From 429e1b1f9efaebecf32f3101360aa8c58cbf36c1 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 9 Sep 2025 15:35:43 -0700
Subject: [PATCH 01/40] Remove graph embedding and UMAP (#2048)

* Remove umap/layout operation

* Remove graph embedding

* Bump unified-search to GR 2.5.0

* Remove graph vis from unified-search
---
 docs/config/yaml.md                           |   23 -
 docs/index/architecture.md                    |    1 -
 docs/index/default_dataflow.md                |   24 +-
 docs/index/methods.md                         |    2 +-
 docs/index/outputs.md                         |    2 -
 docs/visualization_guide.md                   |    7 -
 graphrag/config/defaults.py                   |   23 -
 graphrag/config/init_content.py               |    6 -
 graphrag/config/models/embed_graph_config.py  |   45 -
 graphrag/config/models/graph_rag_config.py    |   13 -
 graphrag/config/models/umap_config.py         |   17 -
 graphrag/data_model/schemas.py                |    4 -
 graphrag/index/operations/cluster_graph.py    |    2 +-
 .../index/operations/embed_graph/__init__.py  |    4 -
 .../operations/embed_graph/embed_graph.py     |   50 -
 .../operations/embed_graph/embed_node2vec.py  |   43 -
 .../index/operations/embed_graph/typing.py    |   12 -
 .../index/operations/finalize_entities.py     |   22 +-
 .../index/operations/layout_graph/__init__.py |    4 -
 .../operations/layout_graph/layout_graph.py   |   84 -
 .../index/operations/layout_graph/typing.py   |   27 -
 .../index/operations/layout_graph/umap.py     |  132 -
 .../index/operations/layout_graph/zero.py     |   96 -
 graphrag/index/update/entities.py             |    2 -
 graphrag/index/workflows/finalize_graph.py    |    9 +-
 tests/unit/config/utils.py                    |   21 -
 tests/verbs/test_finalize_graph.py            |   31 +-
 unified-search-app/README.md                  |   17 +-
 unified-search-app/app/home_page.py           |   13 +-
 unified-search-app/app/ui/full_graph.py       |   56 -
 unified-search-app/app/ui/report_list.py      |    2 +-
 unified-search-app/pyproject.toml             |    2 +-
 unified-search-app/uv.lock                    | 2348 ++++++++---------
 33 files changed, 1189 insertions(+), 1955 deletions(-)
 delete mode 100644 graphrag/config/models/embed_graph_config.py
 delete mode 100644 graphrag/config/models/umap_config.py
 delete mode 100644 graphrag/index/operations/embed_graph/__init__.py
 delete mode 100644 graphrag/index/operations/embed_graph/embed_graph.py
 delete mode 100644 graphrag/index/operations/embed_graph/embed_node2vec.py
 delete mode 100644 graphrag/index/operations/embed_graph/typing.py
 delete mode 100644 graphrag/index/operations/layout_graph/__init__.py
 delete mode 100644 graphrag/index/operations/layout_graph/layout_graph.py
 delete mode 100644 graphrag/index/operations/layout_graph/typing.py
 delete mode 100644 graphrag/index/operations/layout_graph/umap.py
 delete mode 100644 graphrag/index/operations/layout_graph/zero.py
 delete mode 100644 unified-search-app/app/ui/full_graph.py

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 7d7dcdd9e2..4609577214 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -287,29 +287,6 @@ These are the settings used for Leiden hierarchical clustering of the graph to c
 - `max_length` **int** - The maximum number of output tokens per report.
 - `max_input_length` **int** - The maximum number of input tokens to use when generating reports.
 
-### embed_graph
-
-We use node2vec to embed the graph. This is primarily used for visualization, so it is not turned on by default.
-
-#### Fields
-
-- `enabled` **bool** - Whether to enable graph embeddings.
-- `dimensions` **int** - Number of vector dimensions to produce.
-- `num_walks` **int** - The node2vec number of walks.
-- `walk_length` **int** - The node2vec walk length.
-- `window_size` **int** - The node2vec window size.
-- `iterations` **int** - The node2vec number of iterations.
-- `random_seed` **int** - The node2vec random seed.
-- `strategy` **dict** - Fully override the embed graph strategy.
-
-### umap
-
-Indicates whether we should run UMAP dimensionality reduction. This is used to provide an x/y coordinate to each graph node, suitable for visualization. If this is not enabled, nodes will receive a 0/0 x/y coordinate. If this is enabled, you *must* enable graph embedding as well.
-
-#### Fields
-
-- `enabled` **bool** - Whether to enable UMAP layouts.
-
 ### snapshots
 
 #### Fields
diff --git a/docs/index/architecture.md b/docs/index/architecture.md
index b1e58f5488..6febc7c250 100644
--- a/docs/index/architecture.md
+++ b/docs/index/architecture.md
@@ -23,7 +23,6 @@ stateDiagram-v2
     Chunk --> EmbedDocuments
     ExtractGraph --> GenerateReports
     ExtractGraph --> EmbedEntities
-    ExtractGraph --> EmbedGraph
 ```
 
 ### LLM Caching
diff --git a/docs/index/default_dataflow.md b/docs/index/default_dataflow.md
index be290ed7a6..1133963e89 100644
--- a/docs/index/default_dataflow.md
+++ b/docs/index/default_dataflow.md
@@ -46,8 +46,7 @@ flowchart TB
     end
     subgraph phase6[Phase 6: Network Visualization]
     graph_outputs --> graph_embed[Graph Embedding]
-    graph_embed --> umap_entities[Umap Entities]
-    umap_entities --> combine_nodes[Final Entities]
+    graph_embed --> combine_nodes[Final Entities]
     end
     subgraph phase7[Phase 7: Text Embeddings]
     textUnits --> text_embed[Text Embedding]
@@ -176,27 +175,8 @@ In this step, we link each document to the text-units that were created in the f
 
 At this point, we can export the **Documents** table into the knowledge Model.
 
-## Phase 6: Network Visualization (optional)
 
-In this phase of the workflow, we perform some steps to support network visualization of our high-dimensional vector spaces within our existing graphs. At this point there are two logical graphs at play: the _Entity-Relationship_ graph and the _Document_ graph.
-
-```mermaid
----
-title: Network Visualization Workflows
----
-flowchart LR
-    ag[Graph Table] --> ge[Node2Vec Graph Embedding] --> ne[Umap Entities] --> ng[Entities Table]
-```
-
-### Graph Embedding
-
-In this step, we generate a vector representation of our graph using the Node2Vec algorithm. This will allow us to understand the implicit structure of our graph and provide an additional vector-space in which to search for related concepts during our query phase.
-
-### Dimensionality Reduction
-
-For each of the logical graphs, we perform a UMAP dimensionality reduction to generate a 2D representation of the graph. This will allow us to visualize the graph in a 2D space and understand the relationships between the nodes in the graph. The UMAP embeddings are reduced to two dimensions as x/y coordinates.
-
-## Phase 7: Text Embedding
+## Phase 6: Text Embedding
 
 For all artifacts that require downstream vector search, we generate text embeddings as a final step. These embeddings are written directly to a configured vector store. By default we embed entity descriptions, text unit text, and community report text.
 
diff --git a/docs/index/methods.md b/docs/index/methods.md
index 9da2c83d73..9f785a0ae7 100644
--- a/docs/index/methods.md
+++ b/docs/index/methods.md
@@ -41,4 +41,4 @@ You can install it manually by running `python -m spacy download <model_name>`,
 
 ## Choosing a Method
 
-Standard GraphRAG provides a rich description of real-world entities and relationships, but is more expensive that FastGraphRAG. We estimate graph extraction to constitute roughly 75% of indexing cost. FastGraphRAG is therefore much cheaper, but the tradeoff is that the extracted graph is less directly relevant for use outside of GraphRAG, and the graph tends to be quite a bit noisier. If high fidelity entities and graph exploration are important to your use case, we recommend staying with traditional GraphRAG. If your use case is primarily aimed at summary questions using global search, FastGraphRAG provides high quality summarization at much less LLM cost.
+Standard GraphRAG provides a rich description of real-world entities and relationships, but is more expensive than FastGraphRAG. We estimate graph extraction to constitute roughly 75% of indexing cost. FastGraphRAG is therefore much cheaper, but the tradeoff is that the extracted graph is less directly relevant for use outside of GraphRAG, and the graph tends to be quite a bit noisier. If high fidelity entities and graph exploration are important to your use case, we recommend staying with traditional GraphRAG. If your use case is primarily aimed at summary questions using global search, FastGraphRAG provides high quality summarization at much less LLM cost.
diff --git a/docs/index/outputs.md b/docs/index/outputs.md
index add60182b1..0cc43b419b 100644
--- a/docs/index/outputs.md
+++ b/docs/index/outputs.md
@@ -82,8 +82,6 @@ List of all entities found in the data by the LM.
 | text_unit_ids | str[] | List of the text units containing the entity. |
 | frequency     | int   | Count of text units the entity was found within. |
 | degree        | int   | Node degree (connectedness) in the graph. |
-| x             | float | X position of the node for visual layouts. If graph embeddings and UMAP are not turned on, this will be 0. |
-| y             | float | Y position of the node for visual layouts. If graph embeddings and UMAP are not turned on, this will be 0. |
 
 ## relationships
 List of all entity-to-entity relationships found in the data by the LM. This is also the _edge list_ for the graph.
diff --git a/docs/visualization_guide.md b/docs/visualization_guide.md
index d09319e74d..14b4dbe55f 100644
--- a/docs/visualization_guide.md
+++ b/docs/visualization_guide.md
@@ -8,13 +8,6 @@ Before building an index, please review your `settings.yaml` configuration file
 snapshots:
   graphml: true
 ```
-(Optional) To support other visualization tools and exploration, additional parameters can be enabled that provide access to vector embeddings.
-```yaml
-embed_graph:
-  enabled: true # will generate node2vec embeddings for nodes
-umap:
-  enabled: true # will generate UMAP embeddings for nodes, giving the entities table an x/y position to plot
-```
 After running the indexing pipeline over your data, there will be an output folder (defined by the `storage.base_dir` setting).
 
 - **Output Folder**: Contains artifacts from the LLM’s indexing pass.
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index dc84fc4595..136ff25d08 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -125,20 +125,6 @@ class DriftSearchDefaults:
     embedding_model_id: str = DEFAULT_EMBEDDING_MODEL_ID
 
 
-@dataclass
-class EmbedGraphDefaults:
-    """Default values for embedding graph."""
-
-    enabled: bool = False
-    dimensions: int = 1536
-    num_walks: int = 10
-    walk_length: int = 40
-    window_size: int = 2
-    iterations: int = 3
-    random_seed: int = 597832
-    use_lcc: bool = True
-
-
 @dataclass
 class EmbedTextDefaults:
     """Default values for embedding text."""
@@ -367,13 +353,6 @@ class SummarizeDescriptionsDefaults:
     model_id: str = DEFAULT_CHAT_MODEL_ID
 
 
-@dataclass
-class UmapDefaults:
-    """Default values for UMAP."""
-
-    enabled: bool = False
-
-
 @dataclass
 class UpdateIndexOutputDefaults(StorageDefaults):
     """Default values for update index output."""
@@ -410,7 +389,6 @@ class GraphRagConfigDefaults:
     )
     cache: CacheDefaults = field(default_factory=CacheDefaults)
     input: InputDefaults = field(default_factory=InputDefaults)
-    embed_graph: EmbedGraphDefaults = field(default_factory=EmbedGraphDefaults)
     embed_text: EmbedTextDefaults = field(default_factory=EmbedTextDefaults)
     chunks: ChunksDefaults = field(default_factory=ChunksDefaults)
     snapshots: SnapshotsDefaults = field(default_factory=SnapshotsDefaults)
@@ -427,7 +405,6 @@ class GraphRagConfigDefaults:
     extract_claims: ExtractClaimsDefaults = field(default_factory=ExtractClaimsDefaults)
     prune_graph: PruneGraphDefaults = field(default_factory=PruneGraphDefaults)
     cluster_graph: ClusterGraphDefaults = field(default_factory=ClusterGraphDefaults)
-    umap: UmapDefaults = field(default_factory=UmapDefaults)
     local_search: LocalSearchDefaults = field(default_factory=LocalSearchDefaults)
     global_search: GlobalSearchDefaults = field(default_factory=GlobalSearchDefaults)
     drift_search: DriftSearchDefaults = field(default_factory=DriftSearchDefaults)
diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py
index d08abb1c81..98dbd27c59 100644
--- a/graphrag/config/init_content.py
+++ b/graphrag/config/init_content.py
@@ -130,12 +130,6 @@
   max_length: {graphrag_config_defaults.community_reports.max_length}
   max_input_length: {graphrag_config_defaults.community_reports.max_input_length}
 
-embed_graph:
-  enabled: false # if true, will generate node2vec embeddings for nodes
-
-umap:
-  enabled: false # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled)
-
 snapshots:
   graphml: false
   embeddings: false
diff --git a/graphrag/config/models/embed_graph_config.py b/graphrag/config/models/embed_graph_config.py
deleted file mode 100644
index 72827570e2..0000000000
--- a/graphrag/config/models/embed_graph_config.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from pydantic import BaseModel, Field
-
-from graphrag.config.defaults import graphrag_config_defaults
-
-
-class EmbedGraphConfig(BaseModel):
-    """The default configuration section for Node2Vec."""
-
-    enabled: bool = Field(
-        description="A flag indicating whether to enable node2vec.",
-        default=graphrag_config_defaults.embed_graph.enabled,
-    )
-    dimensions: int = Field(
-        description="The node2vec vector dimensions.",
-        default=graphrag_config_defaults.embed_graph.dimensions,
-    )
-    num_walks: int = Field(
-        description="The node2vec number of walks.",
-        default=graphrag_config_defaults.embed_graph.num_walks,
-    )
-    walk_length: int = Field(
-        description="The node2vec walk length.",
-        default=graphrag_config_defaults.embed_graph.walk_length,
-    )
-    window_size: int = Field(
-        description="The node2vec window size.",
-        default=graphrag_config_defaults.embed_graph.window_size,
-    )
-    iterations: int = Field(
-        description="The node2vec iterations.",
-        default=graphrag_config_defaults.embed_graph.iterations,
-    )
-    random_seed: int = Field(
-        description="The node2vec random seed.",
-        default=graphrag_config_defaults.embed_graph.random_seed,
-    )
-    use_lcc: bool = Field(
-        description="Whether to use the largest connected component.",
-        default=graphrag_config_defaults.embed_graph.use_lcc,
-    )
diff --git a/graphrag/config/models/graph_rag_config.py b/graphrag/config/models/graph_rag_config.py
index 39f12e2f4a..ce132bc2b4 100644
--- a/graphrag/config/models/graph_rag_config.py
+++ b/graphrag/config/models/graph_rag_config.py
@@ -19,7 +19,6 @@
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
-from graphrag.config.models.embed_graph_config import EmbedGraphConfig
 from graphrag.config.models.extract_claims_config import ClaimExtractionConfig
 from graphrag.config.models.extract_graph_config import ExtractGraphConfig
 from graphrag.config.models.extract_graph_nlp_config import ExtractGraphNLPConfig
@@ -35,7 +34,6 @@
     SummarizeDescriptionsConfig,
 )
 from graphrag.config.models.text_embedding_config import TextEmbeddingConfig
-from graphrag.config.models.umap_config import UmapConfig
 from graphrag.config.models.vector_store_config import VectorStoreConfig
 
 
@@ -254,17 +252,6 @@ def _validate_reporting_base_dir(self) -> None:
     )
     """The community reports configuration to use."""
 
-    embed_graph: EmbedGraphConfig = Field(
-        description="Graph embedding configuration.",
-        default=EmbedGraphConfig(),
-    )
-    """Graph Embedding configuration."""
-
-    umap: UmapConfig = Field(
-        description="The UMAP configuration to use.", default=UmapConfig()
-    )
-    """The UMAP configuration to use."""
-
     snapshots: SnapshotsConfig = Field(
         description="The snapshots configuration to use.",
         default=SnapshotsConfig(),
diff --git a/graphrag/config/models/umap_config.py b/graphrag/config/models/umap_config.py
deleted file mode 100644
index e7a0c947bc..0000000000
--- a/graphrag/config/models/umap_config.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from pydantic import BaseModel, Field
-
-from graphrag.config.defaults import graphrag_config_defaults
-
-
-class UmapConfig(BaseModel):
-    """Configuration section for UMAP."""
-
-    enabled: bool = Field(
-        description="A flag indicating whether to enable UMAP.",
-        default=graphrag_config_defaults.umap.enabled,
-    )
diff --git a/graphrag/data_model/schemas.py b/graphrag/data_model/schemas.py
index 42d28d9029..059605bfbf 100644
--- a/graphrag/data_model/schemas.py
+++ b/graphrag/data_model/schemas.py
@@ -13,8 +13,6 @@
 NODE_DEGREE = "degree"
 NODE_FREQUENCY = "frequency"
 NODE_DETAILS = "node_details"
-NODE_X = "x"
-NODE_Y = "y"
 
 # POST-PREP EDGE TABLE SCHEMA
 EDGE_SOURCE = "source"
@@ -78,8 +76,6 @@
     TEXT_UNIT_IDS,
     NODE_FREQUENCY,
     NODE_DEGREE,
-    NODE_X,
-    NODE_Y,
 ]
 
 RELATIONSHIPS_FINAL_COLUMNS = [
diff --git a/graphrag/index/operations/cluster_graph.py b/graphrag/index/operations/cluster_graph.py
index de16e2d35c..ae4f83f47d 100644
--- a/graphrag/index/operations/cluster_graph.py
+++ b/graphrag/index/operations/cluster_graph.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing cluster_graph, apply_clustering and run_layout methods definition."""
+"""A module containing cluster_graph, apply_clustering methods definition."""
 
 import logging
 
diff --git a/graphrag/index/operations/embed_graph/__init__.py b/graphrag/index/operations/embed_graph/__init__.py
deleted file mode 100644
index 3c3e4b1ca4..0000000000
--- a/graphrag/index/operations/embed_graph/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The Indexing Engine graph embed package root."""
diff --git a/graphrag/index/operations/embed_graph/embed_graph.py b/graphrag/index/operations/embed_graph/embed_graph.py
deleted file mode 100644
index 0328402db2..0000000000
--- a/graphrag/index/operations/embed_graph/embed_graph.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing embed_graph and run_embeddings methods definition."""
-
-import networkx as nx
-
-from graphrag.config.models.embed_graph_config import EmbedGraphConfig
-from graphrag.index.operations.embed_graph.embed_node2vec import embed_node2vec
-from graphrag.index.operations.embed_graph.typing import (
-    NodeEmbeddings,
-)
-from graphrag.index.utils.stable_lcc import stable_largest_connected_component
-
-
-def embed_graph(
-    graph: nx.Graph,
-    config: EmbedGraphConfig,
-) -> NodeEmbeddings:
-    """
-    Embed a graph into a vector space using node2vec. The graph is expected to be in nx.Graph format. The operation outputs a mapping between node name and vector.
-
-    ## Usage
-    ```yaml
-    dimensions: 1536 # Optional, The number of dimensions to use for the embedding, default: 1536
-    num_walks: 10 # Optional, The number of walks to use for the embedding, default: 10
-    walk_length: 40 # Optional, The walk length to use for the embedding, default: 40
-    window_size: 2 # Optional, The window size to use for the embedding, default: 2
-    iterations: 3 # Optional, The number of iterations to use for the embedding, default: 3
-    random_seed: 86 # Optional, The random seed to use for the embedding, default: 86
-    ```
-    """
-    if config.use_lcc:
-        graph = stable_largest_connected_component(graph)
-
-    # create graph embedding using node2vec
-    embeddings = embed_node2vec(
-        graph=graph,
-        dimensions=config.dimensions,
-        num_walks=config.num_walks,
-        walk_length=config.walk_length,
-        window_size=config.window_size,
-        iterations=config.iterations,
-        random_seed=config.random_seed,
-    )
-
-    pairs = zip(embeddings.nodes, embeddings.embeddings.tolist(), strict=True)
-    sorted_pairs = sorted(pairs, key=lambda x: x[0])
-
-    return dict(sorted_pairs)
diff --git a/graphrag/index/operations/embed_graph/embed_node2vec.py b/graphrag/index/operations/embed_graph/embed_node2vec.py
deleted file mode 100644
index a009c670f6..0000000000
--- a/graphrag/index/operations/embed_graph/embed_node2vec.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Utilities to generate graph embeddings."""
-
-from dataclasses import dataclass
-
-import networkx as nx
-import numpy as np
-
-
-@dataclass
-class NodeEmbeddings:
-    """Node embeddings class definition."""
-
-    nodes: list[str]
-    embeddings: np.ndarray
-
-
-def embed_node2vec(
-    graph: nx.Graph | nx.DiGraph,
-    dimensions: int = 1536,
-    num_walks: int = 10,
-    walk_length: int = 40,
-    window_size: int = 2,
-    iterations: int = 3,
-    random_seed: int = 86,
-) -> NodeEmbeddings:
-    """Generate node embeddings using Node2Vec."""
-    # NOTE: This import is done here to reduce the initial import time of the graphrag package
-    import graspologic as gc
-
-    # generate embedding
-    lcc_tensors = gc.embed.node2vec_embed(  # type: ignore
-        graph=graph,
-        dimensions=dimensions,
-        window_size=window_size,
-        iterations=iterations,
-        num_walks=num_walks,
-        walk_length=walk_length,
-        random_seed=random_seed,
-    )
-    return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1])
diff --git a/graphrag/index/operations/embed_graph/typing.py b/graphrag/index/operations/embed_graph/typing.py
deleted file mode 100644
index fea792c9b1..0000000000
--- a/graphrag/index/operations/embed_graph/typing.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing different lists and dictionaries."""
-
-# Use this for now instead of a wrapper
-from typing import Any
-
-NodeList = list[str]
-EmbeddingList = list[Any]
-NodeEmbeddings = dict[str, list[float]]
-"""Label -> Embedding"""
diff --git a/graphrag/index/operations/finalize_entities.py b/graphrag/index/operations/finalize_entities.py
index cd1dbb83eb..28ac2a55f0 100644
--- a/graphrag/index/operations/finalize_entities.py
+++ b/graphrag/index/operations/finalize_entities.py
@@ -7,38 +7,20 @@
 
 import pandas as pd
 
-from graphrag.config.models.embed_graph_config import EmbedGraphConfig
 from graphrag.data_model.schemas import ENTITIES_FINAL_COLUMNS
 from graphrag.index.operations.compute_degree import compute_degree
 from graphrag.index.operations.create_graph import create_graph
-from graphrag.index.operations.embed_graph.embed_graph import embed_graph
-from graphrag.index.operations.layout_graph.layout_graph import layout_graph
 
 
 def finalize_entities(
     entities: pd.DataFrame,
     relationships: pd.DataFrame,
-    embed_config: EmbedGraphConfig | None = None,
-    layout_enabled: bool = False,
 ) -> pd.DataFrame:
     """All the steps to transform final entities."""
     graph = create_graph(relationships, edge_attr=["weight"])
-    graph_embeddings = None
-    if embed_config is not None and embed_config.enabled:
-        graph_embeddings = embed_graph(
-            graph,
-            embed_config,
-        )
-    layout = layout_graph(
-        graph,
-        layout_enabled,
-        embeddings=graph_embeddings,
-    )
     degrees = compute_degree(graph)
-    final_entities = (
-        entities.merge(layout, left_on="title", right_on="label", how="left")
-        .merge(degrees, on="title", how="left")
-        .drop_duplicates(subset="title")
+    final_entities = entities.merge(degrees, on="title", how="left").drop_duplicates(
+        subset="title"
     )
     final_entities = final_entities.loc[entities["title"].notna()].reset_index()
     # disconnected nodes and those with no community even at level 0 can be missing degree
diff --git a/graphrag/index/operations/layout_graph/__init__.py b/graphrag/index/operations/layout_graph/__init__.py
deleted file mode 100644
index cc80f99a97..0000000000
--- a/graphrag/index/operations/layout_graph/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The Indexing Engine graph layout package root."""
diff --git a/graphrag/index/operations/layout_graph/layout_graph.py b/graphrag/index/operations/layout_graph/layout_graph.py
deleted file mode 100644
index 433ffc8a10..0000000000
--- a/graphrag/index/operations/layout_graph/layout_graph.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing layout_graph, _run_layout and _apply_layout_to_graph methods definition."""
-
-import logging
-
-import networkx as nx
-import pandas as pd
-
-from graphrag.index.operations.embed_graph.typing import NodeEmbeddings
-from graphrag.index.operations.layout_graph.typing import GraphLayout
-
-logger = logging.getLogger(__name__)
-
-
-def layout_graph(
-    graph: nx.Graph,
-    enabled: bool,
-    embeddings: NodeEmbeddings | None,
-):
-    """
-    Apply a layout algorithm to a nx.Graph. The method returns a dataframe containing the node positions.
-
-    ## Usage
-    ```yaml
-    args:
-        graph: The nx.Graph to layout
-        embeddings: Embeddings for each node in the graph
-        strategy: <strategy config> # See strategies section below
-    ```
-
-    ## Strategies
-    The layout graph verb uses a strategy to layout the graph. The strategy is a json object which defines the strategy to use. The following strategies are available:
-
-    ### umap
-    This strategy uses the umap algorithm to layout a graph. The strategy config is as follows:
-    ```yaml
-    strategy:
-        type: umap
-        n_neighbors: 5 # Optional, The number of neighbors to use for the umap algorithm, default: 5
-        min_dist: 0.75 # Optional, The min distance to use for the umap algorithm, default: 0.75
-    ```
-    """
-    layout = _run_layout(
-        graph,
-        enabled,
-        embeddings if embeddings is not None else {},
-    )
-
-    layout_df = pd.DataFrame(layout)
-    return layout_df.loc[
-        :,
-        ["label", "x", "y", "size"],
-    ]
-
-
-def _run_layout(
-    graph: nx.Graph,
-    enabled: bool,
-    embeddings: NodeEmbeddings,
-) -> GraphLayout:
-    if enabled:
-        from graphrag.index.operations.layout_graph.umap import (
-            run as run_umap,
-        )
-
-        return run_umap(
-            graph,
-            embeddings,
-            lambda e, stack, d: logger.error(
-                "Error in Umap", exc_info=e, extra={"stack": stack, "details": d}
-            ),
-        )
-    from graphrag.index.operations.layout_graph.zero import (
-        run as run_zero,
-    )
-
-    return run_zero(
-        graph,
-        lambda e, stack, d: logger.error(
-            "Error in Zero", exc_info=e, extra={"stack": stack, "details": d}
-        ),
-    )
diff --git a/graphrag/index/operations/layout_graph/typing.py b/graphrag/index/operations/layout_graph/typing.py
deleted file mode 100644
index ae46afa928..0000000000
--- a/graphrag/index/operations/layout_graph/typing.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-# Use this for now instead of a wrapper
-"""A module containing 'NodePosition' model."""
-
-from dataclasses import dataclass
-
-
-@dataclass
-class NodePosition:
-    """Node position class definition."""
-
-    label: str
-    cluster: str
-    size: float
-
-    x: float
-    y: float
-    z: float | None = None
-
-    def to_pandas(self) -> tuple[str, float, float, str, float]:
-        """To pandas method definition."""
-        return self.label, self.x, self.y, self.cluster, self.size
-
-
-GraphLayout = list[NodePosition]
diff --git a/graphrag/index/operations/layout_graph/umap.py b/graphrag/index/operations/layout_graph/umap.py
deleted file mode 100644
index 6550188030..0000000000
--- a/graphrag/index/operations/layout_graph/umap.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run and _create_node_position methods definitions."""
-
-import logging
-import traceback
-
-import networkx as nx
-import numpy as np
-
-from graphrag.index.operations.embed_graph.typing import NodeEmbeddings
-from graphrag.index.operations.layout_graph.typing import (
-    GraphLayout,
-    NodePosition,
-)
-from graphrag.index.typing.error_handler import ErrorHandlerFn
-
-# TODO: This could be handled more elegantly, like what columns to use
-# for "size" or "cluster"
-# We could also have a boolean to indicate to use node sizes or clusters
-
-logger = logging.getLogger(__name__)
-
-
-def run(
-    graph: nx.Graph,
-    embeddings: NodeEmbeddings,
-    on_error: ErrorHandlerFn,
-) -> GraphLayout:
-    """Run method definition."""
-    node_clusters = []
-    node_sizes = []
-
-    embeddings = _filter_raw_embeddings(embeddings)
-    nodes = list(embeddings.keys())
-    embedding_vectors = [embeddings[node_id] for node_id in nodes]
-
-    for node_id in nodes:
-        node = graph.nodes[node_id]
-        cluster = node.get("cluster", node.get("community", -1))
-        node_clusters.append(cluster)
-        size = node.get("degree", node.get("size", 0))
-        node_sizes.append(size)
-
-    additional_args = {}
-    if len(node_clusters) > 0:
-        additional_args["node_categories"] = node_clusters
-    if len(node_sizes) > 0:
-        additional_args["node_sizes"] = node_sizes
-
-    try:
-        return compute_umap_positions(
-            embedding_vectors=np.array(embedding_vectors),
-            node_labels=nodes,
-            **additional_args,
-        )
-    except Exception as e:
-        logger.exception("Error running UMAP")
-        on_error(e, traceback.format_exc(), None)
-        # Umap may fail due to input sparseness or memory pressure.
-        # For now, in these cases, we'll just return a layout with all nodes at (0, 0)
-        result = []
-        for i in range(len(nodes)):
-            cluster = node_clusters[i] if len(node_clusters) > 0 else 1
-            result.append(
-                NodePosition(x=0, y=0, label=nodes[i], size=0, cluster=str(cluster))
-            )
-        return result
-
-
-def _filter_raw_embeddings(embeddings: NodeEmbeddings) -> NodeEmbeddings:
-    return {
-        node_id: embedding
-        for node_id, embedding in embeddings.items()
-        if embedding is not None
-    }
-
-
-def compute_umap_positions(
-    embedding_vectors: np.ndarray,
-    node_labels: list[str],
-    node_categories: list[int] | None = None,
-    node_sizes: list[int] | None = None,
-    min_dist: float = 0.75,
-    n_neighbors: int = 5,
-    spread: int = 1,
-    metric: str = "euclidean",
-    n_components: int = 2,
-    random_state: int = 86,
-) -> list[NodePosition]:
-    """Project embedding vectors down to 2D/3D using UMAP."""
-    # NOTE: This import is done here to reduce the initial import time of the graphrag package
-    import umap
-
-    embedding_positions = umap.UMAP(
-        min_dist=min_dist,
-        n_neighbors=n_neighbors,
-        spread=spread,
-        n_components=n_components,
-        metric=metric,
-        random_state=random_state,
-    ).fit_transform(embedding_vectors)
-
-    embedding_position_data: list[NodePosition] = []
-    for index, node_name in enumerate(node_labels):
-        node_points = embedding_positions[index]  # type: ignore
-        node_category = 1 if node_categories is None else node_categories[index]
-        node_size = 1 if node_sizes is None else node_sizes[index]
-
-        if len(node_points) == 2:
-            embedding_position_data.append(
-                NodePosition(
-                    label=str(node_name),
-                    x=float(node_points[0]),
-                    y=float(node_points[1]),
-                    cluster=str(int(node_category)),
-                    size=int(node_size),
-                )
-            )
-        else:
-            embedding_position_data.append(
-                NodePosition(
-                    label=str(node_name),
-                    x=float(node_points[0]),
-                    y=float(node_points[1]),
-                    z=float(node_points[2]),
-                    cluster=str(int(node_category)),
-                    size=int(node_size),
-                )
-            )
-    return embedding_position_data
diff --git a/graphrag/index/operations/layout_graph/zero.py b/graphrag/index/operations/layout_graph/zero.py
deleted file mode 100644
index 934df0030f..0000000000
--- a/graphrag/index/operations/layout_graph/zero.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run and _create_node_position methods definitions."""
-
-import logging
-import traceback
-
-import networkx as nx
-
-from graphrag.index.operations.layout_graph.typing import (
-    GraphLayout,
-    NodePosition,
-)
-from graphrag.index.typing.error_handler import ErrorHandlerFn
-
-# TODO: This could be handled more elegantly, like what columns to use
-# for "size" or "cluster"
-# We could also have a boolean to indicate to use node sizes or clusters
-
-logger = logging.getLogger(__name__)
-
-
-def run(
-    graph: nx.Graph,
-    on_error: ErrorHandlerFn,
-) -> GraphLayout:
-    """Run method definition."""
-    node_clusters = []
-    node_sizes = []
-
-    nodes = list(graph.nodes)
-
-    for node_id in nodes:
-        node = graph.nodes[node_id]
-        cluster = node.get("cluster", node.get("community", -1))
-        node_clusters.append(cluster)
-        size = node.get("degree", node.get("size", 0))
-        node_sizes.append(size)
-
-    additional_args = {}
-    if len(node_clusters) > 0:
-        additional_args["node_categories"] = node_clusters
-    if len(node_sizes) > 0:
-        additional_args["node_sizes"] = node_sizes
-
-    try:
-        return get_zero_positions(node_labels=nodes, **additional_args)
-    except Exception as e:
-        logger.exception("Error running zero-position")
-        on_error(e, traceback.format_exc(), None)
-        # Umap may fail due to input sparseness or memory pressure.
-        # For now, in these cases, we'll just return a layout with all nodes at (0, 0)
-        result = []
-        for i in range(len(nodes)):
-            cluster = node_clusters[i] if len(node_clusters) > 0 else 1
-            result.append(
-                NodePosition(x=0, y=0, label=nodes[i], size=0, cluster=str(cluster))
-            )
-        return result
-
-
-def get_zero_positions(
-    node_labels: list[str],
-    node_categories: list[int] | None = None,
-    node_sizes: list[int] | None = None,
-    three_d: bool | None = False,
-) -> list[NodePosition]:
-    """Project embedding vectors down to 2D/3D using UMAP."""
-    embedding_position_data: list[NodePosition] = []
-    for index, node_name in enumerate(node_labels):
-        node_category = 1 if node_categories is None else node_categories[index]
-        node_size = 1 if node_sizes is None else node_sizes[index]
-
-        if not three_d:
-            embedding_position_data.append(
-                NodePosition(
-                    label=str(node_name),
-                    x=0,
-                    y=0,
-                    cluster=str(int(node_category)),
-                    size=int(node_size),
-                )
-            )
-        else:
-            embedding_position_data.append(
-                NodePosition(
-                    label=str(node_name),
-                    x=0,
-                    y=0,
-                    z=0,
-                    cluster=str(int(node_category)),
-                    size=int(node_size),
-                )
-            )
-    return embedding_position_data
diff --git a/graphrag/index/update/entities.py b/graphrag/index/update/entities.py
index 7a74d1b6e0..56707af01f 100644
--- a/graphrag/index/update/entities.py
+++ b/graphrag/index/update/entities.py
@@ -60,8 +60,6 @@ def _group_and_resolve_entities(
             # Concatenate nd.array into a single list
             "text_unit_ids": lambda x: list(itertools.chain(*x.tolist())),
             "degree": "first",  # todo: we could probably re-compute this with the entire new graph
-            "x": "first",
-            "y": "first",
         })
         .reset_index()
     )
diff --git a/graphrag/index/workflows/finalize_graph.py b/graphrag/index/workflows/finalize_graph.py
index 66827774f2..5681173c59 100644
--- a/graphrag/index/workflows/finalize_graph.py
+++ b/graphrag/index/workflows/finalize_graph.py
@@ -7,7 +7,6 @@
 
 import pandas as pd
 
-from graphrag.config.models.embed_graph_config import EmbedGraphConfig
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.operations.create_graph import create_graph
 from graphrag.index.operations.finalize_entities import finalize_entities
@@ -34,8 +33,6 @@ async def run_workflow(
     final_entities, final_relationships = finalize_graph(
         entities,
         relationships,
-        embed_config=config.embed_graph,
-        layout_enabled=config.umap.enabled,
     )
 
     await write_table_to_storage(final_entities, "entities", context.output_storage)
@@ -65,12 +62,8 @@ async def run_workflow(
 def finalize_graph(
     entities: pd.DataFrame,
     relationships: pd.DataFrame,
-    embed_config: EmbedGraphConfig | None = None,
-    layout_enabled: bool = False,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """All the steps to finalize the entity and relationship formats."""
-    final_entities = finalize_entities(
-        entities, relationships, embed_config, layout_enabled
-    )
+    final_entities = finalize_entities(entities, relationships)
     final_relationships = finalize_relationships(relationships)
     return (final_entities, final_relationships)
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 2fa5b14107..579234ff2f 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -12,7 +12,6 @@
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
-from graphrag.config.models.embed_graph_config import EmbedGraphConfig
 from graphrag.config.models.extract_claims_config import ClaimExtractionConfig
 from graphrag.config.models.extract_graph_config import ExtractGraphConfig
 from graphrag.config.models.extract_graph_nlp_config import (
@@ -32,7 +31,6 @@
     SummarizeDescriptionsConfig,
 )
 from graphrag.config.models.text_embedding_config import TextEmbeddingConfig
-from graphrag.config.models.umap_config import UmapConfig
 from graphrag.config.models.vector_store_config import VectorStoreConfig
 
 FAKE_API_KEY = "NOT_AN_API_KEY"
@@ -181,19 +179,6 @@ def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None:
     assert actual.metadata == expected.metadata
 
 
-def assert_embed_graph_configs(
-    actual: EmbedGraphConfig, expected: EmbedGraphConfig
-) -> None:
-    assert actual.enabled == expected.enabled
-    assert actual.dimensions == expected.dimensions
-    assert actual.num_walks == expected.num_walks
-    assert actual.walk_length == expected.walk_length
-    assert actual.window_size == expected.window_size
-    assert actual.iterations == expected.iterations
-    assert actual.random_seed == expected.random_seed
-    assert actual.use_lcc == expected.use_lcc
-
-
 def assert_text_embedding_configs(
     actual: TextEmbeddingConfig, expected: TextEmbeddingConfig
 ) -> None:
@@ -306,10 +291,6 @@ def assert_cluster_graph_configs(
     assert actual.seed == expected.seed
 
 
-def assert_umap_configs(actual: UmapConfig, expected: UmapConfig) -> None:
-    assert actual.enabled == expected.enabled
-
-
 def assert_local_search_configs(
     actual: LocalSearchConfig, expected: LocalSearchConfig
 ) -> None:
@@ -409,7 +390,6 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) ->
 
     assert_cache_configs(actual.cache, expected.cache)
     assert_input_configs(actual.input, expected.input)
-    assert_embed_graph_configs(actual.embed_graph, expected.embed_graph)
     assert_text_embedding_configs(actual.embed_text, expected.embed_text)
     assert_chunking_configs(actual.chunks, expected.chunks)
     assert_snapshots_configs(actual.snapshots, expected.snapshots)
@@ -426,7 +406,6 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) ->
     assert_extract_claims_configs(actual.extract_claims, expected.extract_claims)
     assert_prune_graph_configs(actual.prune_graph, expected.prune_graph)
     assert_cluster_graph_configs(actual.cluster_graph, expected.cluster_graph)
-    assert_umap_configs(actual.umap, expected.umap)
     assert_local_search_configs(actual.local_search, expected.local_search)
     assert_global_search_configs(actual.global_search, expected.global_search)
     assert_drift_search_configs(actual.drift_search, expected.drift_search)
diff --git a/tests/verbs/test_finalize_graph.py b/tests/verbs/test_finalize_graph.py
index ed0f4b3d69..0950738ae7 100644
--- a/tests/verbs/test_finalize_graph.py
+++ b/tests/verbs/test_finalize_graph.py
@@ -30,35 +30,6 @@ async def test_finalize_graph():
         "relationships", context.output_storage
     )
 
-    # x and y will be zero with the default configuration, because we do not embed/umap
-    assert nodes_actual["x"].sum() == 0
-    assert nodes_actual["y"].sum() == 0
-
-    for column in ENTITIES_FINAL_COLUMNS:
-        assert column in nodes_actual.columns
-    for column in RELATIONSHIPS_FINAL_COLUMNS:
-        assert column in edges_actual.columns
-
-
-async def test_finalize_graph_umap():
-    context = await _prep_tables()
-
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-
-    config.embed_graph.enabled = True
-    config.umap.enabled = True
-
-    await run_workflow(config, context)
-
-    nodes_actual = await load_table_from_storage("entities", context.output_storage)
-    edges_actual = await load_table_from_storage(
-        "relationships", context.output_storage
-    )
-
-    # x and y should have some value other than zero due to umap
-    assert nodes_actual["x"].sum() != 0
-    assert nodes_actual["y"].sum() != 0
-
     for column in ENTITIES_FINAL_COLUMNS:
         assert column in nodes_actual.columns
     for column in RELATIONSHIPS_FINAL_COLUMNS:
@@ -72,7 +43,7 @@ async def _prep_tables():
 
     # edit the tables to eliminate final fields that wouldn't be on the inputs
     entities = load_test_table("entities")
-    entities.drop(columns=["x", "y", "degree"], inplace=True)
+    entities.drop(columns=["degree"], inplace=True)
     await write_table_to_storage(entities, "entities", context.output_storage)
     relationships = load_test_table("relationships")
     relationships.drop(columns=["combined_degree"], inplace=True)
diff --git a/unified-search-app/README.md b/unified-search-app/README.md
index a7097e947c..0f271317c8 100644
--- a/unified-search-app/README.md
+++ b/unified-search-app/README.md
@@ -15,14 +15,7 @@ We recommend always using a virtual environment:
 - `source .venv/bin/activate`
 
 ## Run index
-Use GraphRAG to index your dataset before running Unified Search. We recommend starting with the [Getting Started guide](https://microsoft.github.io/graphrag/get_started/). You need to run GraphRAG indexing with graph embedding umap enabled to use the functionalities of Unified Search. 
-``` yaml
-embed_graph:
-  enabled: true
-
-umap:
-  enabled: true
-```
+Use GraphRAG to index your dataset before running Unified Search. We recommend starting with the [Getting Started guide](https://microsoft.github.io/graphrag/get_started/).
 
 ## Datasets
 Unified Search supports multiple GraphRAG indexes by using a directory listing file. Create a `listing.json` file in the root folder where all your datasets are stored (locally or in blob storage), with the following format (one entry per dataset):
@@ -110,9 +103,9 @@ In the right panel you have several functionalities.
 1. At the top you can see general information related to the chosen dataset (name and description).
 2. Below the dataset information there is a button labeled "Suggest some questions" which analyzes the dataset using global search and generates the most important questions (the number of questions generated is the amount set in the configuration panel). If you want to select a question generated you have to click the checkbox at the left side of the question to select it. 
 3. A textbox that it is labeled as "Ask a question to compare the results" where you can type the question that you want to send.
-4. Two tabs called Search and Graph Explorer:
+4. Two tabs called Search and Community Explorer:
     1. Search: Here all the searches results are displayed with their citations.
-    2. Graph Explorer: This tab is divided in three sections: Community Reports, Entity Graph and Selected Report. 
+    2. Community Explorer: This tab is divided in two sections: Community Reports List, and Selected Report. 
 
 ##### Suggest some question clicked
 ![Suggest some question clicked](images/image-2.png)
@@ -120,8 +113,8 @@ In the right panel you have several functionalities.
 ##### Selected question clicked
 ![Selected question clicked](images/image-3.png)
 
-##### Graph Explorer tab
-![Graph Explorer tab](images/image-4.png)
+##### Community Explorer tab
+![Community Explorer tab](images/image-4.png)
 
 
 
diff --git a/unified-search-app/app/home_page.py b/unified-search-app/app/home_page.py
index 47aa070e6e..6a249f0e5c 100644
--- a/unified-search-app/app/home_page.py
+++ b/unified-search-app/app/home_page.py
@@ -10,7 +10,6 @@
 from rag.typing import SearchType
 from st_tabs import TabBar
 from state.session_variables import SessionVariables
-from ui.full_graph import create_full_graph_ui
 from ui.questions_list import create_questions_list_ui
 from ui.report_details import create_report_details_ui
 from ui.report_list import create_report_list_ui
@@ -78,7 +77,7 @@ def on_change(sv: SessionVariables):
         st.button(label="Reset", on_click=on_click_reset, kwargs={"sv": sv})
 
     tab_id = TabBar(
-        tabs=["Search", "Graph Explorer"],
+        tabs=["Search", "Community Explorer"],
         color="#fc9e9e",
         activeColor="#ff4b4b",
         default=0,
@@ -237,20 +236,12 @@ def on_change(sv: SessionVariables):
                 st.write(e)
 
     if tab_id == 1:
-        report_list, graph, report_content = st.columns([0.20, 0.55, 0.25])
+        report_list, report_content = st.columns([0.33, 0.67])
 
         with report_list:
             st.markdown("##### Community Reports")
             create_report_list_ui(sv)
 
-        with graph:
-            title, dropdown = st.columns([0.80, 0.20])
-            title.markdown("##### Entity Graph (All entities)")
-            dropdown.selectbox(
-                "Community level", options=[0, 1], key=sv.graph_community_level.key
-            )
-            create_full_graph_ui(sv)
-
         with report_content:
             st.markdown("##### Selected Report")
             create_report_details_ui(sv)
diff --git a/unified-search-app/app/ui/full_graph.py b/unified-search-app/app/ui/full_graph.py
deleted file mode 100644
index e2017c9ea8..0000000000
--- a/unified-search-app/app/ui/full_graph.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Full graph module."""
-
-import altair as alt
-import pandas as pd
-import streamlit as st
-from state.session_variables import SessionVariables
-
-
-def create_full_graph_ui(sv: SessionVariables):
-    """Return graph UI object."""
-    entities = sv.entities.value.copy()
-    communities = sv.communities.value.copy()
-
-    if not communities.empty and not entities.empty:
-        communities_entities = (
-            communities.explode("entity_ids")
-            .merge(
-                entities,
-                left_on="entity_ids",
-                right_on="id",
-                suffixes=("_entities", "_communities"),
-            )
-            .dropna(subset=["x", "y"])
-        )
-    else:
-        communities_entities = pd.DataFrame()
-
-    level = sv.graph_community_level.value
-    communities_entities_filtered = communities_entities[
-        communities_entities["level"] == level
-    ]
-
-    graph = (
-        alt.Chart(communities_entities_filtered)
-        .mark_circle()
-        .encode(
-            x="x",
-            y="y",
-            color=alt.Color(
-                "community",
-                scale=alt.Scale(
-                    domain=communities_entities_filtered["community"].unique(),
-                    scheme="category10",
-                ),
-            ),
-            size=alt.Size("degree", scale=alt.Scale(range=[50, 1000]), legend=None),
-            tooltip=["id_entities", "type", "description", "community"],
-        )
-        .properties(height=1000)
-        .configure_axis(disable=True)
-    )
-    st.altair_chart(graph, use_container_width=True)
-    return graph
diff --git a/unified-search-app/app/ui/report_list.py b/unified-search-app/app/ui/report_list.py
index edd221566a..b7f7e1df23 100644
--- a/unified-search-app/app/ui/report_list.py
+++ b/unified-search-app/app/ui/report_list.py
@@ -13,7 +13,7 @@ def create_report_list_ui(sv: SessionVariables):
         sv.community_reports.value,
         height=1000,
         hide_index=True,
-        column_order=["id", "title"],
+        column_order=["human_readable_id", "title"],
         selection_mode="single-row",
         on_select="rerun",
     )
diff --git a/unified-search-app/pyproject.toml b/unified-search-app/pyproject.toml
index 4dc2cf6519..798522a93d 100644
--- a/unified-search-app/pyproject.toml
+++ b/unified-search-app/pyproject.toml
@@ -13,7 +13,7 @@ dependencies = [
     "azure-search-documents>=11.4.0",
     "azure-storage-blob>=12.20.0",
     "azure-identity>=1.16.0",
-    "graphrag==2.0.0",
+    "graphrag==2.5.0",
     "altair>=5.3.0",
     "streamlit-agraph>=0.0.45",
     "st-tabs>=0.1.1",
diff --git a/unified-search-app/uv.lock b/unified-search-app/uv.lock
index 0fdb3dbf47..c200f831cd 100644
--- a/unified-search-app/uv.lock
+++ b/unified-search-app/uv.lock
@@ -1,5 +1,5 @@
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-revision = 2
+revision = 1
 requires-python = ">=3.10, <3.12"
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From 978e79875e1fb0a80d7ea3bec4dbfd021c0dbe80 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 9 Sep 2025 15:36:25 -0700
Subject: [PATCH 02/40] Remove file filtering (#2050)

* Remove document filtering

* Semver

* Fix integ tests

* Fix file find tuple

* Fix another dangling find tuple
---
 .../major-20250909010205372690.json           |  4 +++
 docs/config/yaml.md                           |  1 -
 graphrag/config/defaults.py                   |  1 -
 graphrag/config/models/input_config.py        |  4 ---
 graphrag/index/input/csv.py                   | 11 +------
 graphrag/index/input/json.py                  | 12 +------
 graphrag/index/input/text.py                  |  6 ++--
 graphrag/index/input/util.py                  | 11 ++-----
 graphrag/storage/blob_pipeline_storage.py     | 29 +++++------------
 graphrag/storage/cosmosdb_pipeline_storage.py | 31 +++++--------------
 graphrag/storage/file_pipeline_storage.py     | 31 ++++++-------------
 graphrag/storage/pipeline_storage.py          |  5 ++-
 .../storage/test_blob_pipeline_storage.py     |  7 -----
 .../storage/test_cosmosdb_storage.py          |  3 --
 tests/integration/storage/test_factory.py     |  3 +-
 .../storage/test_file_pipeline_storage.py     |  8 ++---
 tests/unit/config/utils.py                    |  1 -
 17 files changed, 40 insertions(+), 128 deletions(-)
 create mode 100644 .semversioner/next-release/major-20250909010205372690.json

diff --git a/.semversioner/next-release/major-20250909010205372690.json b/.semversioner/next-release/major-20250909010205372690.json
new file mode 100644
index 0000000000..537045e947
--- /dev/null
+++ b/.semversioner/next-release/major-20250909010205372690.json
@@ -0,0 +1,4 @@
+{
+  "type": "major",
+  "description": "Remove document filtering option."
+}
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 4609577214..fbc93bfed3 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -87,7 +87,6 @@ Our pipeline can ingest .csv, .txt, or .json data from an input location. See th
 - `file_type` **text|csv|json** - The type of input data to load. Default is `text`
 - `encoding` **str** - The encoding of the input file. Default is `utf-8`
 - `file_pattern` **str** - A regex to match input files. Default is `.*\.csv$`, `.*\.txt$`, or `.*\.json$` depending on the specified `file_type`, but you can customize it if needed.
-- `file_filter` **dict** - Key/value pairs to filter. Default is None.
 - `text_column` **str** - (CSV/JSON only) The text column name. If unset we expect a column named `text`.
 - `title_column` **str** - (CSV/JSON only) The title column name, filename will be used if unset.
 - `metadata` **list[str]** - (CSV/JSON only) The additional document attributes fields to keep.
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index 136ff25d08..cbb22c8574 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -248,7 +248,6 @@ class InputDefaults:
     file_type: ClassVar[InputFileType] = InputFileType.text
     encoding: str = "utf-8"
     file_pattern: str = ""
-    file_filter: None = None
     text_column: str = "text"
     title_column: None = None
     metadata: None = None
diff --git a/graphrag/config/models/input_config.py b/graphrag/config/models/input_config.py
index 139dd90c46..bc34d9402d 100644
--- a/graphrag/config/models/input_config.py
+++ b/graphrag/config/models/input_config.py
@@ -32,10 +32,6 @@ class InputConfig(BaseModel):
         description="The input file pattern to use.",
         default=graphrag_config_defaults.input.file_pattern,
     )
-    file_filter: dict[str, str] | None = Field(
-        description="The optional file filter for the input files.",
-        default=graphrag_config_defaults.input.file_filter,
-    )
     text_column: str = Field(
         description="The input text column to use.",
         default=graphrag_config_defaults.input.text_column,
diff --git a/graphrag/index/input/csv.py b/graphrag/index/input/csv.py
index eea4864b17..7db033debb 100644
--- a/graphrag/index/input/csv.py
+++ b/graphrag/index/input/csv.py
@@ -22,19 +22,10 @@ async def load_csv(
     """Load csv inputs from a directory."""
     logger.info("Loading csv files from %s", config.storage.base_dir)
 
-    async def load_file(path: str, group: dict | None) -> pd.DataFrame:
-        if group is None:
-            group = {}
+    async def load_file(path: str) -> pd.DataFrame:
         buffer = BytesIO(await storage.get(path, as_bytes=True))
         data = pd.read_csv(buffer, encoding=config.encoding)
-        additional_keys = group.keys()
-        if len(additional_keys) > 0:
-            data[[*additional_keys]] = data.apply(
-                lambda _row: pd.Series([group[key] for key in additional_keys]), axis=1
-            )
-
         data = process_data_columns(data, config, path)
-
         creation_date = await storage.get_creation_date(path)
         data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
 
diff --git a/graphrag/index/input/json.py b/graphrag/index/input/json.py
index e2880bc888..41343fd2b2 100644
--- a/graphrag/index/input/json.py
+++ b/graphrag/index/input/json.py
@@ -22,23 +22,13 @@ async def load_json(
     """Load json inputs from a directory."""
     logger.info("Loading json files from %s", config.storage.base_dir)
 
-    async def load_file(path: str, group: dict | None) -> pd.DataFrame:
-        if group is None:
-            group = {}
+    async def load_file(path: str) -> pd.DataFrame:
         text = await storage.get(path, encoding=config.encoding)
         as_json = json.loads(text)
         # json file could just be a single object, or an array of objects
         rows = as_json if isinstance(as_json, list) else [as_json]
         data = pd.DataFrame(rows)
-
-        additional_keys = group.keys()
-        if len(additional_keys) > 0:
-            data[[*additional_keys]] = data.apply(
-                lambda _row: pd.Series([group[key] for key in additional_keys]), axis=1
-            )
-
         data = process_data_columns(data, config, path)
-
         creation_date = await storage.get_creation_date(path)
         data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
 
diff --git a/graphrag/index/input/text.py b/graphrag/index/input/text.py
index 1834a532eb..f1fc74352f 100644
--- a/graphrag/index/input/text.py
+++ b/graphrag/index/input/text.py
@@ -22,11 +22,9 @@ async def load_text(
 ) -> pd.DataFrame:
     """Load text inputs from a directory."""
 
-    async def load_file(path: str, group: dict | None = None) -> pd.DataFrame:
-        if group is None:
-            group = {}
+    async def load_file(path: str) -> pd.DataFrame:
         text = await storage.get(path, encoding=config.encoding)
-        new_item = {**group, "text": text}
+        new_item = {"text": text}
         new_item["id"] = gen_sha512_hash(new_item, new_item.keys())
         new_item["title"] = str(Path(path).name)
         new_item["creation_date"] = await storage.get_creation_date(path)
diff --git a/graphrag/index/input/util.py b/graphrag/index/input/util.py
index cc15183673..457c1864a9 100644
--- a/graphrag/index/input/util.py
+++ b/graphrag/index/input/util.py
@@ -22,12 +22,7 @@ async def load_files(
     storage: PipelineStorage,
 ) -> pd.DataFrame:
     """Load files from storage and apply a loader function."""
-    files = list(
-        storage.find(
-            re.compile(config.file_pattern),
-            file_filter=config.file_filter,
-        )
-    )
+    files = list(storage.find(re.compile(config.file_pattern)))
 
     if len(files) == 0:
         msg = f"No {config.file_type} files found in {config.storage.base_dir}"
@@ -35,9 +30,9 @@ async def load_files(
 
     files_loaded = []
 
-    for file, group in files:
+    for file in files:
         try:
-            files_loaded.append(await loader(file, group))
+            files_loaded.append(await loader(file))
         except Exception as e:  # noqa: BLE001 (catching Exception is fine here)
             logger.warning("Warning! Error loading file %s. Skipping...", file)
             logger.warning("Error: %s", e)
diff --git a/graphrag/storage/blob_pipeline_storage.py b/graphrag/storage/blob_pipeline_storage.py
index 2a5ba8c41f..5a00af85b5 100644
--- a/graphrag/storage/blob_pipeline_storage.py
+++ b/graphrag/storage/blob_pipeline_storage.py
@@ -101,15 +101,13 @@ def find(
         self,
         file_pattern: re.Pattern[str],
         base_dir: str | None = None,
-        file_filter: dict[str, Any] | None = None,
         max_count=-1,
-    ) -> Iterator[tuple[str, dict[str, Any]]]:
-        """Find blobs in a container using a file pattern, as well as a custom filter function.
+    ) -> Iterator[str]:
+        """Find blobs in a container using a file pattern.
 
         Params:
             base_dir: The name of the base container.
             file_pattern: The file pattern to use.
-            file_filter: A dictionary of key-value pairs to filter the blobs.
             max_count: The maximum number of blobs to return. If -1, all blobs are returned.
 
         Returns
@@ -131,14 +129,6 @@ def _blobname(blob_name: str) -> str:
                 blob_name = blob_name[1:]
             return blob_name
 
-        def item_filter(item: dict[str, Any]) -> bool:
-            if file_filter is None:
-                return True
-
-            return all(
-                re.search(value, item[key]) for key, value in file_filter.items()
-            )
-
         try:
             container_client = self._blob_service_client.get_container_client(
                 self._container_name
@@ -151,14 +141,10 @@ def item_filter(item: dict[str, Any]) -> bool:
             for blob in all_blobs:
                 match = file_pattern.search(blob.name)
                 if match and blob.name.startswith(base_dir):
-                    group = match.groupdict()
-                    if item_filter(group):
-                        yield (_blobname(blob.name), group)
-                        num_loaded += 1
-                        if max_count > 0 and num_loaded >= max_count:
-                            break
-                    else:
-                        num_filtered += 1
+                    yield _blobname(blob.name)
+                    num_loaded += 1
+                    if max_count > 0 and num_loaded >= max_count:
+                        break
                 else:
                     num_filtered += 1
                 logger.debug(
@@ -169,10 +155,9 @@ def item_filter(item: dict[str, Any]) -> bool:
                 )
         except Exception:  # noqa: BLE001
             logger.warning(
-                "Error finding blobs: base_dir=%s, file_pattern=%s, file_filter=%s",
+                "Error finding blobs: base_dir=%s, file_pattern=%s",
                 base_dir,
                 file_pattern,
-                file_filter,
             )
 
     async def get(
diff --git a/graphrag/storage/cosmosdb_pipeline_storage.py b/graphrag/storage/cosmosdb_pipeline_storage.py
index 795d071df8..8d2673e89b 100644
--- a/graphrag/storage/cosmosdb_pipeline_storage.py
+++ b/graphrag/storage/cosmosdb_pipeline_storage.py
@@ -121,15 +121,13 @@ def find(
         self,
         file_pattern: re.Pattern[str],
         base_dir: str | None = None,
-        file_filter: dict[str, Any] | None = None,
         max_count=-1,
-    ) -> Iterator[tuple[str, dict[str, Any]]]:
-        """Find documents in a Cosmos DB container using a file pattern regex and custom file filter (optional).
+    ) -> Iterator[str]:
+        """Find documents in a Cosmos DB container using a file pattern regex.
 
         Params:
             base_dir: The name of the base directory (not used in Cosmos DB context).
             file_pattern: The file pattern to use.
-            file_filter: A dictionary of key-value pairs to filter the documents.
             max_count: The maximum number of documents to return. If -1, all documents are returned.
 
         Returns
@@ -145,23 +143,12 @@ def find(
         if not self._database_client or not self._container_client:
             return
 
-        def item_filter(item: dict[str, Any]) -> bool:
-            if file_filter is None:
-                return True
-            return all(
-                re.search(value, item.get(key, ""))
-                for key, value in file_filter.items()
-            )
-
         try:
             query = "SELECT * FROM c WHERE RegexMatch(c.id, @pattern)"
             parameters: list[dict[str, Any]] = [
                 {"name": "@pattern", "value": file_pattern.pattern}
             ]
-            if file_filter:
-                for key, value in file_filter.items():
-                    query += f" AND c.{key} = @{key}"
-                    parameters.append({"name": f"@{key}", "value": value})
+
             items = list(
                 self._container_client.query_items(
                     query=query,
@@ -177,14 +164,10 @@ def item_filter(item: dict[str, Any]) -> bool:
             for item in items:
                 match = file_pattern.search(item["id"])
                 if match:
-                    group = match.groupdict()
-                    if item_filter(group):
-                        yield (item["id"], group)
-                        num_loaded += 1
-                        if max_count > 0 and num_loaded >= max_count:
-                            break
-                    else:
-                        num_filtered += 1
+                    yield item["id"]
+                    num_loaded += 1
+                    if max_count > 0 and num_loaded >= max_count:
+                        break
                 else:
                     num_filtered += 1
 
diff --git a/graphrag/storage/file_pipeline_storage.py b/graphrag/storage/file_pipeline_storage.py
index a52a92d36d..15445b0d3a 100644
--- a/graphrag/storage/file_pipeline_storage.py
+++ b/graphrag/storage/file_pipeline_storage.py
@@ -41,18 +41,9 @@ def find(
         self,
         file_pattern: re.Pattern[str],
         base_dir: str | None = None,
-        file_filter: dict[str, Any] | None = None,
         max_count=-1,
-    ) -> Iterator[tuple[str, dict[str, Any]]]:
-        """Find files in the storage using a file pattern, as well as a custom filter function."""
-
-        def item_filter(item: dict[str, Any]) -> bool:
-            if file_filter is None:
-                return True
-            return all(
-                re.search(value, item[key]) for key, value in file_filter.items()
-            )
-
+    ) -> Iterator[str]:
+        """Find files in the storage using a file pattern."""
         search_path = Path(self._root_dir) / (base_dir or "")
         logger.info(
             "search %s for files matching %s", search_path, file_pattern.pattern
@@ -64,17 +55,13 @@ def item_filter(item: dict[str, Any]) -> bool:
         for file in all_files:
             match = file_pattern.search(f"{file}")
             if match:
-                group = match.groupdict()
-                if item_filter(group):
-                    filename = f"{file}".replace(self._root_dir, "")
-                    if filename.startswith(os.sep):
-                        filename = filename[1:]
-                    yield (filename, group)
-                    num_loaded += 1
-                    if max_count > 0 and num_loaded >= max_count:
-                        break
-                else:
-                    num_filtered += 1
+                filename = f"{file}".replace(self._root_dir, "")
+                if filename.startswith(os.sep):
+                    filename = filename[1:]
+                yield filename
+                num_loaded += 1
+                if max_count > 0 and num_loaded >= max_count:
+                    break
             else:
                 num_filtered += 1
             logger.debug(
diff --git a/graphrag/storage/pipeline_storage.py b/graphrag/storage/pipeline_storage.py
index ed117a577d..ba3ab86e97 100644
--- a/graphrag/storage/pipeline_storage.py
+++ b/graphrag/storage/pipeline_storage.py
@@ -18,10 +18,9 @@ def find(
         self,
         file_pattern: re.Pattern[str],
         base_dir: str | None = None,
-        file_filter: dict[str, Any] | None = None,
         max_count=-1,
-    ) -> Iterator[tuple[str, dict[str, Any]]]:
-        """Find files in the storage using a file pattern, as well as a custom filter function."""
+    ) -> Iterator[str]:
+        """Find files in the storage using a file pattern."""
 
     @abstractmethod
     async def get(
diff --git a/tests/integration/storage/test_blob_pipeline_storage.py b/tests/integration/storage/test_blob_pipeline_storage.py
index 24e380d20e..f99a74ff74 100644
--- a/tests/integration/storage/test_blob_pipeline_storage.py
+++ b/tests/integration/storage/test_blob_pipeline_storage.py
@@ -21,7 +21,6 @@ async def test_find():
             items = list(
                 storage.find(base_dir="input", file_pattern=re.compile(r".*\.txt$"))
             )
-            items = [item[0] for item in items]
             assert items == []
 
             await storage.set(
@@ -30,12 +29,10 @@ async def test_find():
             items = list(
                 storage.find(base_dir="input", file_pattern=re.compile(r".*\.txt$"))
             )
-            items = [item[0] for item in items]
             assert items == ["input/christmas.txt"]
 
             await storage.set("test.txt", "Hello, World!", encoding="utf-8")
             items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
-            items = [item[0] for item in items]
             assert items == ["input/christmas.txt", "test.txt"]
 
             output = await storage.get("test.txt")
@@ -57,7 +54,6 @@ async def test_dotprefix():
     try:
         await storage.set("input/christmas.txt", "Merry Christmas!", encoding="utf-8")
         items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
-        items = [item[0] for item in items]
         assert items == ["input/christmas.txt"]
     finally:
         storage._delete_container()  # noqa: SLF001
@@ -91,12 +87,10 @@ async def test_child():
             storage = parent.child("input")
             await storage.set("christmas.txt", "Merry Christmas!", encoding="utf-8")
             items = list(storage.find(re.compile(r".*\.txt$")))
-            items = [item[0] for item in items]
             assert items == ["christmas.txt"]
 
             await storage.set("test.txt", "Hello, World!", encoding="utf-8")
             items = list(storage.find(re.compile(r".*\.txt$")))
-            items = [item[0] for item in items]
             print("FOUND", items)
             assert items == ["christmas.txt", "test.txt"]
 
@@ -104,7 +98,6 @@ async def test_child():
             assert output == "Hello, World!"
 
             items = list(parent.find(re.compile(r".*\.txt$")))
-            items = [item[0] for item in items]
             print("FOUND ITEMS", items)
             assert items == ["input/christmas.txt", "input/test.txt"]
         finally:
diff --git a/tests/integration/storage/test_cosmosdb_storage.py b/tests/integration/storage/test_cosmosdb_storage.py
index 529be52256..af08972fb3 100644
--- a/tests/integration/storage/test_cosmosdb_storage.py
+++ b/tests/integration/storage/test_cosmosdb_storage.py
@@ -30,7 +30,6 @@ async def test_find():
     try:
         try:
             items = list(storage.find(file_pattern=re.compile(r".*\.json$")))
-            items = [item[0] for item in items]
             assert items == []
 
             json_content = {
@@ -40,7 +39,6 @@ async def test_find():
                 "christmas.json", json.dumps(json_content), encoding="utf-8"
             )
             items = list(storage.find(file_pattern=re.compile(r".*\.json$")))
-            items = [item[0] for item in items]
             assert items == ["christmas.json"]
 
             json_content = {
@@ -48,7 +46,6 @@ async def test_find():
             }
             await storage.set("test.json", json.dumps(json_content), encoding="utf-8")
             items = list(storage.find(file_pattern=re.compile(r".*\.json$")))
-            items = [item[0] for item in items]
             assert items == ["christmas.json", "test.json"]
 
             output = await storage.get("test.json")
diff --git a/tests/integration/storage/test_factory.py b/tests/integration/storage/test_factory.py
index 3d21e50e9f..0b59366ef9 100644
--- a/tests/integration/storage/test_factory.py
+++ b/tests/integration/storage/test_factory.py
@@ -117,9 +117,8 @@ def find(
             self,
             file_pattern: re.Pattern[str],
             base_dir: str | None = None,
-            file_filter: dict[str, Any] | None = None,
             max_count=-1,
-        ) -> Iterator[tuple[str, dict[str, Any]]]:
+        ) -> Iterator[str]:
             return iter([])
 
         async def get(
diff --git a/tests/integration/storage/test_file_pipeline_storage.py b/tests/integration/storage/test_file_pipeline_storage.py
index be58476480..cc5b3f7c83 100644
--- a/tests/integration/storage/test_file_pipeline_storage.py
+++ b/tests/integration/storage/test_file_pipeline_storage.py
@@ -18,12 +18,10 @@ async def test_find():
     storage = FilePipelineStorage()
     items = list(
         storage.find(
-            base_dir="tests/fixtures/text/input",
-            file_pattern=re.compile(r".*\.txt$"),
-            file_filter=None,
+            base_dir="tests/fixtures/text/input", file_pattern=re.compile(r".*\.txt$")
         )
     )
-    assert items == [(str(Path("tests/fixtures/text/input/dulce.txt")), {})]
+    assert items == [str(Path("tests/fixtures/text/input/dulce.txt"))]
     output = await storage.get("tests/fixtures/text/input/dulce.txt")
     assert len(output) > 0
 
@@ -52,7 +50,7 @@ async def test_child():
     storage = FilePipelineStorage()
     storage = storage.child("tests/fixtures/text/input")
     items = list(storage.find(re.compile(r".*\.txt$")))
-    assert items == [(str(Path("dulce.txt")), {})]
+    assert items == [str(Path("dulce.txt"))]
 
     output = await storage.get("dulce.txt")
     assert len(output) > 0
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 579234ff2f..8ca5e45a8c 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -173,7 +173,6 @@ def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None:
     assert actual.storage.container_name == expected.storage.container_name
     assert actual.encoding == expected.encoding
     assert actual.file_pattern == expected.file_pattern
-    assert actual.file_filter == expected.file_filter
     assert actual.text_column == expected.text_column
     assert actual.title_column == expected.title_column
     assert actual.metadata == expected.metadata

From 97704ab933dfe5ac5eeac4a6c4ad1c3ba73d5cc3 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 9 Sep 2025 16:04:04 -0700
Subject: [PATCH 03/40] Remove text unit grouping (#2052)

* Remove text unit group_by_columns

* Semver

* Fix default token split test

* Fix models in config test samples

* Fix token length in context sort test

* Fix document sort
---
 .../major-20250909205146252760.json           |   4 ++
 docs/config/yaml.md                           |   1 -
 docs/index/default_dataflow.md                |   4 +-
 docs/index/outputs.md                         |   2 +-
 graphrag/config/defaults.py                   |   9 ++-
 graphrag/config/init_content.py               |   1 -
 graphrag/config/models/chunking_config.py     |   4 --
 graphrag/data_model/schemas.py                |   4 +-
 graphrag/data_model/text_unit.py              |   8 +--
 .../index/workflows/create_base_text_units.py |  53 ++++++------------
 .../index/workflows/create_final_documents.py |  18 +++---
 .../workflows/create_final_text_units.py      |   2 +-
 graphrag/prompt_tune/loader/input.py          |   1 -
 graphrag/query/input/loaders/dfs.py           |   4 +-
 .../fixtures/minimal_config/settings.yaml     |   4 +-
 .../settings.yaml                             |   4 +-
 tests/unit/config/utils.py                    |   1 -
 .../community_reports/test_sort_context.py    |   4 +-
 .../text_splitting/test_text_splitting.py     |   3 +-
 tests/verbs/data/communities.parquet          | Bin 25391 -> 39880 bytes
 tests/verbs/data/community_reports.parquet    | Bin 156921 -> 334388 bytes
 tests/verbs/data/covariates.parquet           | Bin 59240 -> 70374 bytes
 tests/verbs/data/documents.parquet            | Bin 126826 -> 126826 bytes
 tests/verbs/data/entities.parquet             | Bin 41136 -> 60987 bytes
 tests/verbs/data/relationships.parquet        | Bin 37492 -> 73213 bytes
 tests/verbs/data/text_units.parquet           | Bin 148830 -> 163602 bytes
 tests/verbs/data/text_units_metadata.parquet  | Bin 128451 -> 163313 bytes
 ...text_units_metadata_included_chunk.parquet | Bin 128513 -> 163634 bytes
 tests/verbs/test_create_base_text_units.py    |  10 +---
 tests/verbs/test_extract_graph_nlp.py         |   4 +-
 tests/verbs/test_prune_graph.py               |   2 +-
 tests/verbs/util.py                           |   6 +-
 32 files changed, 60 insertions(+), 93 deletions(-)
 create mode 100644 .semversioner/next-release/major-20250909205146252760.json

diff --git a/.semversioner/next-release/major-20250909205146252760.json b/.semversioner/next-release/major-20250909205146252760.json
new file mode 100644
index 0000000000..b4e99c5358
--- /dev/null
+++ b/.semversioner/next-release/major-20250909205146252760.json
@@ -0,0 +1,4 @@
+{
+  "type": "major",
+  "description": "Remove text unit group-by ability."
+}
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index fbc93bfed3..1a4ce326e8 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -99,7 +99,6 @@ These settings configure how we parse documents into text chunks. This is necess
 
 - `size` **int** - The max chunk size in tokens.
 - `overlap` **int** - The chunk overlap in tokens.
-- `group_by_columns` **list[str]** - Group documents by these fields before chunking.
 - `strategy` **str**[tokens|sentences] - How to chunk the text. 
 - `encoding_model` **str** - The text encoding model to use for splitting on token boundaries.
 - `prepend_metadata` **bool** - Determines if metadata values should be added at the beginning of each chunk. Default=`False`.
diff --git a/docs/index/default_dataflow.md b/docs/index/default_dataflow.md
index 1133963e89..19f59dfe8a 100644
--- a/docs/index/default_dataflow.md
+++ b/docs/index/default_dataflow.md
@@ -59,9 +59,7 @@ flowchart TB
 
 The first phase of the default-configuration workflow is to transform input documents into _TextUnits_. A _TextUnit_ is a chunk of text that is used for our graph extraction techniques. They are also used as source-references by extracted knowledge items in order to empower breadcrumbs and provenance by concepts back to their original source text.
 
-The chunk size (counted in tokens), is user-configurable. By default this is set to 300 tokens, although we've had positive experience with 1200-token chunks using a single "glean" step. (A "glean" step is a follow-on extraction). Larger chunks result in lower-fidelity output and less meaningful reference texts; however, using larger chunks can result in much faster processing time.
-
-The group-by configuration is also user-configurable. By default, we align our chunks to document boundaries, meaning that there is a strict 1-to-many relationship between Documents and TextUnits. In rare cases, this can be turned into a many-to-many relationship. This is useful when the documents are very short and we need several of them to compose a meaningful analysis unit (e.g. Tweets or a chat log)
+The chunk size (counted in tokens), is user-configurable. By default this is set to 1200 tokens. Larger chunks result in lower-fidelity output and less meaningful reference texts; however, using larger chunks can result in much faster processing time.
 
 ```mermaid
 ---
diff --git a/docs/index/outputs.md b/docs/index/outputs.md
index 0cc43b419b..89a48e8526 100644
--- a/docs/index/outputs.md
+++ b/docs/index/outputs.md
@@ -102,7 +102,7 @@ List of all text chunks parsed from the input documents.
 | ----------------- | ----- | ----------- |
 | text              | str   | Raw full text of the chunk. |
 | n_tokens          | int   | Number of tokens in the chunk. This should normally match the `chunk_size` config parameter, except for the last chunk which is often shorter. |
-| document_ids      | str[] | List of document IDs the chunk came from. This is normally only 1 due to our default groupby, but for very short text documents (e.g., microblogs) it can be configured so text units span multiple documents. |
+| document_id       | str   | ID of the document the chunk came from. |
 | entity_ids        | str[] | List of entities found in the text unit. |
 | relationships_ids | str[] | List of relationships found in the text unit. |
 | covariate_ids     | str[] | Optional list of covariates found in the text unit. |
\ No newline at end of file
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index cbb22c8574..fb9c5578a1 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -27,15 +27,15 @@
 DEFAULT_OUTPUT_BASE_DIR = "output"
 DEFAULT_CHAT_MODEL_ID = "default_chat_model"
 DEFAULT_CHAT_MODEL_TYPE = ModelType.OpenAIChat
-DEFAULT_CHAT_MODEL = "gpt-4-turbo-preview"
+DEFAULT_CHAT_MODEL = "gpt-4o"
 DEFAULT_CHAT_MODEL_AUTH_TYPE = AuthType.APIKey
 DEFAULT_EMBEDDING_MODEL_ID = "default_embedding_model"
 DEFAULT_EMBEDDING_MODEL_TYPE = ModelType.OpenAIEmbedding
-DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
+DEFAULT_EMBEDDING_MODEL = "text-embedding-ada-002"
 DEFAULT_EMBEDDING_MODEL_AUTH_TYPE = AuthType.APIKey
 DEFAULT_VECTOR_STORE_ID = "default_vector_store"
 
-ENCODING_MODEL = "cl100k_base"
+ENCODING_MODEL = "o200k_base"
 COGNITIVE_SERVICES_AUDIENCE = "https://cognitiveservices.azure.com/.default"
 
 
@@ -68,9 +68,8 @@ class ChunksDefaults:
 
     size: int = 1200
     overlap: int = 100
-    group_by_columns: list[str] = field(default_factory=lambda: ["id"])
     strategy: ClassVar[ChunkStrategyType] = ChunkStrategyType.tokens
-    encoding_model: str = "cl100k_base"
+    encoding_model: str = ENCODING_MODEL
     prepend_metadata: bool = False
     chunk_size_includes_metadata: bool = False
 
diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py
index 98dbd27c59..137b3873b5 100644
--- a/graphrag/config/init_content.py
+++ b/graphrag/config/init_content.py
@@ -67,7 +67,6 @@
 chunks:
   size: {graphrag_config_defaults.chunks.size}
   overlap: {graphrag_config_defaults.chunks.overlap}
-  group_by_columns: [{",".join(graphrag_config_defaults.chunks.group_by_columns)}]
 
 ### Output/storage settings ###
 ## If blob storage is specified in the following four sections,
diff --git a/graphrag/config/models/chunking_config.py b/graphrag/config/models/chunking_config.py
index 902bffcbb9..4d1214241c 100644
--- a/graphrag/config/models/chunking_config.py
+++ b/graphrag/config/models/chunking_config.py
@@ -20,10 +20,6 @@ class ChunkingConfig(BaseModel):
         description="The chunk overlap to use.",
         default=graphrag_config_defaults.chunks.overlap,
     )
-    group_by_columns: list[str] = Field(
-        description="The chunk by columns to use.",
-        default=graphrag_config_defaults.chunks.group_by_columns,
-    )
     strategy: ChunkStrategyType = Field(
         description="The chunking strategy to use.",
         default=graphrag_config_defaults.chunks.strategy,
diff --git a/graphrag/data_model/schemas.py b/graphrag/data_model/schemas.py
index 059605bfbf..93ede46191 100644
--- a/graphrag/data_model/schemas.py
+++ b/graphrag/data_model/schemas.py
@@ -52,7 +52,7 @@
 RELATIONSHIP_IDS = "relationship_ids"
 TEXT_UNIT_IDS = "text_unit_ids"
 COVARIATE_IDS = "covariate_ids"
-DOCUMENT_IDS = "document_ids"
+DOCUMENT_ID = "document_id"
 
 PERIOD = "period"
 SIZE = "size"
@@ -142,7 +142,7 @@
     SHORT_ID,
     TEXT,
     N_TOKENS,
-    DOCUMENT_IDS,
+    DOCUMENT_ID,
     ENTITY_IDS,
     RELATIONSHIP_IDS,
     COVARIATE_IDS,
diff --git a/graphrag/data_model/text_unit.py b/graphrag/data_model/text_unit.py
index 07b1b9ae9c..55006ab15b 100644
--- a/graphrag/data_model/text_unit.py
+++ b/graphrag/data_model/text_unit.py
@@ -28,8 +28,8 @@ class TextUnit(Identified):
     n_tokens: int | None = None
     """The number of tokens in the text (optional)."""
 
-    document_ids: list[str] | None = None
-    """List of document IDs in which the text unit appears (optional)."""
+    document_id: str | None = None
+    """ID of the document in which the text unit appears (optional)."""
 
     attributes: dict[str, Any] | None = None
     """A dictionary of additional attributes associated with the text unit (optional)."""
@@ -45,7 +45,7 @@ def from_dict(
         relationships_key: str = "relationship_ids",
         covariates_key: str = "covariate_ids",
         n_tokens_key: str = "n_tokens",
-        document_ids_key: str = "document_ids",
+        document_id_key: str = "document_id",
         attributes_key: str = "attributes",
     ) -> "TextUnit":
         """Create a new text unit from the dict data."""
@@ -57,6 +57,6 @@ def from_dict(
             relationship_ids=d.get(relationships_key),
             covariate_ids=d.get(covariates_key),
             n_tokens=d.get(n_tokens_key),
-            document_ids=d.get(document_ids_key),
+            document_id=d.get(document_id_key),
             attributes=d.get(attributes_key),
         )
diff --git a/graphrag/index/workflows/create_base_text_units.py b/graphrag/index/workflows/create_base_text_units.py
index d94ee5951f..feeba7a065 100644
--- a/graphrag/index/workflows/create_base_text_units.py
+++ b/graphrag/index/workflows/create_base_text_units.py
@@ -35,7 +35,6 @@ async def run_workflow(
     output = create_base_text_units(
         documents,
         context.callbacks,
-        chunks.group_by_columns,
         chunks.size,
         chunks.overlap,
         chunks.encoding_model,
@@ -53,7 +52,6 @@ async def run_workflow(
 def create_base_text_units(
     documents: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    group_by_columns: list[str],
     size: int,
     overlap: int,
     encoding_model: str,
@@ -62,26 +60,9 @@ def create_base_text_units(
     chunk_size_includes_metadata: bool = False,
 ) -> pd.DataFrame:
     """All the steps to transform base text_units."""
-    sort = documents.sort_values(by=["id"], ascending=[True])
+    documents.sort_values(by=["id"], ascending=[True], inplace=True)
 
-    sort["text_with_ids"] = list(
-        zip(*[sort[col] for col in ["id", "text"]], strict=True)
-    )
-
-    agg_dict = {"text_with_ids": list}
-    if "metadata" in documents:
-        agg_dict["metadata"] = "first"  # type: ignore
-
-    aggregated = (
-        (
-            sort.groupby(group_by_columns, sort=False)
-            if len(group_by_columns) > 0
-            else sort.groupby(lambda _x: True)
-        )
-        .agg(agg_dict)
-        .reset_index()
-    )
-    aggregated.rename(columns={"text_with_ids": "texts"}, inplace=True)
+    encode, _ = get_encoding_fn(encoding_model)
 
     def chunker(row: pd.Series) -> Any:
         line_delimiter = ".\n"
@@ -99,7 +80,6 @@ def chunker(row: pd.Series) -> Any:
                 )
 
             if chunk_size_includes_metadata:
-                encode, _ = get_encoding_fn(encoding_model)
                 metadata_tokens = len(encode(metadata_str))
                 if metadata_tokens >= size:
                     message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk."
@@ -107,7 +87,7 @@ def chunker(row: pd.Series) -> Any:
 
         chunked = chunk_text(
             pd.DataFrame([row]).reset_index(drop=True),
-            column="texts",
+            column="text",
             size=size - metadata_tokens,
             overlap=overlap,
             encoding_model=encoding_model,
@@ -128,7 +108,7 @@ def chunker(row: pd.Series) -> Any:
         return row
 
     # Track progress of row-wise apply operation
-    total_rows = len(aggregated)
+    total_rows = len(documents)
     logger.info("Starting chunking process for %d documents", total_rows)
 
     def chunker_with_logging(row: pd.Series, row_index: int) -> Any:
@@ -137,27 +117,26 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any:
         logger.info("chunker progress:  %d/%d", row_index + 1, total_rows)
         return result
 
-    aggregated = aggregated.apply(
+    text_units = documents.apply(
         lambda row: chunker_with_logging(row, row.name), axis=1
     )
 
-    aggregated = cast("pd.DataFrame", aggregated[[*group_by_columns, "chunks"]])
-    aggregated = aggregated.explode("chunks")
-    aggregated.rename(
+    text_units = cast("pd.DataFrame", text_units[["id", "chunks"]])
+    text_units = text_units.explode("chunks")
+    text_units.rename(
         columns={
-            "chunks": "chunk",
+            "id": "document_id",
+            "chunks": "text",
         },
         inplace=True,
     )
-    aggregated["id"] = aggregated.apply(
-        lambda row: gen_sha512_hash(row, ["chunk"]), axis=1
-    )
-    aggregated[["document_ids", "chunk", "n_tokens"]] = pd.DataFrame(
-        aggregated["chunk"].tolist(), index=aggregated.index
+
+    text_units["id"] = text_units.apply(
+        lambda row: gen_sha512_hash(row, ["text"]), axis=1
     )
-    # rename for downstream consumption
-    aggregated.rename(columns={"chunk": "text"}, inplace=True)
+    # get a final token measurement
+    text_units["n_tokens"] = text_units["text"].apply(lambda x: len(encode(x)))
 
     return cast(
-        "pd.DataFrame", aggregated[aggregated["text"].notna()].reset_index(drop=True)
+        "pd.DataFrame", text_units[text_units["text"].notna()].reset_index(drop=True)
     )
diff --git a/graphrag/index/workflows/create_final_documents.py b/graphrag/index/workflows/create_final_documents.py
index af81e8dfa8..f12560bab6 100644
--- a/graphrag/index/workflows/create_final_documents.py
+++ b/graphrag/index/workflows/create_final_documents.py
@@ -37,19 +37,15 @@ def create_final_documents(
     documents: pd.DataFrame, text_units: pd.DataFrame
 ) -> pd.DataFrame:
     """All the steps to transform final documents."""
-    exploded = (
-        text_units.explode("document_ids")
-        .loc[:, ["id", "document_ids", "text"]]
-        .rename(
-            columns={
-                "document_ids": "chunk_doc_id",
-                "id": "chunk_id",
-                "text": "chunk_text",
-            }
-        )
+    renamed = text_units.loc[:, ["id", "document_id", "text"]].rename(
+        columns={
+            "document_id": "chunk_doc_id",
+            "id": "chunk_id",
+            "text": "chunk_text",
+        }
     )
 
-    joined = exploded.merge(
+    joined = renamed.merge(
         documents,
         left_on="chunk_doc_id",
         right_on="id",
diff --git a/graphrag/index/workflows/create_final_text_units.py b/graphrag/index/workflows/create_final_text_units.py
index 373b9b4cb4..fbcf0af890 100644
--- a/graphrag/index/workflows/create_final_text_units.py
+++ b/graphrag/index/workflows/create_final_text_units.py
@@ -59,7 +59,7 @@ def create_final_text_units(
     final_covariates: pd.DataFrame | None,
 ) -> pd.DataFrame:
     """All the steps to transform the text units."""
-    selected = text_units.loc[:, ["id", "text", "document_ids", "n_tokens"]]
+    selected = text_units.loc[:, ["id", "text", "document_id", "n_tokens"]]
     selected["human_readable_id"] = selected.index
 
     entity_join = _entities(final_entities)
diff --git a/graphrag/prompt_tune/loader/input.py b/graphrag/prompt_tune/loader/input.py
index 29010b05a8..36c8fca003 100644
--- a/graphrag/prompt_tune/loader/input.py
+++ b/graphrag/prompt_tune/loader/input.py
@@ -58,7 +58,6 @@ async def load_docs_in_chunks(
     chunks_df = create_base_text_units(
         documents=dataset,
         callbacks=NoopWorkflowCallbacks(),
-        group_by_columns=chunk_config.group_by_columns,
         size=chunk_size,
         overlap=overlap,
         encoding_model=chunk_config.encoding_model,
diff --git a/graphrag/query/input/loaders/dfs.py b/graphrag/query/input/loaders/dfs.py
index 7182090cd2..17aeb604a2 100644
--- a/graphrag/query/input/loaders/dfs.py
+++ b/graphrag/query/input/loaders/dfs.py
@@ -234,7 +234,7 @@ def read_text_units(
     relationships_col: str | None = "relationship_ids",
     covariates_col: str | None = "covariate_ids",
     tokens_col: str | None = "n_tokens",
-    document_ids_col: str | None = "document_ids",
+    document_id_col: str | None = "document_id",
     attributes_cols: list[str] | None = None,
 ) -> list[TextUnit]:
     """Read text units from a dataframe using pre-converted records."""
@@ -250,7 +250,7 @@ def read_text_units(
                 row, covariates_col, key_type=str, value_type=str
             ),
             n_tokens=to_optional_int(row, tokens_col),
-            document_ids=to_optional_list(row, document_ids_col, item_type=str),
+            document_id=to_optional_str(row, document_id_col),
             attributes=(
                 {col: row.get(col) for col in attributes_cols}
                 if attributes_cols
diff --git a/tests/unit/config/fixtures/minimal_config/settings.yaml b/tests/unit/config/fixtures/minimal_config/settings.yaml
index 2fec50a3b9..2848968c06 100644
--- a/tests/unit/config/fixtures/minimal_config/settings.yaml
+++ b/tests/unit/config/fixtures/minimal_config/settings.yaml
@@ -2,8 +2,8 @@ models:
   default_chat_model:
     api_key: ${CUSTOM_API_KEY}
     type: openai_chat
-    model: gpt-4-turbo-preview
+    model: gpt-4o
   default_embedding_model:
     api_key: ${CUSTOM_API_KEY}
     type: openai_embedding
-    model: text-embedding-3-small
\ No newline at end of file
+    model: text-embedding-ada-002
\ No newline at end of file
diff --git a/tests/unit/config/fixtures/minimal_config_missing_env_var/settings.yaml b/tests/unit/config/fixtures/minimal_config_missing_env_var/settings.yaml
index 9e1c45715a..812917da08 100644
--- a/tests/unit/config/fixtures/minimal_config_missing_env_var/settings.yaml
+++ b/tests/unit/config/fixtures/minimal_config_missing_env_var/settings.yaml
@@ -2,8 +2,8 @@ models:
   default_chat_model:
     api_key: ${SOME_NON_EXISTENT_ENV_VAR}
     type: openai_chat
-    model: gpt-4-turbo-preview
+    model: gpt-4o
   default_embedding_model:
     api_key: ${SOME_NON_EXISTENT_ENV_VAR}
     type: openai_embedding
-    model: text-embedding-3-small
\ No newline at end of file
+    model: text-embedding-ada-002
\ No newline at end of file
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 8ca5e45a8c..dc903db9f4 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -192,7 +192,6 @@ def assert_text_embedding_configs(
 def assert_chunking_configs(actual: ChunkingConfig, expected: ChunkingConfig) -> None:
     assert actual.size == expected.size
     assert actual.overlap == expected.overlap
-    assert actual.group_by_columns == expected.group_by_columns
     assert actual.strategy == expected.strategy
     assert actual.encoding_model == expected.encoding_model
     assert actual.prepend_metadata == expected.prepend_metadata
diff --git a/tests/unit/indexing/graph/extractors/community_reports/test_sort_context.py b/tests/unit/indexing/graph/extractors/community_reports/test_sort_context.py
index c5911344b6..68ad54ea32 100644
--- a/tests/unit/indexing/graph/extractors/community_reports/test_sort_context.py
+++ b/tests/unit/indexing/graph/extractors/community_reports/test_sort_context.py
@@ -207,8 +207,8 @@ def test_sort_context():
     ctx = sort_context(context)
     assert ctx is not None, "Context is none"
     num = num_tokens(ctx)
-    assert num == 828 if platform.system() == "Windows" else 826, (
-        f"num_tokens is not matched for platform (win = 827, else 826): {num}"
+    assert num == 825 if platform.system() == "Windows" else 826, (
+        f"num_tokens is not matched for platform (win = 825, else 826): {num}"
     )
 
 
diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/indexing/text_splitting/test_text_splitting.py
index 10a5a06344..e30bb5e721 100644
--- a/tests/unit/indexing/text_splitting/test_text_splitting.py
+++ b/tests/unit/indexing/text_splitting/test_text_splitting.py
@@ -83,8 +83,7 @@ def test_token_text_splitter(mock_tokenizer, mock_split_text):
 def test_encode_basic():
     splitter = TokenTextSplitter()
     result = splitter.encode("abc def")
-
-    assert result == [13997, 711], "Encoding failed to return expected tokens"
+    assert result == [26682, 1056], "Encoding failed to return expected tokens"
 
 
 def test_num_tokens_empty_input():
diff --git a/tests/verbs/data/communities.parquet b/tests/verbs/data/communities.parquet
index d8a5c82dec9fa9191502542c57c62779f72d3c33..d85403d6b6f4f4e9a344b6992442392ef1ba7917 100644
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diff --git a/tests/verbs/data/documents.parquet b/tests/verbs/data/documents.parquet
index 654c5c7f5e49abba4ed1f124f406781d179627dc..a846fecfceef8df5b45ac59e008a04f54dee8a28 100644
GIT binary patch
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diff --git a/tests/verbs/data/entities.parquet b/tests/verbs/data/entities.parquet
index 8de7e6da158cfb611e64022c3876235fbde39e5c..0807395a7fec50c54d8d1b1749f570dbee5dcf0e 100644
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diff --git a/tests/verbs/data/text_units_metadata.parquet b/tests/verbs/data/text_units_metadata.parquet
index 71ec620e2ab50c6b2da900c0d8e83361fdc28c4e..9b6b44342a2c7db760ee0fb5331c575e69c7437d 100644
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diff --git a/tests/verbs/data/text_units_metadata_included_chunk.parquet b/tests/verbs/data/text_units_metadata_included_chunk.parquet
index ccf4b3903ef80963e26cde535d7e48631776d27e..ca2495975ec993ddda61167214bc0e8d41d48ea2 100644
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diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py
index ea34ae8b9c..87148a981d 100644
--- a/tests/verbs/test_create_base_text_units.py
+++ b/tests/verbs/test_create_base_text_units.py
@@ -25,7 +25,7 @@ async def test_create_base_text_units():
 
     actual = await load_table_from_storage("text_units", context.output_storage)
 
-    compare_outputs(actual, expected, columns=["text", "document_ids", "n_tokens"])
+    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])
 
 
 async def test_create_base_text_units_metadata():
@@ -34,8 +34,6 @@ async def test_create_base_text_units_metadata():
     context = await create_test_context()
 
     config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-    # test data was created with 4o, so we need to match the encoding for chunks to be identical
-    config.chunks.encoding_model = "o200k_base"
     config.input.metadata = ["title"]
     config.chunks.prepend_metadata = True
 
@@ -44,7 +42,7 @@ async def test_create_base_text_units_metadata():
     await run_workflow(config, context)
 
     actual = await load_table_from_storage("text_units", context.output_storage)
-    compare_outputs(actual, expected)
+    compare_outputs(actual, expected, ["text", "document_id", "n_tokens"])
 
 
 async def test_create_base_text_units_metadata_included_in_chunk():
@@ -53,8 +51,6 @@ async def test_create_base_text_units_metadata_included_in_chunk():
     context = await create_test_context()
 
     config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-    # test data was created with 4o, so we need to match the encoding for chunks to be identical
-    config.chunks.encoding_model = "o200k_base"
     config.input.metadata = ["title"]
     config.chunks.prepend_metadata = True
     config.chunks.chunk_size_includes_metadata = True
@@ -65,4 +61,4 @@ async def test_create_base_text_units_metadata_included_in_chunk():
 
     actual = await load_table_from_storage("text_units", context.output_storage)
     # only check the columns from the base workflow - our expected table is the final and will have more
-    compare_outputs(actual, expected, columns=["text", "document_ids", "n_tokens"])
+    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])
diff --git a/tests/verbs/test_extract_graph_nlp.py b/tests/verbs/test_extract_graph_nlp.py
index 8c9367ad36..92da89288c 100644
--- a/tests/verbs/test_extract_graph_nlp.py
+++ b/tests/verbs/test_extract_graph_nlp.py
@@ -29,7 +29,7 @@ async def test_extract_graph_nlp():
 
     # this will be the raw count of entities and edges with no pruning
     # with NLP it is deterministic, so we can assert exact row counts
-    assert len(nodes_actual) == 1148
+    assert len(nodes_actual) == 1147
     assert len(nodes_actual.columns) == 5
-    assert len(edges_actual) == 29445
+    assert len(edges_actual) == 29442
     assert len(edges_actual.columns) == 5
diff --git a/tests/verbs/test_prune_graph.py b/tests/verbs/test_prune_graph.py
index 34609d9cb5..6ed0001973 100644
--- a/tests/verbs/test_prune_graph.py
+++ b/tests/verbs/test_prune_graph.py
@@ -28,4 +28,4 @@ async def test_prune_graph():
 
     nodes_actual = await load_table_from_storage("entities", context.output_storage)
 
-    assert len(nodes_actual) == 20
+    assert len(nodes_actual) == 21
diff --git a/tests/verbs/util.py b/tests/verbs/util.py
index 8d342b4756..f28adae4f4 100644
--- a/tests/verbs/util.py
+++ b/tests/verbs/util.py
@@ -66,7 +66,10 @@ def compare_outputs(
     )
 
     for column in cols:
-        assert column in actual.columns
+        try:
+            assert column in actual.columns
+        except AssertionError:
+            print(f"Column '{column}' not found in actual output.")
         try:
             # dtypes can differ since the test data is read from parquet and our workflow runs in memory
             if column != "id":  # don't check uuids
@@ -77,6 +80,7 @@ def compare_outputs(
                     check_index=False,
                 )
         except AssertionError:
+            print(f"Column '{column}' does not match.")
             print("Expected:")
             print(expected[column])
             print("Actual:")

From 04d9f585c8b507651ed55768e85b6dc7a7e55591 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 9 Sep 2025 16:26:07 -0700
Subject: [PATCH 04/40] Re-implement hierarchical Leiden (#2049)

* Use graspologic-native hierarchical leiden

* Re-implement largest_connected_component

* Copy in modularity

* Use graspologic-native directly in pyproject

* Remove directed graph tests (we don't use this)

* Semver

* Remove graspologic dep
---
 .../major-20250909002702300683.json           |   4 +
 graphrag/index/operations/cluster_graph.py    |   2 +-
 graphrag/index/operations/prune_graph.py      |   4 +-
 graphrag/index/utils/graphs.py                | 125 ++++-
 graphrag/index/utils/stable_lcc.py            |   5 +-
 pyproject.toml                                |   4 +-
 .../indexing/graph/utils/test_stable_lcc.py   |  26 -
 uv.lock                                       | 451 +-----------------
 8 files changed, 133 insertions(+), 488 deletions(-)
 create mode 100644 .semversioner/next-release/major-20250909002702300683.json

diff --git a/.semversioner/next-release/major-20250909002702300683.json b/.semversioner/next-release/major-20250909002702300683.json
new file mode 100644
index 0000000000..266194d230
--- /dev/null
+++ b/.semversioner/next-release/major-20250909002702300683.json
@@ -0,0 +1,4 @@
+{
+  "type": "major",
+  "description": "Re-implement graspologic methods to remove dependency. Remove visualization steps."
+}
diff --git a/graphrag/index/operations/cluster_graph.py b/graphrag/index/operations/cluster_graph.py
index ae4f83f47d..d7e1b0c7ca 100644
--- a/graphrag/index/operations/cluster_graph.py
+++ b/graphrag/index/operations/cluster_graph.py
@@ -6,8 +6,8 @@
 import logging
 
 import networkx as nx
-from graspologic.partition import hierarchical_leiden
 
+from graphrag.index.utils.graphs import hierarchical_leiden
 from graphrag.index.utils.stable_lcc import stable_largest_connected_component
 
 Communities = list[tuple[int, int, int, list[str]]]
diff --git a/graphrag/index/operations/prune_graph.py b/graphrag/index/operations/prune_graph.py
index d826558584..b03dfb7b17 100644
--- a/graphrag/index/operations/prune_graph.py
+++ b/graphrag/index/operations/prune_graph.py
@@ -5,11 +5,11 @@
 
 from typing import TYPE_CHECKING, cast
 
-import graspologic as glc
 import networkx as nx
 import numpy as np
 
 import graphrag.data_model.schemas as schemas
+from graphrag.index.utils.graphs import largest_connected_component
 
 if TYPE_CHECKING:
     from networkx.classes.reportviews import DegreeView
@@ -78,7 +78,7 @@ def prune_graph(
         ])
 
     if lcc_only:
-        return glc.utils.largest_connected_component(graph)  # type: ignore
+        return largest_connected_component(graph)
 
     return graph
 
diff --git a/graphrag/index/utils/graphs.py b/graphrag/index/utils/graphs.py
index 6c06e05cbc..5014729a95 100644
--- a/graphrag/index/utils/graphs.py
+++ b/graphrag/index/utils/graphs.py
@@ -1,22 +1,136 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""Collection of graph utility functions."""
+"""
+Collection of graph utility functions.
+
+These are largely copies/re-implementations of graspologic methods to avoid dependency issues.
+"""
 
 import logging
-from typing import cast
+import math
+from collections import defaultdict
+from typing import Any, cast
 
+import graspologic_native as gn
 import networkx as nx
 import numpy as np
 import pandas as pd
-from graspologic.partition import hierarchical_leiden, modularity
-from graspologic.utils import largest_connected_component
 
 from graphrag.config.enums import ModularityMetric
 
 logger = logging.getLogger(__name__)
 
 
+def largest_connected_component(graph: nx.Graph) -> nx.Graph:
+    """Return the largest connected component of the graph."""
+    graph = graph.copy()
+    lcc_nodes = max(nx.connected_components(graph), key=len)
+    lcc = graph.subgraph(lcc_nodes).copy()
+    lcc.remove_nodes_from([n for n in lcc if n not in lcc_nodes])
+    return cast("nx.Graph", lcc)
+
+
+def _nx_to_edge_list(
+    graph: nx.Graph,
+    weight_attribute: str = "weight",
+    weight_default: float = 1.0,
+) -> list[tuple[str, str, float]]:
+    """
+    Convert an undirected, non-multigraph networkx graph to a list of edges.
+
+    Each edge is represented as a tuple of (source_str, target_str, weight).
+    """
+    edge_list: list[tuple[str, str, float]] = []
+
+    # Decide how to retrieve the weight data
+    edge_iter = graph.edges(data=weight_attribute, default=weight_default)  # type: ignore
+
+    for source, target, weight in edge_iter:
+        source_str = str(source)
+        target_str = str(target)
+        edge_list.append((source_str, target_str, float(weight)))
+
+    return edge_list
+
+
+def hierarchical_leiden(
+    graph: nx.Graph,
+    max_cluster_size: int = 10,
+    random_seed: int | None = 0xDEADBEEF,
+) -> Any:
+    """Run hierarchical leiden on the graph."""
+    return gn.hierarchical_leiden(
+        edges=_nx_to_edge_list(graph),
+        max_cluster_size=max_cluster_size,
+        seed=random_seed,
+        starting_communities=None,
+        resolution=1.0,
+        randomness=0.001,
+        use_modularity=True,
+        iterations=1,
+    )
+
+
+def modularity(
+    graph: nx.Graph,
+    partitions: dict[Any, int],
+    weight_attribute: str = "weight",
+    resolution: float = 1.0,
+) -> float:
+    """Given an undirected graph and a dictionary of vertices to community ids, calculate the modularity."""
+    components = _modularity_components(graph, partitions, weight_attribute, resolution)
+    return sum(components.values())
+
+
+def _modularity_component(
+    intra_community_degree: float,
+    total_community_degree: float,
+    network_degree_sum: float,
+    resolution: float,
+) -> float:
+    community_degree_ratio = math.pow(total_community_degree, 2.0) / (
+        2.0 * network_degree_sum
+    )
+    return (intra_community_degree - resolution * community_degree_ratio) / (
+        2.0 * network_degree_sum
+    )
+
+
+def _modularity_components(
+    graph: nx.Graph,
+    partitions: dict[Any, int],
+    weight_attribute: str = "weight",
+    resolution: float = 1.0,
+) -> dict[int, float]:
+    total_edge_weight = 0.0
+    communities = set(partitions.values())
+
+    degree_sums_within_community: dict[int, float] = defaultdict(lambda: 0.0)
+    degree_sums_for_community: dict[int, float] = defaultdict(lambda: 0.0)
+    for vertex, neighbor_vertex, weight in graph.edges(data=weight_attribute):
+        vertex_community = partitions[vertex]
+        neighbor_community = partitions[neighbor_vertex]
+        if vertex_community == neighbor_community:
+            if vertex == neighbor_vertex:
+                degree_sums_within_community[vertex_community] += weight
+            else:
+                degree_sums_within_community[vertex_community] += weight * 2.0
+        degree_sums_for_community[vertex_community] += weight
+        degree_sums_for_community[neighbor_community] += weight
+        total_edge_weight += weight
+
+    return {
+        comm: _modularity_component(
+            degree_sums_within_community[comm],
+            degree_sums_for_community[comm],
+            total_edge_weight,
+            resolution,
+        )
+        for comm in communities
+    }
+
+
 def calculate_root_modularity(
     graph: nx.Graph,
     max_cluster_size: int = 10,
@@ -147,9 +261,6 @@ def calculate_modularity(
                 random_seed=random_seed,
                 use_root_modularity=use_root_modularity,
             )
-        case _:
-            msg = f"Unknown modularity metric type: {modularity_metric}"
-            raise ValueError(msg)
 
 
 def calculate_pmi_edge_weights(
diff --git a/graphrag/index/utils/stable_lcc.py b/graphrag/index/utils/stable_lcc.py
index 070311331b..806c0fd486 100644
--- a/graphrag/index/utils/stable_lcc.py
+++ b/graphrag/index/utils/stable_lcc.py
@@ -8,12 +8,11 @@
 
 import networkx as nx
 
+from graphrag.index.utils.graphs import largest_connected_component
+
 
 def stable_largest_connected_component(graph: nx.Graph) -> nx.Graph:
     """Return the largest connected component of the graph, with nodes and edges sorted in a stable way."""
-    # NOTE: The import is done here to reduce the initial import time of the module
-    from graspologic.utils import largest_connected_component
-
     graph = graph.copy()
     graph = cast("nx.Graph", largest_connected_component(graph))
     graph = normalize_node_names(graph)
diff --git a/pyproject.toml b/pyproject.toml
index 7e67ed01b3..8b08298110 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,7 +46,6 @@ dependencies = [
     "tiktoken>=0.9.0",
     # Data-Science
     "numpy>=1.25.2",
-    "graspologic>=3.4.1",
     "networkx>=3.4.2",
     "pandas>=2.2.3",
     "pyarrow>=17.0.0",
@@ -66,6 +65,7 @@ dependencies = [
     "tqdm>=4.67.1",
     "textblob>=0.18.0.post0",
     "spacy>=3.8.4",
+    "graspologic-native>=1.2.5",
 ]
 
 [project.optional-dependencies]
@@ -260,4 +260,4 @@ exclude = ["**/node_modules", "**/__pycache__"]
 asyncio_default_fixture_loop_scope = "function"
 asyncio_mode = "auto"
 timeout = 1000
-env_files = [".env"]
\ No newline at end of file
+env_files = [".env"]
diff --git a/tests/unit/indexing/graph/utils/test_stable_lcc.py b/tests/unit/indexing/graph/utils/test_stable_lcc.py
index 244f3b905d..1bea2a9185 100644
--- a/tests/unit/indexing/graph/utils/test_stable_lcc.py
+++ b/tests/unit/indexing/graph/utils/test_stable_lcc.py
@@ -20,32 +20,6 @@ def test_undirected_graph_run_twice_produces_same_graph(self):
             nx.generate_graphml(graph_out_2)
         )
 
-    def test_directed_graph_keeps_source_target_intact(self):
-        # create the test graph as a directed graph
-        graph_in = self._create_strongly_connected_graph_with_edges_flipped(
-            digraph=True
-        )
-        graph_out = stable_largest_connected_component(graph_in.copy())
-
-        # Make sure edges are the same and the direction is preserved
-        edges_1 = [f"{edge[0]} -> {edge[1]}" for edge in graph_in.edges(data=True)]
-        edges_2 = [f"{edge[0]} -> {edge[1]}" for edge in graph_out.edges(data=True)]
-
-        assert edges_1 == edges_2
-
-    def test_directed_graph_run_twice_produces_same_graph(self):
-        # create the test graph as a directed graph
-        graph_in = self._create_strongly_connected_graph_with_edges_flipped(
-            digraph=True
-        )
-        graph_out_1 = stable_largest_connected_component(graph_in.copy())
-        graph_out_2 = stable_largest_connected_component(graph_in.copy())
-
-        # Make sure the output is identical when run multiple times
-        assert "".join(nx.generate_graphml(graph_out_1)) == "".join(
-            nx.generate_graphml(graph_out_2)
-        )
-
     def _create_strongly_connected_graph(self, digraph=False):
         graph = nx.Graph() if not digraph else nx.DiGraph()
         graph.add_node("1", node_name=1)
diff --git a/uv.lock b/uv.lock
index f1c4ecb522..59a4e57e86 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -48,15 +49,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
 ]
 
-[[package]]
-name = "anytree"
-version = "2.13.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bc/a8/eb55fab589c56f9b6be2b3fd6997aa04bb6f3da93b01154ce6fc8e799db2/anytree-2.13.0.tar.gz", hash = "sha256:c9d3aa6825fdd06af7ebb05b4ef291d2db63e62bb1f9b7d9b71354be9d362714", size = 48389 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7b/98/f6aa7fe0783e42be3093d8ef1b0ecdc22c34c0d69640dfb37f56925cb141/anytree-2.13.0-py3-none-any.whl", hash = "sha256:4cbcf10df36b1f1cba131b7e487ff3edafc9d6e932a3c70071b5b768bab901ff", size = 45077 },
-]
-
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -145,18 +137,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815 },
 ]
 
-[[package]]
-name = "autograd"
-version = "1.8.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
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-]
-
 [[package]]
 name = "tenacity"
 version = "9.1.2"

From de767cc881e26ad545265a1f25d23e70ee1ce768 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Wed, 24 Sep 2025 14:49:55 -0700
Subject: [PATCH 05/40] Use 4.1 and text-embedding-3-large as defaults

---
 docs/config/yaml.md                                  | 4 ++--
 graphrag/config/defaults.py                          | 6 ++----
 graphrag/config/init_content.py                      | 8 ++++----
 graphrag/config/models/vector_store_schema_config.py | 2 +-
 tests/mock_provider.py                               | 4 ++--
 5 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 1a4ce326e8..0e4ffe3bdf 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -29,12 +29,12 @@ models:
   default_chat_model:
     api_key: ${GRAPHRAG_API_KEY}
     type: openai_chat
-    model: gpt-4o
+    model: gpt-4.1
     model_supports_json: true
   default_embedding_model:
     api_key: ${GRAPHRAG_API_KEY}
     type: openai_embedding
-    model: text-embedding-ada-002
+    model: text-embedding-3-large
 ```
 
 #### Fields
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index 6febd288b1..12029e4574 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -47,11 +47,11 @@
 DEFAULT_OUTPUT_BASE_DIR = "output"
 DEFAULT_CHAT_MODEL_ID = "default_chat_model"
 DEFAULT_CHAT_MODEL_TYPE = ModelType.OpenAIChat
-DEFAULT_CHAT_MODEL = "gpt-4o"
+DEFAULT_CHAT_MODEL = "gpt-4.1"
 DEFAULT_CHAT_MODEL_AUTH_TYPE = AuthType.APIKey
 DEFAULT_EMBEDDING_MODEL_ID = "default_embedding_model"
 DEFAULT_EMBEDDING_MODEL_TYPE = ModelType.OpenAIEmbedding
-DEFAULT_EMBEDDING_MODEL = "text-embedding-ada-002"
+DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large"
 DEFAULT_EMBEDDING_MODEL_AUTH_TYPE = AuthType.APIKey
 DEFAULT_VECTOR_STORE_ID = "default_vector_store"
 
@@ -309,8 +309,6 @@ class LanguageModelDefaults:
     proxy: None = None
     audience: None = None
     model_supports_json: None = None
-    tokens_per_minute: Literal["auto"] = "auto"
-    requests_per_minute: Literal["auto"] = "auto"
     rate_limit_strategy: str | None = "static"
     retry_strategy: str = "native"
     max_retries: int = 10
diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py
index 7d66cd68ad..cf483f6b90 100644
--- a/graphrag/config/init_content.py
+++ b/graphrag/config/init_content.py
@@ -32,10 +32,10 @@
     model_supports_json: true # recommended if this is available for your model.
     concurrent_requests: {language_model_defaults.concurrent_requests} # max number of simultaneous LLM requests allowed
     async_mode: {language_model_defaults.async_mode.value} # or asyncio
-    retry_strategy: native
+    retry_strategy: exponential_backoff
     max_retries: {language_model_defaults.max_retries}
-    tokens_per_minute: {language_model_defaults.tokens_per_minute}              # set to null to disable rate limiting
-    requests_per_minute: {language_model_defaults.requests_per_minute}            # set to null to disable rate limiting
+    tokens_per_minute: null              # set to null to disable rate limiting
+    requests_per_minute: null            # set to null to disable rate limiting
   {defs.DEFAULT_EMBEDDING_MODEL_ID}:
     type: {defs.DEFAULT_EMBEDDING_MODEL_TYPE.value} # or azure_openai_embedding
     # api_base: https://<instance>.openai.azure.com
@@ -50,7 +50,7 @@
     model_supports_json: true # recommended if this is available for your model.
     concurrent_requests: {language_model_defaults.concurrent_requests} # max number of simultaneous LLM requests allowed
     async_mode: {language_model_defaults.async_mode.value} # or asyncio
-    retry_strategy: native
+    retry_strategy: exponential_backoff
     max_retries: {language_model_defaults.max_retries}
     tokens_per_minute: null              # set to null to disable rate limiting or auto for dynamic
     requests_per_minute: null            # set to null to disable rate limiting or auto for dynamic
diff --git a/graphrag/config/models/vector_store_schema_config.py b/graphrag/config/models/vector_store_schema_config.py
index e149192515..2da0cdc17a 100644
--- a/graphrag/config/models/vector_store_schema_config.py
+++ b/graphrag/config/models/vector_store_schema_config.py
@@ -7,7 +7,7 @@
 
 from pydantic import BaseModel, Field, model_validator
 
-DEFAULT_VECTOR_SIZE: int = 1536
+DEFAULT_VECTOR_SIZE: int = 3072
 
 VALID_IDENTIFIER_REGEX = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
 
diff --git a/tests/mock_provider.py b/tests/mock_provider.py
index d68fd762df..2c15ee347f 100644
--- a/tests/mock_provider.py
+++ b/tests/mock_provider.py
@@ -30,7 +30,7 @@ def __init__(
         self.responses = config.responses if config and config.responses else responses
         self.response_index = 0
         self.config = config or LanguageModelConfig(
-            type=ModelType.MockChat, model="gpt-4o", api_key="mock"
+            type=ModelType.MockChat, model="gpt-4.1", api_key="mock"
         )
 
     async def achat(
@@ -99,7 +99,7 @@ class MockEmbeddingLLM:
 
     def __init__(self, **kwargs: Any):
         self.config = LanguageModelConfig(
-            type=ModelType.MockEmbedding, model="text-embedding-ada-002", api_key="mock"
+            type=ModelType.MockEmbedding, model="text-embedding-3-large", api_key="mock"
         )
 
     def embed_batch(self, text_list: list[str], **kwargs: Any) -> list[list[float]]:

From d7516825e276a7ecac79f517023fdcdc80d8722d Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Wed, 24 Sep 2025 14:51:07 -0700
Subject: [PATCH 06/40] Update comment

---
 graphrag/index/text_splitting/text_splitting.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphrag/index/text_splitting/text_splitting.py b/graphrag/index/text_splitting/text_splitting.py
index 83bcaf493b..85b502e639 100644
--- a/graphrag/index/text_splitting/text_splitting.py
+++ b/graphrag/index/text_splitting/text_splitting.py
@@ -50,8 +50,8 @@ class TextSplitter(ABC):
 
     def __init__(
         self,
-        # based on text-ada-002-embedding max input buffer length
-        # https://platform.openai.com/docs/guides/embeddings/second-generation-models
+        # based on OpenAI embedding chunk size limits
+        # https://devblogs.microsoft.com/azure-sql/embedding-models-and-dimensions-optimizing-the-performance-resource-usage-ratio/
         chunk_size: int = 8191,
         chunk_overlap: int = 100,
         length_function: LengthFn = len,

From d7773bd15c74a27eabf583f208928552ffe5893e Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Thu, 25 Sep 2025 21:17:10 -0600
Subject: [PATCH 07/40] Clean vector store (#2077)

* clean vector store code

* fix

* fix launch.json

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 .../local_search/mixed_context.py             |  4 ----
 graphrag/vector_stores/azure_ai_search.py     | 20 ++-----------------
 graphrag/vector_stores/base.py                |  8 ++------
 graphrag/vector_stores/cosmosdb.py            | 18 ++---------------
 graphrag/vector_stores/lancedb.py             | 18 ++---------------
 .../vector_stores/test_azure_ai_search.py     |  9 ---------
 .../vector_stores/test_cosmosdb.py            |  4 ----
 .../integration/vector_stores/test_lancedb.py | 14 +++----------
 8 files changed, 11 insertions(+), 84 deletions(-)

diff --git a/graphrag/query/structured_search/local_search/mixed_context.py b/graphrag/query/structured_search/local_search/mixed_context.py
index 1ee8973fc4..b91272d164 100644
--- a/graphrag/query/structured_search/local_search/mixed_context.py
+++ b/graphrag/query/structured_search/local_search/mixed_context.py
@@ -84,10 +84,6 @@ def __init__(
         self.tokenizer = tokenizer or get_tokenizer()
         self.embedding_vectorstore_key = embedding_vectorstore_key
 
-    def filter_by_entity_keys(self, entity_keys: list[int] | list[str]):
-        """Filter entity text embeddings by entity keys."""
-        self.entity_text_embeddings.filter_by_id(entity_keys)
-
     def build_context(
         self,
         query: str,
diff --git a/graphrag/vector_stores/azure_ai_search.py b/graphrag/vector_stores/azure_ai_search.py
index 3adce873ef..037cc28754 100644
--- a/graphrag/vector_stores/azure_ai_search.py
+++ b/graphrag/vector_stores/azure_ai_search.py
@@ -149,24 +149,8 @@ def load_documents(
         if len(batch) > 0:
             self.db_connection.upload_documents(batch)
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by a list of ids."""
-        if include_ids is None or len(include_ids) == 0:
-            self.query_filter = None
-            # Returning to keep consistency with other methods, but not needed
-            return self.query_filter
-
-        # More info about odata filtering here: https://learn.microsoft.com/en-us/azure/search/search-query-odata-search-in-function
-        # search.in is faster that joined and/or conditions
-        id_filter = ",".join([f"{id!s}" for id in include_ids])
-        self.query_filter = f"search.in({self.id_field}, '{id_filter}', ',')"
-
-        # Returning to keep consistency with other methods, but not needed
-        # TODO: Refactor on a future PR
-        return self.query_filter
-
     def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+        self, query_embedding: list[float], k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
         vectorized_query = VectorizedQuery(
@@ -193,7 +177,7 @@ def similarity_search_by_vector(
         ]
 
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a text-based similarity search."""
         query_embedding = text_embedder(text)
diff --git a/graphrag/vector_stores/base.py b/graphrag/vector_stores/base.py
index 762bc93232..2008fb472d 100644
--- a/graphrag/vector_stores/base.py
+++ b/graphrag/vector_stores/base.py
@@ -71,20 +71,16 @@ def load_documents(
 
     @abstractmethod
     def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+        self, query_embedding: list[float], k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform ANN search by vector."""
 
     @abstractmethod
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform ANN search by text."""
 
-    @abstractmethod
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by id."""
-
     @abstractmethod
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""
diff --git a/graphrag/vector_stores/cosmosdb.py b/graphrag/vector_stores/cosmosdb.py
index 6703c0ff08..186412d6ba 100644
--- a/graphrag/vector_stores/cosmosdb.py
+++ b/graphrag/vector_stores/cosmosdb.py
@@ -179,7 +179,7 @@ def load_documents(
                 self._container_client.upsert_item(doc_json)
 
     def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+        self, query_embedding: list[float], k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
         if self._container_client is None:
@@ -241,7 +241,7 @@ def cosine_similarity(a, b):
         ]
 
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a text-based similarity search."""
         query_embedding = text_embedder(text)
@@ -251,20 +251,6 @@ def similarity_search_by_text(
             )
         return []
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by a list of ids."""
-        if include_ids is None or len(include_ids) == 0:
-            self.query_filter = None
-        else:
-            if isinstance(include_ids[0], str):
-                id_filter = ", ".join([f"'{id}'" for id in include_ids])
-            else:
-                id_filter = ", ".join([str(id) for id in include_ids])
-            self.query_filter = (
-                f"SELECT * FROM c WHERE c.{self.id_field} IN ({id_filter})"  # noqa: S608
-            )
-        return self.query_filter
-
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""
         if self._container_client is None:
diff --git a/graphrag/vector_stores/lancedb.py b/graphrag/vector_stores/lancedb.py
index e2b52858ec..7c03d97386 100644
--- a/graphrag/vector_stores/lancedb.py
+++ b/graphrag/vector_stores/lancedb.py
@@ -100,22 +100,8 @@ def load_documents(
             if data:
                 self.document_collection.add(data)
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by id."""
-        if len(include_ids) == 0:
-            self.query_filter = None
-        else:
-            if isinstance(include_ids[0], str):
-                id_filter = ", ".join([f"'{id}'" for id in include_ids])
-                self.query_filter = f"{self.id_field} in ({id_filter})"
-            else:
-                self.query_filter = (
-                    f"{self.id_field} in ({', '.join([str(id) for id in include_ids])})"
-                )
-        return self.query_filter
-
     def similarity_search_by_vector(
-        self, query_embedding: list[float] | np.ndarray, k: int = 10, **kwargs: Any
+        self, query_embedding: list[float] | np.ndarray, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
         if self.query_filter:
@@ -151,7 +137,7 @@ def similarity_search_by_vector(
         ]
 
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a similarity search using a given input text."""
         query_embedding = text_embedder(text)
diff --git a/tests/integration/vector_stores/test_azure_ai_search.py b/tests/integration/vector_stores/test_azure_ai_search.py
index 2ff3347444..2d67c62ea8 100644
--- a/tests/integration/vector_stores/test_azure_ai_search.py
+++ b/tests/integration/vector_stores/test_azure_ai_search.py
@@ -136,9 +136,6 @@ async def test_vector_store_operations(
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
 
-        filter_query = vector_store.filter_by_id(["doc1", "doc2"])
-        assert filter_query == "search.in(id, 'doc1,doc2', ',')"
-
         vector_results = vector_store.similarity_search_by_vector(
             [0.1, 0.2, 0.3, 0.4, 0.5], k=2
         )
@@ -215,12 +212,6 @@ async def test_vector_store_customization(
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
 
-        filter_query = vector_store_custom.filter_by_id(["doc1", "doc2"])
-        assert (
-            filter_query
-            == f"search.in({vector_store_custom.id_field}, 'doc1,doc2', ',')"
-        )
-
         vector_results = vector_store_custom.similarity_search_by_vector(
             [0.1, 0.2, 0.3, 0.4, 0.5], k=2
         )
diff --git a/tests/integration/vector_stores/test_cosmosdb.py b/tests/integration/vector_stores/test_cosmosdb.py
index 768858d578..f9515beed9 100644
--- a/tests/integration/vector_stores/test_cosmosdb.py
+++ b/tests/integration/vector_stores/test_cosmosdb.py
@@ -50,8 +50,6 @@ def test_vector_store_operations():
         ]
         vector_store.load_documents(docs)
 
-        vector_store.filter_by_id(["doc1"])
-
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
         assert doc.text == "This is document 1"
@@ -140,8 +138,6 @@ def test_vector_store_customization():
         ]
         vector_store.load_documents(docs)
 
-        vector_store.filter_by_id(["doc1"])
-
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
         assert doc.text == "This is document 1"
diff --git a/tests/integration/vector_stores/test_lancedb.py b/tests/integration/vector_stores/test_lancedb.py
index a3fe4e3a70..cca48a1271 100644
--- a/tests/integration/vector_stores/test_lancedb.py
+++ b/tests/integration/vector_stores/test_lancedb.py
@@ -91,9 +91,6 @@ def test_vector_store_operations(self, sample_documents):
             assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
             assert doc.attributes["title"] == "Doc 1"
 
-            filter_query = vector_store.filter_by_id(["1"])
-            assert filter_query == "id in ('1')"
-
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=2
             )
@@ -186,16 +183,14 @@ def test_filter_search(self, sample_documents_categories):
             vector_store.load_documents(sample_documents_categories)
 
             # Filter to include only documents about animals
-            vector_store.filter_by_id(["1", "2"])
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=3
             )
 
-            # Should return at most 2 documents (the filtered ones)
-            assert len(results) <= 2
+            # Should return at most 3 documents (the filtered ones)
+            assert len(results) <= 3
             ids = [result.document.id for result in results]
-            assert "3" not in ids
-            assert set(ids).issubset({"1", "2"})
+            assert set(ids).issubset({"1", "2", "3"})
         finally:
             shutil.rmtree(temp_dir)
 
@@ -230,9 +225,6 @@ def test_vector_store_customization(self, sample_documents):
             assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
             assert doc.attributes["title"] == "Doc 1"
 
-            filter_query = vector_store.filter_by_id(["1"])
-            assert filter_query == f"{vector_store.id_field} in ('1')"
-
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=2
             )

From ebe959a9db7568aeb5e61e576f41d656ca3523ef Mon Sep 17 00:00:00 2001
From: Andres Morales <86074752+andresmor-ms@users.noreply.github.com>
Date: Tue, 30 Sep 2025 15:48:40 -0600
Subject: [PATCH 08/40] Update v3/main missing config + functions (#2082)

---
 graphrag/config/defaults.py                   |    4 +-
 .../index/operations/embed_text/embed_text.py |    8 +-
 graphrag/index/utils/graphs.py                |   35 +-
 graphrag/logger/progress.py                   |    4 +-
 graphrag/prompt_tune/loader/input.py          |    3 +-
 pyproject.toml                                |    1 +
 uv.lock                                       | 3729 +++++++++--------
 7 files changed, 1910 insertions(+), 1874 deletions(-)

diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index 12029e4574..9099e601a4 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -6,7 +6,7 @@
 from collections.abc import Callable
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import ClassVar, Literal
+from typing import ClassVar
 
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
@@ -311,6 +311,8 @@ class LanguageModelDefaults:
     model_supports_json: None = None
     rate_limit_strategy: str | None = "static"
     retry_strategy: str = "native"
+    tokens_per_minute: None = None
+    requests_per_minute: None = None
     max_retries: int = 10
     max_retry_wait: float = 10.0
     concurrent_requests: int = 25
diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py
index 70b4f93fba..14a6fd54b3 100644
--- a/graphrag/index/operations/embed_text/embed_text.py
+++ b/graphrag/index/operations/embed_text/embed_text.py
@@ -89,7 +89,7 @@ async def _text_embed_in_memory(
     strategy_exec = load_strategy(strategy_type)
     strategy_config = {**strategy}
 
-    texts: list[str] = input[embed_column].to_numpy().tolist()
+    texts: list[str] = input[embed_column].tolist()
     result = await strategy_exec(texts, callbacks, cache, strategy_config)
 
     return result.embeddings
@@ -151,9 +151,9 @@ async def _text_embed_with_vector_store(
             insert_batch_size,
         )
         batch = input.iloc[insert_batch_size * i : insert_batch_size * (i + 1)]
-        texts: list[str] = batch[embed_column].to_numpy().tolist()
-        titles: list[str] = batch[title].to_numpy().tolist()
-        ids: list[str] = batch[id_column].to_numpy().tolist()
+        texts: list[str] = batch[embed_column].tolist()
+        titles: list[str] = batch[title].tolist()
+        ids: list[str] = batch[id_column].tolist()
         result = await strategy_exec(texts, callbacks, cache, strategy_config)
         if result.embeddings:
             embeddings = [
diff --git a/graphrag/index/utils/graphs.py b/graphrag/index/utils/graphs.py
index 5014729a95..028f36c838 100644
--- a/graphrag/index/utils/graphs.py
+++ b/graphrag/index/utils/graphs.py
@@ -58,7 +58,7 @@ def hierarchical_leiden(
     graph: nx.Graph,
     max_cluster_size: int = 10,
     random_seed: int | None = 0xDEADBEEF,
-) -> Any:
+) -> list[gn.HierarchicalCluster]:
     """Run hierarchical leiden on the graph."""
     return gn.hierarchical_leiden(
         edges=_nx_to_edge_list(graph),
@@ -140,7 +140,7 @@ def calculate_root_modularity(
     hcs = hierarchical_leiden(
         graph, max_cluster_size=max_cluster_size, random_seed=random_seed
     )
-    root_clusters = hcs.first_level_hierarchical_clustering()
+    root_clusters = first_level_hierarchical_clustering(hcs)
     return modularity(graph, root_clusters)
 
 
@@ -153,7 +153,7 @@ def calculate_leaf_modularity(
     hcs = hierarchical_leiden(
         graph, max_cluster_size=max_cluster_size, random_seed=random_seed
     )
-    leaf_clusters = hcs.final_level_hierarchical_clustering()
+    leaf_clusters = final_level_hierarchical_clustering(hcs)
     return modularity(graph, leaf_clusters)
 
 
@@ -351,3 +351,32 @@ def get_upper_threshold_by_std(data: list[float] | list[int], std_trim: float) -
     mean = np.mean(data)
     std = np.std(data)
     return cast("float", mean + std_trim * std)
+
+
+def first_level_hierarchical_clustering(
+    hcs: list[gn.HierarchicalCluster],
+) -> dict[Any, int]:
+    """first_level_hierarchical_clustering.
+
+    Returns
+    -------
+    dict[Any, int]
+        The initial leiden algorithm clustering results as a dictionary
+        of node id to community id.
+    """
+    return {entry.node: entry.cluster for entry in hcs if entry.level == 0}
+
+
+def final_level_hierarchical_clustering(
+    hcs: list[gn.HierarchicalCluster],
+) -> dict[Any, int]:
+    """
+    final_level_hierarchical_clustering.
+
+    Returns
+    -------
+    dict[Any, int]
+        The last leiden algorithm clustering results as a dictionary
+        of node id to community id.
+    """
+    return {entry.node: entry.cluster for entry in hcs if entry.is_final_cluster}
diff --git a/graphrag/logger/progress.py b/graphrag/logger/progress.py
index 3393091c43..20617a647f 100644
--- a/graphrag/logger/progress.py
+++ b/graphrag/logger/progress.py
@@ -56,9 +56,7 @@ def __call__(self, num_ticks: int = 1) -> None:
                 description=self._description,
             )
             if p.description:
-                logger.info(
-                    "%s%s/%s", p.description, str(p.completed_items), str(p.total_items)
-                )
+                logger.info("%s%s/%s", p.description, p.completed_items, p.total_items)
             self._callback(p)
 
     def done(self) -> None:
diff --git a/graphrag/prompt_tune/loader/input.py b/graphrag/prompt_tune/loader/input.py
index 36c8fca003..235a78fe7a 100644
--- a/graphrag/prompt_tune/loader/input.py
+++ b/graphrag/prompt_tune/loader/input.py
@@ -4,6 +4,7 @@
 """Input loading module."""
 
 import logging
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -27,7 +28,7 @@
 
 def _sample_chunks_from_embeddings(
     text_chunks: pd.DataFrame,
-    embeddings: np.ndarray[float, np.dtype[np.float_]],
+    embeddings: np.ndarray[Any, np.dtype[np.float64]],
     k: int = K,
 ) -> pd.DataFrame:
     """Sample text chunks from embeddings."""
diff --git a/pyproject.toml b/pyproject.toml
index 8720d9281c..d61eeab279 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -239,6 +239,7 @@ ignore = [
     "PERF203", # Needs restructuring of errors, we should bail-out on first error
     "C901",    # needs refactoring to remove cyclomatic complexity
     "B008", # Needs to restructure our cli params with Typer into constants
+    "ASYNC240", 
 ]
 
 [tool.ruff.lint.per-file-ignores]
diff --git a/uv.lock b/uv.lock
index 01bb9a7202..22bb182b0b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -11,18 +11,18 @@ resolution-markers = [
 name = "aiofiles"
 version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
@@ -39,68 +39,68 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/47/dc/ef9394bde9080128ad401ac7ede185267ed637df03b51f05d14d1c99ad67/aiohttp-3.12.15-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b6fc902bff74d9b1879ad55f5404153e2b33a82e72a95c89cec5eb6cc9e92fbc", size = 703921 },
-    { url = "https://files.pythonhosted.org/packages/8f/42/63fccfc3a7ed97eb6e1a71722396f409c46b60a0552d8a56d7aad74e0df5/aiohttp-3.12.15-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:098e92835b8119b54c693f2f88a1dec690e20798ca5f5fe5f0520245253ee0af", size = 480288 },
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From 79ad9b96f3115bb3b535335a2f73d97caf5c4eff Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Thu, 9 Oct 2025 13:41:31 -0600
Subject: [PATCH 09/40] reduce schema fields (#2089)

* reduce schema fields

* fix launch.json

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 .../models/vector_store_schema_config.py      | 12 --------
 .../index/operations/embed_text/embed_text.py |  7 +----
 .../basic_search/basic_context.py             | 15 +++++-----
 graphrag/vector_stores/azure_ai_search.py     | 15 ----------
 graphrag/vector_stores/base.py                |  8 +----
 graphrag/vector_stores/cosmosdb.py            | 11 ++-----
 graphrag/vector_stores/lancedb.py             | 20 ++++---------
 .../vector_stores/test_azure_ai_search.py     | 22 --------------
 .../vector_stores/test_cosmosdb.py            | 16 ----------
 .../integration/vector_stores/test_factory.py |  2 +-
 .../integration/vector_stores/test_lancedb.py | 29 -------------------
 .../context_builder/test_entity_extraction.py | 15 ++++------
 12 files changed, 24 insertions(+), 148 deletions(-)

diff --git a/graphrag/config/models/vector_store_schema_config.py b/graphrag/config/models/vector_store_schema_config.py
index 2da0cdc17a..ccb91b3cbb 100644
--- a/graphrag/config/models/vector_store_schema_config.py
+++ b/graphrag/config/models/vector_store_schema_config.py
@@ -30,16 +30,6 @@ class VectorStoreSchemaConfig(BaseModel):
         default="vector",
     )
 
-    text_field: str = Field(
-        description="The text field to use.",
-        default="text",
-    )
-
-    attributes_field: str = Field(
-        description="The attributes field to use.",
-        default="attributes",
-    )
-
     vector_size: int = Field(
         description="The vector size to use.",
         default=DEFAULT_VECTOR_SIZE,
@@ -52,8 +42,6 @@ def _validate_schema(self) -> None:
         for field in [
             self.id_field,
             self.vector_field,
-            self.text_field,
-            self.attributes_field,
         ]:
             if not is_valid_field_name(field):
                 msg = f"Unsafe or invalid field name: {field}"
diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py
index c1a4b796e3..46b81ce212 100644
--- a/graphrag/index/operations/embed_text/embed_text.py
+++ b/graphrag/index/operations/embed_text/embed_text.py
@@ -152,7 +152,6 @@ async def _text_embed_with_vector_store(
         )
         batch = input.iloc[insert_batch_size * i : insert_batch_size * (i + 1)]
         texts: list[str] = batch[embed_column].tolist()
-        titles: list[str] = batch[title].tolist()
         ids: list[str] = batch[id_column].tolist()
         result = await strategy_exec(texts, callbacks, cache, strategy_config)
         if result.embeddings:
@@ -163,16 +162,12 @@ async def _text_embed_with_vector_store(
 
         vectors = result.embeddings or []
         documents: list[VectorStoreDocument] = []
-        for doc_id, doc_text, doc_title, doc_vector in zip(
-            ids, texts, titles, vectors, strict=True
-        ):
+        for doc_id, doc_vector in zip(ids, vectors, strict=True):
             if type(doc_vector) is np.ndarray:
                 doc_vector = doc_vector.tolist()
             document = VectorStoreDocument(
                 id=doc_id,
-                text=doc_text,
                 vector=doc_vector,
-                attributes={"title": doc_title},
             )
             documents.append(document)
 
diff --git a/graphrag/query/structured_search/basic_search/basic_context.py b/graphrag/query/structured_search/basic_search/basic_context.py
index 8296de3c89..215b26a31e 100644
--- a/graphrag/query/structured_search/basic_search/basic_context.py
+++ b/graphrag/query/structured_search/basic_search/basic_context.py
@@ -59,14 +59,15 @@ def build_context(
                 text_embedder=lambda t: self.text_embedder.embed(t),
                 k=k,
             )
-            related_text_list = [
-                {
-                    text_id_col: self.text_id_map[f"{chunk.document.id}"],
-                    text_col: chunk.document.text,
-                }
-                for chunk in related_texts
+
+            text_unit_ids = {t.document.id for t in related_texts}
+            text_units_filtered = []
+            text_units_filtered = [
+                {text_id_col: t.id, text_col: t.text}
+                for t in self.text_units or []
+                if t.id in text_unit_ids
             ]
-            related_text_df = pd.DataFrame(related_text_list)
+            related_text_df = pd.DataFrame(text_units_filtered)
         else:
             related_text_df = pd.DataFrame({
                 text_id_col: [],
diff --git a/graphrag/vector_stores/azure_ai_search.py b/graphrag/vector_stores/azure_ai_search.py
index 037cc28754..c1eb063d64 100644
--- a/graphrag/vector_stores/azure_ai_search.py
+++ b/graphrag/vector_stores/azure_ai_search.py
@@ -3,7 +3,6 @@
 
 """A package containing the Azure AI Search  vector store implementation."""
 
-import json
 from typing import Any
 
 from azure.core.credentials import AzureKeyCredential
@@ -13,7 +12,6 @@
 from azure.search.documents.indexes.models import (
     HnswAlgorithmConfiguration,
     HnswParameters,
-    SearchableField,
     SearchField,
     SearchFieldDataType,
     SearchIndex,
@@ -121,13 +119,6 @@ def load_documents(
                         vector_search_dimensions=self.vector_size,
                         vector_search_profile_name=self.vector_search_profile_name,
                     ),
-                    SearchableField(
-                        name=self.text_field, type=SearchFieldDataType.String
-                    ),
-                    SimpleField(
-                        name=self.attributes_field,
-                        type=SearchFieldDataType.String,
-                    ),
                 ],
                 vector_search=vector_search,
             )
@@ -139,8 +130,6 @@ def load_documents(
             {
                 self.id_field: doc.id,
                 self.vector_field: doc.vector,
-                self.text_field: doc.text,
-                self.attributes_field: json.dumps(doc.attributes),
             }
             for doc in documents
             if doc.vector is not None
@@ -165,9 +154,7 @@ def similarity_search_by_vector(
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
                     id=doc.get(self.id_field, ""),
-                    text=doc.get(self.text_field, ""),
                     vector=doc.get(self.vector_field, []),
-                    attributes=(json.loads(doc.get(self.attributes_field, "{}"))),
                 ),
                 # Cosine similarity between 0.333 and 1.000
                 # https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking#scores-in-a-hybrid-search-results
@@ -192,7 +179,5 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
         response = self.db_connection.get_document(id)
         return VectorStoreDocument(
             id=response.get(self.id_field, ""),
-            text=response.get(self.text_field, ""),
             vector=response.get(self.vector_field, []),
-            attributes=(json.loads(response.get(self.attributes_field, "{}"))),
         )
diff --git a/graphrag/vector_stores/base.py b/graphrag/vector_stores/base.py
index 2008fb472d..1bee5910af 100644
--- a/graphrag/vector_stores/base.py
+++ b/graphrag/vector_stores/base.py
@@ -4,7 +4,7 @@
 """Base classes for vector stores."""
 
 from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any
 
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
@@ -18,12 +18,8 @@ class VectorStoreDocument:
     id: str | int
     """unique id for the document"""
 
-    text: str | None
     vector: list[float] | None
 
-    attributes: dict[str, Any] = field(default_factory=dict)
-    """store any additional metadata, e.g. title, date ranges, etc"""
-
 
 @dataclass
 class VectorStoreSearchResult:
@@ -54,9 +50,7 @@ def __init__(
 
         self.index_name = vector_store_schema_config.index_name
         self.id_field = vector_store_schema_config.id_field
-        self.text_field = vector_store_schema_config.text_field
         self.vector_field = vector_store_schema_config.vector_field
-        self.attributes_field = vector_store_schema_config.attributes_field
         self.vector_size = vector_store_schema_config.vector_size
 
     @abstractmethod
diff --git a/graphrag/vector_stores/cosmosdb.py b/graphrag/vector_stores/cosmosdb.py
index 186412d6ba..c609aa30b2 100644
--- a/graphrag/vector_stores/cosmosdb.py
+++ b/graphrag/vector_stores/cosmosdb.py
@@ -3,7 +3,6 @@
 
 """A package containing the CosmosDB vector store implementation."""
 
-import json
 from typing import Any
 
 from azure.cosmos import ContainerProxy, CosmosClient, DatabaseProxy
@@ -171,8 +170,6 @@ def load_documents(
                 doc_json = {
                     self.id_field: doc.id,
                     self.vector_field: doc.vector,
-                    self.text_field: doc.text,
-                    self.attributes_field: json.dumps(doc.attributes),
                 }
                 print("Storing document in CosmosDB:")  # noqa: T201
                 print(doc_json)  # noqa: T201
@@ -187,7 +184,7 @@ def similarity_search_by_vector(
             raise ValueError(msg)
 
         try:
-            query = f"SELECT TOP {k} c.{self.id_field}, c.{self.text_field}, c.{self.vector_field}, c.{self.attributes_field}, VectorDistance(c.{self.vector_field}, @embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.{self.vector_field}, @embedding)"  # noqa: S608
+            query = f"SELECT TOP {k} c.{self.id_field}, c.{self.vector_field}, VectorDistance(c.{self.vector_field}, @embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.{self.vector_field}, @embedding)"  # noqa: S608
             query_params = [{"name": "@embedding", "value": query_embedding}]
             items = list(
                 self._container_client.query_items(
@@ -199,7 +196,7 @@ def similarity_search_by_vector(
         except (CosmosHttpResponseError, ValueError):
             # Currently, the CosmosDB emulator does not support the VectorDistance function.
             # For emulator or test environments - fetch all items and calculate distance locally
-            query = f"SELECT c.{self.id_field}, c.{self.text_field}, c.{self.vector_field}, c.{self.attributes_field} FROM c"  # noqa: S608
+            query = f"SELECT c.{self.id_field}, c.{self.vector_field} FROM c"  # noqa: S608
             items = list(
                 self._container_client.query_items(
                     query=query,
@@ -231,9 +228,7 @@ def cosine_similarity(a, b):
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
                     id=item.get(self.id_field, ""),
-                    text=item.get(self.text_field, ""),
                     vector=item.get(self.vector_field, []),
-                    attributes=(json.loads(item.get(self.attributes_field, "{}"))),
                 ),
                 score=item.get("SimilarityScore", 0.0),
             )
@@ -261,8 +256,6 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
         return VectorStoreDocument(
             id=item.get(self.id_field, ""),
             vector=item.get(self.vector_field, []),
-            text=item.get(self.text_field, ""),
-            attributes=(json.loads(item.get(self.attributes_field, "{}"))),
         )
 
     def clear(self) -> None:
diff --git a/graphrag/vector_stores/lancedb.py b/graphrag/vector_stores/lancedb.py
index 7c03d97386..fe53ca7182 100644
--- a/graphrag/vector_stores/lancedb.py
+++ b/graphrag/vector_stores/lancedb.py
@@ -3,19 +3,19 @@
 
 """The LanceDB vector storage implementation package."""
 
-import json  # noqa: I001
 from typing import Any
-import pyarrow as pa
+
+import lancedb
 import numpy as np
+import pyarrow as pa
+
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.data_model.types import TextEmbedder
-
 from graphrag.vector_stores.base import (
     BaseVectorStore,
     VectorStoreDocument,
     VectorStoreSearchResult,
 )
-import lancedb
 
 
 class LanceDBVectorStore(BaseVectorStore):
@@ -41,9 +41,7 @@ def load_documents(
         """Load documents into vector storage."""
         # Step 1: Prepare data columns manually
         ids = []
-        texts = []
         vectors = []
-        attributes = []
 
         for document in documents:
             self.vector_size = (
@@ -51,9 +49,7 @@ def load_documents(
             )
             if document.vector is not None and len(document.vector) == self.vector_size:
                 ids.append(document.id)
-                texts.append(document.text)
                 vectors.append(np.array(document.vector, dtype=np.float32))
-                attributes.append(json.dumps(document.attributes))
 
         # Step 2: Handle empty case
         if len(ids) == 0:
@@ -69,9 +65,7 @@ def load_documents(
             # Step 4: Create PyArrow table (let schema be inferred)
             data = pa.table({
                 self.id_field: pa.array(ids, type=pa.string()),
-                self.text_field: pa.array(texts, type=pa.string()),
                 self.vector_field: vector_column,
-                self.attributes_field: pa.array(attributes, type=pa.string()),
             })
 
         # NOTE: If modifying the next section of code, ensure that the schema remains the same.
@@ -127,9 +121,7 @@ def similarity_search_by_vector(
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
                     id=doc[self.id_field],
-                    text=doc[self.text_field],
                     vector=doc[self.vector_field],
-                    attributes=json.loads(doc[self.attributes_field]),
                 ),
                 score=1 - abs(float(doc["_distance"])),
             )
@@ -155,8 +147,6 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
         if doc:
             return VectorStoreDocument(
                 id=doc[0][self.id_field],
-                text=doc[0][self.text_field],
                 vector=doc[0][self.vector_field],
-                attributes=json.loads(doc[0][self.attributes_field]),
             )
-        return VectorStoreDocument(id=id, text=None, vector=None)
+        return VectorStoreDocument(id=id, vector=None)
diff --git a/tests/integration/vector_stores/test_azure_ai_search.py b/tests/integration/vector_stores/test_azure_ai_search.py
index 2d67c62ea8..aaf6f00374 100644
--- a/tests/integration/vector_stores/test_azure_ai_search.py
+++ b/tests/integration/vector_stores/test_azure_ai_search.py
@@ -63,8 +63,6 @@ def vector_store_custom(self, mock_search_client, mock_index_client):
             vector_store_schema_config=VectorStoreSchemaConfig(
                 index_name="test_vectors",
                 id_field="id_custom",
-                text_field="text_custom",
-                attributes_field="attributes_custom",
                 vector_field="vector_custom",
                 vector_size=5,
             ),
@@ -86,15 +84,11 @@ def sample_documents(self):
         return [
             VectorStoreDocument(
                 id="doc1",
-                text="This is document 1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Doc 1", "category": "test"},
             ),
             VectorStoreDocument(
                 id="doc2",
-                text="This is document 2",
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
-                attributes={"title": "Doc 2", "category": "test"},
             ),
         ]
 
@@ -110,16 +104,12 @@ async def test_vector_store_operations(
         search_results = [
             {
                 "id": "doc1",
-                "text": "This is document 1",
                 "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
-                "attributes": '{"title": "Doc 1", "category": "test"}',
                 "@search.score": 0.9,
             },
             {
                 "id": "doc2",
-                "text": "This is document 2",
                 "vector": [0.2, 0.3, 0.4, 0.5, 0.6],
-                "attributes": '{"title": "Doc 2", "category": "test"}',
                 "@search.score": 0.8,
             },
         ]
@@ -127,9 +117,7 @@ async def test_vector_store_operations(
 
         mock_search_client.get_document.return_value = {
             "id": "doc1",
-            "text": "This is document 1",
             "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
-            "attributes": '{"title": "Doc 1", "category": "test"}',
         }
 
         vector_store.load_documents(sample_documents)
@@ -154,8 +142,6 @@ def mock_embedder(text: str) -> list[float]:
 
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
-        assert doc.text == "This is document 1"
-        assert doc.attributes["title"] == "Doc 1"
 
     async def test_empty_embedding(self, vector_store, mock_search_client):
         """Test similarity search by text with empty embedding."""
@@ -186,16 +172,12 @@ async def test_vector_store_customization(
         search_results = [
             {
                 vector_store_custom.id_field: "doc1",
-                vector_store_custom.text_field: "This is document 1",
                 vector_store_custom.vector_field: [0.1, 0.2, 0.3, 0.4, 0.5],
-                vector_store_custom.attributes_field: '{"title": "Doc 1", "category": "test"}',
                 "@search.score": 0.9,
             },
             {
                 vector_store_custom.id_field: "doc2",
-                vector_store_custom.text_field: "This is document 2",
                 vector_store_custom.vector_field: [0.2, 0.3, 0.4, 0.5, 0.6],
-                vector_store_custom.attributes_field: '{"title": "Doc 2", "category": "test"}',
                 "@search.score": 0.8,
             },
         ]
@@ -203,9 +185,7 @@ async def test_vector_store_customization(
 
         mock_search_client.get_document.return_value = {
             vector_store_custom.id_field: "doc1",
-            vector_store_custom.text_field: "This is document 1",
             vector_store_custom.vector_field: [0.1, 0.2, 0.3, 0.4, 0.5],
-            vector_store_custom.attributes_field: '{"title": "Doc 1", "category": "test"}',
         }
 
         vector_store_custom.load_documents(sample_documents)
@@ -230,5 +210,3 @@ def mock_embedder(text: str) -> list[float]:
 
         doc = vector_store_custom.search_by_id("doc1")
         assert doc.id == "doc1"
-        assert doc.text == "This is document 1"
-        assert doc.attributes["title"] == "Doc 1"
diff --git a/tests/integration/vector_stores/test_cosmosdb.py b/tests/integration/vector_stores/test_cosmosdb.py
index f9515beed9..364a3d85d8 100644
--- a/tests/integration/vector_stores/test_cosmosdb.py
+++ b/tests/integration/vector_stores/test_cosmosdb.py
@@ -37,25 +37,19 @@ def test_vector_store_operations():
         docs = [
             VectorStoreDocument(
                 id="doc1",
-                text="This is document 1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Doc 1", "category": "test"},
             ),
             VectorStoreDocument(
                 id="doc2",
-                text="This is document 2",
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
-                attributes={"title": "Doc 2", "category": "test"},
             ),
         ]
         vector_store.load_documents(docs)
 
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
-        assert doc.text == "This is document 1"
         assert doc.vector is not None
         assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
-        assert doc.attributes["title"] == "Doc 1"
 
         # Define a simple text embedder function for testing
         def mock_embedder(text: str) -> list[float]:
@@ -87,9 +81,7 @@ def test_clear():
 
         doc = VectorStoreDocument(
             id="test",
-            text="Test document",
             vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-            attributes={"title": "Test Doc"},
         )
 
         vector_store.load_documents([doc])
@@ -109,9 +101,7 @@ def test_vector_store_customization():
         vector_store_schema_config=VectorStoreSchemaConfig(
             index_name="text-embeddings",
             id_field="id",
-            text_field="text_custom",
             vector_field="vector_custom",
-            attributes_field="attributes_custom",
             vector_size=5,
         ),
     )
@@ -125,25 +115,19 @@ def test_vector_store_customization():
         docs = [
             VectorStoreDocument(
                 id="doc1",
-                text="This is document 1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Doc 1", "category": "test"},
             ),
             VectorStoreDocument(
                 id="doc2",
-                text="This is document 2",
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
-                attributes={"title": "Doc 2", "category": "test"},
             ),
         ]
         vector_store.load_documents(docs)
 
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
-        assert doc.text == "This is document 1"
         assert doc.vector is not None
         assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
-        assert doc.attributes["title"] == "Doc 1"
 
         # Define a simple text embedder function for testing
         def mock_embedder(text: str) -> list[float]:
diff --git a/tests/integration/vector_stores/test_factory.py b/tests/integration/vector_stores/test_factory.py
index 724c1f8e2d..de819a6ab0 100644
--- a/tests/integration/vector_stores/test_factory.py
+++ b/tests/integration/vector_stores/test_factory.py
@@ -146,7 +146,7 @@ def filter_by_id(self, include_ids):
         def search_by_id(self, id):
             from graphrag.vector_stores.base import VectorStoreDocument
 
-            return VectorStoreDocument(id=id, text="test", vector=None)
+            return VectorStoreDocument(id=id, vector=None)
 
     # VectorStoreFactory allows registering classes directly (no TypeError)
     VectorStoreFactory.register("custom_class", CustomVectorStore)
diff --git a/tests/integration/vector_stores/test_lancedb.py b/tests/integration/vector_stores/test_lancedb.py
index cca48a1271..d0fb19eaaa 100644
--- a/tests/integration/vector_stores/test_lancedb.py
+++ b/tests/integration/vector_stores/test_lancedb.py
@@ -23,21 +23,15 @@ def sample_documents(self):
         return [
             VectorStoreDocument(
                 id="1",
-                text="This is document 1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Doc 1", "category": "test"},
             ),
             VectorStoreDocument(
                 id="2",
-                text="This is document 2",
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
-                attributes={"title": "Doc 2", "category": "test"},
             ),
             VectorStoreDocument(
                 id="3",
-                text="This is document 3",
                 vector=[0.3, 0.4, 0.5, 0.6, 0.7],
-                attributes={"title": "Doc 3", "category": "test"},
             ),
         ]
 
@@ -47,21 +41,15 @@ def sample_documents_categories(self):
         return [
             VectorStoreDocument(
                 id="1",
-                text="Document about cats",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"category": "animals"},
             ),
             VectorStoreDocument(
                 id="2",
-                text="Document about dogs",
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
-                attributes={"category": "animals"},
             ),
             VectorStoreDocument(
                 id="3",
-                text="Document about cars",
                 vector=[0.3, 0.4, 0.5, 0.6, 0.7],
-                attributes={"category": "vehicles"},
             ),
         ]
 
@@ -85,11 +73,8 @@ def test_vector_store_operations(self, sample_documents):
 
             doc = vector_store.search_by_id("1")
             assert doc.id == "1"
-            assert doc.text == "This is document 1"
-
             assert doc.vector is not None
             assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
-            assert doc.attributes["title"] == "Doc 1"
 
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=2
@@ -101,7 +86,6 @@ def test_vector_store_operations(self, sample_documents):
             vector_store.load_documents([sample_documents[2]], overwrite=False)
             result = vector_store.search_by_id("3")
             assert result.id == "3"
-            assert result.text == "This is document 3"
 
             # Define a simple text embedder function for testing
             def mock_embedder(text: str) -> list[float]:
@@ -116,7 +100,6 @@ def mock_embedder(text: str) -> list[float]:
             # Test non-existent document
             non_existent = vector_store.search_by_id("nonexistent")
             assert non_existent.id == "nonexistent"
-            assert non_existent.text is None
             assert non_existent.vector is None
         finally:
             shutil.rmtree(temp_dir)
@@ -136,9 +119,7 @@ def test_empty_collection(self):
             # Load the vector store with a document, then delete it
             sample_doc = VectorStoreDocument(
                 id="tmp",
-                text="Temporary document to create schema",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Tmp"},
             )
             vector_store.load_documents([sample_doc])
             vector_store.db_connection.open_table(
@@ -154,15 +135,12 @@ def test_empty_collection(self):
             # Add a document after creating an empty collection
             doc = VectorStoreDocument(
                 id="1",
-                text="This is document 1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Doc 1"},
             )
             vector_store.load_documents([doc], overwrite=False)
 
             result = vector_store.search_by_id("1")
             assert result.id == "1"
-            assert result.text == "This is document 1"
         finally:
             # Clean up - remove the temporary directory
             shutil.rmtree(temp_dir)
@@ -203,9 +181,7 @@ def test_vector_store_customization(self, sample_documents):
                 vector_store_schema_config=VectorStoreSchemaConfig(
                     index_name="text-embeddings",
                     id_field="id_custom",
-                    text_field="text_custom",
                     vector_field="vector_custom",
-                    attributes_field="attributes_custom",
                     vector_size=5,
                 ),
             )
@@ -219,11 +195,8 @@ def test_vector_store_customization(self, sample_documents):
 
             doc = vector_store.search_by_id("1")
             assert doc.id == "1"
-            assert doc.text == "This is document 1"
-
             assert doc.vector is not None
             assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
-            assert doc.attributes["title"] == "Doc 1"
 
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=2
@@ -235,7 +208,6 @@ def test_vector_store_customization(self, sample_documents):
             vector_store.load_documents([sample_documents[2]], overwrite=False)
             result = vector_store.search_by_id("3")
             assert result.id == "3"
-            assert result.text == "This is document 3"
 
             # Define a simple text embedder function for testing
             def mock_embedder(text: str) -> list[float]:
@@ -250,7 +222,6 @@ def mock_embedder(text: str) -> list[float]:
             # Test non-existent document
             non_existent = vector_store.search_by_id("nonexistent")
             assert non_existent.id == "nonexistent"
-            assert non_existent.text is None
             assert non_existent.vector is None
         finally:
             shutil.rmtree(temp_dir)
diff --git a/tests/unit/query/context_builder/test_entity_extraction.py b/tests/unit/query/context_builder/test_entity_extraction.py
index 8a15e8e929..aa2ff29b09 100644
--- a/tests/unit/query/context_builder/test_entity_extraction.py
+++ b/tests/unit/query/context_builder/test_entity_extraction.py
@@ -47,7 +47,8 @@ def similarity_search_by_text(
         return sorted(
             [
                 VectorStoreSearchResult(
-                    document=document, score=abs(len(text) - len(document.text or ""))
+                    document=document,
+                    score=abs(len(text) - len(str(document.id) or "")),
                 )
                 for document in self.documents
             ],
@@ -94,8 +95,7 @@ def test_map_query_to_entities():
     assert map_query_to_entities(
         query="t22",
         text_embedding_vectorstore=MockBaseVectorStore([
-            VectorStoreDocument(id=entity.id, text=entity.title, vector=None)
-            for entity in entities
+            VectorStoreDocument(id=entity.id, vector=None) for entity in entities
         ]),
         text_embedder=ModelManager().get_or_create_embedding_model(
             model_type="mock_embedding", name="mock"
@@ -116,8 +116,7 @@ def test_map_query_to_entities():
     assert map_query_to_entities(
         query="t22",
         text_embedding_vectorstore=MockBaseVectorStore([
-            VectorStoreDocument(id=entity.title, text=entity.title, vector=None)
-            for entity in entities
+            VectorStoreDocument(id=entity.title, vector=None) for entity in entities
         ]),
         text_embedder=ModelManager().get_or_create_embedding_model(
             model_type="mock_embedding", name="mock"
@@ -138,8 +137,7 @@ def test_map_query_to_entities():
     assert map_query_to_entities(
         query="",
         text_embedding_vectorstore=MockBaseVectorStore([
-            VectorStoreDocument(id=entity.id, text=entity.title, vector=None)
-            for entity in entities
+            VectorStoreDocument(id=entity.id, vector=None) for entity in entities
         ]),
         text_embedder=ModelManager().get_or_create_embedding_model(
             model_type="mock_embedding", name="mock"
@@ -165,8 +163,7 @@ def test_map_query_to_entities():
     assert map_query_to_entities(
         query="",
         text_embedding_vectorstore=MockBaseVectorStore([
-            VectorStoreDocument(id=entity.id, text=entity.title, vector=None)
-            for entity in entities
+            VectorStoreDocument(id=entity.id, vector=None) for entity in entities
         ]),
         text_embedder=ModelManager().get_or_create_embedding_model(
             model_type="mock_embedding", name="mock"

From eb0dfe376bf70da081801f3303347014626efc1b Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Fri, 10 Oct 2025 12:15:23 -0700
Subject: [PATCH 10/40] Remove strategy dicts (#2090)

* Remove "strategy" from community reports config/workflow

* Remove extraction strategy from extract_graph

* Remove summarization strategy from extract_graph

* Remove strategy from claim extraction

* Strongly type prompt templates

* Remove strategy from embed_text

* Push hydrated params into community report workflows

* Push hyrdated params into extract covariates

* Push hydrated params into extract graph NLP

* Push hydrated params into extract graph

* Push hydrated params into text embeddings

* Remove a few more low-level defaults

* Semver

* Remove configurable prompt delimiters

* Update smoke tests
---
 .../major-20251009203808375389.json           |   4 +
 .../index_migration_to_v1.ipynb               |  35 ++-
 graphrag/config/defaults.py                   |   5 +-
 ...ttings.py => get_vector_store_settings.py} |  20 +-
 graphrag/config/init_content.py               |   3 -
 .../config/models/community_reports_config.py |  46 ++--
 ...bedding_config.py => embed_text_config.py} |  21 +-
 .../config/models/extract_claims_config.py    |  35 ++-
 .../config/models/extract_graph_config.py     |  37 ++-
 graphrag/config/models/graph_rag_config.py    |  12 +-
 .../models/summarize_descriptions_config.py   |  38 ++-
 .../build_noun_graph/build_noun_graph.py      |  14 +-
 .../index/operations/embed_text/embed_text.py | 115 ++++-----
 .../openai.py => run_embed_text.py}           |  46 ++--
 .../embed_text/strategies/__init__.py         |   4 -
 .../operations/embed_text/strategies/mock.py  |  35 ---
 .../embed_text/strategies/typing.py           |  28 ---
 .../extract_covariates/claim_extractor.py     | 120 +++------
 .../extract_covariates/extract_covariates.py  |  84 ++-----
 .../operations/extract_graph/extract_graph.py |  94 ++++---
 .../extract_graph/graph_extractor.py          |  83 ++----
 .../graph_intelligence_strategy.py            | 102 --------
 .../index/operations/extract_graph/typing.py  |  12 -
 .../community_reports_extractor.py            |  13 +-
 .../summarize_communities/strategies.py       |  85 -------
 .../summarize_communities.py                  |  94 ++++---
 .../summarize_communities/typing.py           |  21 +-
 .../description_summary_extractor.py          |  11 +-
 .../graph_intelligence_strategy.py            |  65 -----
 .../summarize_descriptions.py                 |  65 +++--
 .../summarize_descriptions/typing.py          |  27 --
 .../index/workflows/create_base_text_units.py |   4 +-
 .../workflows/create_community_reports.py     |  62 ++---
 .../create_community_reports_text.py          |  62 ++---
 .../index/workflows/extract_covariates.py     |  60 +++--
 graphrag/index/workflows/extract_graph.py     |  91 ++++---
 graphrag/index/workflows/extract_graph_nlp.py |  26 +-
 .../workflows/generate_text_embeddings.py     |  61 ++++-
 .../update_entities_relationships.py          |  19 +-
 .../index/workflows/update_text_embeddings.py |  27 +-
 graphrag/prompt_tune/loader/input.py          |  26 +-
 .../prompt_tune/prompt/entity_relationship.py | 236 +++++++++---------
 .../prompt_tune/template/extract_graph.py     |  16 +-
 graphrag/prompts/index/extract_claims.py      |  18 +-
 graphrag/prompts/index/extract_graph.py       | 118 ++++-----
 graphrag/query/llm/text_utils.py              |   3 +-
 tests/fixtures/min-csv/settings.yml           |   6 +-
 tests/fixtures/text/settings.yml              |   6 +-
 tests/unit/config/utils.py                    |  13 +-
 .../text_splitting/test_text_splitting.py     |  11 -
 .../test_gi_entity_extraction.py              |  38 ++-
 tests/unit/utils/test_encoding.py             |   4 +-
 tests/verbs/test_create_community_reports.py  |  14 +-
 tests/verbs/test_extract_covariates.py        |  14 +-
 tests/verbs/test_extract_graph.py             |  31 ++-
 tests/verbs/test_generate_text_embeddings.py  |  11 +-
 56 files changed, 946 insertions(+), 1405 deletions(-)
 create mode 100644 .semversioner/next-release/major-20251009203808375389.json
 rename graphrag/config/{get_embedding_settings.py => get_vector_store_settings.py} (53%)
 rename graphrag/config/models/{text_embedding_config.py => embed_text_config.py} (57%)
 rename graphrag/index/operations/embed_text/{strategies/openai.py => run_embed_text.py} (80%)
 delete mode 100644 graphrag/index/operations/embed_text/strategies/__init__.py
 delete mode 100644 graphrag/index/operations/embed_text/strategies/mock.py
 delete mode 100644 graphrag/index/operations/embed_text/strategies/typing.py
 delete mode 100644 graphrag/index/operations/extract_graph/graph_intelligence_strategy.py
 delete mode 100644 graphrag/index/operations/summarize_communities/strategies.py
 delete mode 100644 graphrag/index/operations/summarize_descriptions/graph_intelligence_strategy.py

diff --git a/.semversioner/next-release/major-20251009203808375389.json b/.semversioner/next-release/major-20251009203808375389.json
new file mode 100644
index 0000000000..4bf235ecb0
--- /dev/null
+++ b/.semversioner/next-release/major-20251009203808375389.json
@@ -0,0 +1,4 @@
+{
+  "type": "major",
+  "description": "Simplify internal args with stronger types and firmer boundaries."
+}
diff --git a/docs/examples_notebooks/index_migration_to_v1.ipynb b/docs/examples_notebooks/index_migration_to_v1.ipynb
index ecff51929a..581f5cef64 100644
--- a/docs/examples_notebooks/index_migration_to_v1.ipynb
+++ b/docs/examples_notebooks/index_migration_to_v1.ipynb
@@ -202,45 +202,44 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from graphrag.index.flows.generate_text_embeddings import generate_text_embeddings\n",
-    "\n",
     "from graphrag.cache.factory import CacheFactory\n",
     "from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks\n",
-    "from graphrag.config.embeddings import get_embedded_fields, get_embedding_settings\n",
+    "from graphrag.config.get_vector_store_settings import get_vector_store_settings\n",
+    "from graphrag.index.workflows.generate_text_embeddings import generate_text_embeddings\n",
     "\n",
     "# We only need to re-run the embeddings workflow, to ensure that embeddings for all required search fields are in place\n",
     "# We'll construct the context and run this function flow directly to avoid everything else\n",
     "\n",
     "\n",
-    "embedded_fields = get_embedded_fields(config)\n",
-    "text_embed = get_embedding_settings(config)\n",
+    "vector_store_config = get_vector_store_settings(config)\n",
+    "model_config = config.get_language_model_config(config.embed_text.model_id)\n",
     "callbacks = NoopWorkflowCallbacks()\n",
     "cache_config = config.cache.model_dump()  # type: ignore\n",
     "cache = CacheFactory().create_cache(\n",
     "    cache_type=cache_config[\"type\"],  # type: ignore\n",
-    "    root_dir=PROJECT_DIRECTORY,\n",
-    "    kwargs=cache_config,\n",
+    "    **cache_config,\n",
     ")\n",
     "\n",
     "await generate_text_embeddings(\n",
-    "    final_documents=None,\n",
-    "    final_relationships=None,\n",
-    "    final_text_units=final_text_units,\n",
-    "    final_entities=final_entities,\n",
-    "    final_community_reports=final_community_reports,\n",
+    "    documents=None,\n",
+    "    relationships=None,\n",
+    "    text_units=final_text_units,\n",
+    "    entities=final_entities,\n",
+    "    community_reports=final_community_reports,\n",
     "    callbacks=callbacks,\n",
     "    cache=cache,\n",
-    "    storage=storage,\n",
-    "    text_embed_config=text_embed,\n",
-    "    embedded_fields=embedded_fields,\n",
-    "    snapshot_embeddings_enabled=False,\n",
+    "    model_config=model_config,\n",
+    "    batch_size=config.embed_text.batch_size,\n",
+    "    batch_max_tokens=config.embed_text.batch_max_tokens,\n",
+    "    vector_store_config=vector_store_config,\n",
+    "    embedded_fields=config.embed_text.names,\n",
     ")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "graphrag",
    "language": "python",
    "name": "python3"
   },
@@ -254,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index c2a0cb6fb9..eadafd9860 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -60,6 +60,7 @@
 ENCODING_MODEL = "o200k_base"
 COGNITIVE_SERVICES_AUDIENCE = "https://cognitiveservices.azure.com/.default"
 
+DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event"]
 
 DEFAULT_RETRY_SERVICES: dict[str, Callable[..., Retry]] = {
     "native": NativeRetry,
@@ -125,7 +126,6 @@ class CommunityReportDefaults:
     text_prompt: None = None
     max_length: int = 2000
     max_input_length: int = 8000
-    strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
 
 
@@ -162,10 +162,9 @@ class DriftSearchDefaults:
 class EmbedTextDefaults:
     """Default values for embedding text."""
 
-    model: str = "text-embedding-3-small"
+    model_id: str = DEFAULT_EMBEDDING_MODEL_ID
     batch_size: int = 16
     batch_max_tokens: int = 8191
-    model_id: str = DEFAULT_EMBEDDING_MODEL_ID
     names: list[str] = field(default_factory=lambda: default_embeddings)
     strategy: None = None
     vector_store_id: str = DEFAULT_VECTOR_STORE_ID
diff --git a/graphrag/config/get_embedding_settings.py b/graphrag/config/get_vector_store_settings.py
similarity index 53%
rename from graphrag/config/get_embedding_settings.py
rename to graphrag/config/get_vector_store_settings.py
index 9522f31359..3771d65ff3 100644
--- a/graphrag/config/get_embedding_settings.py
+++ b/graphrag/config/get_vector_store_settings.py
@@ -1,19 +1,16 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing get_embedding_settings."""
+"""A module containing get_vector_store_settings."""
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 
 
-def get_embedding_settings(
+def get_vector_store_settings(
     settings: GraphRagConfig,
     vector_store_params: dict | None = None,
 ) -> dict:
     """Transform GraphRAG config into settings for workflows."""
-    embeddings_llm_settings = settings.get_language_model_config(
-        settings.embed_text.model_id
-    )
     vector_store_settings = settings.get_vector_store_config(
         settings.embed_text.vector_store_id
     ).model_dump()
@@ -23,16 +20,7 @@ def get_embedding_settings(
     # settings.vector_store.base contains connection information, or may be undefined
     # settings.vector_store.<vector_name> contains the specific settings for this embedding
     #
-    strategy = settings.embed_text.resolved_strategy(
-        embeddings_llm_settings
-    )  # get the default strategy
-    strategy.update({
-        "vector_store": {
-            **(vector_store_params or {}),
-            **(vector_store_settings),
-        }
-    })  # update the default strategy with the vector store settings
-    # This ensures the vector store config is part of the strategy and not the global config
     return {
-        "strategy": strategy,
+        **(vector_store_params or {}),
+        **(vector_store_settings),
     }
diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py
index 421cfe2e30..aadacf8f38 100644
--- a/graphrag/config/init_content.py
+++ b/graphrag/config/init_content.py
@@ -28,7 +28,6 @@
     # api_version: 2024-05-01-preview
     model_supports_json: true # recommended if this is available for your model.
     concurrent_requests: {language_model_defaults.concurrent_requests}
-    async_mode: {language_model_defaults.async_mode.value} # or asyncio
     retry_strategy: {language_model_defaults.retry_strategy}
     max_retries: {language_model_defaults.max_retries}
     tokens_per_minute: null
@@ -42,7 +41,6 @@
     # api_base: https://<instance>.openai.azure.com
     # api_version: 2024-05-01-preview
     concurrent_requests: {language_model_defaults.concurrent_requests}
-    async_mode: {language_model_defaults.async_mode.value} # or asyncio
     retry_strategy: {language_model_defaults.retry_strategy}
     max_retries: {language_model_defaults.max_retries}
     tokens_per_minute: null
@@ -102,7 +100,6 @@
 extract_graph_nlp:
   text_analyzer:
     extractor_type: {graphrag_config_defaults.extract_graph_nlp.text_analyzer.extractor_type.value} # [regex_english, syntactic_parser, cfg]
-  async_mode: {graphrag_config_defaults.extract_graph_nlp.async_mode.value} # or asyncio
 
 cluster_graph:
   max_cluster_size: {graphrag_config_defaults.cluster_graph.max_cluster_size}
diff --git a/graphrag/config/models/community_reports_config.py b/graphrag/config/models/community_reports_config.py
index b4e9259489..5369dea232 100644
--- a/graphrag/config/models/community_reports_config.py
+++ b/graphrag/config/models/community_reports_config.py
@@ -3,12 +3,24 @@
 
 """Parameterization settings for the default configuration."""
 
+from dataclasses import dataclass
 from pathlib import Path
 
 from pydantic import BaseModel, Field
 
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.models.language_model_config import LanguageModelConfig
+from graphrag.prompts.index.community_report import COMMUNITY_REPORT_PROMPT
+from graphrag.prompts.index.community_report_text_units import (
+    COMMUNITY_REPORT_TEXT_PROMPT,
+)
+
+
+@dataclass
+class CommunityReportPrompts:
+    """Community report prompt templates."""
+
+    graph_prompt: str
+    text_prompt: str
 
 
 class CommunityReportsConfig(BaseModel):
@@ -34,32 +46,16 @@ class CommunityReportsConfig(BaseModel):
         description="The maximum input length in tokens to use when generating reports.",
         default=graphrag_config_defaults.community_reports.max_input_length,
     )
-    strategy: dict | None = Field(
-        description="The override strategy to use.",
-        default=graphrag_config_defaults.community_reports.strategy,
-    )
 
-    def resolved_strategy(
-        self, root_dir: str, model_config: LanguageModelConfig
-    ) -> dict:
-        """Get the resolved community report extraction strategy."""
-        from graphrag.index.operations.summarize_communities.typing import (
-            CreateCommunityReportsStrategyType,
-        )
-
-        return self.strategy or {
-            "type": CreateCommunityReportsStrategyType.graph_intelligence,
-            "llm": model_config.model_dump(),
-            "graph_prompt": (Path(root_dir) / self.graph_prompt).read_text(
+    def resolved_prompts(self, root_dir: str) -> CommunityReportPrompts:
+        """Get the resolved community report extraction prompts."""
+        return CommunityReportPrompts(
+            graph_prompt=(Path(root_dir) / self.graph_prompt).read_text(
                 encoding="utf-8"
             )
             if self.graph_prompt
-            else None,
-            "text_prompt": (Path(root_dir) / self.text_prompt).read_text(
-                encoding="utf-8"
-            )
+            else COMMUNITY_REPORT_PROMPT,
+            text_prompt=(Path(root_dir) / self.text_prompt).read_text(encoding="utf-8")
             if self.text_prompt
-            else None,
-            "max_report_length": self.max_length,
-            "max_input_length": self.max_input_length,
-        }
+            else COMMUNITY_REPORT_TEXT_PROMPT,
+        )
diff --git a/graphrag/config/models/text_embedding_config.py b/graphrag/config/models/embed_text_config.py
similarity index 57%
rename from graphrag/config/models/text_embedding_config.py
rename to graphrag/config/models/embed_text_config.py
index e154675a1e..f785bf6eed 100644
--- a/graphrag/config/models/text_embedding_config.py
+++ b/graphrag/config/models/embed_text_config.py
@@ -6,10 +6,9 @@
 from pydantic import BaseModel, Field
 
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.models.language_model_config import LanguageModelConfig
 
 
-class TextEmbeddingConfig(BaseModel):
+class EmbedTextConfig(BaseModel):
     """Configuration section for text embeddings."""
 
     model_id: str = Field(
@@ -32,21 +31,3 @@ class TextEmbeddingConfig(BaseModel):
         description="The specific embeddings to perform.",
         default=graphrag_config_defaults.embed_text.names,
     )
-    strategy: dict | None = Field(
-        description="The override strategy to use.",
-        default=graphrag_config_defaults.embed_text.strategy,
-    )
-
-    def resolved_strategy(self, model_config: LanguageModelConfig) -> dict:
-        """Get the resolved text embedding strategy."""
-        from graphrag.index.operations.embed_text.embed_text import (
-            TextEmbedStrategyType,
-        )
-
-        return self.strategy or {
-            "type": TextEmbedStrategyType.openai,
-            "llm": model_config.model_dump(),
-            "num_threads": model_config.concurrent_requests,
-            "batch_size": self.batch_size,
-            "batch_max_tokens": self.batch_max_tokens,
-        }
diff --git a/graphrag/config/models/extract_claims_config.py b/graphrag/config/models/extract_claims_config.py
index 166cc29d4e..78fe926795 100644
--- a/graphrag/config/models/extract_claims_config.py
+++ b/graphrag/config/models/extract_claims_config.py
@@ -3,15 +3,23 @@
 
 """Parameterization settings for the default configuration."""
 
+from dataclasses import dataclass
 from pathlib import Path
 
 from pydantic import BaseModel, Field
 
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.models.language_model_config import LanguageModelConfig
+from graphrag.prompts.index.extract_claims import EXTRACT_CLAIMS_PROMPT
 
 
-class ClaimExtractionConfig(BaseModel):
+@dataclass
+class ClaimExtractionPrompts:
+    """Claim extraction prompt templates."""
+
+    extraction_prompt: str
+
+
+class ExtractClaimsConfig(BaseModel):
     """Configuration section for claim extraction."""
 
     enabled: bool = Field(
@@ -34,22 +42,11 @@ class ClaimExtractionConfig(BaseModel):
         description="The maximum number of entity gleanings to use.",
         default=graphrag_config_defaults.extract_claims.max_gleanings,
     )
-    strategy: dict | None = Field(
-        description="The override strategy to use.",
-        default=graphrag_config_defaults.extract_claims.strategy,
-    )
 
-    def resolved_strategy(
-        self, root_dir: str, model_config: LanguageModelConfig
-    ) -> dict:
-        """Get the resolved claim extraction strategy."""
-        return self.strategy or {
-            "llm": model_config.model_dump(),
-            "extraction_prompt": (Path(root_dir) / self.prompt).read_text(
-                encoding="utf-8"
-            )
+    def resolved_prompts(self, root_dir: str) -> ClaimExtractionPrompts:
+        """Get the resolved claim extraction prompts."""
+        return ClaimExtractionPrompts(
+            extraction_prompt=(Path(root_dir) / self.prompt).read_text(encoding="utf-8")
             if self.prompt
-            else None,
-            "claim_description": self.description,
-            "max_gleanings": self.max_gleanings,
-        }
+            else EXTRACT_CLAIMS_PROMPT,
+        )
diff --git a/graphrag/config/models/extract_graph_config.py b/graphrag/config/models/extract_graph_config.py
index 915ff5d8a5..b8dfce3e40 100644
--- a/graphrag/config/models/extract_graph_config.py
+++ b/graphrag/config/models/extract_graph_config.py
@@ -3,12 +3,20 @@
 
 """Parameterization settings for the default configuration."""
 
+from dataclasses import dataclass
 from pathlib import Path
 
 from pydantic import BaseModel, Field
 
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.models.language_model_config import LanguageModelConfig
+from graphrag.prompts.index.extract_graph import GRAPH_EXTRACTION_PROMPT
+
+
+@dataclass
+class ExtractGraphPrompts:
+    """Graph extraction prompt templates."""
+
+    extraction_prompt: str
 
 
 class ExtractGraphConfig(BaseModel):
@@ -30,26 +38,11 @@ class ExtractGraphConfig(BaseModel):
         description="The maximum number of entity gleanings to use.",
         default=graphrag_config_defaults.extract_graph.max_gleanings,
     )
-    strategy: dict | None = Field(
-        description="Override the default entity extraction strategy",
-        default=graphrag_config_defaults.extract_graph.strategy,
-    )
 
-    def resolved_strategy(
-        self, root_dir: str, model_config: LanguageModelConfig
-    ) -> dict:
-        """Get the resolved entity extraction strategy."""
-        from graphrag.index.operations.extract_graph.typing import (
-            ExtractEntityStrategyType,
-        )
-
-        return self.strategy or {
-            "type": ExtractEntityStrategyType.graph_intelligence,
-            "llm": model_config.model_dump(),
-            "extraction_prompt": (Path(root_dir) / self.prompt).read_text(
-                encoding="utf-8"
-            )
+    def resolved_prompts(self, root_dir: str) -> ExtractGraphPrompts:
+        """Get the resolved graph extraction prompts."""
+        return ExtractGraphPrompts(
+            extraction_prompt=(Path(root_dir) / self.prompt).read_text(encoding="utf-8")
             if self.prompt
-            else None,
-            "max_gleanings": self.max_gleanings,
-        }
+            else GRAPH_EXTRACTION_PROMPT,
+        )
diff --git a/graphrag/config/models/graph_rag_config.py b/graphrag/config/models/graph_rag_config.py
index 4846984903..2b5961321a 100644
--- a/graphrag/config/models/graph_rag_config.py
+++ b/graphrag/config/models/graph_rag_config.py
@@ -19,7 +19,8 @@
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
-from graphrag.config.models.extract_claims_config import ClaimExtractionConfig
+from graphrag.config.models.embed_text_config import EmbedTextConfig
+from graphrag.config.models.extract_claims_config import ExtractClaimsConfig
 from graphrag.config.models.extract_graph_config import ExtractGraphConfig
 from graphrag.config.models.extract_graph_nlp_config import ExtractGraphNLPConfig
 from graphrag.config.models.global_search_config import GlobalSearchConfig
@@ -33,7 +34,6 @@
 from graphrag.config.models.summarize_descriptions_config import (
     SummarizeDescriptionsConfig,
 )
-from graphrag.config.models.text_embedding_config import TextEmbeddingConfig
 from graphrag.config.models.vector_store_config import VectorStoreConfig
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter_factory import (
     RateLimiterFactory,
@@ -249,9 +249,9 @@ def _validate_reporting_base_dir(self) -> None:
     )
     """List of workflows to run, in execution order."""
 
-    embed_text: TextEmbeddingConfig = Field(
+    embed_text: EmbedTextConfig = Field(
         description="Text embedding configuration.",
-        default=TextEmbeddingConfig(),
+        default=EmbedTextConfig(),
     )
     """Text embedding configuration."""
 
@@ -285,9 +285,9 @@ def _validate_reporting_base_dir(self) -> None:
     )
     """The cluster graph configuration to use."""
 
-    extract_claims: ClaimExtractionConfig = Field(
+    extract_claims: ExtractClaimsConfig = Field(
         description="The claim extraction configuration to use.",
-        default=ClaimExtractionConfig(
+        default=ExtractClaimsConfig(
             enabled=graphrag_config_defaults.extract_claims.enabled,
         ),
     )
diff --git a/graphrag/config/models/summarize_descriptions_config.py b/graphrag/config/models/summarize_descriptions_config.py
index ef293f69c8..3ab1fdaec4 100644
--- a/graphrag/config/models/summarize_descriptions_config.py
+++ b/graphrag/config/models/summarize_descriptions_config.py
@@ -3,12 +3,20 @@
 
 """Parameterization settings for the default configuration."""
 
+from dataclasses import dataclass
 from pathlib import Path
 
 from pydantic import BaseModel, Field
 
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.models.language_model_config import LanguageModelConfig
+from graphrag.prompts.index.summarize_descriptions import SUMMARIZE_PROMPT
+
+
+@dataclass
+class SummarizeDescriptionsPrompts:
+    """Description summarization prompt templates."""
+
+    summarize_prompt: str
 
 
 class SummarizeDescriptionsConfig(BaseModel):
@@ -30,27 +38,11 @@ class SummarizeDescriptionsConfig(BaseModel):
         description="Maximum tokens to submit from the input entity descriptions.",
         default=graphrag_config_defaults.summarize_descriptions.max_input_tokens,
     )
-    strategy: dict | None = Field(
-        description="The override strategy to use.",
-        default=graphrag_config_defaults.summarize_descriptions.strategy,
-    )
 
-    def resolved_strategy(
-        self, root_dir: str, model_config: LanguageModelConfig
-    ) -> dict:
-        """Get the resolved description summarization strategy."""
-        from graphrag.index.operations.summarize_descriptions.summarize_descriptions import (
-            SummarizeStrategyType,
-        )
-
-        return self.strategy or {
-            "type": SummarizeStrategyType.graph_intelligence,
-            "llm": model_config.model_dump(),
-            "summarize_prompt": (Path(root_dir) / self.prompt).read_text(
-                encoding="utf-8"
-            )
+    def resolved_prompts(self, root_dir: str) -> SummarizeDescriptionsPrompts:
+        """Get the resolved description summarization prompts."""
+        return SummarizeDescriptionsPrompts(
+            summarize_prompt=(Path(root_dir) / self.prompt).read_text(encoding="utf-8")
             if self.prompt
-            else None,
-            "max_summary_length": self.max_length,
-            "max_input_tokens": self.max_input_tokens,
-        }
+            else SUMMARIZE_PROMPT,
+        )
diff --git a/graphrag/index/operations/build_noun_graph/build_noun_graph.py b/graphrag/index/operations/build_noun_graph/build_noun_graph.py
index dca2644ca9..8d3310e766 100644
--- a/graphrag/index/operations/build_noun_graph/build_noun_graph.py
+++ b/graphrag/index/operations/build_noun_graph/build_noun_graph.py
@@ -8,7 +8,6 @@
 import numpy as np
 import pandas as pd
 
-from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.enums import AsyncType
 from graphrag.index.operations.build_noun_graph.np_extractors.base import (
@@ -23,9 +22,9 @@ async def build_noun_graph(
     text_unit_df: pd.DataFrame,
     text_analyzer: BaseNounPhraseExtractor,
     normalize_edge_weights: bool,
-    num_threads: int = 4,
-    async_mode: AsyncType = AsyncType.Threaded,
-    cache: PipelineCache | None = None,
+    num_threads: int,
+    async_mode: AsyncType,
+    cache: PipelineCache,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Build a noun graph from text units."""
     text_units = text_unit_df.loc[:, ["id", "text"]]
@@ -43,9 +42,9 @@ async def build_noun_graph(
 async def _extract_nodes(
     text_unit_df: pd.DataFrame,
     text_analyzer: BaseNounPhraseExtractor,
-    num_threads: int = 4,
-    async_mode: AsyncType = AsyncType.Threaded,
-    cache: PipelineCache | None = None,
+    num_threads: int,
+    async_mode: AsyncType,
+    cache: PipelineCache,
 ) -> pd.DataFrame:
     """
     Extract initial nodes and edges from text units.
@@ -53,7 +52,6 @@ async def _extract_nodes(
     Input: text unit df with schema [id, text, document_id]
     Returns a dataframe with schema [id, title, frequency, text_unit_ids].
     """
-    cache = cache or NoopPipelineCache()
     cache = cache.child("extract_noun_phrases")
 
     async def extract(row):
diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py
index 46b81ce212..04099f8109 100644
--- a/graphrag/index/operations/embed_text/embed_text.py
+++ b/graphrag/index/operations/embed_text/embed_text.py
@@ -4,51 +4,37 @@
 """A module containing embed_text, load_strategy and create_row_from_embedding_data methods definition."""
 
 import logging
-from enum import Enum
-from typing import Any
 
 import numpy as np
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.embeddings import create_index_name
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.index.operations.embed_text.strategies.typing import TextEmbeddingStrategy
+from graphrag.index.operations.embed_text.run_embed_text import run_embed_text
+from graphrag.language_model.protocol.base import EmbeddingModel
+from graphrag.tokenizer.tokenizer import Tokenizer
 from graphrag.vector_stores.base import BaseVectorStore, VectorStoreDocument
 from graphrag.vector_stores.factory import VectorStoreFactory
 
 logger = logging.getLogger(__name__)
 
-# Per Azure OpenAI Limits
-# https://learn.microsoft.com/en-us/azure/ai-services/openai/reference
-DEFAULT_EMBEDDING_BATCH_SIZE = 500
-
-
-class TextEmbedStrategyType(str, Enum):
-    """TextEmbedStrategyType class definition."""
-
-    openai = "openai"
-    mock = "mock"
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
-
 
 async def embed_text(
     input: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
+    model: EmbeddingModel,
+    tokenizer: Tokenizer,
     embed_column: str,
-    strategy: dict,
     embedding_name: str,
+    batch_size: int,
+    batch_max_tokens: int,
+    num_threads: int,
+    vector_store_config: dict,
     id_column: str = "id",
     title_column: str | None = None,
 ):
     """Embed a piece of text into a vector space. The operation outputs a new column containing a mapping between doc_id and vector."""
-    vector_store_config = strategy.get("vector_store")
-
     if vector_store_config:
         index_name = _get_index_name(vector_store_config, embedding_name)
         vector_store: BaseVectorStore = _create_vector_store(
@@ -60,11 +46,14 @@ async def embed_text(
         return await _text_embed_with_vector_store(
             input=input,
             callbacks=callbacks,
-            cache=cache,
+            model=model,
+            tokenizer=tokenizer,
             embed_column=embed_column,
-            strategy=strategy,
             vector_store=vector_store,
             vector_store_config=vector_store_workflow_config,
+            batch_size=batch_size,
+            batch_max_tokens=batch_max_tokens,
+            num_threads=num_threads,
             id_column=id_column,
             title_column=title_column,
         )
@@ -72,25 +61,29 @@ async def embed_text(
     return await _text_embed_in_memory(
         input=input,
         callbacks=callbacks,
-        cache=cache,
+        model=model,
+        tokenizer=tokenizer,
         embed_column=embed_column,
-        strategy=strategy,
+        batch_size=batch_size,
+        batch_max_tokens=batch_max_tokens,
+        num_threads=num_threads,
     )
 
 
 async def _text_embed_in_memory(
     input: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
+    model: EmbeddingModel,
+    tokenizer: Tokenizer,
     embed_column: str,
-    strategy: dict,
+    batch_size: int,
+    batch_max_tokens: int,
+    num_threads: int,
 ):
-    strategy_type = strategy["type"]
-    strategy_exec = load_strategy(strategy_type)
-    strategy_config = {**strategy}
-
     texts: list[str] = input[embed_column].tolist()
-    result = await strategy_exec(texts, callbacks, cache, strategy_config)
+    result = await run_embed_text(
+        texts, callbacks, model, tokenizer, batch_size, batch_max_tokens, num_threads
+    )
 
     return result.embeddings
 
@@ -98,22 +91,18 @@ async def _text_embed_in_memory(
 async def _text_embed_with_vector_store(
     input: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
+    model: EmbeddingModel,
+    tokenizer: Tokenizer,
     embed_column: str,
-    strategy: dict[str, Any],
     vector_store: BaseVectorStore,
     vector_store_config: dict,
-    id_column: str = "id",
+    batch_size: int,
+    batch_max_tokens: int,
+    num_threads: int,
+    id_column: str,
     title_column: str | None = None,
 ):
-    strategy_type = strategy["type"]
-    strategy_exec = load_strategy(strategy_type)
-    strategy_config = {**strategy}
-
     # Get vector-storage configuration
-    insert_batch_size: int = (
-        vector_store_config.get("batch_size") or DEFAULT_EMBEDDING_BATCH_SIZE
-    )
 
     overwrite: bool = vector_store_config.get("overwrite", True)
 
@@ -142,18 +131,26 @@ async def _text_embed_with_vector_store(
 
     all_results = []
 
-    num_total_batches = (input.shape[0] + insert_batch_size - 1) // insert_batch_size
-    while insert_batch_size * i < input.shape[0]:
+    num_total_batches = (input.shape[0] + batch_size - 1) // batch_size
+    while batch_size * i < input.shape[0]:
         logger.info(
             "uploading text embeddings batch %d/%d of size %d to vector store",
             i + 1,
             num_total_batches,
-            insert_batch_size,
+            batch_size,
         )
-        batch = input.iloc[insert_batch_size * i : insert_batch_size * (i + 1)]
+        batch = input.iloc[batch_size * i : batch_size * (i + 1)]
         texts: list[str] = batch[embed_column].tolist()
         ids: list[str] = batch[id_column].tolist()
-        result = await strategy_exec(texts, callbacks, cache, strategy_config)
+        result = await run_embed_text(
+            texts,
+            callbacks,
+            model,
+            tokenizer,
+            batch_size,
+            batch_max_tokens,
+            num_threads,
+        )
         if result.embeddings:
             embeddings = [
                 embedding for embedding in result.embeddings if embedding is not None
@@ -219,23 +216,3 @@ def _get_index_name(vector_store_config: dict, embedding_name: str) -> str:
     msg = f"using vector store {vector_store_config.get('type')} with container_name {container_name} for embedding {embedding_name}: {index_name}"
     logger.info(msg)
     return index_name
-
-
-def load_strategy(strategy: TextEmbedStrategyType) -> TextEmbeddingStrategy:
-    """Load strategy method definition."""
-    match strategy:
-        case TextEmbedStrategyType.openai:
-            from graphrag.index.operations.embed_text.strategies.openai import (
-                run as run_openai,
-            )
-
-            return run_openai
-        case TextEmbedStrategyType.mock:
-            from graphrag.index.operations.embed_text.strategies.mock import (
-                run as run_mock,
-            )
-
-            return run_mock
-        case _:
-            msg = f"Unknown strategy: {strategy}"
-            raise ValueError(msg)
diff --git a/graphrag/index/operations/embed_text/strategies/openai.py b/graphrag/index/operations/embed_text/run_embed_text.py
similarity index 80%
rename from graphrag/index/operations/embed_text/strategies/openai.py
rename to graphrag/index/operations/embed_text/run_embed_text.py
index ef8aadff6b..fc3da5ee6c 100644
--- a/graphrag/index/operations/embed_text/strategies/openai.py
+++ b/graphrag/index/operations/embed_text/run_embed_text.py
@@ -5,47 +5,43 @@
 
 import asyncio
 import logging
-from typing import Any
+from dataclasses import dataclass
 
 import numpy as np
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.models.language_model_config import LanguageModelConfig
-from graphrag.index.operations.embed_text.strategies.typing import TextEmbeddingResult
 from graphrag.index.text_splitting.text_splitting import TokenTextSplitter
 from graphrag.index.utils.is_null import is_null
-from graphrag.language_model.manager import ModelManager
 from graphrag.language_model.protocol.base import EmbeddingModel
 from graphrag.logger.progress import ProgressTicker, progress_ticker
-from graphrag.tokenizer.get_tokenizer import get_tokenizer
+from graphrag.tokenizer.tokenizer import Tokenizer
 
 logger = logging.getLogger(__name__)
 
 
-async def run(
+@dataclass
+class TextEmbeddingResult:
+    """Text embedding result class definition."""
+
+    embeddings: list[list[float] | None] | None
+
+
+async def run_embed_text(
     input: list[str],
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    args: dict[str, Any],
+    model: EmbeddingModel,
+    tokenizer: Tokenizer,
+    batch_size: int,
+    batch_max_tokens: int,
+    num_threads: int,
 ) -> TextEmbeddingResult:
     """Run the Claim extraction chain."""
     if is_null(input):
         return TextEmbeddingResult(embeddings=None)
 
-    batch_size = args.get("batch_size", 16)
-    batch_max_tokens = args.get("batch_max_tokens", 8191)
-    llm_config = args["llm"]
-    llm_config = LanguageModelConfig(**args["llm"])
-    splitter = _get_splitter(llm_config, batch_max_tokens)
-    model = ModelManager().get_or_create_embedding_model(
-        name="text_embedding",
-        model_type=llm_config.type,
-        config=llm_config,
-        callbacks=callbacks,
-        cache=cache,
-    )
-    semaphore: asyncio.Semaphore = asyncio.Semaphore(args.get("num_threads", 4))
+    splitter = _get_splitter(tokenizer, batch_max_tokens)
+
+    semaphore: asyncio.Semaphore = asyncio.Semaphore(num_threads)
 
     # Break up the input texts. The sizes here indicate how many snippets are in each input text
     texts, input_sizes = _prepare_embed_texts(input, splitter)
@@ -76,11 +72,9 @@ async def run(
     return TextEmbeddingResult(embeddings=embeddings)
 
 
-def _get_splitter(
-    config: LanguageModelConfig, batch_max_tokens: int
-) -> TokenTextSplitter:
+def _get_splitter(tokenizer: Tokenizer, batch_max_tokens: int) -> TokenTextSplitter:
     return TokenTextSplitter(
-        tokenizer=get_tokenizer(model_config=config),
+        tokenizer=tokenizer,
         chunk_size=batch_max_tokens,
     )
 
diff --git a/graphrag/index/operations/embed_text/strategies/__init__.py b/graphrag/index/operations/embed_text/strategies/__init__.py
deleted file mode 100644
index 8cbe7a580e..0000000000
--- a/graphrag/index/operations/embed_text/strategies/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The Indexing Engine embed strategies package root."""
diff --git a/graphrag/index/operations/embed_text/strategies/mock.py b/graphrag/index/operations/embed_text/strategies/mock.py
deleted file mode 100644
index a65ad9721f..0000000000
--- a/graphrag/index/operations/embed_text/strategies/mock.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run and _embed_text methods definitions."""
-
-import random
-from collections.abc import Iterable
-from typing import Any
-
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.index.operations.embed_text.strategies.typing import TextEmbeddingResult
-from graphrag.logger.progress import ProgressTicker, progress_ticker
-
-
-async def run(  # noqa RUF029 async is required for interface
-    input: list[str],
-    callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    _args: dict[str, Any],
-) -> TextEmbeddingResult:
-    """Run the Claim extraction chain."""
-    input = input if isinstance(input, Iterable) else [input]
-    ticker = progress_ticker(
-        callbacks.progress, len(input), description="generate embeddings progress: "
-    )
-    return TextEmbeddingResult(
-        embeddings=[_embed_text(cache, text, ticker) for text in input]
-    )
-
-
-def _embed_text(_cache: PipelineCache, _text: str, tick: ProgressTicker) -> list[float]:
-    """Embed a single piece of text."""
-    tick(1)
-    return [random.random(), random.random(), random.random()]  # noqa S311
diff --git a/graphrag/index/operations/embed_text/strategies/typing.py b/graphrag/index/operations/embed_text/strategies/typing.py
deleted file mode 100644
index f45a7eb36e..0000000000
--- a/graphrag/index/operations/embed_text/strategies/typing.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'TextEmbeddingResult' model."""
-
-from collections.abc import Awaitable, Callable
-from dataclasses import dataclass
-
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-
-
-@dataclass
-class TextEmbeddingResult:
-    """Text embedding result class definition."""
-
-    embeddings: list[list[float] | None] | None
-
-
-TextEmbeddingStrategy = Callable[
-    [
-        list[str],
-        WorkflowCallbacks,
-        PipelineCache,
-        dict,
-    ],
-    Awaitable[TextEmbeddingResult],
-]
diff --git a/graphrag/index/operations/extract_covariates/claim_extractor.py b/graphrag/index/operations/extract_covariates/claim_extractor.py
index e50d05ce83..99db5d7e2b 100644
--- a/graphrag/index/operations/extract_covariates/claim_extractor.py
+++ b/graphrag/index/operations/extract_covariates/claim_extractor.py
@@ -13,13 +13,18 @@
 from graphrag.language_model.protocol.base import ChatModel
 from graphrag.prompts.index.extract_claims import (
     CONTINUE_PROMPT,
-    EXTRACT_CLAIMS_PROMPT,
     LOOP_PROMPT,
 )
 
-DEFAULT_TUPLE_DELIMITER = "<|>"
-DEFAULT_RECORD_DELIMITER = "##"
-DEFAULT_COMPLETION_DELIMITER = "<|COMPLETE|>"
+INPUT_TEXT_KEY = "input_text"
+INPUT_ENTITY_SPEC_KEY = "entity_specs"
+INPUT_CLAIM_DESCRIPTION_KEY = "claim_description"
+INPUT_RESOLVED_ENTITIES_KEY = "resolved_entities"
+RECORD_DELIMITER_KEY = "record_delimiter"
+COMPLETION_DELIMITER_KEY = "completion_delimiter"
+TUPLE_DELIMITER = "<|>"
+RECORD_DELIMITER = "##"
+COMPLETION_DELIMITER = "<|COMPLETE|>"
 logger = logging.getLogger(__name__)
 
 
@@ -36,47 +41,19 @@ class ClaimExtractor:
 
     _model: ChatModel
     _extraction_prompt: str
-    _summary_prompt: str
-    _output_formatter_prompt: str
-    _input_text_key: str
-    _input_entity_spec_key: str
-    _input_claim_description_key: str
-    _tuple_delimiter_key: str
-    _record_delimiter_key: str
-    _completion_delimiter_key: str
     _max_gleanings: int
     _on_error: ErrorHandlerFn
 
     def __init__(
         self,
-        model_invoker: ChatModel,
-        extraction_prompt: str | None = None,
-        input_text_key: str | None = None,
-        input_entity_spec_key: str | None = None,
-        input_claim_description_key: str | None = None,
-        input_resolved_entities_key: str | None = None,
-        tuple_delimiter_key: str | None = None,
-        record_delimiter_key: str | None = None,
-        completion_delimiter_key: str | None = None,
+        model: ChatModel,
+        extraction_prompt: str,
         max_gleanings: int | None = None,
         on_error: ErrorHandlerFn | None = None,
     ):
         """Init method definition."""
-        self._model = model_invoker
-        self._extraction_prompt = extraction_prompt or EXTRACT_CLAIMS_PROMPT
-        self._input_text_key = input_text_key or "input_text"
-        self._input_entity_spec_key = input_entity_spec_key or "entity_specs"
-        self._tuple_delimiter_key = tuple_delimiter_key or "tuple_delimiter"
-        self._record_delimiter_key = record_delimiter_key or "record_delimiter"
-        self._completion_delimiter_key = (
-            completion_delimiter_key or "completion_delimiter"
-        )
-        self._input_claim_description_key = (
-            input_claim_description_key or "claim_description"
-        )
-        self._input_resolved_entities_key = (
-            input_resolved_entities_key or "resolved_entities"
-        )
+        self._model = model
+        self._extraction_prompt = extraction_prompt
         self._max_gleanings = (
             max_gleanings
             if max_gleanings is not None
@@ -85,35 +62,21 @@ def __init__(
         self._on_error = on_error or (lambda _e, _s, _d: None)
 
     async def __call__(
-        self, inputs: dict[str, Any], prompt_variables: dict | None = None
+        self,
+        texts,
+        entity_spec,
+        resolved_entities,
+        claim_description,
     ) -> ClaimExtractorResult:
         """Call method definition."""
-        if prompt_variables is None:
-            prompt_variables = {}
-        texts = inputs[self._input_text_key]
-        entity_spec = str(inputs[self._input_entity_spec_key])
-        claim_description = inputs[self._input_claim_description_key]
-        resolved_entities = inputs.get(self._input_resolved_entities_key, {})
         source_doc_map = {}
-
-        prompt_args = {
-            self._input_entity_spec_key: entity_spec,
-            self._input_claim_description_key: claim_description,
-            self._tuple_delimiter_key: prompt_variables.get(self._tuple_delimiter_key)
-            or DEFAULT_TUPLE_DELIMITER,
-            self._record_delimiter_key: prompt_variables.get(self._record_delimiter_key)
-            or DEFAULT_RECORD_DELIMITER,
-            self._completion_delimiter_key: prompt_variables.get(
-                self._completion_delimiter_key
-            )
-            or DEFAULT_COMPLETION_DELIMITER,
-        }
-
         all_claims: list[dict] = []
         for doc_index, text in enumerate(texts):
             document_id = f"d{doc_index}"
             try:
-                claims = await self._process_document(prompt_args, text, doc_index)
+                claims = await self._process_document(
+                    text, claim_description, entity_spec
+                )
                 all_claims += [
                     self._clean_claim(c, document_id, resolved_entities) for c in claims
                 ]
@@ -147,23 +110,17 @@ def _clean_claim(
         return claim
 
     async def _process_document(
-        self, prompt_args: dict, doc, doc_index: int
+        self, text: str, claim_description: str, entity_spec: dict
     ) -> list[dict]:
-        record_delimiter = prompt_args.get(
-            self._record_delimiter_key, DEFAULT_RECORD_DELIMITER
-        )
-        completion_delimiter = prompt_args.get(
-            self._completion_delimiter_key, DEFAULT_COMPLETION_DELIMITER
-        )
-
         response = await self._model.achat(
             self._extraction_prompt.format(**{
-                self._input_text_key: doc,
-                **prompt_args,
+                INPUT_TEXT_KEY: text,
+                INPUT_CLAIM_DESCRIPTION_KEY: claim_description,
+                INPUT_ENTITY_SPEC_KEY: entity_spec,
             })
         )
         results = response.output.content or ""
-        claims = results.strip().removesuffix(completion_delimiter)
+        claims = results.strip().removesuffix(COMPLETION_DELIMITER)
 
         # if gleanings are specified, enter a loop to extract more claims
         # there are two exit criteria: (a) we hit the configured max, (b) the model says there are no more claims
@@ -175,8 +132,8 @@ async def _process_document(
                     history=response.history,
                 )
                 extension = response.output.content or ""
-                claims += record_delimiter + extension.strip().removesuffix(
-                    completion_delimiter
+                claims += RECORD_DELIMITER + extension.strip().removesuffix(
+                    COMPLETION_DELIMITER
                 )
 
                 # If this isn't the last loop, check to see if we should continue
@@ -192,37 +149,26 @@ async def _process_document(
                 if response.output.content != "Y":
                     break
 
-        return self._parse_claim_tuples(results, prompt_args)
+        return self._parse_claim_tuples(results)
 
-    def _parse_claim_tuples(
-        self, claims: str, prompt_variables: dict
-    ) -> list[dict[str, Any]]:
+    def _parse_claim_tuples(self, claims: str) -> list[dict[str, Any]]:
         """Parse claim tuples."""
-        record_delimiter = prompt_variables.get(
-            self._record_delimiter_key, DEFAULT_RECORD_DELIMITER
-        )
-        completion_delimiter = prompt_variables.get(
-            self._completion_delimiter_key, DEFAULT_COMPLETION_DELIMITER
-        )
-        tuple_delimiter = prompt_variables.get(
-            self._tuple_delimiter_key, DEFAULT_TUPLE_DELIMITER
-        )
 
         def pull_field(index: int, fields: list[str]) -> str | None:
             return fields[index].strip() if len(fields) > index else None
 
         result: list[dict[str, Any]] = []
         claims_values = (
-            claims.strip().removesuffix(completion_delimiter).split(record_delimiter)
+            claims.strip().removesuffix(COMPLETION_DELIMITER).split(RECORD_DELIMITER)
         )
         for claim in claims_values:
             claim = claim.strip().removeprefix("(").removesuffix(")")
 
             # Ignore the completion delimiter
-            if claim == completion_delimiter:
+            if claim == COMPLETION_DELIMITER:
                 continue
 
-            claim_fields = claim.split(tuple_delimiter)
+            claim_fields = claim.split(TUPLE_DELIMITER)
             result.append({
                 "subject_id": pull_field(0, claim_fields),
                 "object_id": pull_field(1, claim_fields),
diff --git a/graphrag/index/operations/extract_covariates/extract_covariates.py b/graphrag/index/operations/extract_covariates/extract_covariates.py
index d29ca61e9d..bc2e1fa9de 100644
--- a/graphrag/index/operations/extract_covariates/extract_covariates.py
+++ b/graphrag/index/operations/extract_covariates/extract_covariates.py
@@ -10,55 +10,45 @@
 
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import AsyncType
-from graphrag.config.models.language_model_config import LanguageModelConfig
 from graphrag.index.operations.extract_covariates.claim_extractor import ClaimExtractor
 from graphrag.index.operations.extract_covariates.typing import (
     Covariate,
     CovariateExtractionResult,
 )
 from graphrag.index.utils.derive_from_rows import derive_from_rows
-from graphrag.language_model.manager import ModelManager
+from graphrag.language_model.protocol.base import ChatModel
 
 logger = logging.getLogger(__name__)
 
 
-DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event"]
-
-
 async def extract_covariates(
     input: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
+    model: ChatModel,
     column: str,
     covariate_type: str,
-    strategy: dict[str, Any] | None,
-    async_mode: AsyncType = AsyncType.AsyncIO,
-    entity_types: list[str] | None = None,
-    num_threads: int = 4,
+    max_gleanings: int,
+    claim_description: str,
+    prompt: str,
+    entity_types: list[str],
+    num_threads: int,
+    async_type: AsyncType,
 ):
     """Extract claims from a piece of text."""
-    logger.debug("extract_covariates strategy=%s", strategy)
-    if entity_types is None:
-        entity_types = DEFAULT_ENTITY_TYPES
-
     resolved_entities_map = {}
 
-    strategy = strategy or {}
-    strategy_config = {**strategy}
-
     async def run_strategy(row):
         text = row[column]
         result = await run_extract_claims(
             input=text,
             entity_types=entity_types,
             resolved_entities_map=resolved_entities_map,
-            callbacks=callbacks,
-            cache=cache,
-            strategy_config=strategy_config,
+            model=model,
+            max_gleanings=max_gleanings,
+            claim_description=claim_description,
+            prompt=prompt,
         )
         return [
             create_row_from_claim_data(row, item, covariate_type)
@@ -69,8 +59,8 @@ async def run_strategy(row):
         input,
         run_strategy,
         callbacks,
-        async_type=async_mode,
         num_threads=num_threads,
+        async_type=async_type,
         progress_msg="extract covariates progress: ",
     )
     return pd.DataFrame([item for row in results for item in row or []])
@@ -85,53 +75,29 @@ async def run_extract_claims(
     input: str | Iterable[str],
     entity_types: list[str],
     resolved_entities_map: dict[str, str],
-    callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    strategy_config: dict[str, Any],
+    model: ChatModel,
+    max_gleanings: int,
+    claim_description: str,
+    prompt: str,
 ) -> CovariateExtractionResult:
     """Run the Claim extraction chain."""
-    llm_config = LanguageModelConfig(**strategy_config["llm"])
-    llm = ModelManager().get_or_create_chat_model(
-        name="extract_claims",
-        model_type=llm_config.type,
-        config=llm_config,
-        callbacks=callbacks,
-        cache=cache,
-    )
-
-    extraction_prompt = strategy_config.get("extraction_prompt")
-    max_gleanings = strategy_config.get(
-        "max_gleanings", graphrag_config_defaults.extract_claims.max_gleanings
-    )
-    tuple_delimiter = strategy_config.get("tuple_delimiter")
-    record_delimiter = strategy_config.get("record_delimiter")
-    completion_delimiter = strategy_config.get("completion_delimiter")
-
     extractor = ClaimExtractor(
-        model_invoker=llm,
-        extraction_prompt=extraction_prompt,
+        model=model,
+        extraction_prompt=prompt,
         max_gleanings=max_gleanings,
         on_error=lambda e, s, d: logger.error(
             "Claim Extraction Error", exc_info=e, extra={"stack": s, "details": d}
         ),
     )
 
-    claim_description = strategy_config.get("claim_description")
-    if claim_description is None:
-        msg = "claim_description is required for claim extraction"
-        raise ValueError(msg)
-
     input = [input] if isinstance(input, str) else input
 
-    results = await extractor({
-        "input_text": input,
-        "entity_specs": entity_types,
-        "resolved_entities": resolved_entities_map,
-        "claim_description": claim_description,
-        "tuple_delimiter": tuple_delimiter,
-        "record_delimiter": record_delimiter,
-        "completion_delimiter": completion_delimiter,
-    })
+    results = await extractor(
+        texts=input,
+        entity_spec=entity_types,
+        resolved_entities=resolved_entities_map,
+        claim_description=claim_description,
+    )
 
     claim_data = results.output
     return CovariateExtractionResult([create_covariate(item) for item in claim_data])
diff --git a/graphrag/index/operations/extract_graph/extract_graph.py b/graphrag/index/operations/extract_graph/extract_graph.py
index 76bcf40c76..3aa87404ec 100644
--- a/graphrag/index/operations/extract_graph/extract_graph.py
+++ b/graphrag/index/operations/extract_graph/extract_graph.py
@@ -4,58 +4,49 @@
 """A module containing entity_extract methods."""
 
 import logging
-from typing import Any
 
+import networkx as nx
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.enums import AsyncType
+from graphrag.index.operations.extract_graph.graph_extractor import GraphExtractor
 from graphrag.index.operations.extract_graph.typing import (
     Document,
-    EntityExtractStrategy,
-    ExtractEntityStrategyType,
+    EntityExtractionResult,
+    EntityTypes,
 )
 from graphrag.index.utils.derive_from_rows import derive_from_rows
+from graphrag.language_model.protocol.base import ChatModel
 
 logger = logging.getLogger(__name__)
 
 
-DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event"]
-
-
 async def extract_graph(
     text_units: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
     text_column: str,
     id_column: str,
-    strategy: dict[str, Any] | None,
-    async_mode: AsyncType = AsyncType.AsyncIO,
-    entity_types=DEFAULT_ENTITY_TYPES,
-    num_threads: int = 4,
+    model: ChatModel,
+    prompt: str,
+    entity_types: list[str],
+    max_gleanings: int,
+    num_threads: int,
+    async_type: AsyncType,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Extract a graph from a piece of text using a language model."""
-    logger.debug("entity_extract strategy=%s", strategy)
-    if entity_types is None:
-        entity_types = DEFAULT_ENTITY_TYPES
-    strategy = strategy or {}
-    strategy_exec = _load_strategy(
-        strategy.get("type", ExtractEntityStrategyType.graph_intelligence)
-    )
-    strategy_config = {**strategy}
-
     num_started = 0
 
     async def run_strategy(row):
         nonlocal num_started
         text = row[text_column]
         id = row[id_column]
-        result = await strategy_exec(
+        result = await run_extract_graph(
             [Document(text=text, id=id)],
             entity_types,
-            cache,
-            strategy_config,
+            model,
+            prompt,
+            max_gleanings,
         )
         num_started += 1
         return [result.entities, result.relationships, result.graph]
@@ -64,8 +55,8 @@ async def run_strategy(row):
         text_units,
         run_strategy,
         callbacks,
-        async_type=async_mode,
         num_threads=num_threads,
+        async_type=async_type,
         progress_msg="extract graph progress: ",
     )
 
@@ -82,19 +73,52 @@ async def run_strategy(row):
     return (entities, relationships)
 
 
-def _load_strategy(strategy_type: ExtractEntityStrategyType) -> EntityExtractStrategy:
-    """Load strategy method definition."""
-    match strategy_type:
-        case ExtractEntityStrategyType.graph_intelligence:
-            from graphrag.index.operations.extract_graph.graph_intelligence_strategy import (
-                run_graph_intelligence,
+async def run_extract_graph(
+    docs: list[Document],
+    entity_types: EntityTypes,
+    model: ChatModel,
+    prompt: str,
+    max_gleanings: int,
+) -> EntityExtractionResult:
+    """Run the graph intelligence entity extraction strategy."""
+    extractor = GraphExtractor(
+        model=model,
+        prompt=prompt,
+        max_gleanings=max_gleanings,
+        on_error=lambda e, s, d: logger.error(
+            "Entity Extraction Error", exc_info=e, extra={"stack": s, "details": d}
+        ),
+    )
+    text_list = [doc.text.strip() for doc in docs]
+
+    results = await extractor(
+        list(text_list),
+        entity_types=entity_types,
+    )
+
+    graph = results.output
+    # Map the "source_id" back to the "id" field
+    for _, node in graph.nodes(data=True):  # type: ignore
+        if node is not None:
+            node["source_id"] = ",".join(
+                docs[int(id)].id for id in node["source_id"].split(",")
             )
 
-            return run_graph_intelligence
+    for _, _, edge in graph.edges(data=True):  # type: ignore
+        if edge is not None:
+            edge["source_id"] = ",".join(
+                docs[int(id)].id for id in edge["source_id"].split(",")
+            )
+
+    entities = [
+        ({"title": item[0], **(item[1] or {})})
+        for item in graph.nodes(data=True)
+        if item is not None
+    ]
+
+    relationships = nx.to_pandas_edgelist(graph)
 
-        case _:
-            msg = f"Unknown strategy: {strategy_type}"
-            raise ValueError(msg)
+    return EntityExtractionResult(entities, relationships, graph)
 
 
 def _merge_entities(entity_dfs) -> pd.DataFrame:
diff --git a/graphrag/index/operations/extract_graph/graph_extractor.py b/graphrag/index/operations/extract_graph/graph_extractor.py
index d1f66c3e81..8e98eb7ec4 100644
--- a/graphrag/index/operations/extract_graph/graph_extractor.py
+++ b/graphrag/index/operations/extract_graph/graph_extractor.py
@@ -12,19 +12,21 @@
 
 import networkx as nx
 
-from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.index.typing.error_handler import ErrorHandlerFn
 from graphrag.index.utils.string import clean_str
 from graphrag.language_model.protocol.base import ChatModel
 from graphrag.prompts.index.extract_graph import (
     CONTINUE_PROMPT,
-    GRAPH_EXTRACTION_PROMPT,
     LOOP_PROMPT,
 )
 
-DEFAULT_TUPLE_DELIMITER = "<|>"
-DEFAULT_RECORD_DELIMITER = "##"
-DEFAULT_COMPLETION_DELIMITER = "<|COMPLETE|>"
+INPUT_TEXT_KEY = "input_text"
+RECORD_DELIMITER_KEY = "record_delimiter"
+COMPLETION_DELIMITER_KEY = "completion_delimiter"
+ENTITY_TYPES_KEY = "entity_types"
+TUPLE_DELIMITER = "<|>"
+RECORD_DELIMITER = "##"
+COMPLETION_DELIMITER = "<|COMPLETE|>"
 DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event"]
 
 logger = logging.getLogger(__name__)
@@ -43,79 +45,36 @@ class GraphExtractor:
 
     _model: ChatModel
     _join_descriptions: bool
-    _tuple_delimiter_key: str
-    _record_delimiter_key: str
-    _entity_types_key: str
-    _input_text_key: str
-    _completion_delimiter_key: str
-    _entity_name_key: str
-    _input_descriptions_key: str
     _extraction_prompt: str
-    _summarization_prompt: str
     _max_gleanings: int
     _on_error: ErrorHandlerFn
 
     def __init__(
         self,
-        model_invoker: ChatModel,
-        tuple_delimiter_key: str | None = None,
-        record_delimiter_key: str | None = None,
-        input_text_key: str | None = None,
-        entity_types_key: str | None = None,
-        completion_delimiter_key: str | None = None,
-        prompt: str | None = None,
+        model: ChatModel,
+        prompt: str,
+        max_gleanings: int,
         join_descriptions=True,
-        max_gleanings: int | None = None,
         on_error: ErrorHandlerFn | None = None,
     ):
         """Init method definition."""
-        # TODO: streamline construction
-        self._model = model_invoker
+        self._model = model
         self._join_descriptions = join_descriptions
-        self._input_text_key = input_text_key or "input_text"
-        self._tuple_delimiter_key = tuple_delimiter_key or "tuple_delimiter"
-        self._record_delimiter_key = record_delimiter_key or "record_delimiter"
-        self._completion_delimiter_key = (
-            completion_delimiter_key or "completion_delimiter"
-        )
-        self._entity_types_key = entity_types_key or "entity_types"
-        self._extraction_prompt = prompt or GRAPH_EXTRACTION_PROMPT
-        self._max_gleanings = (
-            max_gleanings
-            if max_gleanings is not None
-            else graphrag_config_defaults.extract_graph.max_gleanings
-        )
+        self._extraction_prompt = prompt
+        self._max_gleanings = max_gleanings
         self._on_error = on_error or (lambda _e, _s, _d: None)
 
     async def __call__(
-        self, texts: list[str], prompt_variables: dict[str, Any] | None = None
+        self, texts: list[str], entity_types: list[str]
     ) -> GraphExtractionResult:
         """Call method definition."""
-        if prompt_variables is None:
-            prompt_variables = {}
         all_records: dict[int, str] = {}
         source_doc_map: dict[int, str] = {}
 
-        # Wire defaults into the prompt variables
-        prompt_variables = {
-            **prompt_variables,
-            self._tuple_delimiter_key: prompt_variables.get(self._tuple_delimiter_key)
-            or DEFAULT_TUPLE_DELIMITER,
-            self._record_delimiter_key: prompt_variables.get(self._record_delimiter_key)
-            or DEFAULT_RECORD_DELIMITER,
-            self._completion_delimiter_key: prompt_variables.get(
-                self._completion_delimiter_key
-            )
-            or DEFAULT_COMPLETION_DELIMITER,
-            self._entity_types_key: ",".join(
-                prompt_variables[self._entity_types_key] or DEFAULT_ENTITY_TYPES
-            ),
-        }
-
         for doc_index, text in enumerate(texts):
             try:
                 # Invoke the entity extraction
-                result = await self._process_document(text, prompt_variables)
+                result = await self._process_document(text, entity_types)
                 source_doc_map[doc_index] = text
                 all_records[doc_index] = result
             except Exception as e:
@@ -131,8 +90,8 @@ async def __call__(
 
         output = await self._process_results(
             all_records,
-            prompt_variables.get(self._tuple_delimiter_key, DEFAULT_TUPLE_DELIMITER),
-            prompt_variables.get(self._record_delimiter_key, DEFAULT_RECORD_DELIMITER),
+            TUPLE_DELIMITER,
+            RECORD_DELIMITER,
         )
 
         return GraphExtractionResult(
@@ -140,13 +99,11 @@ async def __call__(
             source_docs=source_doc_map,
         )
 
-    async def _process_document(
-        self, text: str, prompt_variables: dict[str, str]
-    ) -> str:
+    async def _process_document(self, text: str, entity_types: list[str]) -> str:
         response = await self._model.achat(
             self._extraction_prompt.format(**{
-                **prompt_variables,
-                self._input_text_key: text,
+                INPUT_TEXT_KEY: text,
+                ENTITY_TYPES_KEY: ",".join(entity_types),
             }),
         )
         results = response.output.content or ""
diff --git a/graphrag/index/operations/extract_graph/graph_intelligence_strategy.py b/graphrag/index/operations/extract_graph/graph_intelligence_strategy.py
deleted file mode 100644
index b335d191a6..0000000000
--- a/graphrag/index/operations/extract_graph/graph_intelligence_strategy.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run_graph_intelligence,  run_extract_graph and _create_text_splitter methods to run graph intelligence."""
-
-import logging
-
-import networkx as nx
-
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.models.language_model_config import LanguageModelConfig
-from graphrag.index.operations.extract_graph.graph_extractor import GraphExtractor
-from graphrag.index.operations.extract_graph.typing import (
-    Document,
-    EntityExtractionResult,
-    EntityTypes,
-    StrategyConfig,
-)
-from graphrag.language_model.manager import ModelManager
-from graphrag.language_model.protocol.base import ChatModel
-
-logger = logging.getLogger(__name__)
-
-
-async def run_graph_intelligence(
-    docs: list[Document],
-    entity_types: EntityTypes,
-    cache: PipelineCache,
-    args: StrategyConfig,
-) -> EntityExtractionResult:
-    """Run the graph intelligence entity extraction strategy."""
-    llm_config = LanguageModelConfig(**args["llm"])
-
-    llm = ModelManager().get_or_create_chat_model(
-        name="extract_graph",
-        model_type=llm_config.type,
-        config=llm_config,
-        cache=cache,
-    )
-
-    return await run_extract_graph(llm, docs, entity_types, args)
-
-
-async def run_extract_graph(
-    model: ChatModel,
-    docs: list[Document],
-    entity_types: EntityTypes,
-    args: StrategyConfig,
-) -> EntityExtractionResult:
-    """Run the entity extraction chain."""
-    tuple_delimiter = args.get("tuple_delimiter", None)
-    record_delimiter = args.get("record_delimiter", None)
-    completion_delimiter = args.get("completion_delimiter", None)
-    extraction_prompt = args.get("extraction_prompt", None)
-    max_gleanings = args.get(
-        "max_gleanings", graphrag_config_defaults.extract_graph.max_gleanings
-    )
-
-    extractor = GraphExtractor(
-        model_invoker=model,
-        prompt=extraction_prompt,
-        max_gleanings=max_gleanings,
-        on_error=lambda e, s, d: logger.error(
-            "Entity Extraction Error", exc_info=e, extra={"stack": s, "details": d}
-        ),
-    )
-    text_list = [doc.text.strip() for doc in docs]
-
-    results = await extractor(
-        list(text_list),
-        {
-            "entity_types": entity_types,
-            "tuple_delimiter": tuple_delimiter,
-            "record_delimiter": record_delimiter,
-            "completion_delimiter": completion_delimiter,
-        },
-    )
-
-    graph = results.output
-    # Map the "source_id" back to the "id" field
-    for _, node in graph.nodes(data=True):  # type: ignore
-        if node is not None:
-            node["source_id"] = ",".join(
-                docs[int(id)].id for id in node["source_id"].split(",")
-            )
-
-    for _, _, edge in graph.edges(data=True):  # type: ignore
-        if edge is not None:
-            edge["source_id"] = ",".join(
-                docs[int(id)].id for id in edge["source_id"].split(",")
-            )
-
-    entities = [
-        ({"title": item[0], **(item[1] or {})})
-        for item in graph.nodes(data=True)
-        if item is not None
-    ]
-
-    relationships = nx.to_pandas_edgelist(graph)
-
-    return EntityExtractionResult(entities, relationships, graph)
diff --git a/graphrag/index/operations/extract_graph/typing.py b/graphrag/index/operations/extract_graph/typing.py
index 3c7c134753..d74eb9a476 100644
--- a/graphrag/index/operations/extract_graph/typing.py
+++ b/graphrag/index/operations/extract_graph/typing.py
@@ -5,7 +5,6 @@
 
 from collections.abc import Awaitable, Callable
 from dataclasses import dataclass
-from enum import Enum
 from typing import Any
 
 import networkx as nx
@@ -44,14 +43,3 @@ class EntityExtractionResult:
     ],
     Awaitable[EntityExtractionResult],
 ]
-
-
-class ExtractEntityStrategyType(str, Enum):
-    """ExtractEntityStrategyType class definition."""
-
-    graph_intelligence = "graph_intelligence"
-    nltk = "nltk"
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
diff --git a/graphrag/index/operations/summarize_communities/community_reports_extractor.py b/graphrag/index/operations/summarize_communities/community_reports_extractor.py
index 1442a44a1b..3ca29251a0 100644
--- a/graphrag/index/operations/summarize_communities/community_reports_extractor.py
+++ b/graphrag/index/operations/summarize_communities/community_reports_extractor.py
@@ -11,7 +11,6 @@
 
 from graphrag.index.typing.error_handler import ErrorHandlerFn
 from graphrag.language_model.protocol.base import ChatModel
-from graphrag.prompts.index.community_report import COMMUNITY_REPORT_PROMPT
 
 logger = logging.getLogger(__name__)
 
@@ -58,16 +57,16 @@ class CommunityReportsExtractor:
 
     def __init__(
         self,
-        model_invoker: ChatModel,
-        extraction_prompt: str | None = None,
+        model: ChatModel,
+        extraction_prompt: str,
+        max_report_length: int,
         on_error: ErrorHandlerFn | None = None,
-        max_report_length: int | None = None,
     ):
         """Init method definition."""
-        self._model = model_invoker
-        self._extraction_prompt = extraction_prompt or COMMUNITY_REPORT_PROMPT
+        self._model = model
+        self._extraction_prompt = extraction_prompt
         self._on_error = on_error or (lambda _e, _s, _d: None)
-        self._max_report_length = max_report_length or 1500
+        self._max_report_length = max_report_length
 
     async def __call__(self, input_text: str):
         """Call method definition."""
diff --git a/graphrag/index/operations/summarize_communities/strategies.py b/graphrag/index/operations/summarize_communities/strategies.py
deleted file mode 100644
index 06ce44b27d..0000000000
--- a/graphrag/index/operations/summarize_communities/strategies.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run, _run_extractor and _load_nodes_edges_for_claim_chain methods definition."""
-
-import logging
-
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.models.language_model_config import LanguageModelConfig
-from graphrag.index.operations.summarize_communities.community_reports_extractor import (
-    CommunityReportsExtractor,
-)
-from graphrag.index.operations.summarize_communities.typing import (
-    CommunityReport,
-    Finding,
-    StrategyConfig,
-)
-from graphrag.language_model.manager import ModelManager
-from graphrag.language_model.protocol.base import ChatModel
-
-logger = logging.getLogger(__name__)
-
-
-async def run_graph_intelligence(
-    community: str | int,
-    input: str,
-    level: int,
-    callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    args: StrategyConfig,
-) -> CommunityReport | None:
-    """Run the graph intelligence entity extraction strategy."""
-    llm_config = LanguageModelConfig(**args["llm"])
-    llm = ModelManager().get_or_create_chat_model(
-        name="community_reporting",
-        model_type=llm_config.type,
-        config=llm_config,
-        callbacks=callbacks,
-        cache=cache,
-    )
-
-    return await _run_extractor(llm, community, input, level, args)
-
-
-async def _run_extractor(
-    model: ChatModel,
-    community: str | int,
-    input: str,
-    level: int,
-    args: StrategyConfig,
-) -> CommunityReport | None:
-    extractor = CommunityReportsExtractor(
-        model,
-        extraction_prompt=args.get("extraction_prompt", None),
-        max_report_length=args.get("max_report_length", None),
-        on_error=lambda e, stack, _data: logger.error(
-            "Community Report Extraction Error", exc_info=e, extra={"stack": stack}
-        ),
-    )
-
-    try:
-        results = await extractor(input)
-        report = results.structured_output
-        if report is None:
-            logger.warning("No report found for community: %s", community)
-            return None
-
-        return CommunityReport(
-            community=community,
-            full_content=results.output,
-            level=level,
-            rank=report.rating,
-            title=report.title,
-            rating_explanation=report.rating_explanation,
-            summary=report.summary,
-            findings=[
-                Finding(explanation=f.explanation, summary=f.summary)
-                for f in report.findings
-            ],
-            full_content_json=report.model_dump_json(indent=4),
-        )
-    except Exception:
-        logger.exception("Error processing community: %s", community)
-        return None
diff --git a/graphrag/index/operations/summarize_communities/summarize_communities.py b/graphrag/index/operations/summarize_communities/summarize_communities.py
index c31a4b0d77..f6028332bc 100644
--- a/graphrag/index/operations/summarize_communities/summarize_communities.py
+++ b/graphrag/index/operations/summarize_communities/summarize_communities.py
@@ -9,19 +9,22 @@
 import pandas as pd
 
 import graphrag.data_model.schemas as schemas
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.enums import AsyncType
+from graphrag.index.operations.summarize_communities.community_reports_extractor import (
+    CommunityReportsExtractor,
+)
 from graphrag.index.operations.summarize_communities.typing import (
     CommunityReport,
     CommunityReportsStrategy,
-    CreateCommunityReportsStrategyType,
+    Finding,
 )
 from graphrag.index.operations.summarize_communities.utils import (
     get_levels,
 )
 from graphrag.index.utils.derive_from_rows import derive_from_rows
+from graphrag.language_model.protocol.base import ChatModel
 from graphrag.logger.progress import progress_ticker
 from graphrag.tokenizer.tokenizer import Tokenizer
 
@@ -34,18 +37,17 @@ async def summarize_communities(
     local_contexts,
     level_context_builder: Callable,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    strategy: dict,
+    model: ChatModel,
+    prompt: str,
     tokenizer: Tokenizer,
     max_input_length: int,
-    async_mode: AsyncType = AsyncType.AsyncIO,
-    num_threads: int = 4,
+    max_report_length: int,
+    num_threads: int,
+    async_type: AsyncType,
 ):
     """Generate community summaries."""
     reports: list[CommunityReport | None] = []
     tick = progress_ticker(callbacks.progress, len(local_contexts))
-    strategy_exec = load_strategy(strategy["type"])
-    strategy_config = {**strategy}
     community_hierarchy = (
         communities.explode("children")
         .rename({"children": "sub_community"}, axis=1)
@@ -70,13 +72,13 @@ async def summarize_communities(
 
         async def run_generate(record):
             result = await _generate_report(
-                strategy_exec,
+                run_extractor,
                 community_id=record[schemas.COMMUNITY_ID],
                 community_level=record[schemas.COMMUNITY_LEVEL],
                 community_context=record[schemas.CONTEXT_STRING],
-                callbacks=callbacks,
-                cache=cache,
-                strategy=strategy_config,
+                model=model,
+                extraction_prompt=prompt,
+                max_report_length=max_report_length,
             )
             tick()
             return result
@@ -86,7 +88,7 @@ async def run_generate(record):
             run_generate,
             callbacks=NoopWorkflowCallbacks(),
             num_threads=num_threads,
-            async_type=async_mode,
+            async_type=async_type,
             progress_msg=f"level {levels[i]} summarize communities progress: ",
         )
         reports.extend([lr for lr in local_reports if lr is not None])
@@ -96,35 +98,63 @@ async def run_generate(record):
 
 async def _generate_report(
     runner: CommunityReportsStrategy,
-    callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    strategy: dict,
+    model: ChatModel,
+    extraction_prompt: str,
     community_id: int,
     community_level: int,
     community_context: str,
+    max_report_length: int,
 ) -> CommunityReport | None:
     """Generate a report for a single community."""
     return await runner(
         community_id,
         community_context,
         community_level,
-        callbacks,
-        cache,
-        strategy,
+        model,
+        extraction_prompt,
+        max_report_length,
     )
 
 
-def load_strategy(
-    strategy: CreateCommunityReportsStrategyType,
-) -> CommunityReportsStrategy:
-    """Load strategy method definition."""
-    match strategy:
-        case CreateCommunityReportsStrategyType.graph_intelligence:
-            from graphrag.index.operations.summarize_communities.strategies import (
-                run_graph_intelligence,
-            )
+async def run_extractor(
+    community: str | int,
+    input: str,
+    level: int,
+    model: ChatModel,
+    extraction_prompt: str,
+    max_report_length: int,
+) -> CommunityReport | None:
+    """Run the graph intelligence entity extraction strategy."""
+    extractor = CommunityReportsExtractor(
+        model,
+        extraction_prompt=extraction_prompt,
+        max_report_length=max_report_length,
+        on_error=lambda e, stack, _data: logger.error(
+            "Community Report Extraction Error", exc_info=e, extra={"stack": stack}
+        ),
+    )
+
+    try:
+        results = await extractor(input)
+        report = results.structured_output
+        if report is None:
+            logger.warning("No report found for community: %s", community)
+            return None
 
-            return run_graph_intelligence
-        case _:
-            msg = f"Unknown strategy: {strategy}"
-            raise ValueError(msg)
+        return CommunityReport(
+            community=community,
+            full_content=results.output,
+            level=level,
+            rank=report.rating,
+            title=report.title,
+            rating_explanation=report.rating_explanation,
+            summary=report.summary,
+            findings=[
+                Finding(explanation=f.explanation, summary=f.summary)
+                for f in report.findings
+            ],
+            full_content_json=report.model_dump_json(indent=4),
+        )
+    except Exception:
+        logger.exception("Error processing community: %s", community)
+        return None
diff --git a/graphrag/index/operations/summarize_communities/typing.py b/graphrag/index/operations/summarize_communities/typing.py
index 6dddf3d6b1..709c5ccc6a 100644
--- a/graphrag/index/operations/summarize_communities/typing.py
+++ b/graphrag/index/operations/summarize_communities/typing.py
@@ -4,16 +4,13 @@
 """A module containing 'Finding' and 'CommunityReport' models."""
 
 from collections.abc import Awaitable, Callable
-from enum import Enum
 from typing import Any
 
 from typing_extensions import TypedDict
 
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
+from graphrag.language_model.protocol.base import ChatModel
 
 ExtractedEntity = dict[str, Any]
-StrategyConfig = dict[str, Any]
 RowContext = dict[str, Any]
 EntityTypes = list[str]
 Claim = dict[str, Any]
@@ -45,19 +42,9 @@ class CommunityReport(TypedDict):
         str | int,
         str,
         int,
-        WorkflowCallbacks,
-        PipelineCache,
-        StrategyConfig,
+        ChatModel,
+        str,
+        int,
     ],
     Awaitable[CommunityReport | None],
 ]
-
-
-class CreateCommunityReportsStrategyType(str, Enum):
-    """CreateCommunityReportsStrategyType class definition."""
-
-    graph_intelligence = "graph_intelligence"
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
diff --git a/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py b/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
index 6a44ee1df7..4a999fd685 100644
--- a/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
+++ b/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
@@ -8,7 +8,6 @@
 
 from graphrag.index.typing.error_handler import ErrorHandlerFn
 from graphrag.language_model.protocol.base import ChatModel
-from graphrag.prompts.index.summarize_descriptions import SUMMARIZE_PROMPT
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
 
 # these tokens are used in the prompt
@@ -36,17 +35,17 @@ class SummarizeExtractor:
 
     def __init__(
         self,
-        model_invoker: ChatModel,
+        model: ChatModel,
         max_summary_length: int,
         max_input_tokens: int,
-        summarization_prompt: str | None = None,
+        summarization_prompt: str,
         on_error: ErrorHandlerFn | None = None,
     ):
         """Init method definition."""
         # TODO: streamline construction
-        self._model = model_invoker
-        self._tokenizer = get_tokenizer(model_invoker.config)
-        self._summarization_prompt = summarization_prompt or SUMMARIZE_PROMPT
+        self._model = model
+        self._tokenizer = get_tokenizer(model.config)
+        self._summarization_prompt = summarization_prompt
         self._on_error = on_error or (lambda _e, _s, _d: None)
         self._max_summary_length = max_summary_length
         self._max_input_tokens = max_input_tokens
diff --git a/graphrag/index/operations/summarize_descriptions/graph_intelligence_strategy.py b/graphrag/index/operations/summarize_descriptions/graph_intelligence_strategy.py
deleted file mode 100644
index d3259b290f..0000000000
--- a/graphrag/index/operations/summarize_descriptions/graph_intelligence_strategy.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run_graph_intelligence,  run_resolve_entities and _create_text_list_splitter methods to run graph intelligence."""
-
-import logging
-
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.config.models.language_model_config import LanguageModelConfig
-from graphrag.index.operations.summarize_descriptions.description_summary_extractor import (
-    SummarizeExtractor,
-)
-from graphrag.index.operations.summarize_descriptions.typing import (
-    StrategyConfig,
-    SummarizedDescriptionResult,
-)
-from graphrag.language_model.manager import ModelManager
-from graphrag.language_model.protocol.base import ChatModel
-
-logger = logging.getLogger(__name__)
-
-
-async def run_graph_intelligence(
-    id: str | tuple[str, str],
-    descriptions: list[str],
-    cache: PipelineCache,
-    args: StrategyConfig,
-) -> SummarizedDescriptionResult:
-    """Run the graph intelligence entity extraction strategy."""
-    llm_config = LanguageModelConfig(**args["llm"])
-    llm = ModelManager().get_or_create_chat_model(
-        name="summarize_descriptions",
-        model_type=llm_config.type,
-        config=llm_config,
-        cache=cache,
-    )
-
-    return await run_summarize_descriptions(llm, id, descriptions, args)
-
-
-async def run_summarize_descriptions(
-    model: ChatModel,
-    id: str | tuple[str, str],
-    descriptions: list[str],
-    args: StrategyConfig,
-) -> SummarizedDescriptionResult:
-    """Run the entity extraction chain."""
-    # Extraction Arguments
-    summarize_prompt = args.get("summarize_prompt", None)
-    max_input_tokens = args["max_input_tokens"]
-    max_summary_length = args["max_summary_length"]
-    extractor = SummarizeExtractor(
-        model_invoker=model,
-        summarization_prompt=summarize_prompt,
-        on_error=lambda e, stack, details: logger.error(
-            "Entity Extraction Error",
-            exc_info=e,
-            extra={"stack": stack, "details": details},
-        ),
-        max_summary_length=max_summary_length,
-        max_input_tokens=max_input_tokens,
-    )
-
-    result = await extractor(id=id, descriptions=descriptions)
-    return SummarizedDescriptionResult(id=result.id, description=result.description)
diff --git a/graphrag/index/operations/summarize_descriptions/summarize_descriptions.py b/graphrag/index/operations/summarize_descriptions/summarize_descriptions.py
index 780c94b329..48aaf37a00 100644
--- a/graphrag/index/operations/summarize_descriptions/summarize_descriptions.py
+++ b/graphrag/index/operations/summarize_descriptions/summarize_descriptions.py
@@ -5,16 +5,17 @@
 
 import asyncio
 import logging
-from typing import Any
 
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
+from graphrag.index.operations.summarize_descriptions.description_summary_extractor import (
+    SummarizeExtractor,
+)
 from graphrag.index.operations.summarize_descriptions.typing import (
-    SummarizationStrategy,
-    SummarizeStrategyType,
+    SummarizedDescriptionResult,
 )
+from graphrag.language_model.protocol.base import ChatModel
 from graphrag.logger.progress import ProgressTicker, progress_ticker
 
 logger = logging.getLogger(__name__)
@@ -24,17 +25,13 @@ async def summarize_descriptions(
     entities_df: pd.DataFrame,
     relationships_df: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    strategy: dict[str, Any] | None = None,
-    num_threads: int = 4,
+    model: ChatModel,
+    max_summary_length: int,
+    max_input_tokens: int,
+    prompt: str,
+    num_threads: int,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Summarize entity and relationship descriptions from an entity graph, using a language model."""
-    logger.debug("summarize_descriptions strategy=%s", strategy)
-    strategy = strategy or {}
-    strategy_exec = load_strategy(
-        strategy.get("type", SummarizeStrategyType.graph_intelligence)
-    )
-    strategy_config = {**strategy}
 
     async def get_summarized(
         nodes: pd.DataFrame, edges: pd.DataFrame, semaphore: asyncio.Semaphore
@@ -99,7 +96,14 @@ async def do_summarize_descriptions(
         semaphore: asyncio.Semaphore,
     ):
         async with semaphore:
-            results = await strategy_exec(id, descriptions, cache, strategy_config)
+            results = await run_summarize_descriptions(
+                id,
+                descriptions,
+                model,
+                max_summary_length,
+                max_input_tokens,
+                prompt,
+            )
             ticker(1)
         return results
 
@@ -108,15 +112,26 @@ async def do_summarize_descriptions(
     return await get_summarized(entities_df, relationships_df, semaphore)
 
 
-def load_strategy(strategy_type: SummarizeStrategyType) -> SummarizationStrategy:
-    """Load strategy method definition."""
-    match strategy_type:
-        case SummarizeStrategyType.graph_intelligence:
-            from graphrag.index.operations.summarize_descriptions.graph_intelligence_strategy import (
-                run_graph_intelligence,
-            )
+async def run_summarize_descriptions(
+    id: str | tuple[str, str],
+    descriptions: list[str],
+    model: ChatModel,
+    max_summary_length: int,
+    max_input_tokens: int,
+    prompt: str,
+) -> SummarizedDescriptionResult:
+    """Run the graph intelligence entity extraction strategy."""
+    extractor = SummarizeExtractor(
+        model=model,
+        summarization_prompt=prompt,
+        on_error=lambda e, stack, details: logger.error(
+            "Entity Extraction Error",
+            exc_info=e,
+            extra={"stack": stack, "details": details},
+        ),
+        max_summary_length=max_summary_length,
+        max_input_tokens=max_input_tokens,
+    )
 
-            return run_graph_intelligence
-        case _:
-            msg = f"Unknown strategy: {strategy_type}"
-            raise ValueError(msg)
+    result = await extractor(id=id, descriptions=descriptions)
+    return SummarizedDescriptionResult(id=result.id, description=result.description)
diff --git a/graphrag/index/operations/summarize_descriptions/typing.py b/graphrag/index/operations/summarize_descriptions/typing.py
index 55b079090d..5a912caec4 100644
--- a/graphrag/index/operations/summarize_descriptions/typing.py
+++ b/graphrag/index/operations/summarize_descriptions/typing.py
@@ -3,15 +3,9 @@
 
 """A module containing 'SummarizedDescriptionResult' model."""
 
-from collections.abc import Awaitable, Callable
 from dataclasses import dataclass
-from enum import Enum
 from typing import Any, NamedTuple
 
-from graphrag.cache.pipeline_cache import PipelineCache
-
-StrategyConfig = dict[str, Any]
-
 
 @dataclass
 class SummarizedDescriptionResult:
@@ -21,28 +15,7 @@ class SummarizedDescriptionResult:
     description: str
 
 
-SummarizationStrategy = Callable[
-    [
-        str | tuple[str, str],
-        list[str],
-        PipelineCache,
-        StrategyConfig,
-    ],
-    Awaitable[SummarizedDescriptionResult],
-]
-
-
 class DescriptionSummarizeRow(NamedTuple):
     """DescriptionSummarizeRow class definition."""
 
     graph: Any
-
-
-class SummarizeStrategyType(str, Enum):
-    """SummarizeStrategyType class definition."""
-
-    graph_intelligence = "graph_intelligence"
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
diff --git a/graphrag/index/workflows/create_base_text_units.py b/graphrag/index/workflows/create_base_text_units.py
index feeba7a065..df57713126 100644
--- a/graphrag/index/workflows/create_base_text_units.py
+++ b/graphrag/index/workflows/create_base_text_units.py
@@ -56,8 +56,8 @@ def create_base_text_units(
     overlap: int,
     encoding_model: str,
     strategy: ChunkStrategyType,
-    prepend_metadata: bool = False,
-    chunk_size_includes_metadata: bool = False,
+    prepend_metadata: bool,
+    chunk_size_includes_metadata: bool,
 ) -> pd.DataFrame:
     """All the steps to transform base text_units."""
     documents.sort_values(by=["id"], ascending=[True], inplace=True)
diff --git a/graphrag/index/workflows/create_community_reports.py b/graphrag/index/workflows/create_community_reports.py
index bf98655c01..e9f19533ef 100644
--- a/graphrag/index/workflows/create_community_reports.py
+++ b/graphrag/index/workflows/create_community_reports.py
@@ -8,12 +8,9 @@
 import pandas as pd
 
 import graphrag.data_model.schemas as schemas
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import AsyncType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.language_model_config import LanguageModelConfig
 from graphrag.index.operations.finalize_community_reports import (
     finalize_community_reports,
 )
@@ -29,7 +26,10 @@
 )
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
+from graphrag.language_model.manager import ModelManager
+from graphrag.language_model.protocol.base import ChatModel
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
+from graphrag.tokenizer.tokenizer import Tokenizer
 from graphrag.utils.storage import (
     load_table_from_storage,
     storage_has_table,
@@ -54,25 +54,32 @@ async def run_workflow(
     ):
         claims = await load_table_from_storage("covariates", context.output_storage)
 
-    community_reports_llm_settings = config.get_language_model_config(
-        config.community_reports.model_id
-    )
-    async_mode = community_reports_llm_settings.async_mode
-    num_threads = community_reports_llm_settings.concurrent_requests
-    summarization_strategy = config.community_reports.resolved_strategy(
-        config.root_dir, community_reports_llm_settings
+    model_config = config.get_language_model_config(config.community_reports.model_id)
+    prompts = config.community_reports.resolved_prompts(config.root_dir)
+
+    model = ModelManager().get_or_create_chat_model(
+        name="community_reporting",
+        model_type=model_config.type,
+        config=model_config,
+        callbacks=context.callbacks,
+        cache=context.cache,
     )
 
+    tokenizer = get_tokenizer(model_config)
+
     output = await create_community_reports(
         edges_input=edges,
         entities=entities,
         communities=communities,
         claims_input=claims,
         callbacks=context.callbacks,
-        cache=context.cache,
-        summarization_strategy=summarization_strategy,
-        async_mode=async_mode,
-        num_threads=num_threads,
+        model=model,
+        tokenizer=tokenizer,
+        prompt=prompts.graph_prompt,
+        max_input_length=config.community_reports.max_input_length,
+        max_report_length=config.community_reports.max_length,
+        num_threads=model_config.concurrent_requests,
+        async_type=model_config.async_mode,
     )
 
     await write_table_to_storage(output, "community_reports", context.output_storage)
@@ -87,10 +94,13 @@ async def create_community_reports(
     communities: pd.DataFrame,
     claims_input: pd.DataFrame | None,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    summarization_strategy: dict,
-    async_mode: AsyncType = AsyncType.AsyncIO,
-    num_threads: int = 4,
+    model: ChatModel,
+    tokenizer: Tokenizer,
+    prompt: str,
+    max_input_length: int,
+    max_report_length: int,
+    num_threads: int,
+    async_type: AsyncType,
 ) -> pd.DataFrame:
     """All the steps to transform community reports."""
     nodes = explode_communities(communities, entities)
@@ -102,15 +112,6 @@ async def create_community_reports(
     if claims_input is not None:
         claims = _prep_claims(claims_input)
 
-    summarization_strategy["extraction_prompt"] = summarization_strategy["graph_prompt"]
-
-    model_config = LanguageModelConfig(**summarization_strategy["llm"])
-    tokenizer = get_tokenizer(model_config)
-
-    max_input_length = summarization_strategy.get(
-        "max_input_length", graphrag_config_defaults.community_reports.max_input_length
-    )
-
     local_contexts = build_local_context(
         nodes,
         edges,
@@ -126,12 +127,13 @@ async def create_community_reports(
         local_contexts,
         build_level_context,
         callbacks,
-        cache,
-        summarization_strategy,
+        model=model,
+        prompt=prompt,
         tokenizer=tokenizer,
         max_input_length=max_input_length,
-        async_mode=async_mode,
+        max_report_length=max_report_length,
         num_threads=num_threads,
+        async_type=async_type,
     )
 
     return finalize_community_reports(community_reports, communities)
diff --git a/graphrag/index/workflows/create_community_reports_text.py b/graphrag/index/workflows/create_community_reports_text.py
index 20286f7c55..80b236890d 100644
--- a/graphrag/index/workflows/create_community_reports_text.py
+++ b/graphrag/index/workflows/create_community_reports_text.py
@@ -7,12 +7,9 @@
 
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import AsyncType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.language_model_config import LanguageModelConfig
 from graphrag.index.operations.finalize_community_reports import (
     finalize_community_reports,
 )
@@ -28,7 +25,10 @@
 )
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
+from graphrag.language_model.manager import ModelManager
+from graphrag.language_model.protocol.base import ChatModel
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
+from graphrag.tokenizer.tokenizer import Tokenizer
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -45,24 +45,31 @@ async def run_workflow(
 
     text_units = await load_table_from_storage("text_units", context.output_storage)
 
-    community_reports_llm_settings = config.get_language_model_config(
-        config.community_reports.model_id
-    )
-    async_mode = community_reports_llm_settings.async_mode
-    num_threads = community_reports_llm_settings.concurrent_requests
-    summarization_strategy = config.community_reports.resolved_strategy(
-        config.root_dir, community_reports_llm_settings
+    model_config = config.get_language_model_config(config.community_reports.model_id)
+    model = ModelManager().get_or_create_chat_model(
+        name="community_reporting",
+        model_type=model_config.type,
+        config=model_config,
+        callbacks=context.callbacks,
+        cache=context.cache,
     )
 
+    tokenizer = get_tokenizer(model_config)
+
+    prompts = config.community_reports.resolved_prompts(config.root_dir)
+
     output = await create_community_reports_text(
         entities,
         communities,
         text_units,
         context.callbacks,
-        context.cache,
-        summarization_strategy,
-        async_mode=async_mode,
-        num_threads=num_threads,
+        model=model,
+        tokenizer=tokenizer,
+        prompt=prompts.text_prompt,
+        max_input_length=config.community_reports.max_input_length,
+        max_report_length=config.community_reports.max_length,
+        num_threads=model_config.concurrent_requests,
+        async_type=model_config.async_mode,
     )
 
     await write_table_to_storage(output, "community_reports", context.output_storage)
@@ -76,23 +83,17 @@ async def create_community_reports_text(
     communities: pd.DataFrame,
     text_units: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    summarization_strategy: dict,
-    async_mode: AsyncType = AsyncType.AsyncIO,
-    num_threads: int = 4,
+    model: ChatModel,
+    tokenizer: Tokenizer,
+    prompt: str,
+    max_input_length: int,
+    max_report_length: int,
+    num_threads: int,
+    async_type: AsyncType,
 ) -> pd.DataFrame:
     """All the steps to transform community reports."""
     nodes = explode_communities(communities, entities)
 
-    summarization_strategy["extraction_prompt"] = summarization_strategy["text_prompt"]
-
-    max_input_length = summarization_strategy.get(
-        "max_input_length", graphrag_config_defaults.community_reports.max_input_length
-    )
-
-    model_config = LanguageModelConfig(**summarization_strategy["llm"])
-    tokenizer = get_tokenizer(model_config)
-
     local_contexts = build_local_context(
         communities, text_units, nodes, tokenizer, max_input_length
     )
@@ -103,12 +104,13 @@ async def create_community_reports_text(
         local_contexts,
         build_level_context,
         callbacks,
-        cache,
-        summarization_strategy,
+        model=model,
+        prompt=prompt,
         tokenizer=tokenizer,
         max_input_length=max_input_length,
-        async_mode=async_mode,
+        max_report_length=max_report_length,
         num_threads=num_threads,
+        async_type=async_type,
     )
 
     return finalize_community_reports(community_reports, communities)
diff --git a/graphrag/index/workflows/extract_covariates.py b/graphrag/index/workflows/extract_covariates.py
index 63adb1d0b9..76889a56db 100644
--- a/graphrag/index/workflows/extract_covariates.py
+++ b/graphrag/index/workflows/extract_covariates.py
@@ -4,13 +4,12 @@
 """A module containing run_workflow method definition."""
 
 import logging
-from typing import Any
 from uuid import uuid4
 
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
+from graphrag.config.defaults import DEFAULT_ENTITY_TYPES
 from graphrag.config.enums import AsyncType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COVARIATES_FINAL_COLUMNS
@@ -19,6 +18,8 @@
 )
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
+from graphrag.language_model.manager import ModelManager
+from graphrag.language_model.protocol.base import ChatModel
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -34,25 +35,29 @@ async def run_workflow(
     if config.extract_claims.enabled:
         text_units = await load_table_from_storage("text_units", context.output_storage)
 
-        extract_claims_llm_settings = config.get_language_model_config(
-            config.extract_claims.model_id
-        )
-        extraction_strategy = config.extract_claims.resolved_strategy(
-            config.root_dir, extract_claims_llm_settings
+        model_config = config.get_language_model_config(config.extract_claims.model_id)
+
+        model = ModelManager().get_or_create_chat_model(
+            name="extract_claims",
+            model_type=model_config.type,
+            config=model_config,
+            callbacks=context.callbacks,
+            cache=context.cache,
         )
 
-        async_mode = extract_claims_llm_settings.async_mode
-        num_threads = extract_claims_llm_settings.concurrent_requests
+        prompts = config.extract_claims.resolved_prompts(config.root_dir)
 
         output = await extract_covariates(
-            text_units,
-            context.callbacks,
-            context.cache,
-            "claim",
-            extraction_strategy,
-            async_mode=async_mode,
-            entity_types=None,
-            num_threads=num_threads,
+            text_units=text_units,
+            callbacks=context.callbacks,
+            model=model,
+            covariate_type="claim",
+            max_gleanings=config.extract_claims.max_gleanings,
+            claim_description=config.extract_claims.description,
+            prompt=prompts.extraction_prompt,
+            entity_types=DEFAULT_ENTITY_TYPES,
+            num_threads=model_config.concurrent_requests,
+            async_type=model_config.async_mode,
         )
 
         await write_table_to_storage(output, "covariates", context.output_storage)
@@ -64,27 +69,32 @@ async def run_workflow(
 async def extract_covariates(
     text_units: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
+    model: ChatModel,
     covariate_type: str,
-    extraction_strategy: dict[str, Any] | None,
-    async_mode: AsyncType = AsyncType.AsyncIO,
-    entity_types: list[str] | None = None,
-    num_threads: int = 4,
+    max_gleanings: int,
+    claim_description: str,
+    prompt: str,
+    entity_types: list[str],
+    num_threads: int,
+    async_type: AsyncType,
 ) -> pd.DataFrame:
     """All the steps to extract and format covariates."""
     # reassign the id because it will be overwritten in the output by a covariate one
     # this also results in text_unit_id being copied to the output covariate table
     text_units["text_unit_id"] = text_units["id"]
+
     covariates = await extractor(
         input=text_units,
         callbacks=callbacks,
-        cache=cache,
+        model=model,
         column="text",
         covariate_type=covariate_type,
-        strategy=extraction_strategy,
-        async_mode=async_mode,
+        max_gleanings=max_gleanings,
+        claim_description=claim_description,
+        prompt=prompt,
         entity_types=entity_types,
         num_threads=num_threads,
+        async_type=async_type,
     )
     text_units.drop(columns=["text_unit_id"], inplace=True)  # don't pollute the global
     covariates["id"] = covariates["covariate_type"].apply(lambda _x: str(uuid4()))
diff --git a/graphrag/index/workflows/extract_graph.py b/graphrag/index/workflows/extract_graph.py
index 592502f6da..ce466379c7 100644
--- a/graphrag/index/workflows/extract_graph.py
+++ b/graphrag/index/workflows/extract_graph.py
@@ -4,11 +4,9 @@
 """A module containing run_workflow method definition."""
 
 import logging
-from typing import Any
 
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.enums import AsyncType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
@@ -20,6 +18,8 @@
 )
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
+from graphrag.language_model.manager import ModelManager
+from graphrag.language_model.protocol.base import ChatModel
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -33,30 +33,44 @@ async def run_workflow(
     logger.info("Workflow started: extract_graph")
     text_units = await load_table_from_storage("text_units", context.output_storage)
 
-    extract_graph_llm_settings = config.get_language_model_config(
+    extraction_model_config = config.get_language_model_config(
         config.extract_graph.model_id
     )
-    extraction_strategy = config.extract_graph.resolved_strategy(
-        config.root_dir, extract_graph_llm_settings
+    extraction_prompts = config.extract_graph.resolved_prompts(config.root_dir)
+    extraction_model = ModelManager().get_or_create_chat_model(
+        name="extract_graph",
+        model_type=extraction_model_config.type,
+        config=extraction_model_config,
+        cache=context.cache,
     )
 
-    summarization_llm_settings = config.get_language_model_config(
+    summarization_model_config = config.get_language_model_config(
         config.summarize_descriptions.model_id
     )
-    summarization_strategy = config.summarize_descriptions.resolved_strategy(
-        config.root_dir, summarization_llm_settings
+    summarization_prompts = config.summarize_descriptions.resolved_prompts(
+        config.root_dir
+    )
+    summarization_model = ModelManager().get_or_create_chat_model(
+        name="summarize_descriptions",
+        model_type=summarization_model_config.type,
+        config=summarization_model_config,
+        cache=context.cache,
     )
 
     entities, relationships, raw_entities, raw_relationships = await extract_graph(
         text_units=text_units,
         callbacks=context.callbacks,
-        cache=context.cache,
-        extraction_strategy=extraction_strategy,
-        extraction_num_threads=extract_graph_llm_settings.concurrent_requests,
-        extraction_async_mode=extract_graph_llm_settings.async_mode,
+        extraction_model=extraction_model,
+        extraction_prompt=extraction_prompts.extraction_prompt,
         entity_types=config.extract_graph.entity_types,
-        summarization_strategy=summarization_strategy,
-        summarization_num_threads=summarization_llm_settings.concurrent_requests,
+        max_gleanings=config.extract_graph.max_gleanings,
+        extraction_num_threads=extraction_model_config.concurrent_requests,
+        extraction_async_type=extraction_model_config.async_mode,
+        summarization_model=summarization_model,
+        max_summary_length=config.summarize_descriptions.max_length,
+        max_input_tokens=config.summarize_descriptions.max_input_tokens,
+        summarization_prompt=summarization_prompts.summarize_prompt,
+        summarization_num_threads=summarization_model_config.concurrent_requests,
     )
 
     await write_table_to_storage(entities, "entities", context.output_storage)
@@ -82,26 +96,31 @@ async def run_workflow(
 async def extract_graph(
     text_units: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    extraction_strategy: dict[str, Any] | None = None,
-    extraction_num_threads: int = 4,
-    extraction_async_mode: AsyncType = AsyncType.AsyncIO,
-    entity_types: list[str] | None = None,
-    summarization_strategy: dict[str, Any] | None = None,
-    summarization_num_threads: int = 4,
+    extraction_model: ChatModel,
+    extraction_prompt: str,
+    entity_types: list[str],
+    max_gleanings: int,
+    extraction_num_threads: int,
+    extraction_async_type: AsyncType,
+    summarization_model: ChatModel,
+    max_summary_length: int,
+    max_input_tokens: int,
+    summarization_prompt: str,
+    summarization_num_threads: int,
 ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """All the steps to create the base entity graph."""
     # this returns a graph for each text unit, to be merged later
     extracted_entities, extracted_relationships = await extractor(
         text_units=text_units,
         callbacks=callbacks,
-        cache=cache,
         text_column="text",
         id_column="id",
-        strategy=extraction_strategy,
-        async_mode=extraction_async_mode,
+        model=extraction_model,
+        prompt=extraction_prompt,
         entity_types=entity_types,
+        max_gleanings=max_gleanings,
         num_threads=extraction_num_threads,
+        async_type=extraction_async_type,
     )
 
     if not _validate_data(extracted_entities):
@@ -124,9 +143,11 @@ async def extract_graph(
         extracted_entities=extracted_entities,
         extracted_relationships=extracted_relationships,
         callbacks=callbacks,
-        cache=cache,
-        summarization_strategy=summarization_strategy,
-        summarization_num_threads=summarization_num_threads,
+        model=summarization_model,
+        max_summary_length=max_summary_length,
+        max_input_tokens=max_input_tokens,
+        summarization_prompt=summarization_prompt,
+        num_threads=summarization_num_threads,
     )
 
     return (entities, relationships, raw_entities, raw_relationships)
@@ -136,18 +157,22 @@ async def get_summarized_entities_relationships(
     extracted_entities: pd.DataFrame,
     extracted_relationships: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    summarization_strategy: dict[str, Any] | None = None,
-    summarization_num_threads: int = 4,
+    model: ChatModel,
+    max_summary_length: int,
+    max_input_tokens: int,
+    summarization_prompt: str,
+    num_threads: int,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Summarize the entities and relationships."""
     entity_summaries, relationship_summaries = await summarize_descriptions(
         entities_df=extracted_entities,
         relationships_df=extracted_relationships,
         callbacks=callbacks,
-        cache=cache,
-        strategy=summarization_strategy,
-        num_threads=summarization_num_threads,
+        model=model,
+        max_summary_length=max_summary_length,
+        max_input_tokens=max_input_tokens,
+        prompt=summarization_prompt,
+        num_threads=num_threads,
     )
 
     relationships = extracted_relationships.drop(columns=["description"]).merge(
diff --git a/graphrag/index/workflows/extract_graph_nlp.py b/graphrag/index/workflows/extract_graph_nlp.py
index 90afedf46c..b49f3e7a1f 100644
--- a/graphrag/index/workflows/extract_graph_nlp.py
+++ b/graphrag/index/workflows/extract_graph_nlp.py
@@ -8,9 +8,12 @@
 import pandas as pd
 
 from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.config.models.extract_graph_nlp_config import ExtractGraphNLPConfig
+from graphrag.config.enums import AsyncType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.operations.build_noun_graph.build_noun_graph import build_noun_graph
+from graphrag.index.operations.build_noun_graph.np_extractors.base import (
+    BaseNounPhraseExtractor,
+)
 from graphrag.index.operations.build_noun_graph.np_extractors.factory import (
     create_noun_phrase_extractor,
 )
@@ -29,10 +32,16 @@ async def run_workflow(
     logger.info("Workflow started: extract_graph_nlp")
     text_units = await load_table_from_storage("text_units", context.output_storage)
 
+    text_analyzer_config = config.extract_graph_nlp.text_analyzer
+    text_analyzer = create_noun_phrase_extractor(text_analyzer_config)
+
     entities, relationships = await extract_graph_nlp(
         text_units,
         context.cache,
-        extraction_config=config.extract_graph_nlp,
+        text_analyzer=text_analyzer,
+        normalize_edge_weights=config.extract_graph_nlp.normalize_edge_weights,
+        num_threads=config.extract_graph_nlp.concurrent_requests,
+        async_type=config.extract_graph_nlp.async_mode,
     )
 
     await write_table_to_storage(entities, "entities", context.output_storage)
@@ -51,17 +60,18 @@ async def run_workflow(
 async def extract_graph_nlp(
     text_units: pd.DataFrame,
     cache: PipelineCache,
-    extraction_config: ExtractGraphNLPConfig,
+    text_analyzer: BaseNounPhraseExtractor,
+    normalize_edge_weights: bool,
+    num_threads: int,
+    async_type: AsyncType,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """All the steps to create the base entity graph."""
-    text_analyzer_config = extraction_config.text_analyzer
-    text_analyzer = create_noun_phrase_extractor(text_analyzer_config)
     extracted_nodes, extracted_edges = await build_noun_graph(
         text_units,
         text_analyzer=text_analyzer,
-        normalize_edge_weights=extraction_config.normalize_edge_weights,
-        num_threads=extraction_config.concurrent_requests,
-        async_mode=extraction_config.async_mode,
+        normalize_edge_weights=normalize_edge_weights,
+        num_threads=num_threads,
+        async_mode=async_type,
         cache=cache,
     )
 
diff --git a/graphrag/index/workflows/generate_text_embeddings.py b/graphrag/index/workflows/generate_text_embeddings.py
index 9e6dde6d34..3a0ae41259 100644
--- a/graphrag/index/workflows/generate_text_embeddings.py
+++ b/graphrag/index/workflows/generate_text_embeddings.py
@@ -7,7 +7,6 @@
 
 import pandas as pd
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.embeddings import (
     community_full_content_embedding,
@@ -19,11 +18,15 @@
     relationship_description_embedding,
     text_unit_text_embedding,
 )
-from graphrag.config.get_embedding_settings import get_embedding_settings
+from graphrag.config.get_vector_store_settings import get_vector_store_settings
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.operations.embed_text.embed_text import embed_text
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
+from graphrag.language_model.manager import ModelManager
+from graphrag.language_model.protocol.base import EmbeddingModel
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
+from graphrag.tokenizer.tokenizer import Tokenizer
 from graphrag.utils.storage import (
     load_table_from_storage,
     write_table_to_storage,
@@ -67,7 +70,19 @@ async def run_workflow(
             "community_reports", context.output_storage
         )
 
-    text_embed = get_embedding_settings(config)
+    vector_store_config = get_vector_store_settings(config)
+
+    model_config = config.get_language_model_config(config.embed_text.model_id)
+
+    model = ModelManager().get_or_create_embedding_model(
+        name="text_embedding",
+        model_type=model_config.type,
+        config=model_config,
+        callbacks=context.callbacks,
+        cache=context.cache,
+    )
+
+    tokenizer = get_tokenizer(model_config)
 
     output = await generate_text_embeddings(
         documents=documents,
@@ -76,8 +91,12 @@ async def run_workflow(
         entities=entities,
         community_reports=community_reports,
         callbacks=context.callbacks,
-        cache=context.cache,
-        text_embed_config=text_embed,
+        model=model,
+        tokenizer=tokenizer,
+        batch_size=config.embed_text.batch_size,
+        batch_max_tokens=config.embed_text.batch_max_tokens,
+        num_threads=model_config.concurrent_requests,
+        vector_store_config=vector_store_config,
         embedded_fields=embedded_fields,
     )
 
@@ -100,8 +119,12 @@ async def generate_text_embeddings(
     entities: pd.DataFrame | None,
     community_reports: pd.DataFrame | None,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    text_embed_config: dict,
+    model: EmbeddingModel,
+    tokenizer: Tokenizer,
+    batch_size: int,
+    batch_max_tokens: int,
+    num_threads: int,
+    vector_store_config: dict,
     embedded_fields: list[str],
 ) -> dict[str, pd.DataFrame]:
     """All the steps to generate all embeddings."""
@@ -164,8 +187,12 @@ async def generate_text_embeddings(
             outputs[field] = await _run_embeddings(
                 name=field,
                 callbacks=callbacks,
-                cache=cache,
-                text_embed_config=text_embed_config,
+                model=model,
+                tokenizer=tokenizer,
+                vector_store_config=vector_store_config,
+                batch_size=batch_size,
+                batch_max_tokens=batch_max_tokens,
+                num_threads=num_threads,
                 **embedding_param_map[field],
             )
     return outputs
@@ -176,17 +203,25 @@ async def _run_embeddings(
     data: pd.DataFrame,
     embed_column: str,
     callbacks: WorkflowCallbacks,
-    cache: PipelineCache,
-    text_embed_config: dict,
+    model: EmbeddingModel,
+    tokenizer: Tokenizer,
+    batch_size: int,
+    batch_max_tokens: int,
+    num_threads: int,
+    vector_store_config: dict,
 ) -> pd.DataFrame:
     """All the steps to generate single embedding."""
     data["embedding"] = await embed_text(
         input=data,
         callbacks=callbacks,
-        cache=cache,
+        model=model,
+        tokenizer=tokenizer,
         embed_column=embed_column,
         embedding_name=name,
-        strategy=text_embed_config["strategy"],
+        batch_size=batch_size,
+        batch_max_tokens=batch_max_tokens,
+        num_threads=num_threads,
+        vector_store_config=vector_store_config,
     )
 
     return data.loc[:, ["id", "embedding"]]
diff --git a/graphrag/index/workflows/update_entities_relationships.py b/graphrag/index/workflows/update_entities_relationships.py
index cd8ad82553..69bdefa3f2 100644
--- a/graphrag/index/workflows/update_entities_relationships.py
+++ b/graphrag/index/workflows/update_entities_relationships.py
@@ -16,6 +16,7 @@
 from graphrag.index.update.entities import _group_and_resolve_entities
 from graphrag.index.update.relationships import _update_and_merge_relationships
 from graphrag.index.workflows.extract_graph import get_summarized_entities_relationships
+from graphrag.language_model.manager import ModelManager
 from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
@@ -77,11 +78,15 @@ async def _update_entities_and_relationships(
         delta_relationships,
     )
 
-    summarization_llm_settings = config.get_language_model_config(
+    summarization_model_config = config.get_language_model_config(
         config.summarize_descriptions.model_id
     )
-    summarization_strategy = config.summarize_descriptions.resolved_strategy(
-        config.root_dir, summarization_llm_settings
+    prompts = config.summarize_descriptions.resolved_prompts(config.root_dir)
+    model = ModelManager().get_or_create_chat_model(
+        name="summarize_descriptions",
+        model_type=summarization_model_config.type,
+        config=summarization_model_config,
+        cache=cache,
     )
 
     (
@@ -91,9 +96,11 @@ async def _update_entities_and_relationships(
         extracted_entities=merged_entities_df,
         extracted_relationships=merged_relationships_df,
         callbacks=callbacks,
-        cache=cache,
-        summarization_strategy=summarization_strategy,
-        summarization_num_threads=summarization_llm_settings.concurrent_requests,
+        model=model,
+        max_summary_length=config.summarize_descriptions.max_length,
+        max_input_tokens=config.summarize_descriptions.max_input_tokens,
+        summarization_prompt=prompts.summarize_prompt,
+        num_threads=summarization_model_config.concurrent_requests,
     )
 
     # Save the updated entities back to storage
diff --git a/graphrag/index/workflows/update_text_embeddings.py b/graphrag/index/workflows/update_text_embeddings.py
index 11bce16d3e..4b349f5809 100644
--- a/graphrag/index/workflows/update_text_embeddings.py
+++ b/graphrag/index/workflows/update_text_embeddings.py
@@ -5,12 +5,14 @@
 
 import logging
 
-from graphrag.config.get_embedding_settings import get_embedding_settings
+from graphrag.config.get_vector_store_settings import get_vector_store_settings
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.workflows.generate_text_embeddings import generate_text_embeddings
+from graphrag.language_model.manager import ModelManager
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.utils.storage import write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -35,7 +37,20 @@ async def run_workflow(
     ]
 
     embedded_fields = config.embed_text.names
-    text_embed = get_embedding_settings(config)
+    vector_store_config = get_vector_store_settings(config)
+
+    model_config = config.get_language_model_config(config.embed_text.model_id)
+
+    model = ModelManager().get_or_create_embedding_model(
+        name="text_embedding",
+        model_type=model_config.type,
+        config=model_config,
+        callbacks=context.callbacks,
+        cache=context.cache,
+    )
+
+    tokenizer = get_tokenizer(model_config)
+
     result = await generate_text_embeddings(
         documents=final_documents_df,
         relationships=merged_relationships_df,
@@ -43,8 +58,12 @@ async def run_workflow(
         entities=merged_entities_df,
         community_reports=merged_community_reports,
         callbacks=context.callbacks,
-        cache=context.cache,
-        text_embed_config=text_embed,
+        model=model,
+        tokenizer=tokenizer,
+        batch_size=config.embed_text.batch_size,
+        batch_max_tokens=config.embed_text.batch_max_tokens,
+        num_threads=model_config.concurrent_requests,
+        vector_store_config=vector_store_config,
         embedded_fields=embedded_fields,
     )
     if config.snapshots.embeddings:
diff --git a/graphrag/prompt_tune/loader/input.py b/graphrag/prompt_tune/loader/input.py
index 235a78fe7a..bdc42e5b5c 100644
--- a/graphrag/prompt_tune/loader/input.py
+++ b/graphrag/prompt_tune/loader/input.py
@@ -13,16 +13,18 @@
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.input.factory import create_input
-from graphrag.index.operations.embed_text.strategies.openai import (
-    run as run_embed_text,
+from graphrag.index.operations.embed_text.run_embed_text import (
+    run_embed_text,
 )
 from graphrag.index.workflows.create_base_text_units import create_base_text_units
+from graphrag.language_model.manager import ModelManager
 from graphrag.prompt_tune.defaults import (
     LIMIT,
     N_SUBSET_MAX,
     K,
 )
 from graphrag.prompt_tune.types import DocSelectionType
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.utils.api import create_storage_from_config
 
 
@@ -53,6 +55,14 @@ async def load_docs_in_chunks(
     embeddings_llm_settings = config.get_language_model_config(
         config.embed_text.model_id
     )
+    model = ModelManager().get_or_create_embedding_model(
+        name="text_embedding",
+        model_type=embeddings_llm_settings.type,
+        config=embeddings_llm_settings,
+        callbacks=NoopWorkflowCallbacks(),
+        cache=NoopPipelineCache(),
+    )
+    tokenizer = get_tokenizer(embeddings_llm_settings)
     input_storage = create_storage_from_config(config.input.storage)
     dataset = await create_input(config.input, input_storage)
     chunk_config = config.chunks
@@ -89,13 +99,11 @@ async def load_docs_in_chunks(
         embedding_results = await run_embed_text(
             sampled_text_chunks,
             callbacks=NoopWorkflowCallbacks(),
-            cache=NoopPipelineCache(),
-            args={
-                "llm": embeddings_llm_settings.model_dump(),
-                "num_threads": embeddings_llm_settings.concurrent_requests,
-                "batch_size": config.embed_text.batch_size,
-                "batch_max_tokens": config.embed_text.batch_max_tokens,
-            },
+            model=model,
+            tokenizer=tokenizer,
+            batch_size=config.embed_text.batch_size,
+            batch_max_tokens=config.embed_text.batch_max_tokens,
+            num_threads=embeddings_llm_settings.concurrent_requests,
         )
         embeddings = np.array(embedding_results.embeddings)
         chunks_df = _sample_chunks_from_embeddings(chunks_df, embeddings, k=k)
diff --git a/graphrag/prompt_tune/prompt/entity_relationship.py b/graphrag/prompt_tune/prompt/entity_relationship.py
index 66eefa9947..ec7dca55a4 100644
--- a/graphrag/prompt_tune/prompt/entity_relationship.py
+++ b/graphrag/prompt_tune/prompt/entity_relationship.py
@@ -12,7 +12,7 @@
 - entity_name: Name of the entity, capitalized
 - entity_type: One of the following types: [{entity_types}]
 - entity_description: Comprehensive description of the entity's attributes and activities
-Format each entity as ("entity"{{tuple_delimiter}}<entity_name>{{tuple_delimiter}}<entity_type>{{tuple_delimiter}}<entity_description>)
+Format each entity as ("entity"<|><entity_name><|><entity_type><|><entity_description>)
 
 2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
 For each pair of related entities, extract the following information:
@@ -20,13 +20,13 @@
 - target_entity: name of the target entity, as identified in step 1
 - relationship_description: explanation as to why you think the source entity and the target entity are related to each other
 - relationship_strength: an integer score between 1 to 10, indicating strength of the relationship between the source entity and target entity
-Format each relationship as ("relationship"{{tuple_delimiter}}<source_entity>{{tuple_delimiter}}<target_entity>{{tuple_delimiter}}<relationship_description>{{tuple_delimiter}}<relationship_strength>)
+Format each relationship as ("relationship"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)
 
-3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use {{record_delimiter}} as the list delimiter.
+3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use ## as the list delimiter.
 
 4. If you have to translate into {language}, just translate the descriptions, nothing else!
 
-5. When finished, output {{completion_delimiter}}.
+5. When finished, output <|COMPLETE|>.
 
 ######################
 -Examples-
@@ -37,14 +37,14 @@
 The Verdantis's Central Institution is scheduled to meet on Monday and Thursday, with the institution planning to release its latest policy decision on Thursday at 1:30 p.m. PDT, followed by a press conference where Central Institution Chair Martin Smith will take questions. Investors expect the Market Strategy Committee to hold its benchmark interest rate steady in a range of 3.5%-3.75%.
 ######################
 Output:
-("entity"{{tuple_delimiter}}CENTRAL INSTITUTION{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}MARTIN SMITH{{tuple_delimiter}}PERSON{{tuple_delimiter}}Martin Smith is the chair of the Central Institution)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}MARKET STRATEGY COMMITTEE{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}MARTIN SMITH{{tuple_delimiter}}CENTRAL INSTITUTION{{tuple_delimiter}}Martin Smith is the Chair of the Central Institution and will answer questions at a press conference{{tuple_delimiter}}9)
-{{completion_delimiter}}
+("entity"<|>CENTRAL INSTITUTION<|>ORGANIZATION<|>The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)
+##
+("entity"<|>MARTIN SMITH<|>PERSON<|>Martin Smith is the chair of the Central Institution)
+##
+("entity"<|>MARKET STRATEGY COMMITTEE<|>ORGANIZATION<|>The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)
+##
+("relationship"<|>MARTIN SMITH<|>CENTRAL INSTITUTION<|>Martin Smith is the Chair of the Central Institution and will answer questions at a press conference<|>9)
+<|COMPLETE|>
 
 ######################
 Example 2:
@@ -55,12 +55,12 @@
 TechGlobal, a formerly public company, was taken private by Vision Holdings in 2014. The well-established chip designer says it powers 85% of premium smartphones.
 ######################
 Output:
-("entity"{{tuple_delimiter}}TECHGLOBAL{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}VISION HOLDINGS{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}Vision Holdings is a firm that previously owned TechGlobal)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}TECHGLOBAL{{tuple_delimiter}}VISION HOLDINGS{{tuple_delimiter}}Vision Holdings formerly owned TechGlobal from 2014 until present{{tuple_delimiter}}5)
-{{completion_delimiter}}
+("entity"<|>TECHGLOBAL<|>ORGANIZATION<|>TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)
+##
+("entity"<|>VISION HOLDINGS<|>ORGANIZATION<|>Vision Holdings is a firm that previously owned TechGlobal)
+##
+("relationship"<|>TECHGLOBAL<|>VISION HOLDINGS<|>Vision Holdings formerly owned TechGlobal from 2014 until present<|>5)
+<|COMPLETE|>
 
 ######################
 Example 3:
@@ -77,47 +77,47 @@
 The Aurelians include 39-year-old businessman Samuel Namara, who has been held in Tiruzia's Alhamia Prison, as well as journalist Durke Bataglani, 59, and environmentalist Meggie Tazbah, 53, who also holds Bratinas nationality.
 ######################
 Output:
-("entity"{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}GEO{{tuple_delimiter}}Firuzabad held Aurelians as hostages)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}AURELIA{{tuple_delimiter}}GEO{{tuple_delimiter}}Country seeking to release hostages)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}QUINTARA{{tuple_delimiter}}GEO{{tuple_delimiter}}Country that negotiated a swap of money in exchange for hostages)
-{{record_delimiter}}
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}TIRUZIA{{tuple_delimiter}}GEO{{tuple_delimiter}}Capital of Firuzabad where the Aurelians were being held)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}KROHAARA{{tuple_delimiter}}GEO{{tuple_delimiter}}Capital city in Quintara)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}CASHION{{tuple_delimiter}}GEO{{tuple_delimiter}}Capital city in Aurelia)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}PERSON{{tuple_delimiter}}Aurelian who spent time in Tiruzia's Alhamia Prison)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}ALHAMIA PRISON{{tuple_delimiter}}GEO{{tuple_delimiter}}Prison in Tiruzia)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}PERSON{{tuple_delimiter}}Aurelian journalist who was held hostage)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}PERSON{{tuple_delimiter}}Bratinas national and environmentalist who was held hostage)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}AURELIA{{tuple_delimiter}}Firuzabad negotiated a hostage exchange with Aurelia{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}QUINTARA{{tuple_delimiter}}AURELIA{{tuple_delimiter}}Quintara brokered the hostage exchange between Firuzabad and Aurelia{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}QUINTARA{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Quintara brokered the hostage exchange between Firuzabad and Aurelia{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}ALHAMIA PRISON{{tuple_delimiter}}Samuel Namara was a prisoner at Alhamia prison{{tuple_delimiter}}8)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}Samuel Namara and Meggie Tazbah were exchanged in the same hostage release{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}Samuel Namara and Durke Bataglani were exchanged in the same hostage release{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Samuel Namara was a hostage in Firuzabad{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Meggie Tazbah was a hostage in Firuzabad{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Durke Bataglani was a hostage in Firuzabad{{tuple_delimiter}}2)
-{{completion_delimiter}}
+("entity"<|>FIRUZABAD<|>GEO<|>Firuzabad held Aurelians as hostages)
+##
+("entity"<|>AURELIA<|>GEO<|>Country seeking to release hostages)
+##
+("entity"<|>QUINTARA<|>GEO<|>Country that negotiated a swap of money in exchange for hostages)
+##
+##
+("entity"<|>TIRUZIA<|>GEO<|>Capital of Firuzabad where the Aurelians were being held)
+##
+("entity"<|>KROHAARA<|>GEO<|>Capital city in Quintara)
+##
+("entity"<|>CASHION<|>GEO<|>Capital city in Aurelia)
+##
+("entity"<|>SAMUEL NAMARA<|>PERSON<|>Aurelian who spent time in Tiruzia's Alhamia Prison)
+##
+("entity"<|>ALHAMIA PRISON<|>GEO<|>Prison in Tiruzia)
+##
+("entity"<|>DURKE BATAGLANI<|>PERSON<|>Aurelian journalist who was held hostage)
+##
+("entity"<|>MEGGIE TAZBAH<|>PERSON<|>Bratinas national and environmentalist who was held hostage)
+##
+("relationship"<|>FIRUZABAD<|>AURELIA<|>Firuzabad negotiated a hostage exchange with Aurelia<|>2)
+##
+("relationship"<|>QUINTARA<|>AURELIA<|>Quintara brokered the hostage exchange between Firuzabad and Aurelia<|>2)
+##
+("relationship"<|>QUINTARA<|>FIRUZABAD<|>Quintara brokered the hostage exchange between Firuzabad and Aurelia<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>ALHAMIA PRISON<|>Samuel Namara was a prisoner at Alhamia prison<|>8)
+##
+("relationship"<|>SAMUEL NAMARA<|>MEGGIE TAZBAH<|>Samuel Namara and Meggie Tazbah were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>DURKE BATAGLANI<|>Samuel Namara and Durke Bataglani were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>MEGGIE TAZBAH<|>DURKE BATAGLANI<|>Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>FIRUZABAD<|>Samuel Namara was a hostage in Firuzabad<|>2)
+##
+("relationship"<|>MEGGIE TAZBAH<|>FIRUZABAD<|>Meggie Tazbah was a hostage in Firuzabad<|>2)
+##
+("relationship"<|>DURKE BATAGLANI<|>FIRUZABAD<|>Durke Bataglani was a hostage in Firuzabad<|>2)
+<|COMPLETE|>
 
 -Real Data-
 ######################
@@ -242,7 +242,7 @@
 - entity_name: Name of the entity, capitalized
 - entity_type: Suggest several labels or categories for the entity. The categories should not be specific, but should be as general as possible.
 - entity_description: Comprehensive description of the entity's attributes and activities
-Format each entity as ("entity"{{tuple_delimiter}}<entity_name>{{tuple_delimiter}}<entity_type>{{tuple_delimiter}}<entity_description>)
+Format each entity as ("entity"<|><entity_name><|><entity_type><|><entity_description>)
 
 2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
 For each pair of related entities, extract the following information:
@@ -250,13 +250,13 @@
 - target_entity: name of the target entity, as identified in step 1
 - relationship_description: explanation as to why you think the source entity and the target entity are related to each other
 - relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity
-Format each relationship as ("relationship"{{tuple_delimiter}}<source_entity>{{tuple_delimiter}}<target_entity>{{tuple_delimiter}}<relationship_description>{{tuple_delimiter}}<relationship_strength>)
+Format each relationship as ("relationship"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)
 
-3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{{record_delimiter}}** as the list delimiter.
+3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.
 
 4. If you have to translate into {language}, just translate the descriptions, nothing else!
 
-5. When finished, output {{completion_delimiter}}.
+5. When finished, output <|COMPLETE|>.
 
 ######################
 -Examples-
@@ -266,14 +266,14 @@
 The Verdantis's Central Institution is scheduled to meet on Monday and Thursday, with the institution planning to release its latest policy decision on Thursday at 1:30 p.m. PDT, followed by a press conference where Central Institution Chair Martin Smith will take questions. Investors expect the Market Strategy Committee to hold its benchmark interest rate steady in a range of 3.5%-3.75%.
 ######################
 Output:
-("entity"{{tuple_delimiter}}CENTRAL INSTITUTION{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}MARTIN SMITH{{tuple_delimiter}}PERSON{{tuple_delimiter}}Martin Smith is the chair of the Central Institution)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}MARKET STRATEGY COMMITTEE{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}MARTIN SMITH{{tuple_delimiter}}CENTRAL INSTITUTION{{tuple_delimiter}}Martin Smith is the Chair of the Central Institution and will answer questions at a press conference{{tuple_delimiter}}9)
-{{completion_delimiter}}
+("entity"<|>CENTRAL INSTITUTION<|>ORGANIZATION<|>The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)
+##
+("entity"<|>MARTIN SMITH<|>PERSON<|>Martin Smith is the chair of the Central Institution)
+##
+("entity"<|>MARKET STRATEGY COMMITTEE<|>ORGANIZATION<|>The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)
+##
+("relationship"<|>MARTIN SMITH<|>CENTRAL INSTITUTION<|>Martin Smith is the Chair of the Central Institution and will answer questions at a press conference<|>9)
+<|COMPLETE|>
 
 ######################
 Example 2:
@@ -283,12 +283,12 @@
 TechGlobal, a formerly public company, was taken private by Vision Holdings in 2014. The well-established chip designer says it powers 85% of premium smartphones.
 ######################
 Output:
-("entity"{{tuple_delimiter}}TECHGLOBAL{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}VISION HOLDINGS{{tuple_delimiter}}ORGANIZATION{{tuple_delimiter}}Vision Holdings is a firm that previously owned TechGlobal)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}TECHGLOBAL{{tuple_delimiter}}VISION HOLDINGS{{tuple_delimiter}}Vision Holdings formerly owned TechGlobal from 2014 until present{{tuple_delimiter}}5)
-{{completion_delimiter}}
+("entity"<|>TECHGLOBAL<|>ORGANIZATION<|>TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)
+##
+("entity"<|>VISION HOLDINGS<|>ORGANIZATION<|>Vision Holdings is a firm that previously owned TechGlobal)
+##
+("relationship"<|>TECHGLOBAL<|>VISION HOLDINGS<|>Vision Holdings formerly owned TechGlobal from 2014 until present<|>5)
+<|COMPLETE|>
 
 ######################
 Example 3:
@@ -304,47 +304,47 @@
 The Aurelians include 39-year-old businessman Samuel Namara, who has been held in Tiruzia's Alhamia Prison, as well as journalist Durke Bataglani, 59, and environmentalist Meggie Tazbah, 53, who also holds Bratinas nationality.
 ######################
 Output:
-("entity"{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}GEO{{tuple_delimiter}}Firuzabad held Aurelians as hostages)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}AURELIA{{tuple_delimiter}}GEO{{tuple_delimiter}}Country seeking to release hostages)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}QUINTARA{{tuple_delimiter}}GEO{{tuple_delimiter}}Country that negotiated a swap of money in exchange for hostages)
-{{record_delimiter}}
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}TIRUZIA{{tuple_delimiter}}GEO{{tuple_delimiter}}Capital of Firuzabad where the Aurelians were being held)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}KROHAARA{{tuple_delimiter}}GEO{{tuple_delimiter}}Capital city in Quintara)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}CASHION{{tuple_delimiter}}GEO{{tuple_delimiter}}Capital city in Aurelia)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}PERSON{{tuple_delimiter}}Aurelian who spent time in Tiruzia's Alhamia Prison)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}ALHAMIA PRISON{{tuple_delimiter}}GEO{{tuple_delimiter}}Prison in Tiruzia)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}PERSON{{tuple_delimiter}}Aurelian journalist who was held hostage)
-{{record_delimiter}}
-("entity"{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}PERSON{{tuple_delimiter}}Bratinas national and environmentalist who was held hostage)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}AURELIA{{tuple_delimiter}}Firuzabad negotiated a hostage exchange with Aurelia{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}QUINTARA{{tuple_delimiter}}AURELIA{{tuple_delimiter}}Quintara brokered the hostage exchange between Firuzabad and Aurelia{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}QUINTARA{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Quintara brokered the hostage exchange between Firuzabad and Aurelia{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}ALHAMIA PRISON{{tuple_delimiter}}Samuel Namara was a prisoner at Alhamia prison{{tuple_delimiter}}8)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}Samuel Namara and Meggie Tazbah were exchanged in the same hostage release{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}Samuel Namara and Durke Bataglani were exchanged in the same hostage release{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}SAMUEL NAMARA{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Samuel Namara was a hostage in Firuzabad{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}MEGGIE TAZBAH{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Meggie Tazbah was a hostage in Firuzabad{{tuple_delimiter}}2)
-{{record_delimiter}}
-("relationship"{{tuple_delimiter}}DURKE BATAGLANI{{tuple_delimiter}}FIRUZABAD{{tuple_delimiter}}Durke Bataglani was a hostage in Firuzabad{{tuple_delimiter}}2)
-{{completion_delimiter}}
+("entity"<|>FIRUZABAD<|>GEO<|>Firuzabad held Aurelians as hostages)
+##
+("entity"<|>AURELIA<|>GEO<|>Country seeking to release hostages)
+##
+("entity"<|>QUINTARA<|>GEO<|>Country that negotiated a swap of money in exchange for hostages)
+##
+##
+("entity"<|>TIRUZIA<|>GEO<|>Capital of Firuzabad where the Aurelians were being held)
+##
+("entity"<|>KROHAARA<|>GEO<|>Capital city in Quintara)
+##
+("entity"<|>CASHION<|>GEO<|>Capital city in Aurelia)
+##
+("entity"<|>SAMUEL NAMARA<|>PERSON<|>Aurelian who spent time in Tiruzia's Alhamia Prison)
+##
+("entity"<|>ALHAMIA PRISON<|>GEO<|>Prison in Tiruzia)
+##
+("entity"<|>DURKE BATAGLANI<|>PERSON<|>Aurelian journalist who was held hostage)
+##
+("entity"<|>MEGGIE TAZBAH<|>PERSON<|>Bratinas national and environmentalist who was held hostage)
+##
+("relationship"<|>FIRUZABAD<|>AURELIA<|>Firuzabad negotiated a hostage exchange with Aurelia<|>2)
+##
+("relationship"<|>QUINTARA<|>AURELIA<|>Quintara brokered the hostage exchange between Firuzabad and Aurelia<|>2)
+##
+("relationship"<|>QUINTARA<|>FIRUZABAD<|>Quintara brokered the hostage exchange between Firuzabad and Aurelia<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>ALHAMIA PRISON<|>Samuel Namara was a prisoner at Alhamia prison<|>8)
+##
+("relationship"<|>SAMUEL NAMARA<|>MEGGIE TAZBAH<|>Samuel Namara and Meggie Tazbah were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>DURKE BATAGLANI<|>Samuel Namara and Durke Bataglani were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>MEGGIE TAZBAH<|>DURKE BATAGLANI<|>Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>FIRUZABAD<|>Samuel Namara was a hostage in Firuzabad<|>2)
+##
+("relationship"<|>MEGGIE TAZBAH<|>FIRUZABAD<|>Meggie Tazbah was a hostage in Firuzabad<|>2)
+##
+("relationship"<|>DURKE BATAGLANI<|>FIRUZABAD<|>Durke Bataglani was a hostage in Firuzabad<|>2)
+<|COMPLETE|>
 
 ######################
 -Real Data-
diff --git a/graphrag/prompt_tune/template/extract_graph.py b/graphrag/prompt_tune/template/extract_graph.py
index 32d8756ec2..58a095cd0d 100644
--- a/graphrag/prompt_tune/template/extract_graph.py
+++ b/graphrag/prompt_tune/template/extract_graph.py
@@ -12,7 +12,7 @@
 - entity_name: Name of the entity, capitalized
 - entity_type: One of the following types: [{entity_types}]
 - entity_description: Comprehensive description of the entity's attributes and activities
-Format each entity as ("entity"{{tuple_delimiter}}<entity_name>{{tuple_delimiter}}<entity_type>{{tuple_delimiter}}<entity_description>)
+Format each entity as ("entity"<|><entity_name><|><entity_type><|><entity_description>)
 
 2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
 For each pair of related entities, extract the following information:
@@ -20,13 +20,13 @@
 - target_entity: name of the target entity, as identified in step 1
 - relationship_description: explanation as to why you think the source entity and the target entity are related to each other
 - relationship_strength: an integer score between 1 to 10, indicating strength of the relationship between the source entity and target entity
-Format each relationship as ("relationship"{{tuple_delimiter}}<source_entity>{{tuple_delimiter}}<target_entity>{{tuple_delimiter}}<relationship_description>{{tuple_delimiter}}<relationship_strength>)
+Format each relationship as ("relationship"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)
 
-3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{{record_delimiter}}** as the list delimiter.
+3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.
 
 4. If you have to translate into {language}, just translate the descriptions, nothing else!
 
-5. When finished, output {{completion_delimiter}}.
+5. When finished, output <|COMPLETE|>.
 
 -Examples-
 ######################
@@ -113,7 +113,7 @@
 - entity_name: Name of the entity, capitalized
 - entity_type: Suggest several labels or categories for the entity. The categories should not be specific, but should be as general as possible.
 - entity_description: Comprehensive description of the entity's attributes and activities
-Format each entity as ("entity"{{tuple_delimiter}}<entity_name>{{tuple_delimiter}}<entity_type>{{tuple_delimiter}}<entity_description>)
+Format each entity as ("entity"<|><entity_name><|><entity_type><|><entity_description>)
 
 2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
 For each pair of related entities, extract the following information:
@@ -121,13 +121,13 @@
 - target_entity: name of the target entity, as identified in step 1
 - relationship_description: explanation as to why you think the source entity and the target entity are related to each other
 - relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity
-Format each relationship as ("relationship"{{tuple_delimiter}}<source_entity>{{tuple_delimiter}}<target_entity>{{tuple_delimiter}}<relationship_description>{{tuple_delimiter}}<relationship_strength>)
+Format each relationship as ("relationship"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)
 
-3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{{record_delimiter}}** as the list delimiter.
+3. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.
 
 4. If you have to translate into {language}, just translate the descriptions, nothing else!
 
-5. When finished, output {{completion_delimiter}}.
+5. When finished, output <|COMPLETE|>.
 
 -Examples-
 ######################
diff --git a/graphrag/prompts/index/extract_claims.py b/graphrag/prompts/index/extract_claims.py
index 5e0e5570c6..59b19c9061 100644
--- a/graphrag/prompts/index/extract_claims.py
+++ b/graphrag/prompts/index/extract_claims.py
@@ -22,11 +22,11 @@
 - Claim Date: Period (start_date, end_date) when the claim was made. Both start_date and end_date should be in ISO-8601 format. If the claim was made on a single date rather than a date range, set the same date for both start_date and end_date. If date is unknown, return **NONE**.
 - Claim Source Text: List of **all** quotes from the original text that are relevant to the claim.
 
-Format each claim as (<subject_entity>{tuple_delimiter}<object_entity>{tuple_delimiter}<claim_type>{tuple_delimiter}<claim_status>{tuple_delimiter}<claim_start_date>{tuple_delimiter}<claim_end_date>{tuple_delimiter}<claim_description>{tuple_delimiter}<claim_source>)
+Format each claim as (<subject_entity><|><object_entity><|><claim_type><|><claim_status><|><claim_start_date><|><claim_end_date><|><claim_description><|><claim_source>)
 
-3. Return output in English as a single list of all the claims identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.
+3. Return output in English as a single list of all the claims identified in steps 1 and 2. Use **##** as the list delimiter.
 
-4. When finished, output {completion_delimiter}
+4. When finished, output <|COMPLETE|>
 
 -Examples-
 Example 1:
@@ -35,8 +35,8 @@
 Text: According to an article on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B. The company is owned by Person C who was suspected of engaging in corruption activities in 2015.
 Output:
 
-(COMPANY A{tuple_delimiter}GOVERNMENT AGENCY B{tuple_delimiter}ANTI-COMPETITIVE PRACTICES{tuple_delimiter}TRUE{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10{tuple_delimiter}According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.)
-{completion_delimiter}
+(COMPANY A<|>GOVERNMENT AGENCY B<|>ANTI-COMPETITIVE PRACTICES<|>TRUE<|>2022-01-10T00:00:00<|>2022-01-10T00:00:00<|>Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10<|>According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.)
+<|COMPLETE|>
 
 Example 2:
 Entity specification: Company A, Person C
@@ -44,10 +44,10 @@
 Text: According to an article on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B. The company is owned by Person C who was suspected of engaging in corruption activities in 2015.
 Output:
 
-(COMPANY A{tuple_delimiter}GOVERNMENT AGENCY B{tuple_delimiter}ANTI-COMPETITIVE PRACTICES{tuple_delimiter}TRUE{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10{tuple_delimiter}According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.)
-{record_delimiter}
-(PERSON C{tuple_delimiter}NONE{tuple_delimiter}CORRUPTION{tuple_delimiter}SUSPECTED{tuple_delimiter}2015-01-01T00:00:00{tuple_delimiter}2015-12-30T00:00:00{tuple_delimiter}Person C was suspected of engaging in corruption activities in 2015{tuple_delimiter}The company is owned by Person C who was suspected of engaging in corruption activities in 2015)
-{completion_delimiter}
+(COMPANY A<|>GOVERNMENT AGENCY B<|>ANTI-COMPETITIVE PRACTICES<|>TRUE<|>2022-01-10T00:00:00<|>2022-01-10T00:00:00<|>Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10<|>According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.)
+##
+(PERSON C<|>NONE<|>CORRUPTION<|>SUSPECTED<|>2015-01-01T00:00:00<|>2015-12-30T00:00:00<|>Person C was suspected of engaging in corruption activities in 2015<|>The company is owned by Person C who was suspected of engaging in corruption activities in 2015)
+<|COMPLETE|>
 
 -Real Data-
 Use the following input for your answer.
diff --git a/graphrag/prompts/index/extract_graph.py b/graphrag/prompts/index/extract_graph.py
index a94b36142e..91157937d4 100644
--- a/graphrag/prompts/index/extract_graph.py
+++ b/graphrag/prompts/index/extract_graph.py
@@ -12,7 +12,7 @@
 - entity_name: Name of the entity, capitalized
 - entity_type: One of the following types: [{entity_types}]
 - entity_description: Comprehensive description of the entity's attributes and activities
-Format each entity as ("entity"{tuple_delimiter}<entity_name>{tuple_delimiter}<entity_type>{tuple_delimiter}<entity_description>)
+Format each entity as ("entity"<|><entity_name><|><entity_type><|><entity_description>)
  
 2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
 For each pair of related entities, extract the following information:
@@ -20,11 +20,11 @@
 - target_entity: name of the target entity, as identified in step 1
 - relationship_description: explanation as to why you think the source entity and the target entity are related to each other
 - relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity
- Format each relationship as ("relationship"{tuple_delimiter}<source_entity>{tuple_delimiter}<target_entity>{tuple_delimiter}<relationship_description>{tuple_delimiter}<relationship_strength>)
+ Format each relationship as ("relationship"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)
  
-3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.
+3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.
  
-4. When finished, output {completion_delimiter}
+4. When finished, output <|COMPLETE|>
  
 ######################
 -Examples-
@@ -35,14 +35,14 @@
 The Verdantis's Central Institution is scheduled to meet on Monday and Thursday, with the institution planning to release its latest policy decision on Thursday at 1:30 p.m. PDT, followed by a press conference where Central Institution Chair Martin Smith will take questions. Investors expect the Market Strategy Committee to hold its benchmark interest rate steady in a range of 3.5%-3.75%.
 ######################
 Output:
-("entity"{tuple_delimiter}CENTRAL INSTITUTION{tuple_delimiter}ORGANIZATION{tuple_delimiter}The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)
-{record_delimiter}
-("entity"{tuple_delimiter}MARTIN SMITH{tuple_delimiter}PERSON{tuple_delimiter}Martin Smith is the chair of the Central Institution)
-{record_delimiter}
-("entity"{tuple_delimiter}MARKET STRATEGY COMMITTEE{tuple_delimiter}ORGANIZATION{tuple_delimiter}The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)
-{record_delimiter}
-("relationship"{tuple_delimiter}MARTIN SMITH{tuple_delimiter}CENTRAL INSTITUTION{tuple_delimiter}Martin Smith is the Chair of the Central Institution and will answer questions at a press conference{tuple_delimiter}9)
-{completion_delimiter}
+("entity"<|>CENTRAL INSTITUTION<|>ORGANIZATION<|>The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)
+##
+("entity"<|>MARTIN SMITH<|>PERSON<|>Martin Smith is the chair of the Central Institution)
+##
+("entity"<|>MARKET STRATEGY COMMITTEE<|>ORGANIZATION<|>The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)
+##
+("relationship"<|>MARTIN SMITH<|>CENTRAL INSTITUTION<|>Martin Smith is the Chair of the Central Institution and will answer questions at a press conference<|>9)
+<|COMPLETE|>
 
 ######################
 Example 2:
@@ -53,12 +53,12 @@
 TechGlobal, a formerly public company, was taken private by Vision Holdings in 2014. The well-established chip designer says it powers 85% of premium smartphones.
 ######################
 Output:
-("entity"{tuple_delimiter}TECHGLOBAL{tuple_delimiter}ORGANIZATION{tuple_delimiter}TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)
-{record_delimiter}
-("entity"{tuple_delimiter}VISION HOLDINGS{tuple_delimiter}ORGANIZATION{tuple_delimiter}Vision Holdings is a firm that previously owned TechGlobal)
-{record_delimiter}
-("relationship"{tuple_delimiter}TECHGLOBAL{tuple_delimiter}VISION HOLDINGS{tuple_delimiter}Vision Holdings formerly owned TechGlobal from 2014 until present{tuple_delimiter}5)
-{completion_delimiter}
+("entity"<|>TECHGLOBAL<|>ORGANIZATION<|>TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)
+##
+("entity"<|>VISION HOLDINGS<|>ORGANIZATION<|>Vision Holdings is a firm that previously owned TechGlobal)
+##
+("relationship"<|>TECHGLOBAL<|>VISION HOLDINGS<|>Vision Holdings formerly owned TechGlobal from 2014 until present<|>5)
+<|COMPLETE|>
 
 ######################
 Example 3:
@@ -75,47 +75,47 @@
 The Aurelians include 39-year-old businessman Samuel Namara, who has been held in Tiruzia's Alhamia Prison, as well as journalist Durke Bataglani, 59, and environmentalist Meggie Tazbah, 53, who also holds Bratinas nationality.
 ######################
 Output:
-("entity"{tuple_delimiter}FIRUZABAD{tuple_delimiter}GEO{tuple_delimiter}Firuzabad held Aurelians as hostages)
-{record_delimiter}
-("entity"{tuple_delimiter}AURELIA{tuple_delimiter}GEO{tuple_delimiter}Country seeking to release hostages)
-{record_delimiter}
-("entity"{tuple_delimiter}QUINTARA{tuple_delimiter}GEO{tuple_delimiter}Country that negotiated a swap of money in exchange for hostages)
-{record_delimiter}
-{record_delimiter}
-("entity"{tuple_delimiter}TIRUZIA{tuple_delimiter}GEO{tuple_delimiter}Capital of Firuzabad where the Aurelians were being held)
-{record_delimiter}
-("entity"{tuple_delimiter}KROHAARA{tuple_delimiter}GEO{tuple_delimiter}Capital city in Quintara)
-{record_delimiter}
-("entity"{tuple_delimiter}CASHION{tuple_delimiter}GEO{tuple_delimiter}Capital city in Aurelia)
-{record_delimiter}
-("entity"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}PERSON{tuple_delimiter}Aurelian who spent time in Tiruzia's Alhamia Prison)
-{record_delimiter}
-("entity"{tuple_delimiter}ALHAMIA PRISON{tuple_delimiter}GEO{tuple_delimiter}Prison in Tiruzia)
-{record_delimiter}
-("entity"{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}PERSON{tuple_delimiter}Aurelian journalist who was held hostage)
-{record_delimiter}
-("entity"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}PERSON{tuple_delimiter}Bratinas national and environmentalist who was held hostage)
-{record_delimiter}
-("relationship"{tuple_delimiter}FIRUZABAD{tuple_delimiter}AURELIA{tuple_delimiter}Firuzabad negotiated a hostage exchange with Aurelia{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}QUINTARA{tuple_delimiter}AURELIA{tuple_delimiter}Quintara brokered the hostage exchange between Firuzabad and Aurelia{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}QUINTARA{tuple_delimiter}FIRUZABAD{tuple_delimiter}Quintara brokered the hostage exchange between Firuzabad and Aurelia{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}ALHAMIA PRISON{tuple_delimiter}Samuel Namara was a prisoner at Alhamia prison{tuple_delimiter}8)
-{record_delimiter}
-("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}Samuel Namara and Meggie Tazbah were exchanged in the same hostage release{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}Samuel Namara and Durke Bataglani were exchanged in the same hostage release{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}FIRUZABAD{tuple_delimiter}Samuel Namara was a hostage in Firuzabad{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}FIRUZABAD{tuple_delimiter}Meggie Tazbah was a hostage in Firuzabad{tuple_delimiter}2)
-{record_delimiter}
-("relationship"{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}FIRUZABAD{tuple_delimiter}Durke Bataglani was a hostage in Firuzabad{tuple_delimiter}2)
-{completion_delimiter}
+("entity"<|>FIRUZABAD<|>GEO<|>Firuzabad held Aurelians as hostages)
+##
+("entity"<|>AURELIA<|>GEO<|>Country seeking to release hostages)
+##
+("entity"<|>QUINTARA<|>GEO<|>Country that negotiated a swap of money in exchange for hostages)
+##
+##
+("entity"<|>TIRUZIA<|>GEO<|>Capital of Firuzabad where the Aurelians were being held)
+##
+("entity"<|>KROHAARA<|>GEO<|>Capital city in Quintara)
+##
+("entity"<|>CASHION<|>GEO<|>Capital city in Aurelia)
+##
+("entity"<|>SAMUEL NAMARA<|>PERSON<|>Aurelian who spent time in Tiruzia's Alhamia Prison)
+##
+("entity"<|>ALHAMIA PRISON<|>GEO<|>Prison in Tiruzia)
+##
+("entity"<|>DURKE BATAGLANI<|>PERSON<|>Aurelian journalist who was held hostage)
+##
+("entity"<|>MEGGIE TAZBAH<|>PERSON<|>Bratinas national and environmentalist who was held hostage)
+##
+("relationship"<|>FIRUZABAD<|>AURELIA<|>Firuzabad negotiated a hostage exchange with Aurelia<|>2)
+##
+("relationship"<|>QUINTARA<|>AURELIA<|>Quintara brokered the hostage exchange between Firuzabad and Aurelia<|>2)
+##
+("relationship"<|>QUINTARA<|>FIRUZABAD<|>Quintara brokered the hostage exchange between Firuzabad and Aurelia<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>ALHAMIA PRISON<|>Samuel Namara was a prisoner at Alhamia prison<|>8)
+##
+("relationship"<|>SAMUEL NAMARA<|>MEGGIE TAZBAH<|>Samuel Namara and Meggie Tazbah were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>DURKE BATAGLANI<|>Samuel Namara and Durke Bataglani were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>MEGGIE TAZBAH<|>DURKE BATAGLANI<|>Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release<|>2)
+##
+("relationship"<|>SAMUEL NAMARA<|>FIRUZABAD<|>Samuel Namara was a hostage in Firuzabad<|>2)
+##
+("relationship"<|>MEGGIE TAZBAH<|>FIRUZABAD<|>Meggie Tazbah was a hostage in Firuzabad<|>2)
+##
+("relationship"<|>DURKE BATAGLANI<|>FIRUZABAD<|>Durke Bataglani was a hostage in Firuzabad<|>2)
+<|COMPLETE|>
 
 ######################
 -Real Data-
diff --git a/graphrag/query/llm/text_utils.py b/graphrag/query/llm/text_utils.py
index 5ff1983aa1..ddd6abe1ef 100644
--- a/graphrag/query/llm/text_utils.py
+++ b/graphrag/query/llm/text_utils.py
@@ -11,7 +11,6 @@
 
 from json_repair import repair_json
 
-import graphrag.config.defaults as defs
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.tokenizer.tokenizer import Tokenizer
 
@@ -36,7 +35,7 @@ def batched(iterable: Iterator, n: int):
 def chunk_text(text: str, max_tokens: int, tokenizer: Tokenizer | None = None):
     """Chunk text by token length."""
     if tokenizer is None:
-        tokenizer = get_tokenizer(encoding_model=defs.ENCODING_MODEL)
+        tokenizer = get_tokenizer()
     tokens = tokenizer.encode(text)  # type: ignore
     chunk_iterator = batched(iter(tokens), max_tokens)
     yield from (tokenizer.decode(list(chunk)) for chunk in chunk_iterator)
diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml
index ee3ddd03f9..26143840e4 100644
--- a/tests/fixtures/min-csv/settings.yml
+++ b/tests/fixtures/min-csv/settings.yml
@@ -6,14 +6,12 @@ models:
     api_key: ${GRAPHRAG_API_KEY}
     api_base: ${GRAPHRAG_API_BASE}
     api_version: "2025-04-01-preview"
-    deployment_name: gpt-4.1
     model: gpt-4.1
     retry_strategy: exponential_backoff
     tokens_per_minute: null
     requests_per_minute: null
     model_supports_json: true
     concurrent_requests: 25
-    async_mode: threaded
   default_embedding_model:
     azure_auth_type: api_key
     type: embedding
@@ -21,13 +19,11 @@ models:
     api_key: ${GRAPHRAG_API_KEY}
     api_base: ${GRAPHRAG_API_BASE}
     api_version: "2025-04-01-preview"
-    deployment_name: text-embedding-ada-002
-    model: text-embedding-ada-002
+    model: text-embedding-3-large
     retry_strategy: exponential_backoff
     tokens_per_minute: null
     requests_per_minute: null
     concurrent_requests: 25
-    async_mode: threaded
 
 vector_store:
   default_vector_store:
diff --git a/tests/fixtures/text/settings.yml b/tests/fixtures/text/settings.yml
index 163f92ccc4..633c12a03f 100644
--- a/tests/fixtures/text/settings.yml
+++ b/tests/fixtures/text/settings.yml
@@ -6,14 +6,12 @@ models:
     api_key: ${GRAPHRAG_API_KEY}
     api_base: ${GRAPHRAG_API_BASE}
     api_version: "2025-04-01-preview"
-    deployment_name: gpt-4.1
     model: gpt-4.1
     retry_strategy: exponential_backoff
     tokens_per_minute: null
     requests_per_minute: null
     model_supports_json: true
     concurrent_requests: 25
-    async_mode: threaded
   default_embedding_model:
     azure_auth_type: api_key
     type: embedding
@@ -21,13 +19,11 @@ models:
     api_key: ${GRAPHRAG_API_KEY}
     api_base: ${GRAPHRAG_API_BASE}
     api_version: "2025-04-01-preview"
-    deployment_name: text-embedding-ada-002
-    model: text-embedding-ada-002
+    model: text-embedding-3-large
     retry_strategy: exponential_backoff
     tokens_per_minute: null
     requests_per_minute: null
     concurrent_requests: 25
-    async_mode: threaded
 
 vector_store:
   default_vector_store:
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index e322a2179d..f230692560 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -12,7 +12,8 @@
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
-from graphrag.config.models.extract_claims_config import ClaimExtractionConfig
+from graphrag.config.models.embed_text_config import EmbedTextConfig
+from graphrag.config.models.extract_claims_config import ExtractClaimsConfig
 from graphrag.config.models.extract_graph_config import ExtractGraphConfig
 from graphrag.config.models.extract_graph_nlp_config import (
     ExtractGraphNLPConfig,
@@ -30,7 +31,6 @@
 from graphrag.config.models.summarize_descriptions_config import (
     SummarizeDescriptionsConfig,
 )
-from graphrag.config.models.text_embedding_config import TextEmbeddingConfig
 from graphrag.config.models.vector_store_config import VectorStoreConfig
 
 FAKE_API_KEY = "NOT_AN_API_KEY"
@@ -181,12 +181,11 @@ def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None:
 
 
 def assert_text_embedding_configs(
-    actual: TextEmbeddingConfig, expected: TextEmbeddingConfig
+    actual: EmbedTextConfig, expected: EmbedTextConfig
 ) -> None:
     assert actual.batch_size == expected.batch_size
     assert actual.batch_max_tokens == expected.batch_max_tokens
     assert actual.names == expected.names
-    assert actual.strategy == expected.strategy
     assert actual.model_id == expected.model_id
     assert actual.vector_store_id == expected.vector_store_id
 
@@ -213,7 +212,6 @@ def assert_extract_graph_configs(
     assert actual.prompt == expected.prompt
     assert actual.entity_types == expected.entity_types
     assert actual.max_gleanings == expected.max_gleanings
-    assert actual.strategy == expected.strategy
     assert actual.model_id == expected.model_id
 
 
@@ -257,7 +255,6 @@ def assert_summarize_descriptions_configs(
 ) -> None:
     assert actual.prompt == expected.prompt
     assert actual.max_length == expected.max_length
-    assert actual.strategy == expected.strategy
     assert actual.model_id == expected.model_id
 
 
@@ -268,18 +265,16 @@ def assert_community_reports_configs(
     assert actual.text_prompt == expected.text_prompt
     assert actual.max_length == expected.max_length
     assert actual.max_input_length == expected.max_input_length
-    assert actual.strategy == expected.strategy
     assert actual.model_id == expected.model_id
 
 
 def assert_extract_claims_configs(
-    actual: ClaimExtractionConfig, expected: ClaimExtractionConfig
+    actual: ExtractClaimsConfig, expected: ExtractClaimsConfig
 ) -> None:
     assert actual.enabled == expected.enabled
     assert actual.prompt == expected.prompt
     assert actual.description == expected.description
     assert actual.max_gleanings == expected.max_gleanings
-    assert actual.strategy == expected.strategy
     assert actual.model_id == expected.model_id
 
 
diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/indexing/text_splitting/test_text_splitting.py
index 8de8718a99..4ea8b25e5d 100644
--- a/tests/unit/indexing/text_splitting/test_text_splitting.py
+++ b/tests/unit/indexing/text_splitting/test_text_splitting.py
@@ -80,17 +80,6 @@ def test_token_text_splitter(mock_tokenizer, mock_split_text):
     mock_split_text.assert_called_once_with(text=text, tokenizer=mocked_tokenizer)
 
 
-@mock.patch("tiktoken.encoding_for_model", side_effect=KeyError)
-@mock.patch("tiktoken.get_encoding")
-def test_model_name_exception(mock_get_encoding, mock_encoding_for_model):
-    mock_get_encoding.return_value = mock.MagicMock()
-
-    TokenTextSplitter(model_name="mock_model", encoding_name="mock_encoding")
-
-    mock_get_encoding.assert_called_once_with("mock_encoding")
-    mock_encoding_for_model.assert_called_once_with("mock_model")
-
-
 def test_split_single_text_on_tokens():
     text = "This is a test text, meaning to be taken seriously by this test only."
     mocked_tokenizer = MockTokenizer()
diff --git a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
index 13e676e9fb..39f041b10c 100644
--- a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
+++ b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
@@ -2,24 +2,23 @@
 # Licensed under the MIT License
 import unittest
 
-from graphrag.index.operations.extract_graph.graph_intelligence_strategy import (
-    run_extract_graph,
-)
+from graphrag.cache.factory import CacheFactory
+from graphrag.index.operations.extract_graph.extract_graph import run_extract_graph
 from graphrag.index.operations.extract_graph.typing import (
     Document,
 )
+from graphrag.prompts.index.extract_graph import GRAPH_EXTRACTION_PROMPT
 from tests.unit.indexing.verbs.helpers.mock_llm import create_mock_llm
 
+_cache = CacheFactory.create_cache("none", kwargs={})
+
 
 class TestRunChain(unittest.IsolatedAsyncioTestCase):
     async def test_run_extract_graph_single_document_correct_entities_returned(self):
         results = await run_extract_graph(
             docs=[Document("test_text", "1")],
             entity_types=["person"],
-            args={
-                "max_gleanings": 0,
-                "summarize_descriptions": False,
-            },
+            max_gleanings=0,
             model=create_mock_llm(
                 responses=[
                     """
@@ -36,6 +35,7 @@ async def test_run_extract_graph_single_document_correct_entities_returned(self)
                 ],
                 name="test_run_extract_graph_single_document_correct_entities_returned",
             ),
+            prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
         # self.assertItemsEqual isn't available yet, or I am just silly
@@ -50,10 +50,7 @@ async def test_run_extract_graph_multiple_documents_correct_entities_returned(
         results = await run_extract_graph(
             docs=[Document("text_1", "1"), Document("text_2", "2")],
             entity_types=["person"],
-            args={
-                "max_gleanings": 0,
-                "summarize_descriptions": False,
-            },
+            max_gleanings=0,
             model=create_mock_llm(
                 responses=[
                     """
@@ -74,6 +71,7 @@ async def test_run_extract_graph_multiple_documents_correct_entities_returned(
                 ],
                 name="test_run_extract_graph_multiple_documents_correct_entities_returned",
             ),
+            prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
         # self.assertItemsEqual isn't available yet, or I am just silly
@@ -86,10 +84,7 @@ async def test_run_extract_graph_multiple_documents_correct_edges_returned(self)
         results = await run_extract_graph(
             docs=[Document("text_1", "1"), Document("text_2", "2")],
             entity_types=["person"],
-            args={
-                "max_gleanings": 0,
-                "summarize_descriptions": False,
-            },
+            max_gleanings=0,
             model=create_mock_llm(
                 responses=[
                     """
@@ -110,6 +105,7 @@ async def test_run_extract_graph_multiple_documents_correct_edges_returned(self)
                 ],
                 name="test_run_extract_graph_multiple_documents_correct_edges_returned",
             ),
+            prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
         # self.assertItemsEqual isn't available yet, or I am just silly
@@ -130,10 +126,7 @@ async def test_run_extract_graph_multiple_documents_correct_entity_source_ids_ma
         results = await run_extract_graph(
             docs=[Document("text_1", "1"), Document("text_2", "2")],
             entity_types=["person"],
-            args={
-                "max_gleanings": 0,
-                "summarize_descriptions": False,
-            },
+            max_gleanings=0,
             model=create_mock_llm(
                 responses=[
                     """
@@ -154,6 +147,7 @@ async def test_run_extract_graph_multiple_documents_correct_entity_source_ids_ma
                 ],
                 name="test_run_extract_graph_multiple_documents_correct_entity_source_ids_mapped",
             ),
+            prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
         graph = results.graph
@@ -179,10 +173,7 @@ async def test_run_extract_graph_multiple_documents_correct_edge_source_ids_mapp
         results = await run_extract_graph(
             docs=[Document("text_1", "1"), Document("text_2", "2")],
             entity_types=["person"],
-            args={
-                "max_gleanings": 0,
-                "summarize_descriptions": False,
-            },
+            max_gleanings=0,
             model=create_mock_llm(
                 responses=[
                     """
@@ -203,6 +194,7 @@ async def test_run_extract_graph_multiple_documents_correct_edge_source_ids_mapp
                 ],
                 name="test_run_extract_graph_multiple_documents_correct_edge_source_ids_mapped",
             ),
+            prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
         graph = results.graph
diff --git a/tests/unit/utils/test_encoding.py b/tests/unit/utils/test_encoding.py
index aca5575b2e..7ad83b5d7f 100644
--- a/tests/unit/utils/test_encoding.py
+++ b/tests/unit/utils/test_encoding.py
@@ -8,7 +8,9 @@ def test_encode_basic():
     tokenizer = get_tokenizer()
     result = tokenizer.encode("abc def")
 
-    assert result == [13997, 711], "Encoding failed to return expected tokens"
+    assert result == [26682, 1056], (
+        f"Encoding failed to return expected tokens, sent {result}"
+    )
 
 
 def test_num_tokens_empty_input():
diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py
index 56fe4a6221..3947d4dc58 100644
--- a/tests/verbs/test_create_community_reports.py
+++ b/tests/verbs/test_create_community_reports.py
@@ -3,7 +3,6 @@
 
 
 from graphrag.config.create_graphrag_config import create_graphrag_config
-from graphrag.config.enums import ModelType
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
 from graphrag.index.operations.summarize_communities.community_reports_extractor import (
     CommunityReportResponse,
@@ -52,17 +51,8 @@ async def test_create_community_reports():
     )
 
     config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-    llm_settings = config.get_language_model_config(
-        config.community_reports.model_id
-    ).model_dump()
-    llm_settings["type"] = ModelType.MockChat
-    llm_settings["responses"] = MOCK_RESPONSES
-    llm_settings["parse_json"] = True
-    config.community_reports.strategy = {
-        "type": "graph_intelligence",
-        "llm": llm_settings,
-        "graph_prompt": "",
-    }
+    config.models["default_chat_model"].type = "mock_chat"
+    config.models["default_chat_model"].responses = MOCK_RESPONSES  # type: ignore
 
     await run_workflow(config, context)
 
diff --git a/tests/verbs/test_extract_covariates.py b/tests/verbs/test_extract_covariates.py
index b873186541..c9c74f0fa9 100644
--- a/tests/verbs/test_extract_covariates.py
+++ b/tests/verbs/test_extract_covariates.py
@@ -32,17 +32,11 @@ async def test_extract_covariates():
     )
 
     config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-    llm_settings = config.get_language_model_config(
-        config.extract_claims.model_id
-    ).model_dump()
-    llm_settings["type"] = ModelType.MockChat
-    llm_settings["responses"] = MOCK_LLM_RESPONSES
     config.extract_claims.enabled = True
-    config.extract_claims.strategy = {
-        "type": "graph_intelligence",
-        "llm": llm_settings,
-        "claim_description": "description",
-    }
+    config.extract_claims.description = "description"
+    llm_settings = config.get_language_model_config(config.extract_claims.model_id)
+    llm_settings.type = ModelType.MockChat
+    llm_settings.responses = MOCK_LLM_RESPONSES  # type: ignore
 
     await run_workflow(config, context)
 
diff --git a/tests/verbs/test_extract_graph.py b/tests/verbs/test_extract_graph.py
index 145d161d51..28dbfcf3c0 100644
--- a/tests/verbs/test_extract_graph.py
+++ b/tests/verbs/test_extract_graph.py
@@ -9,7 +9,8 @@
 from graphrag.utils.storage import load_table_from_storage
 
 from .util import (
-    DEFAULT_MODEL_CONFIG,
+    DEFAULT_CHAT_MODEL_CONFIG,
+    DEFAULT_EMBEDDING_MODEL_CONFIG,
     create_test_context,
 )
 
@@ -39,27 +40,23 @@ async def test_extract_graph():
         storage=["text_units"],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-    extract_claims_llm_settings = config.get_language_model_config(
-        config.extract_graph.model_id
-    ).model_dump()
-    extract_claims_llm_settings["type"] = ModelType.MockChat
-    extract_claims_llm_settings["responses"] = MOCK_LLM_ENTITY_RESPONSES
-    config.extract_graph.strategy = {
-        "type": "graph_intelligence",
-        "llm": extract_claims_llm_settings,
-    }
+    extraction_model = DEFAULT_CHAT_MODEL_CONFIG.copy()
+    extraction_model["type"] = ModelType.MockChat
+    extraction_model["responses"] = MOCK_LLM_ENTITY_RESPONSES  # type: ignore
+    config = create_graphrag_config({
+        "models": {
+            "default_chat_model": extraction_model,
+            "default_embedding_model": DEFAULT_EMBEDDING_MODEL_CONFIG,
+        }
+    })
+
     summarize_llm_settings = config.get_language_model_config(
         config.summarize_descriptions.model_id
     ).model_dump()
     summarize_llm_settings["type"] = ModelType.MockChat
     summarize_llm_settings["responses"] = MOCK_LLM_SUMMARIZATION_RESPONSES
-    config.summarize_descriptions.strategy = {
-        "type": "graph_intelligence",
-        "llm": summarize_llm_settings,
-        "max_input_tokens": 1000,
-        "max_summary_length": 100,
-    }
+    config.summarize_descriptions.max_input_tokens = 1000
+    config.summarize_descriptions.max_length = 100
 
     await run_workflow(config, context)
 
diff --git a/tests/verbs/test_generate_text_embeddings.py b/tests/verbs/test_generate_text_embeddings.py
index b0e47d1638..33254874e7 100644
--- a/tests/verbs/test_generate_text_embeddings.py
+++ b/tests/verbs/test_generate_text_embeddings.py
@@ -6,7 +6,6 @@
     all_embeddings,
 )
 from graphrag.config.enums import ModelType
-from graphrag.index.operations.embed_text.embed_text import TextEmbedStrategyType
 from graphrag.index.workflows.generate_text_embeddings import (
     run_workflow,
 )
@@ -30,15 +29,9 @@ async def test_generate_text_embeddings():
     )
 
     config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
-    llm_settings = config.get_language_model_config(
-        config.embed_text.model_id
-    ).model_dump()
-    llm_settings["type"] = ModelType.MockEmbedding
+    llm_settings = config.get_language_model_config(config.embed_text.model_id)
+    llm_settings.type = ModelType.MockEmbedding
 
-    config.embed_text.strategy = {
-        "type": TextEmbedStrategyType.openai,
-        "llm": llm_settings,
-    }
     config.embed_text.names = list(all_embeddings)
     config.snapshots.embeddings = True
 

From 6284cdd110c131cf8e8f828c38e65e81b23ba568 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Fri, 10 Oct 2025 16:59:25 -0700
Subject: [PATCH 11/40] Remove fnllm (#2095)

---
 docs/config/env_vars.md                       | 219 ---------
 docs/config/models.md                         |  11 +-
 docs/config/yaml.md                           |   8 +-
 .../custom_vector_store.ipynb                 |  23 +-
 docs/get_started.md                           |   6 +-
 graphrag/config/enums.py                      |   4 -
 .../config/models/language_model_config.py    |  89 +---
 .../index/operations/chunk_text/strategies.py |  25 +-
 graphrag/index/validate_config.py             |   4 +-
 .../index/workflows/create_base_text_units.py |  10 +-
 graphrag/language_model/factory.py            |  20 -
 .../providers/fnllm/__init__.py               |   4 -
 .../language_model/providers/fnllm/cache.py   |  44 --
 .../language_model/providers/fnllm/events.py  |  26 -
 .../language_model/providers/fnllm/models.py  | 443 ------------------
 .../language_model/providers/fnllm/utils.py   | 165 -------
 .../providers/litellm/chat_model.py           |  32 +-
 .../providers/litellm/embedding_model.py      |  32 +-
 graphrag/language_model/util.py               |  41 ++
 graphrag/query/factory.py                     |   2 +-
 .../structured_search/drift_search/search.py  |   2 +-
 pyproject.toml                                |   2 -
 tests/unit/config/test_config.py              |  17 +-
 .../operations/chunk_text/test_strategies.py  |  10 +-
 uv.lock                                       |  46 --
 25 files changed, 122 insertions(+), 1163 deletions(-)
 delete mode 100644 docs/config/env_vars.md
 delete mode 100644 graphrag/language_model/providers/fnllm/__init__.py
 delete mode 100644 graphrag/language_model/providers/fnllm/cache.py
 delete mode 100644 graphrag/language_model/providers/fnllm/events.py
 delete mode 100644 graphrag/language_model/providers/fnllm/models.py
 delete mode 100644 graphrag/language_model/providers/fnllm/utils.py
 create mode 100644 graphrag/language_model/util.py

diff --git a/docs/config/env_vars.md b/docs/config/env_vars.md
deleted file mode 100644
index 004b3757e1..0000000000
--- a/docs/config/env_vars.md
+++ /dev/null
@@ -1,219 +0,0 @@
-# Default Configuration Mode (using Env Vars)
-
-As of version 1.3, GraphRAG no longer supports a full complement of pre-built environment variables. Instead, we support variable replacement within the [settings.yml file](yaml.md) so you can specify any environment variables you like.
-
-The only standard environment variable we expect, and include in the default settings.yml, is `GRAPHRAG_API_KEY`. If you are already using a number of the previous GRAPHRAG_* environment variables, you can insert them with template syntax into settings.yml and they will be adopted.
-
-> **The environment variables below are documented as an aid for migration, but they WILL NOT be read unless you use template syntax in your settings.yml. We also WILL NOT be updating this page as the main config object changes.**
-
----
-
-### Text-Embeddings Customization
-
-By default, the GraphRAG indexer will only export embeddings required for our query methods. However, the model has embeddings defined for all plaintext fields, and these can be generated by setting the `GRAPHRAG_EMBEDDING_TARGET` environment variable to `all`.
-
-#### Embedded Fields
-
-- `text_unit.text`
-- `document.text`
-- `entity.title`
-- `entity.description`
-- `relationship.description`
-- `community.title`
-- `community.summary`
-- `community.full_content`
-
-### Input Data
-
-Our pipeline can ingest .csv or .txt data from an input folder. These files can be nested within subfolders. To configure how input data is handled, what fields are mapped over, and how timestamps are parsed, look for configuration values starting with `GRAPHRAG_INPUT_` below. In general, CSV-based data provides the most customizability. Each CSV should at least contain a `text` field (which can be mapped with environment variables), but it's helpful if they also have `title`, `timestamp`, and `source` fields. Additional fields can be included as well, which will land as extra fields on the `Document` table.
-
-### Base LLM Settings
-
-These are the primary settings for configuring LLM connectivity.
-
-| Parameter                   | Required?                             | Description                                                                                                                      | Type  | Default Value |
-| --------------------------- | ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | ----- | ------------- |
-| `GRAPHRAG_API_KEY`          | **Yes for OpenAI. Optional for AOAI** | The API key. (Note: `OPENAI_API_KEY` is also used as a fallback). If not defined when using AOAI, managed identity will be used. | `str` | `None`        |
-| `GRAPHRAG_API_BASE`         | **For AOAI**                          | The API Base URL                                                                                                                 | `str` | `None`        |
-| `GRAPHRAG_API_VERSION`      | **For AOAI**                          | The AOAI API version.                                                                                                            | `str` | `None`        |
-| `GRAPHRAG_API_ORGANIZATION` |                                       | The AOAI organization.                                                                                                           | `str` | `None`        |
-| `GRAPHRAG_API_PROXY`        |                                       | The AOAI proxy.                                                                                                                  | `str` | `None`        |
-
-### Text Generation Settings
-
-These settings control the text generation model used by the pipeline. Any settings with a fallback will use the base LLM settings, if available.
-
-| Parameter                                         | Required?                | Description                                                                    | Type    | Default Value         |
-| ------------------------------------------------- | ------------------------ | ------------------------------------------------------------------------------ | ------- | --------------------- |
-| `GRAPHRAG_LLM_TYPE`                               | **For AOAI**             | The LLM operation type. Either `openai_chat` or `azure_openai_chat`            | `str`   | `openai_chat`         |
-| `GRAPHRAG_LLM_DEPLOYMENT_NAME`                    | **For AOAI**             | The AOAI model deployment name.                                                | `str`   | `None`                |
-| `GRAPHRAG_LLM_API_KEY`                            | Yes (uses fallback)      | The API key. If not defined when using AOAI, managed identity will be used.    | `str`   | `None`                |
-| `GRAPHRAG_LLM_API_BASE`                           | For AOAI (uses fallback) | The API Base URL                                                               | `str`   | `None`                |
-| `GRAPHRAG_LLM_API_VERSION`                        | For AOAI (uses fallback) | The AOAI API version.                                                          | `str`   | `None`                |
-| `GRAPHRAG_LLM_API_ORGANIZATION`                   | For AOAI (uses fallback) | The AOAI organization.                                                         | `str`   | `None`                |
-| `GRAPHRAG_LLM_API_PROXY`                          |                          | The AOAI proxy.                                                                | `str`   | `None`                |
-| `GRAPHRAG_LLM_MODEL`                              |                          | The LLM model.                                                                 | `str`   | `gpt-4-turbo-preview` |
-| `GRAPHRAG_LLM_MAX_TOKENS`                         |                          | The maximum number of tokens.                                                  | `int`   | `4000`                |
-| `GRAPHRAG_LLM_REQUEST_TIMEOUT`                    |                          | The maximum number of seconds to wait for a response from the chat client.     | `int`   | `180`                 |
-| `GRAPHRAG_LLM_MODEL_SUPPORTS_JSON`                |                          | Indicates whether the given model supports JSON output mode. `True` to enable. | `str`   | `None`                |
-| `GRAPHRAG_LLM_THREAD_COUNT`                       |                          | The number of threads to use for LLM parallelization.                          | `int`   | 50                    |
-| `GRAPHRAG_LLM_THREAD_STAGGER`                     |                          | The time to wait (in seconds) between starting each thread.                    | `float` | 0.3                   |
-| `GRAPHRAG_LLM_CONCURRENT_REQUESTS`                |                          | The number of concurrent requests to allow for the embedding client.           | `int`   | 25                    |
-| `GRAPHRAG_LLM_TOKENS_PER_MINUTE`                  |                          | The number of tokens per minute to allow for the LLM client. 0 = Bypass        | `int`   | 0                     |
-| `GRAPHRAG_LLM_REQUESTS_PER_MINUTE`                |                          | The number of requests per minute to allow for the LLM client. 0 = Bypass      | `int`   | 0                     |
-| `GRAPHRAG_LLM_MAX_RETRIES`                        |                          | The maximum number of retries to attempt when a request fails.                 | `int`   | 10                    |
-| `GRAPHRAG_LLM_MAX_RETRY_WAIT`                     |                          | The maximum number of seconds to wait between retries.                         | `int`   | 10                    |
-| `GRAPHRAG_LLM_SLEEP_ON_RATE_LIMIT_RECOMMENDATION` |                          | Whether to sleep on rate limit recommendation. (Azure Only)                    | `bool`  | `True`                |
-| `GRAPHRAG_LLM_TEMPERATURE`                        |                          | The temperature to use generation.                                             | `float` | 0                     |
-| `GRAPHRAG_LLM_TOP_P`                              |                          | The top_p to use for sampling.                                                 | `float` | 1                     |
-| `GRAPHRAG_LLM_N`                                  |                          | The number of responses to generate.                                           | `int`   | 1                     |
-
-### Text Embedding Settings
-
-These settings control the text embedding model used by the pipeline. Any settings with a fallback will use the base LLM settings, if available.
-
-| Parameter                                               | Required ?               | Description                                                                                                                | Type    | Default                  |
-| ------------------------------------------------------- | ------------------------ | -------------------------------------------------------------------------------------------------------------------------- | ------- | ------------------------ |
-| `GRAPHRAG_EMBEDDING_TYPE`                               | **For AOAI**             | The embedding client to use. Either `openai_embedding` or `azure_openai_embedding`                                         | `str`   | `openai_embedding`       |
-| `GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME`                    | **For AOAI**             | The AOAI deployment name.                                                                                                  | `str`   | `None`                   |
-| `GRAPHRAG_EMBEDDING_API_KEY`                            | Yes (uses fallback)      | The API key to use for the embedding client. If not defined when using AOAI, managed identity will be used.                | `str`   | `None`                   |
-| `GRAPHRAG_EMBEDDING_API_BASE`                           | For AOAI (uses fallback) | The API base URL.                                                                                                          | `str`   | `None`                   |
-| `GRAPHRAG_EMBEDDING_API_VERSION`                        | For AOAI (uses fallback) | The AOAI API version to use for the embedding client.                                                                      | `str`   | `None`                   |
-| `GRAPHRAG_EMBEDDING_API_ORGANIZATION`                   | For AOAI (uses fallback) | The AOAI organization to use for the embedding client.                                                                     | `str`   | `None`                   |
-| `GRAPHRAG_EMBEDDING_API_PROXY`                          |                          | The AOAI proxy to use for the embedding client.                                                                            | `str`   | `None`                   |
-| `GRAPHRAG_EMBEDDING_MODEL`                              |                          | The model to use for the embedding client.                                                                                 | `str`   | `text-embedding-3-small` |
-| `GRAPHRAG_EMBEDDING_BATCH_SIZE`                         |                          | The number of texts to embed at once. [(Azure limit is 16)](https://learn.microsoft.com/en-us/azure/ai-ce)                 | `int`   | 16                       |
-| `GRAPHRAG_EMBEDDING_BATCH_MAX_TOKENS`                   |                          | The maximum tokens per batch [(Azure limit is 8191)](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference) | `int`   | 8191                     |
-| `GRAPHRAG_EMBEDDING_TARGET`                             |                          | The target fields to embed. Either `required` or `all`.                                                                    | `str`   | `required`               |         |
-| `GRAPHRAG_EMBEDDING_THREAD_COUNT`                       |                          | The number of threads to use for parallelization for embeddings.                                                           | `int`   |                          |
-| `GRAPHRAG_EMBEDDING_THREAD_STAGGER`                     |                          | The time to wait (in seconds) between starting each thread for embeddings.                                                 | `float` | 50                       |
-| `GRAPHRAG_EMBEDDING_CONCURRENT_REQUESTS`                |                          | The number of concurrent requests to allow for the embedding client.                                                       | `int`   | 25                       |
-| `GRAPHRAG_EMBEDDING_TOKENS_PER_MINUTE`                  |                          | The number of tokens per minute to allow for the embedding client. 0 = Bypass                                              | `int`   | 0                        |
-| `GRAPHRAG_EMBEDDING_REQUESTS_PER_MINUTE`                |                          | The number of requests per minute to allow for the embedding client. 0 = Bypass                                            | `int`   | 0                        |
-| `GRAPHRAG_EMBEDDING_MAX_RETRIES`                        |                          | The maximum number of retries to attempt when a request fails.                                                             | `int`   | 10                       |
-| `GRAPHRAG_EMBEDDING_MAX_RETRY_WAIT`                     |                          | The maximum number of seconds to wait between retries.                                                                     | `int`   | 10                       |
-| `GRAPHRAG_EMBEDDING_SLEEP_ON_RATE_LIMIT_RECOMMENDATION` |                          | Whether to sleep on rate limit recommendation. (Azure Only)                                                                | `bool`  | `True`                   |
-
-### Input Settings
-
-These settings control the data input used by the pipeline. Any settings with a fallback will use the base LLM settings, if available.
-
-#### Plaintext Input Data (`GRAPHRAG_INPUT_FILE_TYPE`=text)
-
-| Parameter                     | Description                                                                       | Type  | Required or Optional | Default    |
-| ----------------------------- | --------------------------------------------------------------------------------- | ----- | -------------------- | ---------- |
-| `GRAPHRAG_INPUT_FILE_PATTERN` | The file pattern regexp to use when reading input files from the input directory. | `str` | optional             | `.*\.txt$` |
-
-#### CSV Input Data (`GRAPHRAG_INPUT_FILE_TYPE`=csv)
-
-| Parameter                                   | Description                                                                                                                                                        | Type  | Required or Optional | Default    |
-| ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----- | -------------------- | ---------- |
-| `GRAPHRAG_INPUT_TYPE`                       | The input storage type to use when reading files. (`file` or `blob`)                                                                                               | `str` | optional             | `file`     |
-| `GRAPHRAG_INPUT_FILE_PATTERN`               | The file pattern regexp to use when reading input files from the input directory.                                                                                  | `str` | optional             | `.*\.txt$` |
-| `GRAPHRAG_INPUT_TEXT_COLUMN`                | The 'text' column to use when reading CSV input files.                                                                                                             | `str` | optional             | `text`     |
-| `GRAPHRAG_INPUT_METADATA` | A list of CSV columns, comma-separated, to incorporate as JSON in a metadata column.                                                                                         | `str` | optional             | `None`       |
-| `GRAPHRAG_INPUT_TITLE_COLUMN`               | The 'title' column to use when reading CSV input files.                                                                                                            | `str` | optional             | `title`    |
-| `GRAPHRAG_INPUT_STORAGE_ACCOUNT_BLOB_URL`   | The Azure Storage blob endpoint to use when in `blob` mode and using managed identity. Will have the format `https://<storage_account_name>.blob.core.windows.net` | `str` | optional             | `None`     |
-| `GRAPHRAG_INPUT_CONNECTION_STRING`          | The connection string to use when reading CSV input files from Azure Blob Storage.                                                                                 | `str` | optional             | `None`     |
-| `GRAPHRAG_INPUT_CONTAINER_NAME`             | The container name to use when reading CSV input files from Azure Blob Storage.                                                                                    | `str` | optional             | `None`     |
-| `GRAPHRAG_INPUT_BASE_DIR`                   | The base directory to read input files from.                                                                                                                       | `str` | optional             | `None`     |
-
-### Data Mapping Settings
-
-| Parameter                  | Description                                              | Type  | Required or Optional | Default |
-| -------------------------- | -------------------------------------------------------- | ----- | -------------------- | ------- |
-| `GRAPHRAG_INPUT_FILE_TYPE` | The type of input data, `csv` or `text`                  | `str` | optional             | `text`  |
-| `GRAPHRAG_INPUT_ENCODING`  | The encoding to apply when reading CSV/text input files. | `str` | optional             | `utf-8` |
-
-### Data Chunking
-
-| Parameter                       | Description                                                                                 | Type  | Required or Optional | Default                       |
-| ------------------------------- | ------------------------------------------------------------------------------------------- | ----- | -------------------- | ----------------------------- |
-| `GRAPHRAG_CHUNK_SIZE`           | The chunk size in tokens for text-chunk analysis windows.                                   | `str` | optional             | 1200                          |
-| `GRAPHRAG_CHUNK_OVERLAP`        | The chunk overlap in tokens for text-chunk analysis windows.                                | `str` | optional             | 100                           |
-| `GRAPHRAG_CHUNK_BY_COLUMNS`     | A comma-separated list of document attributes to groupby when performing TextUnit chunking. | `str` | optional             | `id`                          |
-| `GRAPHRAG_CHUNK_ENCODING_MODEL` | The encoding model to use for chunking.                                                     | `str` | optional             | The top-level encoding model. |
-
-### Prompting Overrides
-
-| Parameter                                     | Description                                                                                | Type     | Required or Optional | Default                                                          |
-| --------------------------------------------- | ------------------------------------------------------------------------------------------ | -------- | -------------------- | ---------------------------------------------------------------- |
-| `GRAPHRAG_ENTITY_EXTRACTION_PROMPT_FILE`      | The path (relative to the root) of an entity extraction prompt template text file.         | `str`    | optional             | `None`                                                           |
-| `GRAPHRAG_ENTITY_EXTRACTION_MAX_GLEANINGS`    | The maximum number of redrives (gleanings) to invoke when extracting entities in a loop.   | `int`    | optional             | 1                                                                |
-| `GRAPHRAG_ENTITY_EXTRACTION_ENTITY_TYPES`     | A comma-separated list of entity types to extract.                                         | `str`    | optional             | `organization,person,event,geo`                                  |
-| `GRAPHRAG_ENTITY_EXTRACTION_ENCODING_MODEL`		| The encoding model to use for entity extraction.                                           | `str`    | optional             | The top-level encoding model.                                    |
-| `GRAPHRAG_SUMMARIZE_DESCRIPTIONS_PROMPT_FILE` | The path (relative to the root) of an description summarization prompt template text file. | `str`    | optional             | `None`                                                           |
-| `GRAPHRAG_SUMMARIZE_DESCRIPTIONS_MAX_LENGTH`  | The maximum number of tokens to generate per description summarization.                    | `int`    | optional             | 500                                                              |
-| `GRAPHRAG_CLAIM_EXTRACTION_ENABLED`           | Whether claim extraction is enabled for this pipeline.                                     | `bool`   | optional             | `False`                                                          |
-| `GRAPHRAG_CLAIM_EXTRACTION_DESCRIPTION`       | The claim_description prompting argument to utilize.                                       | `string` | optional             | "Any claims or facts that could be relevant to threat analysis." |
-| `GRAPHRAG_CLAIM_EXTRACTION_PROMPT_FILE`       | The claim extraction prompt to utilize.                                                    | `string` | optional             | `None`                                                           |
-| `GRAPHRAG_CLAIM_EXTRACTION_MAX_GLEANINGS`     | The maximum number of redrives (gleanings) to invoke when extracting claims in a loop.     | `int`    | optional             | 1                                                                |
-| `GRAPHRAG_CLAIM_EXTRACTION_ENCODING_MODEL`		| The encoding model to use for claim extraction.                                            | `str`    | optional             | The top-level encoding model                                     |
-| `GRAPHRAG_COMMUNITY_REPORTS_PROMPT_FILE`      | The community reports extraction prompt to utilize.                                        | `string` | optional             | `None`                                                           |
-| `GRAPHRAG_COMMUNITY_REPORTS_MAX_LENGTH`       | The maximum number of tokens to generate per community reports.                            | `int`    | optional             | 1500                                                             |
-
-### Storage
-
-This section controls the storage mechanism used by the pipeline used for exporting output tables.
-
-| Parameter                                   | Description                                                                                                                                                        | Type  | Required or Optional | Default |
-| ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----- | -------------------- | ------- |
-| `GRAPHRAG_STORAGE_TYPE`                     | The type of storage to use. Options are `file`, `memory`, or `blob`                                                                                               | `str` | optional             | `file`  |
-| `GRAPHRAG_STORAGE_STORAGE_ACCOUNT_BLOB_URL` | The Azure Storage blob endpoint to use when in `blob` mode and using managed identity. Will have the format `https://<storage_account_name>.blob.core.windows.net` | `str` | optional             | None    |
-| `GRAPHRAG_STORAGE_CONNECTION_STRING`        | The Azure Storage connection string to use when in `blob` mode.                                                                                                    | `str` | optional             | None    |
-| `GRAPHRAG_STORAGE_CONTAINER_NAME`           | The Azure Storage container name to use when in `blob` mode.                                                                                                       | `str` | optional             | None    |
-| `GRAPHRAG_STORAGE_BASE_DIR`                 | The base path to data outputs outputs.                                                                                                                             | `str` | optional             | None    |
-
-### Cache
-
-This section controls the cache mechanism used by the pipeline. This is used to cache LLM invocation results.
-
-| Parameter                                 | Description                                                                                                                                                        | Type  | Required or Optional | Default |
-| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----- | -------------------- | ------- |
-| `GRAPHRAG_CACHE_TYPE`                     | The type of cache to use. Options are `file`, `memory`, `none` or `blob`                                                                                           | `str` | optional             | `file`  |
-| `GRAPHRAG_CACHE_STORAGE_ACCOUNT_BLOB_URL` | The Azure Storage blob endpoint to use when in `blob` mode and using managed identity. Will have the format `https://<storage_account_name>.blob.core.windows.net` | `str` | optional             | None    |
-| `GRAPHRAG_CACHE_CONNECTION_STRING`        | The Azure Storage connection string to use when in `blob` mode.                                                                                                    | `str` | optional             | None    |
-| `GRAPHRAG_CACHE_CONTAINER_NAME`           | The Azure Storage container name to use when in `blob` mode.                                                                                                       | `str` | optional             | None    |
-| `GRAPHRAG_CACHE_BASE_DIR`                 | The base path to the cache files.                                                                                                                                  | `str` | optional             | None    |
-
-### Reporting
-
-This section controls the reporting mechanism used by the pipeline, for common events and error messages. The default is to write reports to a file in the output directory. However, you can also choose to write reports to an Azure Blob Storage container.
-
-| Parameter                                     | Description                                                                                                                                                        | Type  | Required or Optional | Default |
-| --------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----- | -------------------- | ------- |
-| `GRAPHRAG_REPORTING_TYPE`                     | The type of reporter to use. Options are `file` or `blob`                                                                                              | `str` | optional             | `file`  |
-| `GRAPHRAG_REPORTING_STORAGE_ACCOUNT_BLOB_URL` | The Azure Storage blob endpoint to use when in `blob` mode and using managed identity. Will have the format `https://<storage_account_name>.blob.core.windows.net` | `str` | optional             | None    |
-| `GRAPHRAG_REPORTING_CONNECTION_STRING`        | The Azure Storage connection string to use when in `blob` mode.                                                                                                    | `str` | optional             | None    |
-| `GRAPHRAG_REPORTING_CONTAINER_NAME`           | The Azure Storage container name to use when in `blob` mode.                                                                                                       | `str` | optional             | None    |
-| `GRAPHRAG_REPORTING_BASE_DIR`                 | The base path to the reporting outputs.                                                                                                                            | `str` | optional             | None    |
-
-### Node2Vec Parameters
-
-| Parameter                       | Description                              | Type   | Required or Optional | Default |
-| ------------------------------- | ---------------------------------------- | ------ | -------------------- | ------- |
-| `GRAPHRAG_NODE2VEC_ENABLED`     | Whether to enable Node2Vec               | `bool` | optional             | False   |
-| `GRAPHRAG_NODE2VEC_NUM_WALKS`   | The Node2Vec number of walks to perform  | `int`  | optional             | 10      |
-| `GRAPHRAG_NODE2VEC_WALK_LENGTH` | The Node2Vec walk length                 | `int`  | optional             | 40      |
-| `GRAPHRAG_NODE2VEC_WINDOW_SIZE` | The Node2Vec window size                 | `int`  | optional             | 2       |
-| `GRAPHRAG_NODE2VEC_ITERATIONS`  | The number of iterations to run node2vec | `int`  | optional             | 3       |
-| `GRAPHRAG_NODE2VEC_RANDOM_SEED` | The random seed to use for node2vec      | `int`  | optional             | 597832  |
-
-### Data Snapshotting
-
-| Parameter                              | Description                                     | Type   | Required or Optional | Default |
-| -------------------------------------- | ----------------------------------------------- | ------ | -------------------- | ------- |
-| `GRAPHRAG_SNAPSHOT_EMBEDDINGS`         | Whether to enable embeddings snapshots.         | `bool` | optional             | False   |
-| `GRAPHRAG_SNAPSHOT_GRAPHML`            | Whether to enable GraphML snapshots.            | `bool` | optional             | False   |
-| `GRAPHRAG_SNAPSHOT_RAW_ENTITIES`       | Whether to enable raw entity snapshots.         | `bool` | optional             | False   |
-| `GRAPHRAG_SNAPSHOT_TOP_LEVEL_NODES`    | Whether to enable top-level node snapshots.     | `bool` | optional             | False   |
-| `GRAPHRAG_SNAPSHOT_TRANSIENT`          | Whether to enable transient table snapshots.    | `bool` | optional             | False   |
-
-# Miscellaneous Settings
-
-| Parameter                   | Description                                                           | Type   | Required or Optional | Default       |
-| --------------------------- | --------------------------------------------------------------------- | ------ | -------------------- | ------------- |
-| `GRAPHRAG_ASYNC_MODE`       | Which async mode to use. Either `asyncio` or `threaded`.              | `str`  | optional             | `asyncio`     |
-| `GRAPHRAG_ENCODING_MODEL`   | The text encoding model, used in tiktoken, to encode text.            | `str`  | optional             | `cl100k_base` |
-| `GRAPHRAG_MAX_CLUSTER_SIZE` | The maximum number of entities to include in a single Leiden cluster. | `int`  | optional             | 10            |
-| `GRAPHRAG_UMAP_ENABLED`     | Whether to enable UMAP layouts                                        | `bool` | optional             | False         |
diff --git a/docs/config/models.md b/docs/config/models.md
index eaa4eea0e7..686304a510 100644
--- a/docs/config/models.md
+++ b/docs/config/models.md
@@ -6,9 +6,7 @@ This page contains information on selecting a model to use and options to supply
 
 GraphRAG was built and tested using OpenAI models, so this is the default model set we support. This is not intended to be a limiter or statement of quality or fitness for your use case, only that it's the set we are most familiar with for prompting, tuning, and debugging.
 
-GraphRAG also utilizes a language model wrapper library used by several projects within our team, called fnllm. fnllm provides two important functions for GraphRAG: rate limiting configuration to help us maximize throughput for large indexing jobs, and robust caching of API calls to minimize consumption on repeated indexes for testing, experimentation, or incremental ingest. fnllm uses the OpenAI Python SDK under the covers, so OpenAI-compliant endpoints are a base requirement out-of-the-box.
-
-Starting with version 2.6.0, GraphRAG supports using [LiteLLM](https://docs.litellm.ai/) instead of fnllm for calling language models. LiteLLM provides support for 100+ models though it is important to note that when choosing a model it must support returning [structured outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/) adhering to a [JSON schema](https://docs.litellm.ai/docs/completion/json_mode). 
+Starting with version 2.6.0, GraphRAG supports using [LiteLLM](https://docs.litellm.ai/) for calling language models. LiteLLM provides support for 100+ models though it is important to note that when choosing a model it must support returning [structured outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/) adhering to a [JSON schema](https://docs.litellm.ai/docs/completion/json_mode). 
 
 Example using LiteLLm as the language model tool for GraphRAG:
 
@@ -54,13 +52,15 @@ Example config with asymmetric model use:
 models:
   extraction_chat_model:
     api_key: ${GRAPHRAG_API_KEY}
-    type: openai_chat
+    type: chat
+    model_provider: openai
     auth_type: api_key
     model: gpt-4o
     model_supports_json: true
   query_chat_model:
     api_key: ${GRAPHRAG_API_KEY}
-    type: openai_chat
+    type: chat
+    model_provider: openai
     auth_type: api_key
     model: o1
     model_supports_json: true
@@ -98,7 +98,6 @@ Many users have used platforms such as [ollama](https://ollama.com/) and [LiteLL
 As of GraphRAG 2.0.0, we support model injection through the use of a standard chat and embedding Protocol and an accompanying ModelFactory that you can use to register your model implementation. This is not supported with the CLI, so you'll need to use GraphRAG as a library.
 
 - Our Protocol is [defined here](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/protocol/base.py)
-- Our base implementation, which wraps fnllm, [is here](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/providers/fnllm/models.py)
 - We have a simple mock implementation in our tests that you can [reference here](https://github.com/microsoft/graphrag/blob/main/tests/mock_provider.py)
 
 Once you have a model implementation, you need to register it with our ModelFactory:
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 0d128273a2..059d99eada 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -28,12 +28,14 @@ For example:
 models:
   default_chat_model:
     api_key: ${GRAPHRAG_API_KEY}
-    type: openai_chat
+    type: chat
+    model_provider: openai
     model: gpt-4.1
     model_supports_json: true
   default_embedding_model:
     api_key: ${GRAPHRAG_API_KEY}
-    type: openai_embedding
+    type: embedding
+    model_provider: openai
     model: text-embedding-3-large
 ```
 
@@ -41,7 +43,7 @@ models:
 
 - `api_key` **str** - The OpenAI API key to use.
 - `auth_type` **api_key|azure_managed_identity** - Indicate how you want to authenticate requests.
-- `type` **chat**|**embedding**|**openai_chat|azure_openai_chat|openai_embedding|azure_openai_embedding|mock_chat|mock_embeddings** - The type of LLM to use.
+- `type` **chat**|**embedding**|mock_chat|mock_embeddings** - The type of LLM to use.
 - `model_provider` **str|None** - The model provider to use, e.g., openai, azure, anthropic, etc. Required when `type == chat|embedding`. When `type == chat|embedding`, [LiteLLM](https://docs.litellm.ai/) is used under the hood which has support for calling 100+ models. [View LiteLLm basic usage](https://docs.litellm.ai/docs/#basic-usage) for details on how models are called (The `model_provider` is the portion prior to `/` while the `model` is the portion following the `/`). [View Language Model Selection](models.md) for more details and examples on using LiteLLM.
 - `model` **str** - The model name.
 - `encoding_model` **str** - The text encoding model to use. Default is to use the encoding model aligned with the language model (i.e., it is retrieved from tiktoken if unset).
diff --git a/docs/examples_notebooks/custom_vector_store.ipynb b/docs/examples_notebooks/custom_vector_store.ipynb
index 4c3d223c69..bce47f8bac 100644
--- a/docs/examples_notebooks/custom_vector_store.ipynb
+++ b/docs/examples_notebooks/custom_vector_store.ipynb
@@ -318,27 +318,19 @@
     "sample_documents = [\n",
     "    VectorStoreDocument(\n",
     "        id=\"doc_1\",\n",
-    "        text=\"GraphRAG is a powerful knowledge graph extraction and reasoning framework.\",\n",
     "        vector=create_mock_embedding(),\n",
-    "        attributes={\"category\": \"technology\", \"source\": \"documentation\"},\n",
     "    ),\n",
     "    VectorStoreDocument(\n",
     "        id=\"doc_2\",\n",
-    "        text=\"Vector stores enable efficient similarity search over high-dimensional data.\",\n",
     "        vector=create_mock_embedding(),\n",
-    "        attributes={\"category\": \"technology\", \"source\": \"research\"},\n",
     "    ),\n",
     "    VectorStoreDocument(\n",
     "        id=\"doc_3\",\n",
-    "        text=\"Machine learning models can process and understand natural language text.\",\n",
     "        vector=create_mock_embedding(),\n",
-    "        attributes={\"category\": \"AI\", \"source\": \"article\"},\n",
     "    ),\n",
     "    VectorStoreDocument(\n",
     "        id=\"doc_4\",\n",
-    "        text=\"Custom implementations allow for specialized behavior and integration.\",\n",
     "        vector=create_mock_embedding(),\n",
-    "        attributes={\"category\": \"development\", \"source\": \"tutorial\"},\n",
     "    ),\n",
     "]\n",
     "\n",
@@ -395,9 +387,7 @@
     "for i, result in enumerate(search_results, 1):\n",
     "    doc = result.document\n",
     "    print(f\"{i}. ID: {doc.id}\")\n",
-    "    print(f\"   Text: {doc.text[:60]}...\")\n",
     "    print(f\"   Similarity Score: {result.score:.4f}\")\n",
-    "    print(f\"   Category: {doc.attributes.get('category', 'N/A')}\")\n",
     "    print()"
    ]
   },
@@ -412,14 +402,8 @@
     "    found_doc = vector_store.search_by_id(\"doc_2\")\n",
     "    print(\"✅ Found document by ID:\")\n",
     "    print(f\"   ID: {found_doc.id}\")\n",
-    "    print(f\"   Text: {found_doc.text}\")\n",
-    "    print(f\"   Attributes: {found_doc.attributes}\")\n",
     "except KeyError as e:\n",
-    "    print(f\"� Error: {e}\")\n",
-    "\n",
-    "# Test filter by ID\n",
-    "id_filter = vector_store.filter_by_id([\"doc_1\", \"doc_3\"])\n",
-    "print(f\"\\n🔧 ID filter result: {id_filter}\")"
+    "    print(f\"� Error: {e}\")"
    ]
   },
   {
@@ -450,7 +434,8 @@
     "    # Other GraphRAG configuration...\n",
     "    \"models\": {\n",
     "        \"default_embedding_model\": {\n",
-    "            \"type\": \"openai_embedding\",\n",
+    "            \"type\": \"embedding\",\n",
+    "            \"model_provider\": \"openai\",\n",
     "            \"model\": \"text-embedding-3-small\",\n",
     "        }\n",
     "    },\n",
@@ -500,9 +485,7 @@
     "    entity_documents = [\n",
     "        VectorStoreDocument(\n",
     "            id=f\"entity_{i}\",\n",
-    "            text=f\"Entity {i} description: Important concept in the knowledge graph\",\n",
     "            vector=create_mock_embedding(),\n",
-    "            attributes={\"type\": \"entity\", \"importance\": i % 3 + 1},\n",
     "        )\n",
     "        for i in range(10)\n",
     "    ]\n",
diff --git a/docs/get_started.md b/docs/get_started.md
index a9d5ff5436..4af43b8160 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -60,12 +60,14 @@ If running in OpenAI mode, you only need to update the value of `GRAPHRAG_API_KE
 In addition to setting your API key, Azure OpenAI users should set the variables below in the settings.yaml file. To find the appropriate sections, just search for the `models:` root configuration; you should see two sections, one for the default chat endpoint and one for the default embeddings endpoint. Here is an example of what to add to the chat model config:
 
 ```yaml
-type: azure_openai_chat # Or azure_openai_embedding for embeddings
+type: chat
+model_provider: azure
 api_base: https://<instance>.openai.azure.com
 api_version: 2024-02-15-preview # You can customize this for other versions
-deployment_name: <azure_model_deployment_name>
 ```
 
+Most people tend to name their deployments the same as their model - if yours are different, add the `deployment_name` as well.
+
 #### Using Managed Auth on Azure
 To use managed auth, edit the auth_type in your model config and *remove* the api_key line:
 
diff --git a/graphrag/config/enums.py b/graphrag/config/enums.py
index ef99ee6a1a..4835763afc 100644
--- a/graphrag/config/enums.py
+++ b/graphrag/config/enums.py
@@ -84,13 +84,9 @@ class ModelType(str, Enum):
     """LLMType enum class definition."""
 
     # Embeddings
-    OpenAIEmbedding = "openai_embedding"
-    AzureOpenAIEmbedding = "azure_openai_embedding"
     Embedding = "embedding"
 
     # Chat Completion
-    OpenAIChat = "openai_chat"
-    AzureOpenAIChat = "azure_openai_chat"
     Chat = "chat"
 
     # Debug
diff --git a/graphrag/config/models/language_model_config.py b/graphrag/config/models/language_model_config.py
index e31e311762..33aee99893 100644
--- a/graphrag/config/models/language_model_config.py
+++ b/graphrag/config/models/language_model_config.py
@@ -4,9 +4,7 @@
 """Language model configuration."""
 
 import logging
-from typing import Literal
 
-import tiktoken
 from pydantic import BaseModel, Field, model_validator
 
 from graphrag.config.defaults import language_model_defaults
@@ -77,9 +75,7 @@ def _validate_auth_type(self) -> None:
         """
         if (
             self.auth_type == AuthType.AzureManagedIdentity
-            and self.type != ModelType.AzureOpenAIChat
-            and self.type != ModelType.AzureOpenAIEmbedding
-            and self.model_provider != "azure"  # indicates Litellm + AOI
+            and self.model_provider != "azure"
         ):
             msg = f"auth_type of azure_managed_identity is not supported for model type {self.type}. Please rerun `graphrag init` and set the auth_type to api_key."
             raise ConflictingSettingsError(msg)
@@ -98,14 +94,6 @@ def _validate_type(self) -> None:
         if not ModelFactory.is_supported_model(self.type):
             msg = f"Model type {self.type} is not recognized, must be one of {ModelFactory.get_chat_models() + ModelFactory.get_embedding_models()}."
             raise KeyError(msg)
-        if self.type in [
-            "openai_chat",
-            "openai_embedding",
-            "azure_openai_chat",
-            "azure_openai_embedding",
-        ]:
-            msg = f"Model config based on fnllm is deprecated and will be removed in GraphRAG v3, please use {ModelType.Chat} or {ModelType.Embedding} instead to switch to LiteLLM config."
-            logger.warning(msg)
 
     model_provider: str | None = Field(
         description="The model provider to use.",
@@ -134,32 +122,6 @@ def _validate_model_provider(self) -> None:
         default=language_model_defaults.encoding_model,
     )
 
-    def _validate_encoding_model(self) -> None:
-        """Validate the encoding model.
-
-        The default behavior is to use an encoding model that matches the LLM model.
-        LiteLLM supports 100+ models and their tokenization. There is no need to
-        set the encoding model when using the new LiteLLM provider as was done with fnllm provider.
-
-        Users can still manually specify a tiktoken based encoding model to use even with the LiteLLM provider
-        in which case the specified encoding model will be used regardless of the LLM model being used, even if
-        it is not an openai based model.
-
-        If not using LiteLLM provider, set the encoding model based on the LLM model name.
-        This is for backward compatibility with existing fnllm provider until fnllm is removed.
-
-        Raises
-        ------
-        KeyError
-            If the model name is not recognized.
-        """
-        if (
-            self.type != ModelType.Chat
-            and self.type != ModelType.Embedding
-            and self.encoding_model.strip() == ""
-        ):
-            self.encoding_model = tiktoken.encoding_name_for_model(self.model)
-
     api_base: str | None = Field(
         description="The base URL for the LLM API.",
         default=language_model_defaults.api_base,
@@ -175,11 +137,9 @@ def _validate_api_base(self) -> None:
         AzureApiBaseMissingError
             If the API base is missing and is required.
         """
-        if (
-            self.type == ModelType.AzureOpenAIChat
-            or self.type == ModelType.AzureOpenAIEmbedding
-            or self.model_provider == "azure"  # indicates Litellm + AOI
-        ) and (self.api_base is None or self.api_base.strip() == ""):
+        if (self.model_provider == "azure") and (
+            self.api_base is None or self.api_base.strip() == ""
+        ):
             raise AzureApiBaseMissingError(self.type)
 
     api_version: str | None = Field(
@@ -197,11 +157,9 @@ def _validate_api_version(self) -> None:
         AzureApiBaseMissingError
             If the API base is missing and is required.
         """
-        if (
-            self.type == ModelType.AzureOpenAIChat
-            or self.type == ModelType.AzureOpenAIEmbedding
-            or self.model_provider == "azure"  # indicates Litellm + AOI
-        ) and (self.api_version is None or self.api_version.strip() == ""):
+        if (self.model_provider == "azure") and (
+            self.api_version is None or self.api_version.strip() == ""
+        ):
             raise AzureApiVersionMissingError(self.type)
 
     deployment_name: str | None = Field(
@@ -219,11 +177,9 @@ def _validate_deployment_name(self) -> None:
         AzureDeploymentNameMissingError
             If the deployment name is missing and is required.
         """
-        if (
-            self.type == ModelType.AzureOpenAIChat
-            or self.type == ModelType.AzureOpenAIEmbedding
-            or self.model_provider == "azure"  # indicates Litellm + AOI
-        ) and (self.deployment_name is None or self.deployment_name.strip() == ""):
+        if (self.model_provider == "azure") and (
+            self.deployment_name is None or self.deployment_name.strip() == ""
+        ):
             msg = f"deployment_name is not set for Azure-hosted model. This will default to your model name ({self.model}). If different, this should be set."
             logger.debug(msg)
 
@@ -247,7 +203,7 @@ def _validate_deployment_name(self) -> None:
         description="The request timeout to use.",
         default=language_model_defaults.request_timeout,
     )
-    tokens_per_minute: int | Literal["auto"] | None = Field(
+    tokens_per_minute: int | None = Field(
         description="The number of tokens per minute to use for the LLM service.",
         default=language_model_defaults.tokens_per_minute,
     )
@@ -262,18 +218,10 @@ def _validate_tokens_per_minute(self) -> None:
         """
         # If the value is a number, check if it is less than 1
         if isinstance(self.tokens_per_minute, int) and self.tokens_per_minute < 1:
-            msg = f"Tokens per minute must be a non zero positive number, 'auto' or null. Suggested value: {language_model_defaults.tokens_per_minute}."
+            msg = f"Tokens per minute must be a non zero positive number or null. Suggested value: {language_model_defaults.tokens_per_minute}."
             raise ValueError(msg)
 
-        if (
-            (self.type == ModelType.Chat or self.type == ModelType.Embedding)
-            and self.rate_limit_strategy is not None
-            and self.tokens_per_minute == "auto"
-        ):
-            msg = f"tokens_per_minute cannot be set to 'auto' when using type '{self.type}'. Please set it to a positive integer or null to disable."
-            raise ValueError(msg)
-
-    requests_per_minute: int | Literal["auto"] | None = Field(
+    requests_per_minute: int | None = Field(
         description="The number of requests per minute to use for the LLM service.",
         default=language_model_defaults.requests_per_minute,
     )
@@ -288,15 +236,7 @@ def _validate_requests_per_minute(self) -> None:
         """
         # If the value is a number, check if it is less than 1
         if isinstance(self.requests_per_minute, int) and self.requests_per_minute < 1:
-            msg = f"Requests per minute must be a non zero positive number, 'auto' or null. Suggested value: {language_model_defaults.requests_per_minute}."
-            raise ValueError(msg)
-
-        if (
-            (self.type == ModelType.Chat or self.type == ModelType.Embedding)
-            and self.rate_limit_strategy is not None
-            and self.requests_per_minute == "auto"
-        ):
-            msg = f"requests_per_minute cannot be set to 'auto' when using type '{self.type}'. Please set it to a positive integer or null to disable."
+            msg = f"Requests per minute must be a non zero positive number or null. Suggested value: {language_model_defaults.requests_per_minute}."
             raise ValueError(msg)
 
     rate_limit_strategy: str | None = Field(
@@ -399,5 +339,4 @@ def _validate_model(self):
         self._validate_requests_per_minute()
         self._validate_max_retries()
         self._validate_azure_settings()
-        self._validate_encoding_model()
         return self
diff --git a/graphrag/index/operations/chunk_text/strategies.py b/graphrag/index/operations/chunk_text/strategies.py
index 4be14e408d..9d48786e7f 100644
--- a/graphrag/index/operations/chunk_text/strategies.py
+++ b/graphrag/index/operations/chunk_text/strategies.py
@@ -6,7 +6,6 @@
 from collections.abc import Iterable
 
 import nltk
-import tiktoken
 
 from graphrag.config.models.chunking_config import ChunkingConfig
 from graphrag.index.operations.chunk_text.typing import TextChunk
@@ -15,21 +14,7 @@
     split_multiple_texts_on_tokens,
 )
 from graphrag.logger.progress import ProgressTicker
-
-
-def get_encoding_fn(encoding_name):
-    """Get the encoding model."""
-    enc = tiktoken.get_encoding(encoding_name)
-
-    def encode(text: str) -> list[int]:
-        if not isinstance(text, str):
-            text = f"{text}"
-        return enc.encode(text)
-
-    def decode(tokens: list[int]) -> str:
-        return enc.decode(tokens)
-
-    return encode, decode
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
 
 
 def run_tokens(
@@ -40,16 +25,14 @@ def run_tokens(
     """Chunks text into chunks based on encoding tokens."""
     tokens_per_chunk = config.size
     chunk_overlap = config.overlap
-    encoding_name = config.encoding_model
-
-    encode, decode = get_encoding_fn(encoding_name)
+    tokenizer = get_tokenizer(encoding_model=config.encoding_model)
     return split_multiple_texts_on_tokens(
         input,
         TokenChunkerOptions(
             chunk_overlap=chunk_overlap,
             tokens_per_chunk=tokens_per_chunk,
-            encode=encode,
-            decode=decode,
+            encode=tokenizer.encode,
+            decode=tokenizer.decode,
         ),
         tick,
     )
diff --git a/graphrag/index/validate_config.py b/graphrag/index/validate_config.py
index 2ba4ec1efe..014592df47 100644
--- a/graphrag/index/validate_config.py
+++ b/graphrag/index/validate_config.py
@@ -17,7 +17,7 @@
 def validate_config_names(parameters: GraphRagConfig) -> None:
     """Validate config file for model deployment name typos, by running a quick test message for each."""
     for id, config in parameters.models.items():
-        if config.type in ["chat", "azure_openai", "openai"]:
+        if config.type == "chat":
             llm = ModelManager().register_chat(
                 name="test-llm",
                 model_type=config.type,
@@ -34,7 +34,7 @@ def validate_config_names(parameters: GraphRagConfig) -> None:
                 logger.error(f"LLM configuration error detected.\n{e}")  # noqa
                 print(f"Failed to validate language model ({id}) params", e)  # noqa: T201
                 sys.exit(1)
-        elif config.type in ["embedding", "azure_openai_embedding", "openai_embedding"]:
+        elif config.type == "embedding":
             embed_llm = ModelManager().register_embedding(
                 name="test-embed-llm",
                 model_type=config.type,
diff --git a/graphrag/index/workflows/create_base_text_units.py b/graphrag/index/workflows/create_base_text_units.py
index df57713126..b80cf6d9f4 100644
--- a/graphrag/index/workflows/create_base_text_units.py
+++ b/graphrag/index/workflows/create_base_text_units.py
@@ -13,10 +13,10 @@
 from graphrag.config.models.chunking_config import ChunkStrategyType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.operations.chunk_text.chunk_text import chunk_text
-from graphrag.index.operations.chunk_text.strategies import get_encoding_fn
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.utils.hashing import gen_sha512_hash
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -62,7 +62,7 @@ def create_base_text_units(
     """All the steps to transform base text_units."""
     documents.sort_values(by=["id"], ascending=[True], inplace=True)
 
-    encode, _ = get_encoding_fn(encoding_model)
+    tokenizer = get_tokenizer(encoding_model=encoding_model)
 
     def chunker(row: pd.Series) -> Any:
         line_delimiter = ".\n"
@@ -80,7 +80,7 @@ def chunker(row: pd.Series) -> Any:
                 )
 
             if chunk_size_includes_metadata:
-                metadata_tokens = len(encode(metadata_str))
+                metadata_tokens = len(tokenizer.encode(metadata_str))
                 if metadata_tokens >= size:
                     message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk."
                     raise ValueError(message)
@@ -135,7 +135,9 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any:
         lambda row: gen_sha512_hash(row, ["text"]), axis=1
     )
     # get a final token measurement
-    text_units["n_tokens"] = text_units["text"].apply(lambda x: len(encode(x)))
+    text_units["n_tokens"] = text_units["text"].apply(
+        lambda x: len(tokenizer.encode(x))
+    )
 
     return cast(
         "pd.DataFrame", text_units[text_units["text"].notna()].reset_index(drop=True)
diff --git a/graphrag/language_model/factory.py b/graphrag/language_model/factory.py
index 92bee9dde8..840dada3dc 100644
--- a/graphrag/language_model/factory.py
+++ b/graphrag/language_model/factory.py
@@ -8,12 +8,6 @@
 
 from graphrag.config.enums import ModelType
 from graphrag.language_model.protocol.base import ChatModel, EmbeddingModel
-from graphrag.language_model.providers.fnllm.models import (
-    AzureOpenAIChatFNLLM,
-    AzureOpenAIEmbeddingFNLLM,
-    OpenAIChatFNLLM,
-    OpenAIEmbeddingFNLLM,
-)
 from graphrag.language_model.providers.litellm.chat_model import LitellmChatModel
 from graphrag.language_model.providers.litellm.embedding_model import (
     LitellmEmbeddingModel,
@@ -103,21 +97,7 @@ def is_supported_model(cls, model_type: str) -> bool:
 
 
 # --- Register default implementations ---
-ModelFactory.register_chat(
-    ModelType.AzureOpenAIChat.value, lambda **kwargs: AzureOpenAIChatFNLLM(**kwargs)
-)
-ModelFactory.register_chat(
-    ModelType.OpenAIChat.value, lambda **kwargs: OpenAIChatFNLLM(**kwargs)
-)
 ModelFactory.register_chat(ModelType.Chat, lambda **kwargs: LitellmChatModel(**kwargs))
-
-ModelFactory.register_embedding(
-    ModelType.AzureOpenAIEmbedding.value,
-    lambda **kwargs: AzureOpenAIEmbeddingFNLLM(**kwargs),
-)
-ModelFactory.register_embedding(
-    ModelType.OpenAIEmbedding.value, lambda **kwargs: OpenAIEmbeddingFNLLM(**kwargs)
-)
 ModelFactory.register_embedding(
     ModelType.Embedding, lambda **kwargs: LitellmEmbeddingModel(**kwargs)
 )
diff --git a/graphrag/language_model/providers/fnllm/__init__.py b/graphrag/language_model/providers/fnllm/__init__.py
deleted file mode 100644
index 8132a1b5ab..0000000000
--- a/graphrag/language_model/providers/fnllm/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2025 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""FNLLM provider module."""
diff --git a/graphrag/language_model/providers/fnllm/cache.py b/graphrag/language_model/providers/fnllm/cache.py
deleted file mode 100644
index 4554f1d158..0000000000
--- a/graphrag/language_model/providers/fnllm/cache.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) 2025 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""FNLLM Cache provider."""
-
-from typing import Any
-
-from fnllm.caching import Cache as FNLLMCache
-
-from graphrag.cache.pipeline_cache import PipelineCache
-
-
-class FNLLMCacheProvider(FNLLMCache):
-    """A cache for the pipeline."""
-
-    def __init__(self, cache: PipelineCache):
-        self._cache = cache
-
-    async def has(self, key: str) -> bool:
-        """Check if the cache has a value."""
-        return await self._cache.has(key)
-
-    async def get(self, key: str) -> Any | None:
-        """Retrieve a value from the cache."""
-        return await self._cache.get(key)
-
-    async def set(
-        self, key: str, value: Any, metadata: dict[str, Any] | None = None
-    ) -> None:
-        """Write a value into the cache."""
-        await self._cache.set(key, value, metadata)
-
-    async def remove(self, key: str) -> None:
-        """Remove a value from the cache."""
-        await self._cache.delete(key)
-
-    async def clear(self) -> None:
-        """Clear the cache."""
-        await self._cache.clear()
-
-    def child(self, key: str) -> "FNLLMCacheProvider":
-        """Create a child cache."""
-        child_cache = self._cache.child(key)
-        return FNLLMCacheProvider(child_cache)
diff --git a/graphrag/language_model/providers/fnllm/events.py b/graphrag/language_model/providers/fnllm/events.py
deleted file mode 100644
index e6f75eafe8..0000000000
--- a/graphrag/language_model/providers/fnllm/events.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) 2025 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""FNLLM llm events provider."""
-
-from typing import Any
-
-from fnllm.events import LLMEvents
-
-from graphrag.index.typing.error_handler import ErrorHandlerFn
-
-
-class FNLLMEvents(LLMEvents):
-    """FNLLM events handler that calls the error handler."""
-
-    def __init__(self, on_error: ErrorHandlerFn):
-        self._on_error = on_error
-
-    async def on_error(
-        self,
-        error: BaseException | None,
-        traceback: str | None = None,
-        arguments: dict[str, Any] | None = None,
-    ) -> None:
-        """Handle an fnllm error."""
-        self._on_error(error, traceback, arguments)
diff --git a/graphrag/language_model/providers/fnllm/models.py b/graphrag/language_model/providers/fnllm/models.py
deleted file mode 100644
index 059b0412ca..0000000000
--- a/graphrag/language_model/providers/fnllm/models.py
+++ /dev/null
@@ -1,443 +0,0 @@
-# Copyright (c) 2025 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing fnllm model provider definitions."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from fnllm.openai import (
-    create_openai_chat_llm,
-    create_openai_client,
-    create_openai_embeddings_llm,
-)
-
-from graphrag.language_model.providers.fnllm.events import FNLLMEvents
-from graphrag.language_model.providers.fnllm.utils import (
-    _create_cache,
-    _create_error_handler,
-    _create_openai_config,
-    run_coroutine_sync,
-)
-from graphrag.language_model.response.base import (
-    BaseModelOutput,
-    BaseModelResponse,
-    ModelResponse,
-)
-
-if TYPE_CHECKING:
-    from collections.abc import AsyncGenerator, Generator
-
-    from fnllm.openai.types.client import OpenAIChatLLM as FNLLMChatLLM
-    from fnllm.openai.types.client import OpenAIEmbeddingsLLM as FNLLMEmbeddingLLM
-
-    from graphrag.cache.pipeline_cache import PipelineCache
-    from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-    from graphrag.config.models.language_model_config import (
-        LanguageModelConfig,
-    )
-
-
-class OpenAIChatFNLLM:
-    """An OpenAI Chat Model provider using the fnllm library."""
-
-    model: FNLLMChatLLM
-
-    def __init__(
-        self,
-        *,
-        name: str,
-        config: LanguageModelConfig,
-        callbacks: WorkflowCallbacks | None = None,
-        cache: PipelineCache | None = None,
-    ) -> None:
-        model_config = _create_openai_config(config, azure=False)
-        error_handler = _create_error_handler(callbacks) if callbacks else None
-        model_cache = _create_cache(cache, name)
-        client = create_openai_client(model_config)
-        self.model = create_openai_chat_llm(
-            model_config,
-            client=client,
-            cache=model_cache,
-            events=FNLLMEvents(error_handler) if error_handler else None,
-        )
-        self.config = config
-
-    async def achat(
-        self, prompt: str, history: list | None = None, **kwargs
-    ) -> ModelResponse:
-        """
-        Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The response from the Model.
-        """
-        if history is None:
-            response = await self.model(prompt, **kwargs)
-        else:
-            response = await self.model(prompt, history=history, **kwargs)
-        return BaseModelResponse(
-            output=BaseModelOutput(
-                content=response.output.content,
-                full_response=response.output.raw_model.to_dict(),
-            ),
-            parsed_response=response.parsed_json,
-            history=response.history,
-            cache_hit=response.cache_hit,
-            tool_calls=response.tool_calls,
-            metrics=response.metrics,
-        )
-
-    async def achat_stream(
-        self, prompt: str, history: list | None = None, **kwargs
-    ) -> AsyncGenerator[str, None]:
-        """
-        Stream Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            A generator that yields strings representing the response.
-        """
-        if history is None:
-            response = await self.model(prompt, stream=True, **kwargs)
-        else:
-            response = await self.model(prompt, history=history, stream=True, **kwargs)
-        async for chunk in response.output.content:
-            if chunk is not None:
-                yield chunk
-
-    def chat(self, prompt: str, history: list | None = None, **kwargs) -> ModelResponse:
-        """
-        Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The response from the Model.
-        """
-        return run_coroutine_sync(self.achat(prompt, history=history, **kwargs))
-
-    def chat_stream(
-        self, prompt: str, history: list | None = None, **kwargs
-    ) -> Generator[str, None]:
-        """
-        Stream Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            A generator that yields strings representing the response.
-        """
-        msg = "chat_stream is not supported for synchronous execution"
-        raise NotImplementedError(msg)
-
-
-class OpenAIEmbeddingFNLLM:
-    """An OpenAI Embedding Model provider using the fnllm library."""
-
-    model: FNLLMEmbeddingLLM
-
-    def __init__(
-        self,
-        *,
-        name: str,
-        config: LanguageModelConfig,
-        callbacks: WorkflowCallbacks | None = None,
-        cache: PipelineCache | None = None,
-    ) -> None:
-        model_config = _create_openai_config(config, azure=False)
-        error_handler = _create_error_handler(callbacks) if callbacks else None
-        model_cache = _create_cache(cache, name)
-        client = create_openai_client(model_config)
-        self.model = create_openai_embeddings_llm(
-            model_config,
-            client=client,
-            cache=model_cache,
-            events=FNLLMEvents(error_handler) if error_handler else None,
-        )
-        self.config = config
-
-    async def aembed_batch(self, text_list: list[str], **kwargs) -> list[list[float]]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the LLM.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        response = await self.model(text_list, **kwargs)
-        if response.output.embeddings is None:
-            msg = "No embeddings found in response"
-            raise ValueError(msg)
-        embeddings: list[list[float]] = response.output.embeddings
-        return embeddings
-
-    async def aembed(self, text: str, **kwargs) -> list[float]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        response = await self.model([text], **kwargs)
-        if response.output.embeddings is None:
-            msg = "No embeddings found in response"
-            raise ValueError(msg)
-        embeddings: list[float] = response.output.embeddings[0]
-        return embeddings
-
-    def embed_batch(self, text_list: list[str], **kwargs) -> list[list[float]]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the LLM.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        return run_coroutine_sync(self.aembed_batch(text_list, **kwargs))
-
-    def embed(self, text: str, **kwargs) -> list[float]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        return run_coroutine_sync(self.aembed(text, **kwargs))
-
-
-class AzureOpenAIChatFNLLM:
-    """An Azure OpenAI Chat LLM provider using the fnllm library."""
-
-    model: FNLLMChatLLM
-
-    def __init__(
-        self,
-        *,
-        name: str,
-        config: LanguageModelConfig,
-        callbacks: WorkflowCallbacks | None = None,
-        cache: PipelineCache | None = None,
-    ) -> None:
-        model_config = _create_openai_config(config, azure=True)
-        error_handler = _create_error_handler(callbacks) if callbacks else None
-        model_cache = _create_cache(cache, name)
-        client = create_openai_client(model_config)
-        self.model = create_openai_chat_llm(
-            model_config,
-            client=client,
-            cache=model_cache,
-            events=FNLLMEvents(error_handler) if error_handler else None,
-        )
-        self.config = config
-
-    async def achat(
-        self, prompt: str, history: list | None = None, **kwargs
-    ) -> ModelResponse:
-        """
-        Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            history: The conversation history.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The response from the Model.
-        """
-        if history is None:
-            response = await self.model(prompt, **kwargs)
-        else:
-            response = await self.model(prompt, history=history, **kwargs)
-        return BaseModelResponse(
-            output=BaseModelOutput(
-                content=response.output.content,
-                full_response=response.output.raw_model.to_dict(),
-            ),
-            parsed_response=response.parsed_json,
-            history=response.history,
-            cache_hit=response.cache_hit,
-            tool_calls=response.tool_calls,
-            metrics=response.metrics,
-        )
-
-    async def achat_stream(
-        self, prompt: str, history: list | None = None, **kwargs
-    ) -> AsyncGenerator[str, None]:
-        """
-        Stream Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            history: The conversation history.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            A generator that yields strings representing the response.
-        """
-        if history is None:
-            response = await self.model(prompt, stream=True, **kwargs)
-        else:
-            response = await self.model(prompt, history=history, stream=True, **kwargs)
-        async for chunk in response.output.content:
-            if chunk is not None:
-                yield chunk
-
-    def chat(self, prompt: str, history: list | None = None, **kwargs) -> ModelResponse:
-        """
-        Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The response from the Model.
-        """
-        return run_coroutine_sync(self.achat(prompt, history=history, **kwargs))
-
-    def chat_stream(
-        self, prompt: str, history: list | None = None, **kwargs
-    ) -> Generator[str, None]:
-        """
-        Stream Chat with the Model using the given prompt.
-
-        Args:
-            prompt: The prompt to chat with.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            A generator that yields strings representing the response.
-        """
-        msg = "chat_stream is not supported for synchronous execution"
-        raise NotImplementedError(msg)
-
-
-class AzureOpenAIEmbeddingFNLLM:
-    """An Azure OpenAI Embedding Model provider using the fnllm library."""
-
-    model: FNLLMEmbeddingLLM
-
-    def __init__(
-        self,
-        *,
-        name: str,
-        config: LanguageModelConfig,
-        callbacks: WorkflowCallbacks | None = None,
-        cache: PipelineCache | None = None,
-    ) -> None:
-        model_config = _create_openai_config(config, azure=True)
-        error_handler = _create_error_handler(callbacks) if callbacks else None
-        model_cache = _create_cache(cache, name)
-        client = create_openai_client(model_config)
-        self.model = create_openai_embeddings_llm(
-            model_config,
-            client=client,
-            cache=model_cache,
-            events=FNLLMEvents(error_handler) if error_handler else None,
-        )
-        self.config = config
-
-    async def aembed_batch(self, text_list: list[str], **kwargs) -> list[list[float]]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        response = await self.model(text_list, **kwargs)
-        if response.output.embeddings is None:
-            msg = "No embeddings found in response"
-            raise ValueError(msg)
-        embeddings: list[list[float]] = response.output.embeddings
-        return embeddings
-
-    async def aembed(self, text: str, **kwargs) -> list[float]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        response = await self.model([text], **kwargs)
-        if response.output.embeddings is None:
-            msg = "No embeddings found in response"
-            raise ValueError(msg)
-        embeddings: list[float] = response.output.embeddings[0]
-        return embeddings
-
-    def embed_batch(self, text_list: list[str], **kwargs) -> list[list[float]]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        return run_coroutine_sync(self.aembed_batch(text_list, **kwargs))
-
-    def embed(self, text: str, **kwargs) -> list[float]:
-        """
-        Embed the given text using the Model.
-
-        Args:
-            text: The text to embed.
-            kwargs: Additional arguments to pass to the Model.
-
-        Returns
-        -------
-            The embeddings of the text.
-        """
-        return run_coroutine_sync(self.aembed(text, **kwargs))
diff --git a/graphrag/language_model/providers/fnllm/utils.py b/graphrag/language_model/providers/fnllm/utils.py
deleted file mode 100644
index b12b0e27b0..0000000000
--- a/graphrag/language_model/providers/fnllm/utils.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) 2025 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing utils for fnllm."""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import threading
-from typing import TYPE_CHECKING, Any, TypeVar
-
-from fnllm.base.config import JsonStrategy, RetryStrategy
-from fnllm.openai import AzureOpenAIConfig, OpenAIConfig, PublicOpenAIConfig
-from fnllm.openai.types.chat.parameters import OpenAIChatParameters
-
-import graphrag.config.defaults as defs
-from graphrag.language_model.providers.fnllm.cache import FNLLMCacheProvider
-
-if TYPE_CHECKING:
-    from collections.abc import Coroutine
-
-    from graphrag.cache.pipeline_cache import PipelineCache
-    from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-    from graphrag.config.models.language_model_config import (
-        LanguageModelConfig,
-    )
-    from graphrag.index.typing.error_handler import ErrorHandlerFn
-
-logger = logging.getLogger(__name__)
-
-
-def _create_cache(cache: PipelineCache | None, name: str) -> FNLLMCacheProvider | None:
-    """Create an FNLLM cache from a pipeline cache."""
-    if cache is None:
-        return None
-    return FNLLMCacheProvider(cache).child(name)
-
-
-def _create_error_handler(callbacks: WorkflowCallbacks) -> ErrorHandlerFn:  # noqa: ARG001
-    """Create an error handler from a WorkflowCallbacks."""
-
-    def on_error(
-        error: BaseException | None = None,
-        stack: str | None = None,
-        details: dict | None = None,
-    ) -> None:
-        logger.error(
-            "Error Invoking LLM",
-            exc_info=error,
-            extra={"stack": stack, "details": details},
-        )
-
-    return on_error
-
-
-def _create_openai_config(config: LanguageModelConfig, azure: bool) -> OpenAIConfig:
-    """Create an OpenAIConfig from a LanguageModelConfig."""
-    encoding_model = config.encoding_model
-    json_strategy = (
-        JsonStrategy.VALID if config.model_supports_json else JsonStrategy.LOOSE
-    )
-    chat_parameters = OpenAIChatParameters(
-        **get_openai_model_parameters_from_config(config)
-    )
-
-    if azure:
-        if config.api_base is None:
-            msg = "Azure OpenAI Chat LLM requires an API base"
-            raise ValueError(msg)
-
-        audience = config.audience or defs.COGNITIVE_SERVICES_AUDIENCE
-        return AzureOpenAIConfig(
-            api_key=config.api_key,
-            endpoint=config.api_base,
-            json_strategy=json_strategy,
-            api_version=config.api_version,
-            organization=config.organization,
-            max_retries=config.max_retries,
-            max_retry_wait=config.max_retry_wait,
-            requests_per_minute=config.requests_per_minute,
-            tokens_per_minute=config.tokens_per_minute,
-            audience=audience,
-            retry_strategy=RetryStrategy(config.retry_strategy),
-            timeout=config.request_timeout,
-            max_concurrency=config.concurrent_requests,
-            model=config.model,
-            encoding=encoding_model,
-            deployment=config.deployment_name,
-            chat_parameters=chat_parameters,
-        )
-    return PublicOpenAIConfig(
-        api_key=config.api_key,
-        base_url=config.api_base,
-        json_strategy=json_strategy,
-        organization=config.organization,
-        retry_strategy=RetryStrategy(config.retry_strategy),
-        max_retries=config.max_retries,
-        max_retry_wait=config.max_retry_wait,
-        requests_per_minute=config.requests_per_minute,
-        tokens_per_minute=config.tokens_per_minute,
-        timeout=config.request_timeout,
-        max_concurrency=config.concurrent_requests,
-        model=config.model,
-        encoding=encoding_model,
-        chat_parameters=chat_parameters,
-    )
-
-
-# FNLLM does not support sync operations, so we workaround running in an available loop/thread.
-T = TypeVar("T")
-
-_loop = asyncio.new_event_loop()
-
-_thr = threading.Thread(target=_loop.run_forever, name="Async Runner", daemon=True)
-
-
-def run_coroutine_sync(coroutine: Coroutine[Any, Any, T]) -> T:
-    """
-    Run a coroutine synchronously.
-
-    Args:
-        coroutine: The coroutine to run.
-
-    Returns
-    -------
-        The result of the coroutine.
-    """
-    if not _thr.is_alive():
-        _thr.start()
-    future = asyncio.run_coroutine_threadsafe(coroutine, _loop)
-    return future.result()
-
-
-def is_reasoning_model(model: str) -> bool:
-    """Return whether the model uses a known OpenAI reasoning model."""
-    return model.lower() in {"o1", "o1-mini", "o3-mini"}
-
-
-def get_openai_model_parameters_from_config(
-    config: LanguageModelConfig,
-) -> dict[str, Any]:
-    """Get the model parameters for a given config, adjusting for reasoning API differences."""
-    return get_openai_model_parameters_from_dict(config.model_dump())
-
-
-def get_openai_model_parameters_from_dict(config: dict[str, Any]) -> dict[str, Any]:
-    """Get the model parameters for a given config, adjusting for reasoning API differences."""
-    params = {
-        "n": config.get("n"),
-    }
-    if is_reasoning_model(config["model"]):
-        params["max_completion_tokens"] = config.get("max_completion_tokens")
-        params["reasoning_effort"] = config.get("reasoning_effort")
-    else:
-        params["max_tokens"] = config.get("max_tokens")
-        params["temperature"] = config.get("temperature")
-        params["frequency_penalty"] = config.get("frequency_penalty")
-        params["presence_penalty"] = config.get("presence_penalty")
-        params["top_p"] = config.get("top_p")
-
-    if config.get("response_format"):
-        params["response_format"] = config["response_format"]
-
-    return params
diff --git a/graphrag/language_model/providers/litellm/chat_model.py b/graphrag/language_model/providers/litellm/chat_model.py
index 43cb7ece98..bf4af8aff2 100644
--- a/graphrag/language_model/providers/litellm/chat_model.py
+++ b/graphrag/language_model/providers/litellm/chat_model.py
@@ -137,29 +137,17 @@ def _create_completions(
     """
     completion, acompletion = _create_base_completions(model_config)
 
-    # TODO: For v2.x release, rpm/tpm can be int or str (auto) for backwards compatibility with fnllm.
-    # LiteLLM does not support "auto", so we have to check those values here.
-    # For v3 release, force rpm/tpm to be int and remove the type checks below
-    # and just check if rate_limit_strategy is enabled.
-    if model_config.rate_limit_strategy is not None:
-        rpm = (
-            model_config.requests_per_minute
-            if type(model_config.requests_per_minute) is int
-            else None
-        )
-        tpm = (
-            model_config.tokens_per_minute
-            if type(model_config.tokens_per_minute) is int
-            else None
+    if model_config.rate_limit_strategy is not None and (
+        model_config.requests_per_minute is not None
+        or model_config.tokens_per_minute is not None
+    ):
+        completion, acompletion = with_rate_limiter(
+            sync_fn=completion,
+            async_fn=acompletion,
+            model_config=model_config,
+            rpm=model_config.requests_per_minute,
+            tpm=model_config.tokens_per_minute,
         )
-        if rpm is not None or tpm is not None:
-            completion, acompletion = with_rate_limiter(
-                sync_fn=completion,
-                async_fn=acompletion,
-                model_config=model_config,
-                rpm=rpm,
-                tpm=tpm,
-            )
 
     if model_config.retry_strategy != "none":
         completion, acompletion = with_retries(
diff --git a/graphrag/language_model/providers/litellm/embedding_model.py b/graphrag/language_model/providers/litellm/embedding_model.py
index 779222c37e..17eea1e153 100644
--- a/graphrag/language_model/providers/litellm/embedding_model.py
+++ b/graphrag/language_model/providers/litellm/embedding_model.py
@@ -123,29 +123,17 @@ def _create_embeddings(
     """
     embedding, aembedding = _create_base_embeddings(model_config)
 
-    # TODO: For v2.x release, rpm/tpm can be int or str (auto) for backwards compatibility with fnllm.
-    # LiteLLM does not support "auto", so we have to check those values here.
-    # For v3 release, force rpm/tpm to be int and remove the type checks below
-    # and just check if rate_limit_strategy is enabled.
-    if model_config.rate_limit_strategy is not None:
-        rpm = (
-            model_config.requests_per_minute
-            if type(model_config.requests_per_minute) is int
-            else None
-        )
-        tpm = (
-            model_config.tokens_per_minute
-            if type(model_config.tokens_per_minute) is int
-            else None
+    if model_config.rate_limit_strategy is not None and (
+        model_config.requests_per_minute is not None
+        or model_config.tokens_per_minute is not None
+    ):
+        embedding, aembedding = with_rate_limiter(
+            sync_fn=embedding,
+            async_fn=aembedding,
+            model_config=model_config,
+            rpm=model_config.requests_per_minute,
+            tpm=model_config.tokens_per_minute,
         )
-        if rpm is not None or tpm is not None:
-            embedding, aembedding = with_rate_limiter(
-                sync_fn=embedding,
-                async_fn=aembedding,
-                model_config=model_config,
-                rpm=rpm,
-                tpm=tpm,
-            )
 
     if model_config.retry_strategy != "none":
         embedding, aembedding = with_retries(
diff --git a/graphrag/language_model/util.py b/graphrag/language_model/util.py
new file mode 100644
index 0000000000..fd33077afd
--- /dev/null
+++ b/graphrag/language_model/util.py
@@ -0,0 +1,41 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Utility functions for language models."""
+
+from typing import Any
+
+from graphrag.config.models.language_model_config import LanguageModelConfig
+
+
+def is_reasoning_model(model: str) -> bool:
+    """Return whether the model uses a known OpenAI reasoning model."""
+    return model.lower() in {"o1", "o1-mini", "o3-mini"}
+
+
+def get_openai_model_parameters_from_config(
+    config: LanguageModelConfig,
+) -> dict[str, Any]:
+    """Get the model parameters for a given config, adjusting for reasoning API differences."""
+    return get_openai_model_parameters_from_dict(config.model_dump())
+
+
+def get_openai_model_parameters_from_dict(config: dict[str, Any]) -> dict[str, Any]:
+    """Get the model parameters for a given config, adjusting for reasoning API differences."""
+    params = {
+        "n": config.get("n"),
+    }
+    if is_reasoning_model(config["model"]):
+        params["max_completion_tokens"] = config.get("max_completion_tokens")
+        params["reasoning_effort"] = config.get("reasoning_effort")
+    else:
+        params["max_tokens"] = config.get("max_tokens")
+        params["temperature"] = config.get("temperature")
+        params["frequency_penalty"] = config.get("frequency_penalty")
+        params["presence_penalty"] = config.get("presence_penalty")
+        params["top_p"] = config.get("top_p")
+
+    if config.get("response_format"):
+        params["response_format"] = config["response_format"]
+
+    return params
diff --git a/graphrag/query/factory.py b/graphrag/query/factory.py
index 10dfd3ba1f..8ee35e4bb9 100644
--- a/graphrag/query/factory.py
+++ b/graphrag/query/factory.py
@@ -12,7 +12,7 @@
 from graphrag.data_model.relationship import Relationship
 from graphrag.data_model.text_unit import TextUnit
 from graphrag.language_model.manager import ModelManager
-from graphrag.language_model.providers.fnllm.utils import (
+from graphrag.language_model.util import (
     get_openai_model_parameters_from_config,
 )
 from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey
diff --git a/graphrag/query/structured_search/drift_search/search.py b/graphrag/query/structured_search/drift_search/search.py
index ab1c85ccdb..64a8e52b43 100644
--- a/graphrag/query/structured_search/drift_search/search.py
+++ b/graphrag/query/structured_search/drift_search/search.py
@@ -12,7 +12,7 @@
 
 from graphrag.callbacks.query_callbacks import QueryCallbacks
 from graphrag.language_model.protocol.base import ChatModel
-from graphrag.language_model.providers.fnllm.utils import (
+from graphrag.language_model.util import (
     get_openai_model_parameters_from_dict,
 )
 from graphrag.query.context_builder.conversation_history import ConversationHistory
diff --git a/pyproject.toml b/pyproject.toml
index de0e7b7822..e03ad76274 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,8 +38,6 @@ dependencies = [
     "lancedb>=0.17.0",
     # Async IO
     "aiofiles>=24.1.0",
-    # LLM
-    "fnllm[azure,openai]>=0.4.1",
     "json-repair>=0.30.3",
     "openai>=1.68.0",
     "nltk==3.9.1",
diff --git a/tests/unit/config/test_config.py b/tests/unit/config/test_config.py
index a741de7c40..0f67f451ec 100644
--- a/tests/unit/config/test_config.py
+++ b/tests/unit/config/test_config.py
@@ -24,7 +24,8 @@
 def test_missing_openai_required_api_key() -> None:
     model_config_missing_api_key = {
         defs.DEFAULT_CHAT_MODEL_ID: {
-            "type": ModelType.OpenAIChat,
+            "type": ModelType.Chat,
+            "model_provider": "openai",
             "model": defs.DEFAULT_CHAT_MODEL,
         },
         defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
@@ -36,7 +37,7 @@ def test_missing_openai_required_api_key() -> None:
 
     # API Key required for OpenAIEmbedding
     model_config_missing_api_key[defs.DEFAULT_CHAT_MODEL_ID]["type"] = (
-        ModelType.OpenAIEmbedding
+        ModelType.Embedding
     )
     with pytest.raises(ValidationError):
         create_graphrag_config({"models": model_config_missing_api_key})
@@ -45,7 +46,8 @@ def test_missing_openai_required_api_key() -> None:
 def test_missing_azure_api_key() -> None:
     model_config_missing_api_key = {
         defs.DEFAULT_CHAT_MODEL_ID: {
-            "type": ModelType.AzureOpenAIChat,
+            "type": ModelType.Chat,
+            "model_provider": "azure",
             "auth_type": AuthType.APIKey,
             "model": defs.DEFAULT_CHAT_MODEL,
             "api_base": "some_api_base",
@@ -69,7 +71,8 @@ def test_conflicting_auth_type() -> None:
     model_config_invalid_auth_type = {
         defs.DEFAULT_CHAT_MODEL_ID: {
             "auth_type": AuthType.AzureManagedIdentity,
-            "type": ModelType.OpenAIChat,
+            "type": ModelType.Chat,
+            "model_provider": "openai",
             "model": defs.DEFAULT_CHAT_MODEL,
         },
         defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
@@ -82,7 +85,8 @@ def test_conflicting_auth_type() -> None:
 def test_conflicting_azure_api_key() -> None:
     model_config_conflicting_api_key = {
         defs.DEFAULT_CHAT_MODEL_ID: {
-            "type": ModelType.AzureOpenAIChat,
+            "type": ModelType.Chat,
+            "model_provider": "azure",
             "auth_type": AuthType.AzureManagedIdentity,
             "model": defs.DEFAULT_CHAT_MODEL,
             "api_base": "some_api_base",
@@ -98,7 +102,8 @@ def test_conflicting_azure_api_key() -> None:
 
 
 base_azure_model_config = {
-    "type": ModelType.AzureOpenAIChat,
+    "type": ModelType.Chat,
+    "model_provider": "azure",
     "auth_type": AuthType.AzureManagedIdentity,
     "model": defs.DEFAULT_CHAT_MODEL,
     "api_base": "some_api_base",
diff --git a/tests/unit/indexing/operations/chunk_text/test_strategies.py b/tests/unit/indexing/operations/chunk_text/test_strategies.py
index ecc95da0c0..3eae0d74d9 100644
--- a/tests/unit/indexing/operations/chunk_text/test_strategies.py
+++ b/tests/unit/indexing/operations/chunk_text/test_strategies.py
@@ -6,11 +6,11 @@
 from graphrag.config.models.chunking_config import ChunkingConfig
 from graphrag.index.operations.chunk_text.bootstrap import bootstrap
 from graphrag.index.operations.chunk_text.strategies import (
-    get_encoding_fn,
     run_sentences,
     run_tokens,
 )
 from graphrag.index.operations.chunk_text.typing import TextChunk
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
 
 
 class TestRunSentences:
@@ -101,10 +101,10 @@ def test_get_encoding_fn_encode(mock_get_encoding):
     mock_get_encoding.return_value = mock_encoding
 
     # Call the function to get encode and decode functions
-    encode, _ = get_encoding_fn("mock_encoding")
+    tokenizer = get_tokenizer(encoding_model="mock_encoding")
 
     # Test the encode function
-    encoded_text = encode("test text")
+    encoded_text = tokenizer.encode("test text")
     assert encoded_text == [1, 2, 3]
     mock_encoding.encode.assert_called_once_with("test text")
 
@@ -120,8 +120,8 @@ def test_get_encoding_fn_decode(mock_get_encoding):
     mock_get_encoding.return_value = mock_encoding
 
     # Call the function to get encode and decode functions
-    _, decode = get_encoding_fn("mock_encoding")
+    tokenizer = get_tokenizer(encoding_model="mock_encoding")
 
-    decoded_text = decode([1, 2, 3])
+    decoded_text = tokenizer.decode([1, 2, 3])
     assert decoded_text == "decoded text"
     mock_encoding.decode.assert_called_once_with([1, 2, 3])
diff --git a/uv.lock b/uv.lock
index 7b9a19c781..05453382b1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -94,15 +94,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2b/d8/fa65d2a349fe938b76d309db1a56a75c4fb8cc7b17a398b698488a939903/aiohttp-3.12.15-cp312-cp312-win_amd64.whl", hash = "sha256:b390ef5f62bb508a9d67cb3bba9b8356e23b3996da7062f1a57ce1a79d2b3d34", size = 450266 },
 ]
 
-[[package]]
-name = "aiolimiter"
-version = "1.2.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f1/23/b52debf471f7a1e42e362d959a3982bdcb4fe13a5d46e63d28868807a79c/aiolimiter-1.2.1.tar.gz", hash = "sha256:e02a37ea1a855d9e832252a105420ad4d15011505512a1a1d814647451b5cca9", size = 7185 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f3/ba/df6e8e1045aebc4778d19b8a3a9bc1808adb1619ba94ca354d9ba17d86c3/aiolimiter-1.2.1-py3-none-any.whl", hash = "sha256:d3f249e9059a20badcb56b61601a83556133655c11d1eb3dd3e04ff069e5f3c7", size = 6711 },
-]
-
 [[package]]
 name = "aiosignal"
 version = "1.4.0"
@@ -944,32 +935,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/42/14/42b2651a2f46b022ccd948bca9f2d5af0fd8929c4eec235b8d6d844fbe67/filelock-3.19.1-py3-none-any.whl", hash = "sha256:d38e30481def20772f5baf097c122c3babc4fcdb7e14e57049eb9d88c6dc017d", size = 15988 },
 ]
 
-[[package]]
-name = "fnllm"
-version = "0.4.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "aiolimiter" },
-    { name = "httpx" },
-    { name = "json-repair" },
-    { name = "pydantic" },
-    { name = "tenacity" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/33/84/bc3d02134a46dd267afbed66a47dc281b252bd8171c94ad22bcc8f924f8b/fnllm-0.4.1.tar.gz", hash = "sha256:80a7450693691bf0832e12a2d70420647bfea35a43cb91c4a9cb5e2f39172b50", size = 93566 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/ac/6a/04db92a7e8d9cf9b73d3c29c38e16d5728069ec1be06a4723f74579499fa/fnllm-0.4.1-py3-none-any.whl", hash = "sha256:22f1b3316a90f29fde94bfe651e0e4963ff68cddb438035ef7c2161e39789ccf", size = 79273 },
-]
-
-[package.optional-dependencies]
-azure = [
-    { name = "azure-identity" },
-    { name = "azure-storage-blob" },
-]
-openai = [
-    { name = "openai" },
-    { name = "tiktoken" },
-]
-
 [[package]]
 name = "fqdn"
 version = "1.5.1"
@@ -1081,7 +1046,6 @@ dependencies = [
     { name = "azure-storage-blob" },
     { name = "devtools" },
     { name = "environs" },
-    { name = "fnllm", extra = ["azure", "openai"] },
     { name = "future" },
     { name = "graspologic-native" },
     { name = "json-repair" },
@@ -1139,7 +1103,6 @@ requires-dist = [
     { name = "azure-storage-blob", specifier = ">=12.24.0" },
     { name = "devtools", specifier = ">=0.12.2" },
     { name = "environs", specifier = ">=11.0.0" },
-    { name = "fnllm", extras = ["azure", "openai"], specifier = ">=0.4.1" },
     { name = "future", specifier = ">=1.0.0" },
     { name = "graspologic-native", specifier = ">=1.2.5" },
     { name = "json-repair", specifier = ">=0.30.3" },
@@ -3864,15 +3827,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521 },
 ]
 
-[[package]]
-name = "tenacity"
-version = "9.1.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0a/d4/2b0cd0fe285e14b36db076e78c93766ff1d529d70408bd1d2a5a84f1d929/tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb", size = 48036 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248 },
-]
-
 [[package]]
 name = "terminado"
 version = "0.18.1"

From 715be614047472f066257aa15bdd091aa2de5383 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Fri, 10 Oct 2025 17:10:42 -0700
Subject: [PATCH 12/40] Sort deps alpha

---
 pyproject.toml |  55 ++++++-------
 uv.lock        | 216 -------------------------------------------------
 2 files changed, 24 insertions(+), 247 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c08a6c937d..96f01b5d9d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,62 +32,55 @@ classifiers = [
 ]
 
 dependencies = [
-    "environs>=11.0.0",
-    # Vector Stores
-    "azure-search-documents>=11.5.2",
-    "lancedb>=0.17.0",
-    # Async IO
     "aiofiles>=24.1.0",
+    "azure-cosmos>=4.9.0",
+    "azure-identity>=1.19.0",
+    "azure-search-documents>=11.5.2",
+    "azure-storage-blob>=12.24.0",
+    "devtools>=0.12.2",
+    "environs>=11.0.0",
+    "graspologic-native>=1.2.5",
     "json-repair>=0.30.3",
-    "openai>=1.68.0",
+    "lancedb>=0.17.0",
+    "litellm>=1.77.1",
+    "networkx>=3.4.2",
     "nltk==3.9.1",
-    "tiktoken>=0.11.0",
-    # Data-Science
     "numpy>=1.25.2",
-    "networkx>=3.4.2",
+    "openai>=1.68.0",
     "pandas>=2.2.3",
     "pyarrow>=17.0.0",
-    "umap-learn>=0.5.6",
-    # Configuration
-    "pyyaml>=6.0.2",
-    "python-dotenv>=1.0.1",
     "pydantic>=2.10.3",
-    "devtools>=0.12.2",
+    "python-dotenv>=1.0.1",
+    "pyyaml>=6.0.2",
+    "spacy>=3.8.4",
+    "textblob>=0.18.0.post0",
+    "tiktoken>=0.11.0",
+    "tqdm>=4.67.1",
     "typing-extensions>=4.12.2",
-    # Azure
-    "azure-cosmos>=4.9.0",
-    "azure-identity>=1.19.0",
-    "azure-storage-blob>=12.24.0",
-    "future>=1.0.0", # Needed until graspologic fixes their dependency
     "typer>=0.16.0",
-    "tqdm>=4.67.1",
-    "textblob>=0.18.0.post0",
-    "spacy>=3.8.4",
-    "graspologic-native>=1.2.5",
-    "litellm>=1.77.1",
 ]
 
 [dependency-groups]
 dev = [
     "coverage>=7.6.9",
+    "deptry>=0.21.1",
     "ipykernel>=6.29.5",
     "jupyter>=1.1.1",
+    "mkdocs-material>=9.5.48",
+    "mkdocs-jupyter>=0.25.1",
+    "mkdocs-exclude-search>=0.6.6",
+    "mkdocs-typer>=0.0.3",
     "nbconvert>=7.16.4",
-    "poethepoet>=0.31.1",
     "pandas-stubs>=2.3.0.250703",
+    "poethepoet>=0.31.1",
     "pyright>=1.1.390",
     "pytest>=8.3.4",
     "pytest-asyncio>=0.24.0",
+    "pytest-dotenv>=0.5.2",
     "pytest-timeout>=2.3.1",
     "ruff>=0.8.2",
     "semversioner>=2.0.5",
     "update-toml>=0.2.1",
-    "deptry>=0.21.1",
-    "mkdocs-material>=9.5.48",
-    "mkdocs-jupyter>=0.25.1",
-    "mkdocs-exclude-search>=0.6.6",
-    "pytest-dotenv>=0.5.2",
-    "mkdocs-typer>=0.0.3",
 ]
 
 [project.scripts]
diff --git a/uv.lock b/uv.lock
index 8f8d140d0b..0f3dbb7a7f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1013,15 +1013,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/47/71/70db47e4f6ce3e5c37a607355f80da8860a33226be640226ac52cb05ef2e/fsspec-2025.9.0-py3-none-any.whl", hash = "sha256:530dc2a2af60a414a832059574df4a6e10cce927f6f4a78209390fe38955cfb7", size = 199289 },
 ]
 
-[[package]]
-name = "future"
-version = "1.0.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a7/b2/4140c69c6a66432916b26158687e821ba631a4c9273c474343badf84d3ba/future-1.0.0.tar.gz", hash = "sha256:bd2968309307861edae1458a4f8a4f3598c03be43b97521076aebf5d94c07b05", size = 1228490 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/da/71/ae30dadffc90b9006d77af76b393cb9dfbfc9629f339fc1574a1c52e6806/future-1.0.0-py3-none-any.whl", hash = "sha256:929292d34f5872e70396626ef385ec22355a1fae8ad29e1a734c3e43f9fbc216", size = 491326 },
-]
-
 [[package]]
 name = "ghp-import"
 version = "2.1.0"
@@ -1046,7 +1037,6 @@ dependencies = [
     { name = "azure-storage-blob" },
     { name = "devtools" },
     { name = "environs" },
-    { name = "future" },
     { name = "graspologic-native" },
     { name = "json-repair" },
     { name = "lancedb" },
@@ -1068,7 +1058,6 @@ dependencies = [
     { name = "tqdm" },
     { name = "typer" },
     { name = "typing-extensions" },
-    { name = "umap-learn" },
 ]
 
 [package.dev-dependencies]
@@ -1103,7 +1092,6 @@ requires-dist = [
     { name = "azure-storage-blob", specifier = ">=12.24.0" },
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 name = "semversioner"
 version = "2.0.8"
@@ -3897,15 +3709,6 @@ wheels = [
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-name = "threadpoolctl"
-version = "3.6.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274 }
-wheels = [
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 name = "tiktoken"
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@@ -4114,25 +3917,6 @@ wheels = [
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-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "pynndescent" },
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-    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
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From b7324455350ec299c96896f4fdc2e2585f5f5835 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Fri, 10 Oct 2025 17:20:53 -0700
Subject: [PATCH 13/40] Remove multi search (#2093)

* Remove multi-search from CLI

* Remove multi-search from API

* Flatten vector_store config

* Push hydrated vector store down to embed_text

* Remove outputs from config

* Remove multi-search notebook/docs

* Add missing response_type in basic search API

* Fix basic search context and id mapping

* Fix v1 migration notebook

* Fix query entity search tests
---
 .../index_migration_to_v1.ipynb               |  21 +-
 .../multi_index_search.ipynb                  | 558 --------------
 docs/query/multi_index_search.md              |  20 -
 graphrag/api/__init__.py                      |   8 -
 graphrag/api/query.py                         | 696 +-----------------
 graphrag/cli/main.py                          |   1 +
 graphrag/cli/query.py                         | 239 ++----
 graphrag/config/defaults.py                   |   8 +-
 graphrag/config/get_vector_store_settings.py  |  26 -
 graphrag/config/init_content.py               |   8 +-
 graphrag/config/models/embed_text_config.py   |   4 -
 graphrag/config/models/graph_rag_config.py    |  63 +-
 .../index/operations/embed_text/embed_text.py | 122 +--
 .../workflows/generate_text_embeddings.py     |  67 +-
 .../index/workflows/update_text_embeddings.py |   4 +-
 .../query/basic_search_system_prompt.py       |   4 +-
 graphrag/query/factory.py                     |   2 +-
 .../basic_search/basic_context.py             |  15 +-
 graphrag/utils/api.py                         | 215 +-----
 tests/unit/config/utils.py                    |  34 +-
 .../context_builder/test_entity_extraction.py |  50 --
 21 files changed, 195 insertions(+), 1970 deletions(-)
 delete mode 100644 docs/examples_notebooks/multi_index_search.ipynb
 delete mode 100644 docs/query/multi_index_search.md
 delete mode 100644 graphrag/config/get_vector_store_settings.py

diff --git a/docs/examples_notebooks/index_migration_to_v1.ipynb b/docs/examples_notebooks/index_migration_to_v1.ipynb
index 581f5cef64..4a89d95305 100644
--- a/docs/examples_notebooks/index_migration_to_v1.ipynb
+++ b/docs/examples_notebooks/index_migration_to_v1.ipynb
@@ -204,14 +204,13 @@
    "source": [
     "from graphrag.cache.factory import CacheFactory\n",
     "from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks\n",
-    "from graphrag.config.get_vector_store_settings import get_vector_store_settings\n",
     "from graphrag.index.workflows.generate_text_embeddings import generate_text_embeddings\n",
+    "from graphrag.language_model.manager import ModelManager\n",
+    "from graphrag.tokenizer.get_tokenizer import get_tokenizer\n",
     "\n",
     "# We only need to re-run the embeddings workflow, to ensure that embeddings for all required search fields are in place\n",
     "# We'll construct the context and run this function flow directly to avoid everything else\n",
     "\n",
-    "\n",
-    "vector_store_config = get_vector_store_settings(config)\n",
     "model_config = config.get_language_model_config(config.embed_text.model_id)\n",
     "callbacks = NoopWorkflowCallbacks()\n",
     "cache_config = config.cache.model_dump()  # type: ignore\n",
@@ -219,6 +218,15 @@
     "    cache_type=cache_config[\"type\"],  # type: ignore\n",
     "    **cache_config,\n",
     ")\n",
+    "model = ModelManager().get_or_create_embedding_model(\n",
+    "    name=\"text_embedding\",\n",
+    "    model_type=model_config.type,\n",
+    "    config=model_config,\n",
+    "    callbacks=callbacks,\n",
+    "    cache=cache,\n",
+    ")\n",
+    "\n",
+    "tokenizer = get_tokenizer(model_config)\n",
     "\n",
     "await generate_text_embeddings(\n",
     "    documents=None,\n",
@@ -227,11 +235,12 @@
     "    entities=final_entities,\n",
     "    community_reports=final_community_reports,\n",
     "    callbacks=callbacks,\n",
-    "    cache=cache,\n",
-    "    model_config=model_config,\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
     "    batch_size=config.embed_text.batch_size,\n",
     "    batch_max_tokens=config.embed_text.batch_max_tokens,\n",
-    "    vector_store_config=vector_store_config,\n",
+    "    num_threads=model_config.concurrent_requests,\n",
+    "    vector_store_config=config.vector_store,\n",
     "    embedded_fields=config.embed_text.names,\n",
     ")"
    ]
diff --git a/docs/examples_notebooks/multi_index_search.ipynb b/docs/examples_notebooks/multi_index_search.ipynb
deleted file mode 100644
index 2e70ed5086..0000000000
--- a/docs/examples_notebooks/multi_index_search.ipynb
+++ /dev/null
@@ -1,558 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Copyright (c) 2024 Microsoft Corporation.\n",
-    "# Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multi Index Search\n",
-    "This notebook demonstrates multi-index search using the GraphRAG API.\n",
-    "\n",
-    "Indexes created from Wikipedia state articles for Alaska, California, DC, Maryland, NY and Washington are used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "from graphrag.api.query import (\n",
-    "    multi_index_basic_search,\n",
-    "    multi_index_drift_search,\n",
-    "    multi_index_global_search,\n",
-    "    multi_index_local_search,\n",
-    ")\n",
-    "from graphrag.config.create_graphrag_config import create_graphrag_config\n",
-    "\n",
-    "indexes = [\"alaska\", \"california\", \"dc\", \"maryland\", \"ny\", \"washington\"]\n",
-    "indexes = sorted(indexes)\n",
-    "\n",
-    "print(indexes)\n",
-    "\n",
-    "vector_store_configs = {\n",
-    "    index: {\n",
-    "        \"type\": \"lancedb\",\n",
-    "        \"db_uri\": f\"inputs/{index}/lancedb\",\n",
-    "        \"container_name\": \"default\",\n",
-    "        \"overwrite\": True,\n",
-    "        \"index_name\": f\"{index}\",\n",
-    "    }\n",
-    "    for index in indexes\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config_data = {\n",
-    "    \"models\": {\n",
-    "        \"default_chat_model\": {\n",
-    "            \"model_supports_json\": True,\n",
-    "            \"parallelization_num_threads\": 50,\n",
-    "            \"parallelization_stagger\": 0.3,\n",
-    "            \"async_mode\": \"threaded\",\n",
-    "            \"type\": \"azure_openai_chat\",\n",
-    "            \"model\": \"gpt-4o\",\n",
-    "            \"auth_type\": \"azure_managed_identity\",\n",
-    "            \"api_base\": \"<API_BASE_URL>\",\n",
-    "            \"api_version\": \"2024-02-15-preview\",\n",
-    "            \"deployment_name\": \"gpt-4o\",\n",
-    "        },\n",
-    "        \"default_embedding_model\": {\n",
-    "            \"parallelization_num_threads\": 50,\n",
-    "            \"parallelization_stagger\": 0.3,\n",
-    "            \"async_mode\": \"threaded\",\n",
-    "            \"type\": \"azure_openai_embedding\",\n",
-    "            \"model\": \"text-embedding-3-large\",\n",
-    "            \"auth_type\": \"azure_managed_identity\",\n",
-    "            \"api_base\": \"<API_BASE_URL>\",\n",
-    "            \"api_version\": \"2024-02-15-preview\",\n",
-    "            \"deployment_name\": \"text-embedding-3-large\",\n",
-    "        },\n",
-    "    },\n",
-    "    \"vector_store\": vector_store_configs,\n",
-    "    \"local_search\": {\n",
-    "        \"prompt\": \"prompts/local_search_system_prompt.txt\",\n",
-    "        \"llm_max_tokens\": 12000,\n",
-    "    },\n",
-    "    \"global_search\": {\n",
-    "        \"map_prompt\": \"prompts/global_search_map_system_prompt.txt\",\n",
-    "        \"reduce_prompt\": \"prompts/global_search_reduce_system_prompt.txt\",\n",
-    "        \"knowledge_prompt\": \"prompts/global_search_knowledge_system_prompt.txt\",\n",
-    "    },\n",
-    "    \"drift_search\": {\n",
-    "        \"prompt\": \"prompts/drift_search_system_prompt.txt\",\n",
-    "        \"reduce_prompt\": \"prompts/drift_search_reduce_prompt.txt\",\n",
-    "    },\n",
-    "    \"basic_search\": {\"prompt\": \"prompts/basic_search_system_prompt.txt\"},\n",
-    "}\n",
-    "parameters = create_graphrag_config(config_data, \".\")\n",
-    "loop = asyncio.get_event_loop()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-index Global Search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entities = [pd.read_parquet(f\"inputs/{index}/entities.parquet\") for index in indexes]\n",
-    "communities = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/communities.parquet\") for index in indexes\n",
-    "]\n",
-    "community_reports = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/community_reports.parquet\") for index in indexes\n",
-    "]\n",
-    "\n",
-    "task = loop.create_task(\n",
-    "    multi_index_global_search(\n",
-    "        parameters,\n",
-    "        entities,\n",
-    "        communities,\n",
-    "        community_reports,\n",
-    "        indexes,\n",
-    "        1,\n",
-    "        False,\n",
-    "        \"Multiple Paragraphs\",\n",
-    "        False,\n",
-    "        \"Describe this dataset.\",\n",
-    "    )\n",
-    ")\n",
-    "results = await task"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Print report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(results[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Show context links back to original index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for report_id in [120, 129, 40, 16, 204, 143, 85, 122, 83]:\n",
-    "    index_name = [i for i in results[1][\"reports\"] if i[\"id\"] == str(report_id)][0][  # noqa: RUF015\n",
-    "        \"index_name\"\n",
-    "    ]\n",
-    "    index_id = [i for i in results[1][\"reports\"] if i[\"id\"] == str(report_id)][0][  # noqa: RUF015\n",
-    "        \"index_id\"\n",
-    "    ]\n",
-    "    print(report_id, index_name, index_id)\n",
-    "    index_reports = pd.read_parquet(\n",
-    "        f\"inputs/{index_name}/create_final_community_reports.parquet\"\n",
-    "    )\n",
-    "    print([i for i in results[1][\"reports\"] if i[\"id\"] == str(report_id)][0][\"title\"])  # noqa: RUF015\n",
-    "    print(\n",
-    "        index_reports[index_reports[\"community\"] == int(index_id)][\"title\"].to_numpy()[\n",
-    "            0\n",
-    "        ]\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Multi-index Local Search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entities = [pd.read_parquet(f\"inputs/{index}/entities.parquet\") for index in indexes]\n",
-    "communities = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/communities.parquet\") for index in indexes\n",
-    "]\n",
-    "community_reports = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/community_reports.parquet\") for index in indexes\n",
-    "]\n",
-    "covariates = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/covariates.parquet\") for index in indexes\n",
-    "]\n",
-    "text_units = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/text_units.parquet\") for index in indexes\n",
-    "]\n",
-    "relationships = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/relationships.parquet\") for index in indexes\n",
-    "]\n",
-    "\n",
-    "task = loop.create_task(\n",
-    "    multi_index_local_search(\n",
-    "        parameters,\n",
-    "        entities,\n",
-    "        communities,\n",
-    "        community_reports,\n",
-    "        text_units,\n",
-    "        relationships,\n",
-    "        covariates,\n",
-    "        indexes,\n",
-    "        1,\n",
-    "        \"Multiple Paragraphs\",\n",
-    "        False,\n",
-    "        \"weather\",\n",
-    "    )\n",
-    ")\n",
-    "results = await task"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Print report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(results[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Show context links back to original index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for report_id in [47, 213]:\n",
-    "    index_name = [i for i in results[1][\"reports\"] if i[\"id\"] == str(report_id)][0][  # noqa: RUF015\n",
-    "        \"index_name\"\n",
-    "    ]\n",
-    "    index_id = [i for i in results[1][\"reports\"] if i[\"id\"] == str(report_id)][0][  # noqa: RUF015\n",
-    "        \"index_id\"\n",
-    "    ]\n",
-    "    print(report_id, index_name, index_id)\n",
-    "    index_reports = pd.read_parquet(\n",
-    "        f\"inputs/{index_name}/create_final_community_reports.parquet\"\n",
-    "    )\n",
-    "    print([i for i in results[1][\"reports\"] if i[\"id\"] == str(report_id)][0][\"title\"])  # noqa: RUF015\n",
-    "    print(\n",
-    "        index_reports[index_reports[\"community\"] == int(index_id)][\"title\"].to_numpy()[\n",
-    "            0\n",
-    "        ]\n",
-    "    )\n",
-    "for entity_id in [500, 502, 506, 1960, 1961, 1962]:\n",
-    "    index_name = [i for i in results[1][\"entities\"] if i[\"id\"] == str(entity_id)][0][  # noqa: RUF015\n",
-    "        \"index_name\"\n",
-    "    ]\n",
-    "    index_id = [i for i in results[1][\"entities\"] if i[\"id\"] == str(entity_id)][0][  # noqa: RUF015\n",
-    "        \"index_id\"\n",
-    "    ]\n",
-    "    print(entity_id, index_name, index_id)\n",
-    "    index_entities = pd.read_parquet(\n",
-    "        f\"inputs/{index_name}/create_final_entities.parquet\"\n",
-    "    )\n",
-    "    print(\n",
-    "        [i for i in results[1][\"entities\"] if i[\"id\"] == str(entity_id)][0][  # noqa: RUF015\n",
-    "            \"description\"\n",
-    "        ][:100]\n",
-    "    )\n",
-    "    print(\n",
-    "        index_entities[index_entities[\"human_readable_id\"] == int(index_id)][\n",
-    "            \"description\"\n",
-    "        ].to_numpy()[0][:100]\n",
-    "    )\n",
-    "for relationship_id in [1805, 1806]:\n",
-    "    index_name = [  # noqa: RUF015\n",
-    "        i for i in results[1][\"relationships\"] if i[\"id\"] == str(relationship_id)\n",
-    "    ][0][\"index_name\"]\n",
-    "    index_id = [  # noqa: RUF015\n",
-    "        i for i in results[1][\"relationships\"] if i[\"id\"] == str(relationship_id)\n",
-    "    ][0][\"index_id\"]\n",
-    "    print(relationship_id, index_name, index_id)\n",
-    "    index_relationships = pd.read_parquet(\n",
-    "        f\"inputs/{index_name}/create_final_relationships.parquet\"\n",
-    "    )\n",
-    "    print(\n",
-    "        [i for i in results[1][\"relationships\"] if i[\"id\"] == str(relationship_id)][0][  # noqa: RUF015\n",
-    "            \"description\"\n",
-    "        ]\n",
-    "    )\n",
-    "    print(\n",
-    "        index_relationships[index_relationships[\"human_readable_id\"] == int(index_id)][\n",
-    "            \"description\"\n",
-    "        ].to_numpy()[0]\n",
-    "    )\n",
-    "for claim_id in [100]:\n",
-    "    index_name = [i for i in results[1][\"claims\"] if i[\"id\"] == str(claim_id)][0][  # noqa: RUF015\n",
-    "        \"index_name\"\n",
-    "    ]\n",
-    "    index_id = [i for i in results[1][\"claims\"] if i[\"id\"] == str(claim_id)][0][  # noqa: RUF015\n",
-    "        \"index_id\"\n",
-    "    ]\n",
-    "    print(relationship_id, index_name, index_id)\n",
-    "    index_claims = pd.read_parquet(\n",
-    "        f\"inputs/{index_name}/create_final_covariates.parquet\"\n",
-    "    )\n",
-    "    print(\n",
-    "        [i for i in results[1][\"claims\"] if i[\"id\"] == str(claim_id)][0][\"description\"]  # noqa: RUF015\n",
-    "    )\n",
-    "    print(\n",
-    "        index_claims[index_claims[\"human_readable_id\"] == int(index_id)][\n",
-    "            \"description\"\n",
-    "        ].to_numpy()[0]\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-index Drift Search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entities = [pd.read_parquet(f\"inputs/{index}/entities.parquet\") for index in indexes]\n",
-    "communities = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/communities.parquet\") for index in indexes\n",
-    "]\n",
-    "community_reports = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/community_reports.parquet\") for index in indexes\n",
-    "]\n",
-    "text_units = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/text_units.parquet\") for index in indexes\n",
-    "]\n",
-    "relationships = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/relationships.parquet\") for index in indexes\n",
-    "]\n",
-    "\n",
-    "task = loop.create_task(\n",
-    "    multi_index_drift_search(\n",
-    "        parameters,\n",
-    "        entities,\n",
-    "        communities,\n",
-    "        community_reports,\n",
-    "        text_units,\n",
-    "        relationships,\n",
-    "        indexes,\n",
-    "        1,\n",
-    "        \"Multiple Paragraphs\",\n",
-    "        False,\n",
-    "        \"agriculture\",\n",
-    "    )\n",
-    ")\n",
-    "results = await task"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Print report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(results[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Show context links back to original index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for report_id in [47, 236]:\n",
-    "    for question in results[1]:\n",
-    "        resq = results[1][question]\n",
-    "        if len(resq[\"reports\"]) == 0:\n",
-    "            continue\n",
-    "        if len([i for i in resq[\"reports\"] if i[\"id\"] == str(report_id)]) == 0:\n",
-    "            continue\n",
-    "        index_name = [i for i in resq[\"reports\"] if i[\"id\"] == str(report_id)][0][  # noqa: RUF015\n",
-    "            \"index_name\"\n",
-    "        ]\n",
-    "        index_id = [i for i in resq[\"reports\"] if i[\"id\"] == str(report_id)][0][  # noqa: RUF015\n",
-    "            \"index_id\"\n",
-    "        ]\n",
-    "        print(question, report_id, index_name, index_id)\n",
-    "        index_reports = pd.read_parquet(\n",
-    "            f\"inputs/{index_name}/create_final_community_reports.parquet\"\n",
-    "        )\n",
-    "        print([i for i in resq[\"reports\"] if i[\"id\"] == str(report_id)][0][\"title\"])  # noqa: RUF015\n",
-    "        print(\n",
-    "            index_reports[index_reports[\"community\"] == int(index_id)][\n",
-    "                \"title\"\n",
-    "            ].to_numpy()[0]\n",
-    "        )\n",
-    "        break\n",
-    "for source_id in [10, 16, 19, 20, 21, 22, 24, 29, 93, 95]:\n",
-    "    for question in results[1]:\n",
-    "        resq = results[1][question]\n",
-    "        if len(resq[\"sources\"]) == 0:\n",
-    "            continue\n",
-    "        if len([i for i in resq[\"sources\"] if i[\"id\"] == str(source_id)]) == 0:\n",
-    "            continue\n",
-    "        index_name = [i for i in resq[\"sources\"] if i[\"id\"] == str(source_id)][0][  # noqa: RUF015\n",
-    "            \"index_name\"\n",
-    "        ]\n",
-    "        index_id = [i for i in resq[\"sources\"] if i[\"id\"] == str(source_id)][0][  # noqa: RUF015\n",
-    "            \"index_id\"\n",
-    "        ]\n",
-    "        print(question, source_id, index_name, index_id)\n",
-    "        index_sources = pd.read_parquet(\n",
-    "            f\"inputs/{index_name}/create_final_text_units.parquet\"\n",
-    "        )\n",
-    "        print(\n",
-    "            [i for i in resq[\"sources\"] if i[\"id\"] == str(source_id)][0][\"text\"][:250]  # noqa: RUF015\n",
-    "        )\n",
-    "        print(index_sources.loc[int(index_id)][\"text\"][:250])\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-index Basic Search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text_units = [\n",
-    "    pd.read_parquet(f\"inputs/{index}/text_units.parquet\") for index in indexes\n",
-    "]\n",
-    "\n",
-    "task = loop.create_task(\n",
-    "    multi_index_basic_search(\n",
-    "        parameters, text_units, indexes, False, \"industry in maryland\"\n",
-    "    )\n",
-    ")\n",
-    "results = await task"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Print report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(results[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Show context links back to original text\n",
-    "\n",
-    "Note that original index name is not saved in context data for basic search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for source_id in [0, 1]:\n",
-    "    print(results[1][\"sources\"][source_id][\"text\"][:250])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/query/multi_index_search.md b/docs/query/multi_index_search.md
deleted file mode 100644
index 6b6ff2b41a..0000000000
--- a/docs/query/multi_index_search.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# Multi Index Search 🔎
-
-## Multi Dataset Reasoning
-
-GraphRAG takes in unstructured data contained in text documents and uses large languages models to “read� the documents in a targeted fashion and create a knowledge graph. This knowledge graph, or index, contains information about specific entities in the data, how the entities relate to one another, and high-level reports about communities and topics found in the data. Indexes can be searched by users to get meaningful information about the underlying data, including reports with citations that point back to the original unstructured text. 
-
-Multi-index search is a new capability that has been added to the GraphRAG python library to query multiple knowledge stores at once. Multi-index search allows for many new search scenarios, including: 
-
-- Combining knowledge from different domains – Many documents contain similar types of entities: person, place, thing. But GraphRAG can be tuned for highly specialized domains, such as science and engineering. With the recent updates to search, GraphRAG can now simultaneously query multiple datasets with completely different schemas and entity definitions.
-
-- Combining knowledge with different access levels – Not all datasets are accessible to all people, even within an organization. Some datasets are publicly available. Some datasets, such as internal financial information or intellectual property, may only be accessible by a small number of employees at a company. Multi-index search allows multiple sources with different access controls to be queried at the same time, creating more nuanced and informative reports. Internal R&D findings can be seamlessly combined with open-source scientific publications. 
-
-- Combining knowledge in different locations – With multi-index search, indexes do not need to be in the same location or type of storage to be queried. Indexes in the cloud in Azure Storage can be queried at the same time as indexes stored on a personal computer. Multi-index search makes these types of data joins easy and accessible. 
-
-To search across multiple datasets, the underlying contexts from each index, based on the user query, are combined in-memory at query time, saving on computation and allowing the joint querying of indexes that can’t be joined inherently, either do access controls or differing schemas. Multi-index search automatically keeps track of provenance information, so that any references can be traced back to the correct indexes and correct original documents. 
-
-
-## How to Use
-
-An example of a global search scenario can be found in the following [notebook](../examples_notebooks/multi_index_search.ipynb).
\ No newline at end of file
diff --git a/graphrag/api/__init__.py b/graphrag/api/__init__.py
index a3a06033bc..05692c4182 100644
--- a/graphrag/api/__init__.py
+++ b/graphrag/api/__init__.py
@@ -18,10 +18,6 @@
     global_search_streaming,
     local_search,
     local_search_streaming,
-    multi_index_basic_search,
-    multi_index_drift_search,
-    multi_index_global_search,
-    multi_index_local_search,
 )
 from graphrag.prompt_tune.types import DocSelectionType
 
@@ -37,10 +33,6 @@
     "drift_search_streaming",
     "basic_search",
     "basic_search_streaming",
-    "multi_index_basic_search",
-    "multi_index_drift_search",
-    "multi_index_global_search",
-    "multi_index_local_search",
     # prompt tuning API
     "DocSelectionType",
     "generate_indexing_prompts",
diff --git a/graphrag/api/query.py b/graphrag/api/query.py
index b92cd8145b..e49a0976df 100644
--- a/graphrag/api/query.py
+++ b/graphrag/api/query.py
@@ -52,7 +52,6 @@
     get_embedding_store,
     load_search_prompt,
     truncate,
-    update_context_data,
 )
 from graphrag.utils.cli import redact
 
@@ -192,152 +191,6 @@ def global_search_streaming(
     return search_engine.stream_search(query=query)
 
 
-@validate_call(config={"arbitrary_types_allowed": True})
-async def multi_index_global_search(
-    config: GraphRagConfig,
-    entities_list: list[pd.DataFrame],
-    communities_list: list[pd.DataFrame],
-    community_reports_list: list[pd.DataFrame],
-    index_names: list[str],
-    community_level: int | None,
-    dynamic_community_selection: bool,
-    response_type: str,
-    streaming: bool,
-    query: str,
-    callbacks: list[QueryCallbacks] | None = None,
-    verbose: bool = False,
-) -> tuple[
-    str | dict[str, Any] | list[dict[str, Any]],
-    str | list[pd.DataFrame] | dict[str, pd.DataFrame],
-]:
-    """Perform a global search across multiple indexes and return the context data and response.
-
-    Parameters
-    ----------
-    - config (GraphRagConfig): A graphrag configuration (from settings.yaml)
-    - entities_list (list[pd.DataFrame]): A list of DataFrames containing the final entities (from entities.parquet)
-    - communities_list (list[pd.DataFrame]): A list of DataFrames containing the final communities (from communities.parquet)
-    - community_reports_list (list[pd.DataFrame]): A list of DataFrames containing the final community reports (from community_reports.parquet)
-    - index_names (list[str]): A list of index names.
-    - community_level (int): The community level to search at.
-    - dynamic_community_selection (bool): Enable dynamic community selection instead of using all community reports at a fixed level. Note that you can still provide community_level cap the maximum level to search.
-    - response_type (str): The type of response to return.
-    - streaming (bool): Whether to stream the results or not.
-    - query (str): The user query to search for.
-
-    Returns
-    -------
-    TODO: Document the search response type and format.
-    """
-    init_loggers(config=config, verbose=verbose, filename="query.log")
-
-    logger.warning(
-        "Multi-index search is deprecated and will be removed in GraphRAG v3."
-    )
-
-    # Streaming not supported yet
-    if streaming:
-        message = "Streaming not yet implemented for multi_global_search"
-        raise NotImplementedError(message)
-
-    links = {
-        "communities": {},
-        "community_reports": {},
-        "entities": {},
-    }
-    max_vals = {
-        "communities": -1,
-        "community_reports": -1,
-        "entities": -1,
-    }
-
-    communities_dfs = []
-    community_reports_dfs = []
-    entities_dfs = []
-
-    for idx, index_name in enumerate(index_names):
-        # Prepare each index's community reports dataframe for merging
-        community_reports_df = community_reports_list[idx]
-        community_reports_df["community"] = community_reports_df["community"].astype(
-            int
-        )
-        for i in community_reports_df["community"]:
-            links["community_reports"][i + max_vals["community_reports"] + 1] = {
-                "index_name": index_name,
-                "id": str(i),
-            }
-        community_reports_df["community"] += max_vals["community_reports"] + 1
-        community_reports_df["human_readable_id"] += max_vals["community_reports"] + 1
-        max_vals["community_reports"] = int(community_reports_df["community"].max())
-        community_reports_dfs.append(community_reports_df)
-
-        # Prepare each index's communities dataframe for merging
-        communities_df = communities_list[idx]
-        communities_df["community"] = communities_df["community"].astype(int)
-        communities_df["parent"] = communities_df["parent"].astype(int)
-        for i in communities_df["community"]:
-            links["communities"][i + max_vals["communities"] + 1] = {
-                "index_name": index_name,
-                "id": str(i),
-            }
-        communities_df["community"] += max_vals["communities"] + 1
-        communities_df["parent"] = communities_df["parent"].apply(
-            lambda x: x if x == -1 else x + max_vals["communities"] + 1
-        )
-        communities_df["human_readable_id"] += max_vals["communities"] + 1
-        # concat the index name to the entity_ids, since this is used for joining later
-        communities_df["entity_ids"] = communities_df["entity_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["communities"] = int(communities_df["community"].max())
-        communities_dfs.append(communities_df)
-
-        # Prepare each index's entities dataframe for merging
-        entities_df = entities_list[idx]
-        for i in entities_df["human_readable_id"]:
-            links["entities"][i + max_vals["entities"] + 1] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        entities_df["human_readable_id"] += max_vals["entities"] + 1
-        entities_df["title"] = entities_df["title"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        entities_df["text_unit_ids"] = entities_df["text_unit_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["entities"] = int(entities_df["human_readable_id"].max())
-        entities_dfs.append(entities_df)
-
-    # Merge the dataframes
-    community_reports_combined = pd.concat(
-        community_reports_dfs, axis=0, ignore_index=True, sort=False
-    )
-    entities_combined = pd.concat(entities_dfs, axis=0, ignore_index=True, sort=False)
-    communities_combined = pd.concat(
-        communities_dfs, axis=0, ignore_index=True, sort=False
-    )
-
-    logger.debug("Executing multi-index global search query: %s", query)
-    result = await global_search(
-        config,
-        entities=entities_combined,
-        communities=communities_combined,
-        community_reports=community_reports_combined,
-        community_level=community_level,
-        dynamic_community_selection=dynamic_community_selection,
-        response_type=response_type,
-        query=query,
-        callbacks=callbacks,
-    )
-
-    # Update the context data by linking index names and community ids
-    context = update_context_data(result[1], links)
-
-    logger.debug("Query response: %s", truncate(result[0], 400))  # type: ignore
-    return (result[0], context)
-
-
 @validate_call(config={"arbitrary_types_allowed": True})
 async def local_search(
     config: GraphRagConfig,
@@ -441,14 +294,11 @@ def local_search_streaming(
     """
     init_loggers(config=config, verbose=verbose, filename="query.log")
 
-    vector_store_args = {}
-    for index, store in config.vector_store.items():
-        vector_store_args[index] = store.model_dump()
-    msg = f"Vector Store Args: {redact(vector_store_args)}"
+    msg = f"Vector Store Args: {redact(config.vector_store.model_dump())}"
     logger.debug(msg)
 
     description_embedding_store = get_embedding_store(
-        config_args=vector_store_args,
+        store=config.vector_store.model_dump(),
         embedding_name=entity_description_embedding,
     )
 
@@ -472,238 +322,6 @@ def local_search_streaming(
     return search_engine.stream_search(query=query)
 
 
-@validate_call(config={"arbitrary_types_allowed": True})
-async def multi_index_local_search(
-    config: GraphRagConfig,
-    entities_list: list[pd.DataFrame],
-    communities_list: list[pd.DataFrame],
-    community_reports_list: list[pd.DataFrame],
-    text_units_list: list[pd.DataFrame],
-    relationships_list: list[pd.DataFrame],
-    covariates_list: list[pd.DataFrame] | None,
-    index_names: list[str],
-    community_level: int,
-    response_type: str,
-    streaming: bool,
-    query: str,
-    callbacks: list[QueryCallbacks] | None = None,
-    verbose: bool = False,
-) -> tuple[
-    str | dict[str, Any] | list[dict[str, Any]],
-    str | list[pd.DataFrame] | dict[str, pd.DataFrame],
-]:
-    """Perform a local search across multiple indexes and return the context data and response.
-
-    Parameters
-    ----------
-    - config (GraphRagConfig): A graphrag configuration (from settings.yaml)
-    - entities_list (list[pd.DataFrame]): A list of DataFrames containing the final entities (from entities.parquet)
-    - community_reports_list (list[pd.DataFrame]): A list of DataFrames containing the final community reports (from community_reports.parquet)
-    - text_units_list (list[pd.DataFrame]): A list of DataFrames containing the final text units (from text_units.parquet)
-    - relationships_list (list[pd.DataFrame]): A list of DataFrames containing the final relationships (from relationships.parquet)
-    - covariates_list (list[pd.DataFrame]): [Optional] A list of DataFrames containing the final covariates (from covariates.parquet)
-    - index_names (list[str]): A list of index names.
-    - community_level (int): The community level to search at.
-    - response_type (str): The response type to return.
-    - streaming (bool): Whether to stream the results or not.
-    - query (str): The user query to search for.
-
-    Returns
-    -------
-    TODO: Document the search response type and format.
-    """
-    init_loggers(config=config, verbose=verbose, filename="query.log")
-
-    logger.warning(
-        "Multi-index search is deprecated and will be removed in GraphRAG v3."
-    )
-    # Streaming not supported yet
-    if streaming:
-        message = "Streaming not yet implemented for multi_index_local_search"
-        raise NotImplementedError(message)
-
-    links = {
-        "community_reports": {},
-        "communities": {},
-        "entities": {},
-        "text_units": {},
-        "relationships": {},
-        "covariates": {},
-    }
-    max_vals = {
-        "community_reports": -1,
-        "communities": -1,
-        "entities": -1,
-        "text_units": 0,
-        "relationships": -1,
-        "covariates": 0,
-    }
-    community_reports_dfs = []
-    communities_dfs = []
-    entities_dfs = []
-    relationships_dfs = []
-    text_units_dfs = []
-    covariates_dfs = []
-
-    for idx, index_name in enumerate(index_names):
-        # Prepare each index's communities dataframe for merging
-        communities_df = communities_list[idx]
-        communities_df["community"] = communities_df["community"].astype(int)
-        for i in communities_df["community"]:
-            links["communities"][i + max_vals["communities"] + 1] = {
-                "index_name": index_name,
-                "id": str(i),
-            }
-        communities_df["community"] += max_vals["communities"] + 1
-        communities_df["human_readable_id"] += max_vals["communities"] + 1
-        # concat the index name to the entity_ids, since this is used for joining later
-        communities_df["entity_ids"] = communities_df["entity_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["communities"] = int(communities_df["community"].max())
-        communities_dfs.append(communities_df)
-
-        # Prepare each index's community reports dataframe for merging
-        community_reports_df = community_reports_list[idx]
-        community_reports_df["community"] = community_reports_df["community"].astype(
-            int
-        )
-        for i in community_reports_df["community"]:
-            links["community_reports"][i + max_vals["community_reports"] + 1] = {
-                "index_name": index_name,
-                "id": str(i),
-            }
-        community_reports_df["community"] += max_vals["community_reports"] + 1
-        community_reports_df["human_readable_id"] += max_vals["community_reports"] + 1
-        max_vals["community_reports"] = int(community_reports_df["community"].max())
-        community_reports_dfs.append(community_reports_df)
-
-        # Prepare each index's entities dataframe for merging
-        entities_df = entities_list[idx]
-        for i in entities_df["human_readable_id"]:
-            links["entities"][i + max_vals["entities"] + 1] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        entities_df["human_readable_id"] += max_vals["entities"] + 1
-        entities_df["title"] = entities_df["title"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        entities_df["id"] = entities_df["id"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        entities_df["text_unit_ids"] = entities_df["text_unit_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["entities"] = int(entities_df["human_readable_id"].max())
-        entities_dfs.append(entities_df)
-
-        # Prepare each index's relationships dataframe for merging
-        relationships_df = relationships_list[idx]
-        for i in relationships_df["human_readable_id"].astype(int):
-            links["relationships"][i + max_vals["relationships"] + 1] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        if max_vals["relationships"] != -1:
-            col = (
-                relationships_df["human_readable_id"].astype(int)
-                + max_vals["relationships"]
-                + 1
-            )
-            relationships_df["human_readable_id"] = col.astype(str)
-        relationships_df["source"] = relationships_df["source"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        relationships_df["target"] = relationships_df["target"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        relationships_df["text_unit_ids"] = relationships_df["text_unit_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["relationships"] = int(relationships_df["human_readable_id"].max())
-        relationships_dfs.append(relationships_df)
-
-        # Prepare each index's text units dataframe for merging
-        text_units_df = text_units_list[idx]
-        for i in range(text_units_df.shape[0]):
-            links["text_units"][i + max_vals["text_units"]] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        text_units_df["id"] = text_units_df["id"].apply(
-            lambda x, index_name=index_name: f"{x}-{index_name}"
-        )
-        text_units_df["human_readable_id"] = (
-            text_units_df["human_readable_id"] + max_vals["text_units"]
-        )
-        max_vals["text_units"] += text_units_df.shape[0]
-        text_units_dfs.append(text_units_df)
-
-        # If presents, prepare each index's covariates dataframe for merging
-        if covariates_list is not None:
-            covariates_df = covariates_list[idx]
-            for i in covariates_df["human_readable_id"].astype(int):
-                links["covariates"][i + max_vals["covariates"]] = {
-                    "index_name": index_name,
-                    "id": i,
-                }
-            covariates_df["id"] = covariates_df["id"].apply(
-                lambda x, index_name=index_name: f"{x}-{index_name}"
-            )
-            covariates_df["human_readable_id"] = (
-                covariates_df["human_readable_id"] + max_vals["covariates"]
-            )
-            covariates_df["text_unit_id"] = covariates_df["text_unit_id"].apply(
-                lambda x, index_name=index_name: x + f"-{index_name}"
-            )
-            covariates_df["subject_id"] = covariates_df["subject_id"].apply(
-                lambda x, index_name=index_name: x + f"-{index_name}"
-            )
-            max_vals["covariates"] += covariates_df.shape[0]
-            covariates_dfs.append(covariates_df)
-
-    # Merge the dataframes
-    communities_combined = pd.concat(
-        communities_dfs, axis=0, ignore_index=True, sort=False
-    )
-    community_reports_combined = pd.concat(
-        community_reports_dfs, axis=0, ignore_index=True, sort=False
-    )
-    entities_combined = pd.concat(entities_dfs, axis=0, ignore_index=True, sort=False)
-    relationships_combined = pd.concat(
-        relationships_dfs, axis=0, ignore_index=True, sort=False
-    )
-    text_units_combined = pd.concat(
-        text_units_dfs, axis=0, ignore_index=True, sort=False
-    )
-    covariates_combined = None
-    if len(covariates_dfs) > 0:
-        covariates_combined = pd.concat(
-            covariates_dfs, axis=0, ignore_index=True, sort=False
-        )
-    logger.debug("Executing multi-index local search query: %s", query)
-    result = await local_search(
-        config,
-        entities=entities_combined,
-        communities=communities_combined,
-        community_reports=community_reports_combined,
-        text_units=text_units_combined,
-        relationships=relationships_combined,
-        covariates=covariates_combined,
-        community_level=community_level,
-        response_type=response_type,
-        query=query,
-        callbacks=callbacks,
-    )
-
-    # Update the context data by linking index names and community ids
-    context = update_context_data(result[1], links)
-
-    logger.debug("Query response: %s", truncate(result[0], 400))  # type: ignore
-    return (result[0], context)
-
-
 @validate_call(config={"arbitrary_types_allowed": True})
 async def drift_search(
     config: GraphRagConfig,
@@ -801,19 +419,16 @@ def drift_search_streaming(
     """
     init_loggers(config=config, verbose=verbose, filename="query.log")
 
-    vector_store_args = {}
-    for index, store in config.vector_store.items():
-        vector_store_args[index] = store.model_dump()
-    msg = f"Vector Store Args: {redact(vector_store_args)}"
+    msg = f"Vector Store Args: {redact(config.vector_store.model_dump())}"
     logger.debug(msg)
 
     description_embedding_store = get_embedding_store(
-        config_args=vector_store_args,
+        store=config.vector_store.model_dump(),
         embedding_name=entity_description_embedding,
     )
 
     full_content_embedding_store = get_embedding_store(
-        config_args=vector_store_args,
+        store=config.vector_store.model_dump(),
         embedding_name=community_full_content_embedding,
     )
 
@@ -841,222 +456,11 @@ def drift_search_streaming(
     return search_engine.stream_search(query=query)
 
 
-@validate_call(config={"arbitrary_types_allowed": True})
-async def multi_index_drift_search(
-    config: GraphRagConfig,
-    entities_list: list[pd.DataFrame],
-    communities_list: list[pd.DataFrame],
-    community_reports_list: list[pd.DataFrame],
-    text_units_list: list[pd.DataFrame],
-    relationships_list: list[pd.DataFrame],
-    index_names: list[str],
-    community_level: int,
-    response_type: str,
-    streaming: bool,
-    query: str,
-    callbacks: list[QueryCallbacks] | None = None,
-    verbose: bool = False,
-) -> tuple[
-    str | dict[str, Any] | list[dict[str, Any]],
-    str | list[pd.DataFrame] | dict[str, pd.DataFrame],
-]:
-    """Perform a DRIFT search across multiple indexes and return the context data and response.
-
-    Parameters
-    ----------
-    - config (GraphRagConfig): A graphrag configuration (from settings.yaml)
-    - entities_list (list[pd.DataFrame]): A list of DataFrames containing the final entities (from entities.parquet)
-    - community_reports_list (list[pd.DataFrame]): A list of DataFrames containing the final community reports (from community_reports.parquet)
-    - text_units_list (list[pd.DataFrame]): A list of DataFrames containing the final text units (from text_units.parquet)
-    - relationships_list (list[pd.DataFrame]): A list of DataFrames containing the final relationships (from relationships.parquet)
-    - index_names (list[str]): A list of index names.
-    - community_level (int): The community level to search at.
-    - response_type (str): The response type to return.
-    - streaming (bool): Whether to stream the results or not.
-    - query (str): The user query to search for.
-
-    Returns
-    -------
-    TODO: Document the search response type and format.
-    """
-    init_loggers(config=config, verbose=verbose, filename="query.log")
-
-    logger.warning(
-        "Multi-index search is deprecated and will be removed in GraphRAG v3."
-    )
-
-    # Streaming not supported yet
-    if streaming:
-        message = "Streaming not yet implemented for multi_drift_search"
-        raise NotImplementedError(message)
-
-    links = {
-        "community_reports": {},
-        "communities": {},
-        "entities": {},
-        "text_units": {},
-        "relationships": {},
-    }
-    max_vals = {
-        "community_reports": -1,
-        "communities": -1,
-        "entities": -1,
-        "text_units": 0,
-        "relationships": -1,
-    }
-
-    communities_dfs = []
-    community_reports_dfs = []
-    entities_dfs = []
-    relationships_dfs = []
-    text_units_dfs = []
-
-    for idx, index_name in enumerate(index_names):
-        # Prepare each index's communities dataframe for merging
-        communities_df = communities_list[idx]
-        communities_df["community"] = communities_df["community"].astype(int)
-        for i in communities_df["community"]:
-            links["communities"][i + max_vals["communities"] + 1] = {
-                "index_name": index_name,
-                "id": str(i),
-            }
-        communities_df["community"] += max_vals["communities"] + 1
-        communities_df["human_readable_id"] += max_vals["communities"] + 1
-        # concat the index name to the entity_ids, since this is used for joining later
-        communities_df["entity_ids"] = communities_df["entity_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["communities"] = int(communities_df["community"].max())
-        communities_dfs.append(communities_df)
-
-        # Prepare each index's community reports dataframe for merging
-        community_reports_df = community_reports_list[idx]
-        community_reports_df["community"] = community_reports_df["community"].astype(
-            int
-        )
-        for i in community_reports_df["community"]:
-            links["community_reports"][i + max_vals["community_reports"] + 1] = {
-                "index_name": index_name,
-                "id": str(i),
-            }
-        community_reports_df["community"] += max_vals["community_reports"] + 1
-        community_reports_df["human_readable_id"] += max_vals["community_reports"] + 1
-        community_reports_df["id"] = community_reports_df["id"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        max_vals["community_reports"] = int(community_reports_df["community"].max())
-        community_reports_dfs.append(community_reports_df)
-
-        # Prepare each index's entities dataframe for merging
-        entities_df = entities_list[idx]
-        for i in entities_df["human_readable_id"]:
-            links["entities"][i + max_vals["entities"] + 1] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        entities_df["human_readable_id"] += max_vals["entities"] + 1
-        entities_df["title"] = entities_df["title"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        entities_df["id"] = entities_df["id"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        entities_df["text_unit_ids"] = entities_df["text_unit_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["entities"] = int(entities_df["human_readable_id"].max())
-        entities_dfs.append(entities_df)
-
-        # Prepare each index's relationships dataframe for merging
-        relationships_df = relationships_list[idx]
-        for i in relationships_df["human_readable_id"].astype(int):
-            links["relationships"][i + max_vals["relationships"] + 1] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        if max_vals["relationships"] != -1:
-            col = (
-                relationships_df["human_readable_id"].astype(int)
-                + max_vals["relationships"]
-                + 1
-            )
-            relationships_df["human_readable_id"] = col.astype(str)
-        relationships_df["source"] = relationships_df["source"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        relationships_df["target"] = relationships_df["target"].apply(
-            lambda x, index_name=index_name: x + f"-{index_name}"
-        )
-        relationships_df["text_unit_ids"] = relationships_df["text_unit_ids"].apply(
-            lambda x, index_name=index_name: [i + f"-{index_name}" for i in x]
-        )
-        max_vals["relationships"] = int(
-            relationships_df["human_readable_id"].astype(int).max()
-        )
-
-        relationships_dfs.append(relationships_df)
-
-        # Prepare each index's text units dataframe for merging
-        text_units_df = text_units_list[idx]
-        for i in range(text_units_df.shape[0]):
-            links["text_units"][i + max_vals["text_units"]] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        text_units_df["id"] = text_units_df["id"].apply(
-            lambda x, index_name=index_name: f"{x}-{index_name}"
-        )
-        text_units_df["human_readable_id"] = (
-            text_units_df["human_readable_id"] + max_vals["text_units"]
-        )
-        max_vals["text_units"] += text_units_df.shape[0]
-        text_units_dfs.append(text_units_df)
-
-    # Merge the dataframes
-    communities_combined = pd.concat(
-        communities_dfs, axis=0, ignore_index=True, sort=False
-    )
-    community_reports_combined = pd.concat(
-        community_reports_dfs, axis=0, ignore_index=True, sort=False
-    )
-    entities_combined = pd.concat(entities_dfs, axis=0, ignore_index=True, sort=False)
-    relationships_combined = pd.concat(
-        relationships_dfs, axis=0, ignore_index=True, sort=False
-    )
-    text_units_combined = pd.concat(
-        text_units_dfs, axis=0, ignore_index=True, sort=False
-    )
-
-    logger.debug("Executing multi-index drift search query: %s", query)
-    result = await drift_search(
-        config,
-        entities=entities_combined,
-        communities=communities_combined,
-        community_reports=community_reports_combined,
-        text_units=text_units_combined,
-        relationships=relationships_combined,
-        community_level=community_level,
-        response_type=response_type,
-        query=query,
-        callbacks=callbacks,
-    )
-
-    # Update the context data by linking index names and community ids
-    context = {}
-    if type(result[1]) is dict:
-        for key in result[1]:
-            context[key] = update_context_data(result[1][key], links)
-    else:
-        context = result[1]
-
-    logger.debug("Query response: %s", truncate(result[0], 400))  # type: ignore
-    return (result[0], context)
-
-
 @validate_call(config={"arbitrary_types_allowed": True})
 async def basic_search(
     config: GraphRagConfig,
     text_units: pd.DataFrame,
+    response_type: str,
     query: str,
     callbacks: list[QueryCallbacks] | None = None,
     verbose: bool = False,
@@ -1094,6 +498,7 @@ def on_context(context: Any) -> None:
     async for chunk in basic_search_streaming(
         config=config,
         text_units=text_units,
+        response_type=response_type,
         query=query,
         callbacks=callbacks,
     ):
@@ -1106,6 +511,7 @@ def on_context(context: Any) -> None:
 def basic_search_streaming(
     config: GraphRagConfig,
     text_units: pd.DataFrame,
+    response_type: str,
     query: str,
     callbacks: list[QueryCallbacks] | None = None,
     verbose: bool = False,
@@ -1124,14 +530,11 @@ def basic_search_streaming(
     """
     init_loggers(config=config, verbose=verbose, filename="query.log")
 
-    vector_store_args = {}
-    for index, store in config.vector_store.items():
-        vector_store_args[index] = store.model_dump()
-    msg = f"Vector Store Args: {redact(vector_store_args)}"
+    msg = f"Vector Store Args: {redact(config.vector_store.model_dump())}"
     logger.debug(msg)
 
     embedding_store = get_embedding_store(
-        config_args=vector_store_args,
+        store=config.vector_store.model_dump(),
         embedding_name=text_unit_text_embedding,
     )
 
@@ -1142,85 +545,8 @@ def basic_search_streaming(
         config=config,
         text_units=read_indexer_text_units(text_units),
         text_unit_embeddings=embedding_store,
+        response_type=response_type,
         system_prompt=prompt,
         callbacks=callbacks,
     )
     return search_engine.stream_search(query=query)
-
-
-@validate_call(config={"arbitrary_types_allowed": True})
-async def multi_index_basic_search(
-    config: GraphRagConfig,
-    text_units_list: list[pd.DataFrame],
-    index_names: list[str],
-    streaming: bool,
-    query: str,
-    callbacks: list[QueryCallbacks] | None = None,
-    verbose: bool = False,
-) -> tuple[
-    str | dict[str, Any] | list[dict[str, Any]],
-    str | list[pd.DataFrame] | dict[str, pd.DataFrame],
-]:
-    """Perform a basic search across multiple indexes and return the context data and response.
-
-    Parameters
-    ----------
-    - config (GraphRagConfig): A graphrag configuration (from settings.yaml)
-    - text_units_list (list[pd.DataFrame]): A list of DataFrames containing the final text units (from text_units.parquet)
-    - index_names (list[str]): A list of index names.
-    - streaming (bool): Whether to stream the results or not.
-    - query (str): The user query to search for.
-
-    Returns
-    -------
-    TODO: Document the search response type and format.
-    """
-    init_loggers(config=config, verbose=verbose, filename="query.log")
-
-    logger.warning(
-        "Multi-index search is deprecated and will be removed in GraphRAG v3."
-    )
-
-    # Streaming not supported yet
-    if streaming:
-        message = "Streaming not yet implemented for multi_basic_search"
-        raise NotImplementedError(message)
-
-    links = {
-        "text_units": {},
-    }
-    max_vals = {
-        "text_units": 0,
-    }
-
-    text_units_dfs = []
-
-    for idx, index_name in enumerate(index_names):
-        # Prepare each index's text units dataframe for merging
-        text_units_df = text_units_list[idx]
-        for i in range(text_units_df.shape[0]):
-            links["text_units"][i + max_vals["text_units"]] = {
-                "index_name": index_name,
-                "id": i,
-            }
-        text_units_df["id"] = text_units_df["id"].apply(
-            lambda x, index_name=index_name: f"{x}-{index_name}"
-        )
-        text_units_df["human_readable_id"] = (
-            text_units_df["human_readable_id"] + max_vals["text_units"]
-        )
-        max_vals["text_units"] += text_units_df.shape[0]
-        text_units_dfs.append(text_units_df)
-
-    # Merge the dataframes
-    text_units_combined = pd.concat(
-        text_units_dfs, axis=0, ignore_index=True, sort=False
-    )
-
-    logger.debug("Executing multi-index basic search query: %s", query)
-    return await basic_search(
-        config,
-        text_units=text_units_combined,
-        query=query,
-        callbacks=callbacks,
-    )
diff --git a/graphrag/cli/main.py b/graphrag/cli/main.py
index bc0e9f39ac..7b4d3bb0cd 100644
--- a/graphrag/cli/main.py
+++ b/graphrag/cli/main.py
@@ -537,6 +537,7 @@ def _query_cli(
                 config_filepath=config,
                 data_dir=data,
                 root_dir=root,
+                response_type=response_type,
                 streaming=streaming,
                 query=query,
                 verbose=verbose,
diff --git a/graphrag/cli/query.py b/graphrag/cli/query.py
index 0075734859..914196b865 100644
--- a/graphrag/cli/query.py
+++ b/graphrag/cli/query.py
@@ -52,35 +52,9 @@ def run_global_search(
         optional_list=[],
     )
 
-    # Call the Multi-Index Global Search API
-    if dataframe_dict["multi-index"]:
-        final_entities_list = dataframe_dict["entities"]
-        final_communities_list = dataframe_dict["communities"]
-        final_community_reports_list = dataframe_dict["community_reports"]
-        index_names = dataframe_dict["index_names"]
-
-        response, context_data = asyncio.run(
-            api.multi_index_global_search(
-                config=config,
-                entities_list=final_entities_list,
-                communities_list=final_communities_list,
-                community_reports_list=final_community_reports_list,
-                index_names=index_names,
-                community_level=community_level,
-                dynamic_community_selection=dynamic_community_selection,
-                response_type=response_type,
-                streaming=streaming,
-                query=query,
-                verbose=verbose,
-            )
-        )
-        print(response)
-        return response, context_data
-
-    # Otherwise, call the Single-Index Global Search API
-    final_entities: pd.DataFrame = dataframe_dict["entities"]
-    final_communities: pd.DataFrame = dataframe_dict["communities"]
-    final_community_reports: pd.DataFrame = dataframe_dict["community_reports"]
+    entities: pd.DataFrame = dataframe_dict["entities"]
+    communities: pd.DataFrame = dataframe_dict["communities"]
+    community_reports: pd.DataFrame = dataframe_dict["community_reports"]
 
     if streaming:
 
@@ -97,9 +71,9 @@ def on_context(context: Any) -> None:
 
             async for stream_chunk in api.global_search_streaming(
                 config=config,
-                entities=final_entities,
-                communities=final_communities,
-                community_reports=final_community_reports,
+                entities=entities,
+                communities=communities,
+                community_reports=community_reports,
                 community_level=community_level,
                 dynamic_community_selection=dynamic_community_selection,
                 response_type=response_type,
@@ -118,9 +92,9 @@ def on_context(context: Any) -> None:
     response, context_data = asyncio.run(
         api.global_search(
             config=config,
-            entities=final_entities,
-            communities=final_communities,
-            community_reports=final_community_reports,
+            entities=entities,
+            communities=communities,
+            community_reports=community_reports,
             community_level=community_level,
             dynamic_community_selection=dynamic_community_selection,
             response_type=response_type,
@@ -166,49 +140,13 @@ def run_local_search(
             "covariates",
         ],
     )
-    # Call the Multi-Index Local Search API
-    if dataframe_dict["multi-index"]:
-        final_entities_list = dataframe_dict["entities"]
-        final_communities_list = dataframe_dict["communities"]
-        final_community_reports_list = dataframe_dict["community_reports"]
-        final_text_units_list = dataframe_dict["text_units"]
-        final_relationships_list = dataframe_dict["relationships"]
-        index_names = dataframe_dict["index_names"]
-
-        # If any covariates tables are missing from any index, set the covariates list to None
-        if len(dataframe_dict["covariates"]) != dataframe_dict["num_indexes"]:
-            final_covariates_list = None
-        else:
-            final_covariates_list = dataframe_dict["covariates"]
-
-        response, context_data = asyncio.run(
-            api.multi_index_local_search(
-                config=config,
-                entities_list=final_entities_list,
-                communities_list=final_communities_list,
-                community_reports_list=final_community_reports_list,
-                text_units_list=final_text_units_list,
-                relationships_list=final_relationships_list,
-                covariates_list=final_covariates_list,
-                index_names=index_names,
-                community_level=community_level,
-                response_type=response_type,
-                streaming=streaming,
-                query=query,
-                verbose=verbose,
-            )
-        )
-        print(response)
 
-        return response, context_data
-
-    # Otherwise, call the Single-Index Local Search API
-    final_communities: pd.DataFrame = dataframe_dict["communities"]
-    final_community_reports: pd.DataFrame = dataframe_dict["community_reports"]
-    final_text_units: pd.DataFrame = dataframe_dict["text_units"]
-    final_relationships: pd.DataFrame = dataframe_dict["relationships"]
-    final_entities: pd.DataFrame = dataframe_dict["entities"]
-    final_covariates: pd.DataFrame | None = dataframe_dict["covariates"]
+    communities: pd.DataFrame = dataframe_dict["communities"]
+    community_reports: pd.DataFrame = dataframe_dict["community_reports"]
+    text_units: pd.DataFrame = dataframe_dict["text_units"]
+    relationships: pd.DataFrame = dataframe_dict["relationships"]
+    entities: pd.DataFrame = dataframe_dict["entities"]
+    covariates: pd.DataFrame | None = dataframe_dict["covariates"]
 
     if streaming:
 
@@ -225,12 +163,12 @@ def on_context(context: Any) -> None:
 
             async for stream_chunk in api.local_search_streaming(
                 config=config,
-                entities=final_entities,
-                communities=final_communities,
-                community_reports=final_community_reports,
-                text_units=final_text_units,
-                relationships=final_relationships,
-                covariates=final_covariates,
+                entities=entities,
+                communities=communities,
+                community_reports=community_reports,
+                text_units=text_units,
+                relationships=relationships,
+                covariates=covariates,
                 community_level=community_level,
                 response_type=response_type,
                 query=query,
@@ -248,12 +186,12 @@ def on_context(context: Any) -> None:
     response, context_data = asyncio.run(
         api.local_search(
             config=config,
-            entities=final_entities,
-            communities=final_communities,
-            community_reports=final_community_reports,
-            text_units=final_text_units,
-            relationships=final_relationships,
-            covariates=final_covariates,
+            entities=entities,
+            communities=communities,
+            community_reports=community_reports,
+            text_units=text_units,
+            relationships=relationships,
+            covariates=covariates,
             community_level=community_level,
             response_type=response_type,
             query=query,
@@ -296,41 +234,11 @@ def run_drift_search(
         ],
     )
 
-    # Call the Multi-Index Drift Search API
-    if dataframe_dict["multi-index"]:
-        final_entities_list = dataframe_dict["entities"]
-        final_communities_list = dataframe_dict["communities"]
-        final_community_reports_list = dataframe_dict["community_reports"]
-        final_text_units_list = dataframe_dict["text_units"]
-        final_relationships_list = dataframe_dict["relationships"]
-        index_names = dataframe_dict["index_names"]
-
-        response, context_data = asyncio.run(
-            api.multi_index_drift_search(
-                config=config,
-                entities_list=final_entities_list,
-                communities_list=final_communities_list,
-                community_reports_list=final_community_reports_list,
-                text_units_list=final_text_units_list,
-                relationships_list=final_relationships_list,
-                index_names=index_names,
-                community_level=community_level,
-                response_type=response_type,
-                streaming=streaming,
-                query=query,
-                verbose=verbose,
-            )
-        )
-        print(response)
-
-        return response, context_data
-
-    # Otherwise, call the Single-Index Drift Search API
-    final_communities: pd.DataFrame = dataframe_dict["communities"]
-    final_community_reports: pd.DataFrame = dataframe_dict["community_reports"]
-    final_text_units: pd.DataFrame = dataframe_dict["text_units"]
-    final_relationships: pd.DataFrame = dataframe_dict["relationships"]
-    final_entities: pd.DataFrame = dataframe_dict["entities"]
+    communities: pd.DataFrame = dataframe_dict["communities"]
+    community_reports: pd.DataFrame = dataframe_dict["community_reports"]
+    text_units: pd.DataFrame = dataframe_dict["text_units"]
+    relationships: pd.DataFrame = dataframe_dict["relationships"]
+    entities: pd.DataFrame = dataframe_dict["entities"]
 
     if streaming:
 
@@ -347,11 +255,11 @@ def on_context(context: Any) -> None:
 
             async for stream_chunk in api.drift_search_streaming(
                 config=config,
-                entities=final_entities,
-                communities=final_communities,
-                community_reports=final_community_reports,
-                text_units=final_text_units,
-                relationships=final_relationships,
+                entities=entities,
+                communities=communities,
+                community_reports=community_reports,
+                text_units=text_units,
+                relationships=relationships,
                 community_level=community_level,
                 response_type=response_type,
                 query=query,
@@ -370,11 +278,11 @@ def on_context(context: Any) -> None:
     response, context_data = asyncio.run(
         api.drift_search(
             config=config,
-            entities=final_entities,
-            communities=final_communities,
-            community_reports=final_community_reports,
-            text_units=final_text_units,
-            relationships=final_relationships,
+            entities=entities,
+            communities=communities,
+            community_reports=community_reports,
+            text_units=text_units,
+            relationships=relationships,
             community_level=community_level,
             response_type=response_type,
             query=query,
@@ -390,6 +298,7 @@ def run_basic_search(
     config_filepath: Path | None,
     data_dir: Path | None,
     root_dir: Path,
+    response_type: str,
     streaming: bool,
     query: str,
     verbose: bool,
@@ -411,27 +320,7 @@ def run_basic_search(
         ],
     )
 
-    # Call the Multi-Index Basic Search API
-    if dataframe_dict["multi-index"]:
-        final_text_units_list = dataframe_dict["text_units"]
-        index_names = dataframe_dict["index_names"]
-
-        response, context_data = asyncio.run(
-            api.multi_index_basic_search(
-                config=config,
-                text_units_list=final_text_units_list,
-                index_names=index_names,
-                streaming=streaming,
-                query=query,
-                verbose=verbose,
-            )
-        )
-        print(response)
-
-        return response, context_data
-
-    # Otherwise, call the Single-Index Basic Search API
-    final_text_units: pd.DataFrame = dataframe_dict["text_units"]
+    text_units: pd.DataFrame = dataframe_dict["text_units"]
 
     if streaming:
 
@@ -448,7 +337,8 @@ def on_context(context: Any) -> None:
 
             async for stream_chunk in api.basic_search_streaming(
                 config=config,
-                text_units=final_text_units,
+                text_units=text_units,
+                response_type=response_type,
                 query=query,
                 callbacks=[callbacks],
                 verbose=verbose,
@@ -464,7 +354,8 @@ def on_context(context: Any) -> None:
     response, context_data = asyncio.run(
         api.basic_search(
             config=config,
-            text_units=final_text_units,
+            text_units=text_units,
+            response_type=response_type,
             query=query,
             verbose=verbose,
         )
@@ -481,40 +372,6 @@ def _resolve_output_files(
 ) -> dict[str, Any]:
     """Read indexing output files to a dataframe dict."""
     dataframe_dict = {}
-
-    # Loading output files for multi-index search
-    if config.outputs:
-        dataframe_dict["multi-index"] = True
-        dataframe_dict["num_indexes"] = len(config.outputs)
-        dataframe_dict["index_names"] = config.outputs.keys()
-        for output in config.outputs.values():
-            storage_obj = create_storage_from_config(output)
-            for name in output_list:
-                if name not in dataframe_dict:
-                    dataframe_dict[name] = []
-                df_value = asyncio.run(
-                    load_table_from_storage(name=name, storage=storage_obj)
-                )
-                dataframe_dict[name].append(df_value)
-
-            # for optional output files, do not append if the dataframe does not exist
-            if optional_list:
-                for optional_file in optional_list:
-                    if optional_file not in dataframe_dict:
-                        dataframe_dict[optional_file] = []
-                    file_exists = asyncio.run(
-                        storage_has_table(optional_file, storage_obj)
-                    )
-                    if file_exists:
-                        df_value = asyncio.run(
-                            load_table_from_storage(
-                                name=optional_file, storage=storage_obj
-                            )
-                        )
-                        dataframe_dict[optional_file].append(df_value)
-        return dataframe_dict
-    # Loading output files for single-index search
-    dataframe_dict["multi-index"] = False
     storage_obj = create_storage_from_config(config.output)
     for name in output_list:
         df_value = asyncio.run(load_table_from_storage(name=name, storage=storage_obj))
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index eadafd9860..e81c5ac31c 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -55,8 +55,6 @@
 DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large"
 DEFAULT_MODEL_PROVIDER = "openai"
 
-DEFAULT_VECTOR_STORE_ID = "default_vector_store"
-
 ENCODING_MODEL = "o200k_base"
 COGNITIVE_SERVICES_AUDIENCE = "https://cognitiveservices.azure.com/.default"
 
@@ -167,7 +165,6 @@ class EmbedTextDefaults:
     batch_max_tokens: int = 8191
     names: list[str] = field(default_factory=lambda: default_embeddings)
     strategy: None = None
-    vector_store_id: str = DEFAULT_VECTOR_STORE_ID
 
 
 @dataclass
@@ -418,7 +415,6 @@ class GraphRagConfigDefaults:
     reporting: ReportingDefaults = field(default_factory=ReportingDefaults)
     storage: StorageDefaults = field(default_factory=StorageDefaults)
     output: OutputDefaults = field(default_factory=OutputDefaults)
-    outputs: None = None
     update_index_output: UpdateIndexOutputDefaults = field(
         default_factory=UpdateIndexOutputDefaults
     )
@@ -444,8 +440,8 @@ class GraphRagConfigDefaults:
     global_search: GlobalSearchDefaults = field(default_factory=GlobalSearchDefaults)
     drift_search: DriftSearchDefaults = field(default_factory=DriftSearchDefaults)
     basic_search: BasicSearchDefaults = field(default_factory=BasicSearchDefaults)
-    vector_store: dict[str, VectorStoreDefaults] = field(
-        default_factory=lambda: {DEFAULT_VECTOR_STORE_ID: VectorStoreDefaults()}
+    vector_store: VectorStoreDefaults = field(
+        default_factory=lambda: VectorStoreDefaults()
     )
     workflows: None = None
 
diff --git a/graphrag/config/get_vector_store_settings.py b/graphrag/config/get_vector_store_settings.py
deleted file mode 100644
index 3771d65ff3..0000000000
--- a/graphrag/config/get_vector_store_settings.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing get_vector_store_settings."""
-
-from graphrag.config.models.graph_rag_config import GraphRagConfig
-
-
-def get_vector_store_settings(
-    settings: GraphRagConfig,
-    vector_store_params: dict | None = None,
-) -> dict:
-    """Transform GraphRAG config into settings for workflows."""
-    vector_store_settings = settings.get_vector_store_config(
-        settings.embed_text.vector_store_id
-    ).model_dump()
-
-    #
-    # If we get to this point, settings.vector_store is defined, and there's a specific setting for this embedding.
-    # settings.vector_store.base contains connection information, or may be undefined
-    # settings.vector_store.<vector_name> contains the specific settings for this embedding
-    #
-    return {
-        **(vector_store_params or {}),
-        **(vector_store_settings),
-    }
diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py
index aadacf8f38..69b05015fb 100644
--- a/graphrag/config/init_content.py
+++ b/graphrag/config/init_content.py
@@ -75,16 +75,14 @@
   base_dir: "{graphrag_config_defaults.reporting.base_dir}"
 
 vector_store:
-  {defs.DEFAULT_VECTOR_STORE_ID}:
-    type: {vector_store_defaults.type}
-    db_uri: {vector_store_defaults.db_uri}
-    container_name: {vector_store_defaults.container_name}
+  type: {vector_store_defaults.type}
+  db_uri: {vector_store_defaults.db_uri}
+  container_name: {vector_store_defaults.container_name}
 
 ### Workflow settings ###
 
 embed_text:
   model_id: {graphrag_config_defaults.embed_text.model_id}
-  vector_store_id: {graphrag_config_defaults.embed_text.vector_store_id}
 
 extract_graph:
   model_id: {graphrag_config_defaults.extract_graph.model_id}
diff --git a/graphrag/config/models/embed_text_config.py b/graphrag/config/models/embed_text_config.py
index f785bf6eed..5e5963811c 100644
--- a/graphrag/config/models/embed_text_config.py
+++ b/graphrag/config/models/embed_text_config.py
@@ -15,10 +15,6 @@ class EmbedTextConfig(BaseModel):
         description="The model ID to use for text embeddings.",
         default=graphrag_config_defaults.embed_text.model_id,
     )
-    vector_store_id: str = Field(
-        description="The vector store ID to use for text embeddings.",
-        default=graphrag_config_defaults.embed_text.vector_store_id,
-    )
     batch_size: int = Field(
         description="The batch size to use.",
         default=graphrag_config_defaults.embed_text.batch_size,
diff --git a/graphrag/config/models/graph_rag_config.py b/graphrag/config/models/graph_rag_config.py
index 2b5961321a..c8cdca819c 100644
--- a/graphrag/config/models/graph_rag_config.py
+++ b/graphrag/config/models/graph_rag_config.py
@@ -3,7 +3,6 @@
 
 """Parameterization settings for the default configuration."""
 
-from dataclasses import asdict
 from pathlib import Path
 
 from devtools import pformat
@@ -179,23 +178,6 @@ def _validate_output_base_dir(self) -> None:
                 (Path(self.root_dir) / self.output.base_dir).resolve()
             )
 
-    outputs: dict[str, StorageConfig] | None = Field(
-        description="A list of output configurations used for multi-index query.",
-        default=graphrag_config_defaults.outputs,
-    )
-
-    def _validate_multi_output_base_dirs(self) -> None:
-        """Validate the outputs dict base directories."""
-        if self.outputs:
-            for output in self.outputs.values():
-                if output.type == defs.StorageType.file:
-                    if output.base_dir.strip() == "":
-                        msg = "Output base directory is required for file output. Please rerun `graphrag init` and set the output configuration."
-                        raise ValueError(msg)
-                    output.base_dir = str(
-                        (Path(self.root_dir) / output.base_dir).resolve()
-                    )
-
     update_index_output: StorageConfig = Field(
         description="The output configuration for the updated index.",
         default=StorageConfig(
@@ -234,12 +216,8 @@ def _validate_reporting_base_dir(self) -> None:
                 (Path(self.root_dir) / self.reporting.base_dir).resolve()
             )
 
-    vector_store: dict[str, VectorStoreConfig] = Field(
-        description="The vector store configuration.",
-        default_factory=lambda: {
-            k: VectorStoreConfig(**asdict(v))
-            for k, v in graphrag_config_defaults.vector_store.items()
-        },
+    vector_store: VectorStoreConfig = Field(
+        description="The vector store configuration.", default=VectorStoreConfig()
     )
     """The vector store configuration."""
 
@@ -327,12 +305,12 @@ def _validate_reporting_base_dir(self) -> None:
 
     def _validate_vector_store_db_uri(self) -> None:
         """Validate the vector store configuration."""
-        for store in self.vector_store.values():
-            if store.type == VectorStoreType.LanceDB:
-                if not store.db_uri or store.db_uri.strip == "":
-                    msg = "Vector store URI is required for LanceDB. Please rerun `graphrag init` and set the vector store configuration."
-                    raise ValueError(msg)
-                store.db_uri = str((Path(self.root_dir) / store.db_uri).resolve())
+        store = self.vector_store
+        if store.type == VectorStoreType.LanceDB:
+            if not store.db_uri or store.db_uri.strip == "":
+                msg = "Vector store URI is required for LanceDB. Please rerun `graphrag init` and set the vector store configuration."
+                raise ValueError(msg)
+            store.db_uri = str((Path(self.root_dir) / store.db_uri).resolve())
 
     def _validate_factories(self) -> None:
         """Validate the factories used in the configuration."""
@@ -363,30 +341,6 @@ def get_language_model_config(self, model_id: str) -> LanguageModelConfig:
 
         return self.models[model_id]
 
-    def get_vector_store_config(self, vector_store_id: str) -> VectorStoreConfig:
-        """Get a vector store configuration by ID.
-
-        Parameters
-        ----------
-        vector_store_id : str
-            The ID of the vector store to get. Should match an ID in the vector_store list.
-
-        Returns
-        -------
-        VectorStoreConfig
-            The vector store configuration if found.
-
-        Raises
-        ------
-        ValueError
-            If the vector store ID is not found in the configuration.
-        """
-        if vector_store_id not in self.vector_store:
-            err_msg = f"Vector Store ID {vector_store_id} not found in configuration. Please rerun `graphrag init` and set the vector store configuration."
-            raise ValueError(err_msg)
-
-        return self.vector_store[vector_store_id]
-
     @model_validator(mode="after")
     def _validate_model(self):
         """Validate the model configuration."""
@@ -396,7 +350,6 @@ def _validate_model(self):
         self._validate_input_base_dir()
         self._validate_reporting_base_dir()
         self._validate_output_base_dir()
-        self._validate_multi_output_base_dirs()
         self._validate_update_index_output_base_dir()
         self._validate_vector_store_db_uri()
         self._validate_factories()
diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py
index 04099f8109..f01682604f 100644
--- a/graphrag/index/operations/embed_text/embed_text.py
+++ b/graphrag/index/operations/embed_text/embed_text.py
@@ -9,13 +9,10 @@
 import pandas as pd
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.embeddings import create_index_name
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.index.operations.embed_text.run_embed_text import run_embed_text
 from graphrag.language_model.protocol.base import EmbeddingModel
 from graphrag.tokenizer.tokenizer import Tokenizer
 from graphrag.vector_stores.base import BaseVectorStore, VectorStoreDocument
-from graphrag.vector_stores.factory import VectorStoreFactory
 
 logger = logging.getLogger(__name__)
 
@@ -26,86 +23,14 @@ async def embed_text(
     model: EmbeddingModel,
     tokenizer: Tokenizer,
     embed_column: str,
-    embedding_name: str,
     batch_size: int,
     batch_max_tokens: int,
     num_threads: int,
-    vector_store_config: dict,
+    vector_store: BaseVectorStore,
     id_column: str = "id",
     title_column: str | None = None,
 ):
     """Embed a piece of text into a vector space. The operation outputs a new column containing a mapping between doc_id and vector."""
-    if vector_store_config:
-        index_name = _get_index_name(vector_store_config, embedding_name)
-        vector_store: BaseVectorStore = _create_vector_store(
-            vector_store_config, index_name, embedding_name
-        )
-        vector_store_workflow_config = vector_store_config.get(
-            embedding_name, vector_store_config
-        )
-        return await _text_embed_with_vector_store(
-            input=input,
-            callbacks=callbacks,
-            model=model,
-            tokenizer=tokenizer,
-            embed_column=embed_column,
-            vector_store=vector_store,
-            vector_store_config=vector_store_workflow_config,
-            batch_size=batch_size,
-            batch_max_tokens=batch_max_tokens,
-            num_threads=num_threads,
-            id_column=id_column,
-            title_column=title_column,
-        )
-
-    return await _text_embed_in_memory(
-        input=input,
-        callbacks=callbacks,
-        model=model,
-        tokenizer=tokenizer,
-        embed_column=embed_column,
-        batch_size=batch_size,
-        batch_max_tokens=batch_max_tokens,
-        num_threads=num_threads,
-    )
-
-
-async def _text_embed_in_memory(
-    input: pd.DataFrame,
-    callbacks: WorkflowCallbacks,
-    model: EmbeddingModel,
-    tokenizer: Tokenizer,
-    embed_column: str,
-    batch_size: int,
-    batch_max_tokens: int,
-    num_threads: int,
-):
-    texts: list[str] = input[embed_column].tolist()
-    result = await run_embed_text(
-        texts, callbacks, model, tokenizer, batch_size, batch_max_tokens, num_threads
-    )
-
-    return result.embeddings
-
-
-async def _text_embed_with_vector_store(
-    input: pd.DataFrame,
-    callbacks: WorkflowCallbacks,
-    model: EmbeddingModel,
-    tokenizer: Tokenizer,
-    embed_column: str,
-    vector_store: BaseVectorStore,
-    vector_store_config: dict,
-    batch_size: int,
-    batch_max_tokens: int,
-    num_threads: int,
-    id_column: str,
-    title_column: str | None = None,
-):
-    # Get vector-storage configuration
-
-    overwrite: bool = vector_store_config.get("overwrite", True)
-
     if embed_column not in input.columns:
         msg = f"Column {embed_column} not found in input dataframe with columns {input.columns}"
         raise ValueError(msg)
@@ -168,51 +93,8 @@ async def _text_embed_with_vector_store(
             )
             documents.append(document)
 
-        vector_store.load_documents(documents, overwrite and i == 0)
+        vector_store.load_documents(documents, True)
         starting_index += len(documents)
         i += 1
 
     return all_results
-
-
-def _create_vector_store(
-    vector_store_config: dict, index_name: str, embedding_name: str | None = None
-) -> BaseVectorStore:
-    vector_store_type: str = str(vector_store_config.get("type"))
-
-    embeddings_schema: dict[str, VectorStoreSchemaConfig] = vector_store_config.get(
-        "embeddings_schema", {}
-    )
-    single_embedding_config: VectorStoreSchemaConfig = VectorStoreSchemaConfig()
-
-    if (
-        embeddings_schema is not None
-        and embedding_name is not None
-        and embedding_name in embeddings_schema
-    ):
-        raw_config = embeddings_schema[embedding_name]
-        if isinstance(raw_config, dict):
-            single_embedding_config = VectorStoreSchemaConfig(**raw_config)
-        else:
-            single_embedding_config = raw_config
-
-    if single_embedding_config.index_name is None:
-        single_embedding_config.index_name = index_name
-
-    vector_store = VectorStoreFactory().create_vector_store(
-        vector_store_schema_config=single_embedding_config,
-        vector_store_type=vector_store_type,
-        **vector_store_config,
-    )
-
-    vector_store.connect(**vector_store_config)
-    return vector_store
-
-
-def _get_index_name(vector_store_config: dict, embedding_name: str) -> str:
-    container_name = vector_store_config.get("container_name", "default")
-    index_name = create_index_name(container_name, embedding_name)
-
-    msg = f"using vector store {vector_store_config.get('type')} with container_name {container_name} for embedding {embedding_name}: {index_name}"
-    logger.info(msg)
-    return index_name
diff --git a/graphrag/index/workflows/generate_text_embeddings.py b/graphrag/index/workflows/generate_text_embeddings.py
index 3a0ae41259..848b57d292 100644
--- a/graphrag/index/workflows/generate_text_embeddings.py
+++ b/graphrag/index/workflows/generate_text_embeddings.py
@@ -12,14 +12,16 @@
     community_full_content_embedding,
     community_summary_embedding,
     community_title_embedding,
+    create_index_name,
     document_text_embedding,
     entity_description_embedding,
     entity_title_embedding,
     relationship_description_embedding,
     text_unit_text_embedding,
 )
-from graphrag.config.get_vector_store_settings import get_vector_store_settings
 from graphrag.config.models.graph_rag_config import GraphRagConfig
+from graphrag.config.models.vector_store_config import VectorStoreConfig
+from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.index.operations.embed_text.embed_text import embed_text
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
@@ -31,6 +33,8 @@
     load_table_from_storage,
     write_table_to_storage,
 )
+from graphrag.vector_stores.base import BaseVectorStore
+from graphrag.vector_stores.factory import VectorStoreFactory
 
 logger = logging.getLogger(__name__)
 
@@ -70,8 +74,6 @@ async def run_workflow(
             "community_reports", context.output_storage
         )
 
-    vector_store_config = get_vector_store_settings(config)
-
     model_config = config.get_language_model_config(config.embed_text.model_id)
 
     model = ModelManager().get_or_create_embedding_model(
@@ -96,7 +98,7 @@ async def run_workflow(
         batch_size=config.embed_text.batch_size,
         batch_max_tokens=config.embed_text.batch_max_tokens,
         num_threads=model_config.concurrent_requests,
-        vector_store_config=vector_store_config,
+        vector_store_config=config.vector_store,
         embedded_fields=embedded_fields,
     )
 
@@ -124,7 +126,7 @@ async def generate_text_embeddings(
     batch_size: int,
     batch_max_tokens: int,
     num_threads: int,
-    vector_store_config: dict,
+    vector_store_config: VectorStoreConfig,
     embedded_fields: list[str],
 ) -> dict[str, pd.DataFrame]:
     """All the steps to generate all embeddings."""
@@ -208,20 +210,69 @@ async def _run_embeddings(
     batch_size: int,
     batch_max_tokens: int,
     num_threads: int,
-    vector_store_config: dict,
+    vector_store_config: VectorStoreConfig,
 ) -> pd.DataFrame:
     """All the steps to generate single embedding."""
+    index_name = _get_index_name(vector_store_config, name)
+    vector_store = _create_vector_store(vector_store_config, index_name, name)
+
     data["embedding"] = await embed_text(
         input=data,
         callbacks=callbacks,
         model=model,
         tokenizer=tokenizer,
         embed_column=embed_column,
-        embedding_name=name,
         batch_size=batch_size,
         batch_max_tokens=batch_max_tokens,
         num_threads=num_threads,
-        vector_store_config=vector_store_config,
+        vector_store=vector_store,
     )
 
     return data.loc[:, ["id", "embedding"]]
+
+
+def _create_vector_store(
+    vector_store_config: VectorStoreConfig,
+    index_name: str,
+    embedding_name: str | None = None,
+) -> BaseVectorStore:
+    vector_store_type: str = str(vector_store_config.type)
+
+    embeddings_schema: dict[str, VectorStoreSchemaConfig] = (
+        vector_store_config.embeddings_schema
+    )
+
+    single_embedding_config: VectorStoreSchemaConfig = VectorStoreSchemaConfig()
+
+    if (
+        embeddings_schema is not None
+        and embedding_name is not None
+        and embedding_name in embeddings_schema
+    ):
+        raw_config = embeddings_schema[embedding_name]
+        if isinstance(raw_config, dict):
+            single_embedding_config = VectorStoreSchemaConfig(**raw_config)
+        else:
+            single_embedding_config = raw_config
+
+    if single_embedding_config.index_name is None:
+        single_embedding_config.index_name = index_name
+
+    args = vector_store_config.model_dump()
+    vector_store = VectorStoreFactory().create_vector_store(
+        vector_store_schema_config=single_embedding_config,
+        vector_store_type=vector_store_type,
+        **args,
+    )
+
+    vector_store.connect(**args)
+    return vector_store
+
+
+def _get_index_name(vector_store_config: VectorStoreConfig, embedding_name: str) -> str:
+    container_name = vector_store_config.container_name
+    index_name = create_index_name(container_name, embedding_name)
+
+    msg = f"using vector store {vector_store_config.type} with container_name {container_name} for embedding {embedding_name}: {index_name}"
+    logger.info(msg)
+    return index_name
diff --git a/graphrag/index/workflows/update_text_embeddings.py b/graphrag/index/workflows/update_text_embeddings.py
index 4b349f5809..0fc5f1fdeb 100644
--- a/graphrag/index/workflows/update_text_embeddings.py
+++ b/graphrag/index/workflows/update_text_embeddings.py
@@ -5,7 +5,6 @@
 
 import logging
 
-from graphrag.config.get_vector_store_settings import get_vector_store_settings
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
 from graphrag.index.typing.context import PipelineRunContext
@@ -37,7 +36,6 @@ async def run_workflow(
     ]
 
     embedded_fields = config.embed_text.names
-    vector_store_config = get_vector_store_settings(config)
 
     model_config = config.get_language_model_config(config.embed_text.model_id)
 
@@ -63,7 +61,7 @@ async def run_workflow(
         batch_size=config.embed_text.batch_size,
         batch_max_tokens=config.embed_text.batch_max_tokens,
         num_threads=model_config.concurrent_requests,
-        vector_store_config=vector_store_config,
+        vector_store_config=config.vector_store,
         embedded_fields=embedded_fields,
     )
     if config.snapshots.embeddings:
diff --git a/graphrag/prompts/query/basic_search_system_prompt.py b/graphrag/prompts/query/basic_search_system_prompt.py
index a20fb6ad10..bc37c70d98 100644
--- a/graphrag/prompts/query/basic_search_system_prompt.py
+++ b/graphrag/prompts/query/basic_search_system_prompt.py
@@ -27,7 +27,7 @@
 
 "Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Sources (1, 3)]"
 
-where 1, 2, 3, 7, 34, 46, and 64 represent the source id taken from the "source_id" column in the provided tables.
+where 1, 2, 3, 7, 34, 46, and 64 represent the source id taken from the "id" column in the provided tables.
 
 Do not include information where the supporting evidence for it is not provided.
 
@@ -60,7 +60,7 @@
 
 "Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Sources (1, 3)]"
 
-where 1, 2, 3, 7, 34, 46, and 64 represent the source id taken from the "source_id" column in the provided tables.
+where 1, 2, 3, 7, 34, 46, and 64 represent the source id taken from the "id" column in the provided tables.
 
 Do not include information where the supporting evidence for it is not provided.
 
diff --git a/graphrag/query/factory.py b/graphrag/query/factory.py
index 8ee35e4bb9..3f73c8c1ab 100644
--- a/graphrag/query/factory.py
+++ b/graphrag/query/factory.py
@@ -251,8 +251,8 @@ def get_basic_search_engine(
     text_units: list[TextUnit],
     text_unit_embeddings: BaseVectorStore,
     config: GraphRagConfig,
+    response_type: str,
     system_prompt: str | None = None,
-    response_type: str = "multiple paragraphs",
     callbacks: list[QueryCallbacks] | None = None,
 ) -> BasicSearch:
     """Create a basic search engine based on data + configuration."""
diff --git a/graphrag/query/structured_search/basic_search/basic_context.py b/graphrag/query/structured_search/basic_search/basic_context.py
index 215b26a31e..b7390017fc 100644
--- a/graphrag/query/structured_search/basic_search/basic_context.py
+++ b/graphrag/query/structured_search/basic_search/basic_context.py
@@ -38,7 +38,6 @@ def __init__(
         self.text_units = text_units
         self.text_unit_embeddings = text_unit_embeddings
         self.embedding_vectorstore_key = embedding_vectorstore_key
-        self.text_id_map = self._map_ids()
 
     def build_context(
         self,
@@ -48,7 +47,7 @@ def build_context(
         max_context_tokens: int = 12_000,
         context_name: str = "Sources",
         column_delimiter: str = "|",
-        text_id_col: str = "source_id",
+        text_id_col: str = "id",
         text_col: str = "text",
         **kwargs,
     ) -> ContextBuilderResult:
@@ -63,7 +62,7 @@ def build_context(
             text_unit_ids = {t.document.id for t in related_texts}
             text_units_filtered = []
             text_units_filtered = [
-                {text_id_col: t.id, text_col: t.text}
+                {text_id_col: t.short_id, text_col: t.text}
                 for t in self.text_units or []
                 if t.id in text_unit_ids
             ]
@@ -102,13 +101,5 @@ def build_context(
 
         return ContextBuilderResult(
             context_chunks=final_text,
-            context_records={context_name: final_text_df},
+            context_records={context_name.lower(): final_text_df},
         )
-
-    def _map_ids(self) -> dict[str, str]:
-        """Map id to short id in the text units."""
-        id_map = {}
-        text_units = self.text_units or []
-        for unit in text_units:
-            id_map[unit.id] = unit.short_id
-        return id_map
diff --git a/graphrag/utils/api.py b/graphrag/utils/api.py
index db3d94790d..16a5f9ed52 100644
--- a/graphrag/utils/api.py
+++ b/graphrag/utils/api.py
@@ -12,133 +12,51 @@
 from graphrag.config.models.cache_config import CacheConfig
 from graphrag.config.models.storage_config import StorageConfig
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.data_model.types import TextEmbedder
 from graphrag.storage.factory import StorageFactory
 from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.vector_stores.base import (
     BaseVectorStore,
-    VectorStoreDocument,
-    VectorStoreSearchResult,
 )
 from graphrag.vector_stores.factory import VectorStoreFactory
 
 
-class MultiVectorStore(BaseVectorStore):
-    """Multi Vector Store wrapper implementation."""
-
-    def __init__(
-        self,
-        embedding_stores: list[BaseVectorStore],
-        index_names: list[str],
-    ):
-        self.embedding_stores = embedding_stores
-        self.index_names = index_names
-
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
-        """Load documents into the vector store."""
-        msg = "load_documents method not implemented"
-        raise NotImplementedError(msg)
-
-    def connect(self, **kwargs: Any) -> Any:
-        """Connect to vector storage."""
-        msg = "connect method not implemented"
-        raise NotImplementedError(msg)
-
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by id."""
-        msg = "filter_by_id method not implemented"
-        raise NotImplementedError(msg)
-
-    def search_by_id(self, id: str) -> VectorStoreDocument:
-        """Search for a document by id."""
-        search_index_id = id.split("-")[0]
-        search_index_name = id.split("-")[1]
-        for index_name, embedding_store in zip(
-            self.index_names, self.embedding_stores, strict=False
-        ):
-            if index_name == search_index_name:
-                return embedding_store.search_by_id(search_index_id)
-        else:
-            message = f"Index {search_index_name} not found."
-            raise ValueError(message)
-
-    def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
-    ) -> list[VectorStoreSearchResult]:
-        """Perform a vector-based similarity search."""
-        all_results = []
-        for index_name, embedding_store in zip(
-            self.index_names, self.embedding_stores, strict=False
-        ):
-            results = embedding_store.similarity_search_by_vector(
-                query_embedding=query_embedding, k=k
-            )
-            mod_results = []
-            for r in results:
-                r.document.id = str(r.document.id) + f"-{index_name}"
-                mod_results += [r]
-            all_results += mod_results
-        return sorted(all_results, key=lambda x: x.score, reverse=True)[:k]
-
-    def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
-    ) -> list[VectorStoreSearchResult]:
-        """Perform a text-based similarity search."""
-        query_embedding = text_embedder(text)
-        if query_embedding:
-            return self.similarity_search_by_vector(
-                query_embedding=query_embedding, k=k
-            )
-        return []
-
-
 def get_embedding_store(
-    config_args: dict[str, dict],
+    store: dict[str, Any],
     embedding_name: str,
 ) -> BaseVectorStore:
     """Get the embedding description store."""
-    num_indexes = len(config_args)
-    embedding_stores = []
-    index_names = []
-    for index, store in config_args.items():
-        vector_store_type = store["type"]
-        index_name = create_index_name(
-            store.get("container_name", "default"), embedding_name
-        )
+    vector_store_type = store["type"]
+    index_name = create_index_name(
+        store.get("container_name", "default"), embedding_name
+    )
 
-        embeddings_schema: dict[str, VectorStoreSchemaConfig] = store.get(
-            "embeddings_schema", {}
-        )
-        single_embedding_config: VectorStoreSchemaConfig = VectorStoreSchemaConfig()
+    embeddings_schema: dict[str, VectorStoreSchemaConfig] = store.get(
+        "embeddings_schema", {}
+    )
+    embedding_config: VectorStoreSchemaConfig = VectorStoreSchemaConfig()
 
-        if (
-            embeddings_schema is not None
-            and embedding_name is not None
-            and embedding_name in embeddings_schema
-        ):
-            raw_config = embeddings_schema[embedding_name]
-            if isinstance(raw_config, dict):
-                single_embedding_config = VectorStoreSchemaConfig(**raw_config)
-            else:
-                single_embedding_config = raw_config
+    if (
+        embeddings_schema is not None
+        and embedding_name is not None
+        and embedding_name in embeddings_schema
+    ):
+        raw_config = embeddings_schema[embedding_name]
+        if isinstance(raw_config, dict):
+            embedding_config = VectorStoreSchemaConfig(**raw_config)
+        else:
+            embedding_config = raw_config
 
-        if single_embedding_config.index_name is None:
-            single_embedding_config.index_name = index_name
+    if embedding_config.index_name is None:
+        embedding_config.index_name = index_name
 
-        embedding_store = VectorStoreFactory().create_vector_store(
-            vector_store_type=vector_store_type,
-            vector_store_schema_config=single_embedding_config,
-            **store,
-        )
-        embedding_store.connect(**store)
-        # If there is only a single index, return the embedding store directly
-        if num_indexes == 1:
-            return embedding_store
-        embedding_stores.append(embedding_store)
-        index_names.append(index)
-    return MultiVectorStore(embedding_stores, index_names)
+    embedding_store = VectorStoreFactory().create_vector_store(
+        vector_store_type=vector_store_type,
+        vector_store_schema_config=embedding_config,
+        **store,
+    )
+    embedding_store.connect(**store)
+
+    return embedding_store
 
 
 def reformat_context_data(context_data: dict) -> dict:
@@ -172,81 +90,6 @@ def reformat_context_data(context_data: dict) -> dict:
     return final_format
 
 
-def update_context_data(
-    context_data: Any,
-    links: dict[str, Any],
-) -> Any:
-    """
-    Update context data with the links dict so that it contains both the index name and community id.
-
-    Parameters
-    ----------
-    - context_data (str | list[pd.DataFrame] | dict[str, pd.DataFrame]): The context data to update.
-    - links (dict[str, Any]): A dictionary of links to the original dataframes.
-
-    Returns
-    -------
-    str | list[pd.DataFrame] | dict[str, pd.DataFrame]: The updated context data.
-    """
-    updated_context_data = {}
-    for key in context_data:
-        entries = context_data[key].to_dict(orient="records")
-        updated_entry = []
-        if key == "reports":
-            updated_entry = [
-                dict(
-                    entry,
-                    index_name=links["community_reports"][int(entry["id"])][
-                        "index_name"
-                    ],
-                    index_id=links["community_reports"][int(entry["id"])]["id"],
-                )
-                for entry in entries
-            ]
-        if key == "entities":
-            updated_entry = [
-                dict(
-                    entry,
-                    entity=entry["entity"].split("-")[0],
-                    index_name=links["entities"][int(entry["id"])]["index_name"],
-                    index_id=links["entities"][int(entry["id"])]["id"],
-                )
-                for entry in entries
-            ]
-        if key == "relationships":
-            updated_entry = [
-                dict(
-                    entry,
-                    source=entry["source"].split("-")[0],
-                    target=entry["target"].split("-")[0],
-                    index_name=links["relationships"][int(entry["id"])]["index_name"],
-                    index_id=links["relationships"][int(entry["id"])]["id"],
-                )
-                for entry in entries
-            ]
-        if key == "claims":
-            updated_entry = [
-                dict(
-                    entry,
-                    entity=entry["entity"].split("-")[0],
-                    index_name=links["covariates"][int(entry["id"])]["index_name"],
-                    index_id=links["covariates"][int(entry["id"])]["id"],
-                )
-                for entry in entries
-            ]
-        if key == "sources":
-            updated_entry = [
-                dict(
-                    entry,
-                    index_name=links["text_units"][int(entry["id"])]["index_name"],
-                    index_id=links["text_units"][int(entry["id"])]["id"],
-                )
-                for entry in entries
-            ]
-        updated_context_data[key] = updated_entry
-    return updated_context_data
-
-
 def load_search_prompt(root_dir: str, prompt_config: str | None) -> str | None:
     """
     Load the search prompt from disk if configured.
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index f230692560..7588335a87 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -105,23 +105,18 @@ def assert_language_model_configs(
 
 
 def assert_vector_store_configs(
-    actual: dict[str, VectorStoreConfig],
-    expected: dict[str, VectorStoreConfig],
+    actual: VectorStoreConfig,
+    expected: VectorStoreConfig,
 ):
     assert type(actual) is type(expected)
-    assert len(actual) == len(expected)
-    for (index_a, store_a), (index_e, store_e) in zip(
-        actual.items(), expected.items(), strict=True
-    ):
-        assert index_a == index_e
-        assert store_a.type == store_e.type
-        assert store_a.db_uri == store_e.db_uri
-        assert store_a.url == store_e.url
-        assert store_a.api_key == store_e.api_key
-        assert store_a.audience == store_e.audience
-        assert store_a.container_name == store_e.container_name
-        assert store_a.overwrite == store_e.overwrite
-        assert store_a.database_name == store_e.database_name
+    assert actual.type == expected.type
+    assert actual.db_uri == expected.db_uri
+    assert actual.url == expected.url
+    assert actual.api_key == expected.api_key
+    assert actual.audience == expected.audience
+    assert actual.container_name == expected.container_name
+    assert actual.overwrite == expected.overwrite
+    assert actual.database_name == expected.database_name
 
 
 def assert_reporting_configs(
@@ -187,7 +182,6 @@ def assert_text_embedding_configs(
     assert actual.batch_max_tokens == expected.batch_max_tokens
     assert actual.names == expected.names
     assert actual.model_id == expected.model_id
-    assert actual.vector_store_id == expected.vector_store_id
 
 
 def assert_chunking_configs(actual: ChunkingConfig, expected: ChunkingConfig) -> None:
@@ -371,14 +365,6 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) ->
     assert_reporting_configs(actual.reporting, expected.reporting)
     assert_output_configs(actual.output, expected.output)
 
-    if expected.outputs is not None:
-        assert actual.outputs is not None
-        assert len(actual.outputs) == len(expected.outputs)
-        for a, e in zip(actual.outputs.keys(), expected.outputs.keys(), strict=True):
-            assert_output_configs(actual.outputs[a], expected.outputs[e])
-    else:
-        assert actual.outputs is None
-
     assert_update_output_configs(
         actual.update_index_output, expected.update_index_output
     )
diff --git a/tests/unit/query/context_builder/test_entity_extraction.py b/tests/unit/query/context_builder/test_entity_extraction.py
index aa2ff29b09..a0b7b77c52 100644
--- a/tests/unit/query/context_builder/test_entity_extraction.py
+++ b/tests/unit/query/context_builder/test_entity_extraction.py
@@ -55,9 +55,6 @@ def similarity_search_by_text(
             key=lambda x: x.score,
         )[:k]
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        return [document for document in self.documents if document.id in include_ids]
-
     def search_by_id(self, id: str) -> VectorStoreDocument:
         result = self.documents[0]
         result.id = id
@@ -92,27 +89,6 @@ def test_map_query_to_entities():
         ),
     ]
 
-    assert map_query_to_entities(
-        query="t22",
-        text_embedding_vectorstore=MockBaseVectorStore([
-            VectorStoreDocument(id=entity.id, vector=None) for entity in entities
-        ]),
-        text_embedder=ModelManager().get_or_create_embedding_model(
-            model_type="mock_embedding", name="mock"
-        ),
-        all_entities_dict={entity.id: entity for entity in entities},
-        embedding_vectorstore_key=EntityVectorStoreKey.ID,
-        k=1,
-        oversample_scaler=1,
-    ) == [
-        Entity(
-            id="c4f93564-4507-4ee4-b102-98add401a965",
-            short_id="sid2",
-            title="t22",
-            rank=4,
-        )
-    ]
-
     assert map_query_to_entities(
         query="t22",
         text_embedding_vectorstore=MockBaseVectorStore([
@@ -134,32 +110,6 @@ def test_map_query_to_entities():
         )
     ]
 
-    assert map_query_to_entities(
-        query="",
-        text_embedding_vectorstore=MockBaseVectorStore([
-            VectorStoreDocument(id=entity.id, vector=None) for entity in entities
-        ]),
-        text_embedder=ModelManager().get_or_create_embedding_model(
-            model_type="mock_embedding", name="mock"
-        ),
-        all_entities_dict={entity.id: entity for entity in entities},
-        embedding_vectorstore_key=EntityVectorStoreKey.ID,
-        k=2,
-    ) == [
-        Entity(
-            id="c4f93564-4507-4ee4-b102-98add401a965",
-            short_id="sid2",
-            title="t22",
-            rank=4,
-        ),
-        Entity(
-            id="8fd6d72a-8e9d-4183-8a97-c38bcc971c83",
-            short_id="sid4",
-            title="t4444",
-            rank=3,
-        ),
-    ]
-
     assert map_query_to_entities(
         query="",
         text_embedding_vectorstore=MockBaseVectorStore([

From 5ec49fd39cfae2f2257efe134393f248b2750ce5 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Wed, 15 Oct 2025 13:47:19 -0700
Subject: [PATCH 14/40] V3 docs and cleanup (#2100)

* Remove community contrib notebooks

* Add migration notebook and breaking changes page edits

* Update/polish docs

* Make model instance name configurable

* Add vector schema updates to v3 migration notebook

* Spellcheck

* Bump smoke test runtimes
---
 breaking-changes.md                           |   29 +
 docs/config/models.md                         |    4 +-
 docs/config/overview.md                       |    1 -
 docs/config/yaml.md                           |   14 +-
 .../index_migration_to_v3.ipynb               |  175 +++
 .../community_contrib/README.md               |    5 -
 .../neo4j/graphrag_import_neo4j_cypher.ipynb  | 1215 -----------------
 .../graph-visualization.ipynb                 |  523 -------
 .../_latest.manifest                          |  Bin 305 -> 0 bytes
 ...0-498c6e24-dd0a-42b9-8f7e-5e3d2ab258b0.txn |    1 -
 ...1-bf5aa024-a229-461f-8d78-699841a302fe.txn |  Bin 100 -> 0 bytes
 .../_versions/1.manifest                      |  Bin 249 -> 0 bytes
 .../_versions/2.manifest                      |  Bin 305 -> 0 bytes
 ...fe64774f-5412-4c9c-8dea-f6ed55c81119.lance |  Bin 1394345 -> 0 bytes
 graphrag/config/defaults.py                   |    5 +
 .../config/models/community_reports_config.py |    4 +
 graphrag/config/models/embed_text_config.py   |    4 +
 .../config/models/extract_claims_config.py    |    4 +
 .../config/models/extract_graph_config.py     |    4 +
 .../models/summarize_descriptions_config.py   |    4 +
 .../workflows/create_community_reports.py     |    2 +-
 .../create_community_reports_text.py          |    2 +-
 .../index/workflows/extract_covariates.py     |    2 +-
 graphrag/index/workflows/extract_graph.py     |    4 +-
 .../workflows/generate_text_embeddings.py     |    2 +-
 pyproject.toml                                |    2 +-
 tests/fixtures/min-csv/config.json            |    2 +-
 tests/fixtures/text/config.json               |    4 +-
 28 files changed, 250 insertions(+), 1762 deletions(-)
 create mode 100644 docs/examples_notebooks/index_migration_to_v3.ipynb
 delete mode 100644 examples_notebooks/community_contrib/README.md
 delete mode 100644 examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb
 delete mode 100644 examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb
 delete mode 100644 examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/_latest.manifest
 delete mode 100644 examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/_transactions/0-498c6e24-dd0a-42b9-8f7e-5e3d2ab258b0.txn
 delete mode 100644 examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/_transactions/1-bf5aa024-a229-461f-8d78-699841a302fe.txn
 delete mode 100644 examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/_versions/1.manifest
 delete mode 100644 examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/_versions/2.manifest
 delete mode 100644 examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/data/fe64774f-5412-4c9c-8dea-f6ed55c81119.lance

diff --git a/breaking-changes.md b/breaking-changes.md
index 4c0505d6dd..b07aa6fca8 100644
--- a/breaking-changes.md
+++ b/breaking-changes.md
@@ -12,6 +12,35 @@ There are five surface areas that may be impacted on any given release. They are
 
 > TL;DR: Always run `graphrag init --path [path] --force` between minor version bumps to ensure you have the latest config format. Run the provided migration notebook between major version bumps if you want to avoid re-indexing prior datasets. Note that this will overwrite your configuration and prompts, so backup if necessary.
 
+# v3
+Run the [migration notebook](./docs/examples_notebooks/index_migration_to_v3.ipynb) to convert older tables to the v3 format. Our main goals with v3 were to slim down the core library to minimize long-term maintenance of features that are either largely unused or should have been out of scope for a long time anyway.
+
+## Data Model
+We made minimal data model changes that will affect your index for v3. The primary breaking change is that we removed a rarely-used document-grouping capability that resulted in the `text_units` table having a `document_ids` column with a list instead of a single entry in a column called `document_id`. v3 fixes that, and the migration notebook applies the change so you don't need to re-index.
+
+Most of the other changes we made are removal of fields that are no longer used or are out of scope. For example, we removed the UMAP step that generates x/y coordinates for the entities - new indexes will not produce these columns, but they won't hurt anything if they are in your existing tables.
+
+## API
+We have removed the multi-search variant from each search method in the API.
+
+## Config
+
+We did make several changes to the configuration model. The best way forward is to re-run `init`, which we always recommend for minor and major version bumps.
+
+This is a summary of changes:
+- Removed fnllm as underlying model manager, so the model types "openai_chat", "azure_openai_chat", "openai_embedding", and "azure_openai_embedding" are all invalid. Use "chat" or "embedding".
+- fnllm also had an experimental rate limiting "auto" setting, which is no longer allowed. Use `null` in your config as a default, or set explicit limits to tpm/rpm.
+- LiteLLM does require a model_provider, so add yours as appropriate. For example, if you previously used "openai_chat" for your model type, this would be "openai", and for "azure_openai_chat" this would be "azure".
+- Collapsed the `vector_store` dict into a single root-level object. This is because we no longer support multi-search, and this dict required a lot of downstream complexity for that single use case.
+- Removed the `outputs` block that was also only used for multi-search.
+- Most workflows had an undocumented `strategy` config dict that allowed fine tuning of internal settings. These fine tunings are never used and had associated complexity, so we removed it.
+- Vector store configuration now allows custom schema per embedded field. This overrides the need for the `container_name` prefix, which caused confusion anyway. Now, the default container name will simply be the embedded field name - if you need something custom, add the `embeddings_schema` block and populate as needed.
+- We previously supported the ability to embed any text field in the data model. However, we only ever use text_unit_text, entity_description, and community_full_content, so all others have been removed.
+- Removed the `umap` and `embed_graph` blocks which were only used to add x/y fields to the entities. This fixed a long-standing dependency issue with graspologic. If you need x/y positions, see the [visualization guide](https://microsoft.github.io/graphrag/visualization_guide/) for using gephi.
+- Removed file filtering from input document loading. This was essentially unused.
+- Removed the groupby ability for text chunking. This was intended to allow short documents to be grouped before chunking, but is never used and added a bunch of complexity to the chunking process.
+
+
 # v2
 
 Run the [migration notebook](./docs/examples_notebooks/index_migration_to_v2.ipynb) to convert older tables to the v2 format.
diff --git a/docs/config/models.md b/docs/config/models.md
index 686304a510..676cb4b2ff 100644
--- a/docs/config/models.md
+++ b/docs/config/models.md
@@ -31,9 +31,9 @@ To use LiteLLM one must
 - Set `type` to either `chat` or `embedding`.
 - Provide a `model_provider`, e.g., `openai`, `azure`, `gemini`, etc.
 - Set the `model` to a one supported by the `model_provider`'s API.
-- Provide a `deployment_name` if using `azure` as the `model_provider`.
+- Provide a `deployment_name` if using `azure` as the `model_provider` if your deployment name differs from the model name.
 
-See [Detailed Configuration](yaml.md) for more details on configuration. [View LiteLLm basic usage](https://docs.litellm.ai/docs/#basic-usage) for details on how models are called (The `model_provider` is the portion prior to `/` while the `model` is the portion following the `/`).
+See [Detailed Configuration](yaml.md) for more details on configuration. [View LiteLLM basic usage](https://docs.litellm.ai/docs/#basic-usage) for details on how models are called (The `model_provider` is the portion prior to `/` while the `model` is the portion following the `/`).
 
 ## Model Selection Considerations
 
diff --git a/docs/config/overview.md b/docs/config/overview.md
index 025f5d718e..278d939ce2 100644
--- a/docs/config/overview.md
+++ b/docs/config/overview.md
@@ -8,4 +8,3 @@ The default configuration mode is the simplest way to get started with the Graph
 
 - [Init command](init.md) (recommended first step)
 - [Edit settings.yaml for deeper control](yaml.md)
-- [Purely using environment variables](env_vars.md) (not recommended)
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 059d99eada..ae4d42790d 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -11,7 +11,7 @@ For example:
 GRAPHRAG_API_KEY=some_api_key
 
 # settings.yml
-llm: 
+default_chat_model: 
   api_key: ${GRAPHRAG_API_KEY}
 ```
 
@@ -44,12 +44,12 @@ models:
 - `api_key` **str** - The OpenAI API key to use.
 - `auth_type` **api_key|azure_managed_identity** - Indicate how you want to authenticate requests.
 - `type` **chat**|**embedding**|mock_chat|mock_embeddings** - The type of LLM to use.
-- `model_provider` **str|None** - The model provider to use, e.g., openai, azure, anthropic, etc. Required when `type == chat|embedding`. When `type == chat|embedding`, [LiteLLM](https://docs.litellm.ai/) is used under the hood which has support for calling 100+ models. [View LiteLLm basic usage](https://docs.litellm.ai/docs/#basic-usage) for details on how models are called (The `model_provider` is the portion prior to `/` while the `model` is the portion following the `/`). [View Language Model Selection](models.md) for more details and examples on using LiteLLM.
+- `model_provider` **str|None** - The model provider to use, e.g., openai, azure, anthropic, etc. [LiteLLM](https://docs.litellm.ai/) is used under the hood which has support for calling 100+ models. [View LiteLLm basic usage](https://docs.litellm.ai/docs/#basic-usage) for details on how models are called (The `model_provider` is the portion prior to `/` while the `model` is the portion following the `/`). [View Language Model Selection](models.md) for more details and examples on using LiteLLM.
 - `model` **str** - The model name.
 - `encoding_model` **str** - The text encoding model to use. Default is to use the encoding model aligned with the language model (i.e., it is retrieved from tiktoken if unset).
 - `api_base` **str** - The API base url to use.
 - `api_version` **str** - The API version.
-- `deployment_name` **str** - The deployment name to use (Azure).
+- `deployment_name` **str** - The deployment name to use if your model is hosted on Azure. Note that if your deployment name on Azure matches the model name, this is unnecessary.
 - `organization` **str** - The client organization.
 - `proxy` **str** - The proxy URL to use.
 - `audience` **str** - (Azure OpenAI only) The URI of the target Azure resource/service for which a managed identity token is requested. Used if `api_key` is not defined. Default=`https://cognitiveservices.azure.com/.default`
@@ -57,7 +57,7 @@ models:
 - `request_timeout` **float** - The per-request timeout.
 - `tokens_per_minute` **int** - Set a leaky-bucket throttle on tokens-per-minute.
 - `requests_per_minute` **int** - Set a leaky-bucket throttle on requests-per-minute.
-- `retry_strategy` **str** - Retry strategy to use, "native" is the default and uses the strategy built into the OpenAI SDK. Other allowable values include "exponential_backoff", "random_wait", and "incremental_wait".
+- `retry_strategy` **str** - Retry strategy to use, "exponential_backoff" is the default. Other allowable values include "native", "random_wait", and "incremental_wait".
 - `max_retries` **int** - The maximum number of retries to use.
 - `max_retry_wait` **float** - The maximum backoff time.
 - `concurrent_requests` **int** The number of open requests to allow at once.
@@ -201,7 +201,7 @@ Supported embeddings names are:
 #### Fields
 
 - `model_id` **str** - Name of the model definition to use for text embedding.
-- `vector_store_id` **str** - Name of vector store definition to write to.
+- `model_instance_name` **str** - Name of the model singleton instance. Default is "text_embedding". This primarily affects the cache storage partitioning.
 - `batch_size` **int** - The maximum batch size to use.
 - `batch_max_tokens` **int** - The maximum batch # of tokens.
 - `names` **list[str]** - List of the embeddings names to run (must be in supported list).
@@ -213,6 +213,7 @@ Tune the language model-based graph extraction process.
 #### Fields
 
 - `model_id` **str** - Name of the model definition to use for API calls.
+- `model_instance_name` **str** - Name of the model singleton instance. Default is "extract_graph". This primarily affects the cache storage partitioning.
 - `prompt` **str** - The prompt file to use.
 - `entity_types` **list[str]** - The entity types to identify.
 - `max_gleanings` **int** - The maximum number of gleaning cycles to use.
@@ -222,6 +223,7 @@ Tune the language model-based graph extraction process.
 #### Fields
 
 - `model_id` **str** - Name of the model definition to use for API calls.
+- `model_instance_name` **str** - Name of the model singleton instance. Default is "summarize_descriptions". This primarily affects the cache storage partitioning.
 - `prompt` **str** - The prompt file to use.
 - `max_length` **int** - The maximum number of output tokens per summarization.
 - `max_input_length` **int** - The maximum number of tokens to collect for summarization (this will limit how many descriptions you send to be summarized for a given entity or relationship).
@@ -275,6 +277,7 @@ These are the settings used for Leiden hierarchical clustering of the graph to c
 
 - `enabled` **bool** - Whether to enable claim extraction. Off by default, because claim prompts really need user tuning.
 - `model_id` **str** - Name of the model definition to use for API calls.
+- `model_instance_name` **str** - Name of the model singleton instance. Default is "extract_claims". This primarily affects the cache storage partitioning.
 - `prompt` **str** - The prompt file to use.
 - `description` **str** - Describes the types of claims we want to extract.
 - `max_gleanings` **int** - The maximum number of gleaning cycles to use.
@@ -284,6 +287,7 @@ These are the settings used for Leiden hierarchical clustering of the graph to c
 #### Fields
 
 - `model_id` **str** - Name of the model definition to use for API calls.
+- `model_instance_name` **str** - Name of the model singleton instance. Default is "community_reporting". This primarily affects the cache storage partitioning.
 - `prompt` **str** - The prompt file to use.
 - `max_length` **int** - The maximum number of output tokens per report.
 - `max_input_length` **int** - The maximum number of input tokens to use when generating reports.
diff --git a/docs/examples_notebooks/index_migration_to_v3.ipynb b/docs/examples_notebooks/index_migration_to_v3.ipynb
new file mode 100644
index 0000000000..2b76133e80
--- /dev/null
+++ b/docs/examples_notebooks/index_migration_to_v3.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) 2024 Microsoft Corporation.\n",
+    "# Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Index Migration (v2 to v3)\n",
+    "\n",
+    "This notebook is used to maintain data model parity with older indexes for version 3.0 of GraphRAG. If you have a pre-3.0 index and need to migrate without re-running the entire pipeline, you can use this notebook to only update the pieces necessary for alignment. If you have a pre-2.0 index, please run the v2 migration notebook first!\n",
+    "\n",
+    "NOTE: we recommend regenerating your settings.yml with the latest version of GraphRAG using `graphrag init`. Copy your LLM settings into it before running this notebook. This ensures your config is aligned with the latest version for the migration.\n",
+    "\n",
+    "This notebook will also update your settings.yaml to ensure compatibility with our newer vector store collection naming scheme in order to avoid re-ingesting.\n",
+    "\n",
+    "WARNING: This will overwrite your parquet files, you may want to make a backup!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the directory that has your settings.yaml\n",
+    "PROJECT_DIRECTORY = \"/Users/naevans/graphrag/working/migration\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from graphrag.config.load_config import load_config\n",
+    "from graphrag.storage.factory import StorageFactory\n",
+    "\n",
+    "config = load_config(Path(PROJECT_DIRECTORY))\n",
+    "storage_config = config.output.model_dump()\n",
+    "storage = StorageFactory().create_storage(\n",
+    "    storage_type=storage_config[\"type\"],\n",
+    "    kwargs=storage_config,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_columns(df, columns):\n",
+    "    \"\"\"Remove columns from a DataFrame, suppressing errors.\"\"\"\n",
+    "    df.drop(labels=columns, axis=1, errors=\"ignore\", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphrag.utils.storage import (\n",
+    "    load_table_from_storage,\n",
+    "    write_table_to_storage,\n",
+    ")\n",
+    "\n",
+    "text_units = await load_table_from_storage(\"text_units\", storage)\n",
+    "\n",
+    "text_units[\"document_id\"] = text_units[\"document_ids\"].apply(lambda ids: ids[0])\n",
+    "remove_columns(text_units, [\"document_ids\"])\n",
+    "\n",
+    "await write_table_to_storage(text_units, \"text_units\", storage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Update settings.yaml\n",
+    "This next section will attempt to insert index names for each vector index using our new schema structure. It depends on most things being default. If you have already customized your vector store schema it may not be necessary.\n",
+    "\n",
+    "The primary goal is to align v2 indexes using our old default naming schema with the new customizability. If don't need this done or you have a more complicated config, comment it out and update your config manually to ensure each index name is set.\n",
+    "\n",
+    "Old default index names:\n",
+    "- default-text_unit-text\n",
+    "- default-entity-description\n",
+    "- default-community-full_content\n",
+    "\n",
+    "v3 versions are:\n",
+    "- text_unit_text\n",
+    "- entity_description\n",
+    "- community_full_content\n",
+    "\n",
+    "Therefore, with a v2 index we will explicitly set the old index names so it connects correctly.\n",
+    "\n",
+    "NOTE: we are also setting the default vector_size for each index, under the assumption that you are using a prior default with 1536 dimensions. Our new default of text-embedding-3-large has 3072 dimensions, which will be populated as the default if unset. Again, if you have a more complicated situation you may want to manually configure this.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "\n",
+    "EMBEDDING_DIMENSIONS = 1536\n",
+    "\n",
+    "settings = Path(PROJECT_DIRECTORY) / \"settings.yaml\"\n",
+    "with Path.open(settings) as f:\n",
+    "    conf = yaml.safe_load(f)\n",
+    "\n",
+    "vector_store = conf.get(\"vector_store\", {})\n",
+    "container_name = vector_store.get(\"container_name\", \"default\")\n",
+    "embeddings_schema = vector_store.get(\"embeddings_schema\", {})\n",
+    "text_unit_schema = embeddings_schema.get(\"text_unit.text\", {})\n",
+    "if \"index_name\" not in text_unit_schema:\n",
+    "    text_unit_schema[\"index_name\"] = f\"{container_name}-text_unit-text\"\n",
+    "if \"vector_size\" not in text_unit_schema:\n",
+    "    text_unit_schema[\"vector_size\"] = EMBEDDING_DIMENSIONS\n",
+    "embeddings_schema[\"text_unit.text\"] = text_unit_schema\n",
+    "entity_schema = embeddings_schema.get(\"entity.description\", {})\n",
+    "if \"index_name\" not in entity_schema:\n",
+    "    entity_schema[\"index_name\"] = f\"{container_name}-entity-description\"\n",
+    "if \"vector_size\" not in entity_schema:\n",
+    "    entity_schema[\"vector_size\"] = EMBEDDING_DIMENSIONS\n",
+    "embeddings_schema[\"entity.description\"] = entity_schema\n",
+    "community_schema = embeddings_schema.get(\"community.full_content\", {})\n",
+    "if \"index_name\" not in community_schema:\n",
+    "    community_schema[\"index_name\"] = f\"{container_name}-community-full_content\"\n",
+    "if \"vector_size\" not in community_schema:\n",
+    "    community_schema[\"vector_size\"] = EMBEDDING_DIMENSIONS\n",
+    "embeddings_schema[\"community.full_content\"] = community_schema\n",
+    "vector_store[\"embeddings_schema\"] = embeddings_schema\n",
+    "conf[\"vector_store\"] = vector_store\n",
+    "\n",
+    "with Path.open(settings, \"w\") as f:\n",
+    "    yaml.safe_dump(conf, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "graphrag",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples_notebooks/community_contrib/README.md b/examples_notebooks/community_contrib/README.md
deleted file mode 100644
index 0915dc1a60..0000000000
--- a/examples_notebooks/community_contrib/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-## Disclaimer
-
-This folder contains community contributed notebooks that are not officially supported by the GraphRAG team. The notebooks are provided as-is and are not guaranteed to work with the latest version of GraphRAG. If you have any questions or issues, please reach out to the author of the notebook directly.
-
-For more information on how to contribute to the GraphRAG project, please refer to the [contribution guidelines](https://github.com/microsoft/graphrag/blob/main/CONTRIBUTING.md)
diff --git a/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb b/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb
deleted file mode 100644
index a8b30a819b..0000000000
--- a/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb
+++ /dev/null
@@ -1,1215 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4fea928",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Copyright (c) 2024 Microsoft Corporation.\n",
-    "# Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c4bc9ba",
-   "metadata": {},
-   "source": [
-    "# Neo4j Import of GraphRAG Result Parquet files\n",
-    "\n",
-    "This notebook imports the results of the GraphRAG indexing process into the Neo4j Graph database for further processing, analysis or visualization. \n",
-    "\n",
-    "You can also build your own GenAI applications using Neo4j and a number of RAG strategies with LangChain, LlamaIndex, Haystack, and many other frameworks.\n",
-    "See: https://neo4j.com/labs/genai-ecosystem\n",
-    "\n",
-    "Here is what the end result looks like:\n",
-    "\n",
-    "![](https://dev.assets.neo4j.com/wp-content/uploads/graphrag-neo4j-visualization.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3924e246",
-   "metadata": {},
-   "source": [
-    "## How does it work?\n",
-    "\n",
-    "The notebook loads the parquet files from the `output` folder of your indexing process and loads them into Pandas dataframes.\n",
-    "It then uses a batching approach to send a slice of the data into Neo4j to create nodes and relationships and add relevant properties. The id-arrays on most entities are turned into relationships. \n",
-    "\n",
-    "All operations use MERGE, so they are idempotent, and you can run the script multiple times.\n",
-    "\n",
-    "If you need to clean out the database, you can run the following statement\n",
-    "\n",
-    "```cypher\n",
-    "MATCH (n)\n",
-    "CALL { WITH n DETACH DELETE n } IN TRANSACTIONS OF 25000 ROWS;\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "adca1803",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GRAPHRAG_FOLDER = \"PATH_TO_OUTPUT/artifacts\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7fb27b941602401d91542211134fc71a",
-   "metadata": {},
-   "source": [
-    "### Depedendencies\n",
-    "\n",
-    "We only need Pandas and the neo4j Python driver with the rust extension for faster network transport."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b57beec0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --quiet pandas neo4j-rust-ext"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "3eeee95f-e4f2-4052-94fb-a5dc8ab542ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "\n",
-    "import pandas as pd\n",
-    "from neo4j import GraphDatabase"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "307dd2f4",
-   "metadata": {},
-   "source": [
-    "## Neo4j Installation\n",
-    "\n",
-    "You can create a free instance of Neo4j [online](https://console.neo4j.io). You get a credentials file that you can use for the connection credentials. You can also get an instance in any of the cloud marketplaces.\n",
-    "\n",
-    "If you want to install Neo4j locally either use [Neo4j Desktop](https://neo4j.com/download) or \n",
-    "the official Docker image: `docker run -e NEO4J_AUTH=neo4j/password -p 7687:7687 -p 7474:7474 neo4j` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "b6c15443-4acb-4f91-88ea-4e08abaa4c29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NEO4J_URI = \"neo4j://localhost\"  # or neo4j+s://xxxx.databases.neo4j.io\n",
-    "NEO4J_USERNAME = \"neo4j\"\n",
-    "NEO4J_PASSWORD = \"\"  # your password\n",
-    "NEO4J_DATABASE = \"neo4j\"\n",
-    "\n",
-    "# Create a Neo4j driver\n",
-    "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70f37ab6",
-   "metadata": {},
-   "source": [
-    "## Batched Import\n",
-    "\n",
-    "The batched import function takes a Cypher insert statement (needs to use the variable `value` for the row) and a dataframe to import.\n",
-    "It will send by default 1k rows at a time as query parameter to the database to be inserted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "d787bf7b-ac9b-4bfb-b140-a50a3fd205c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def batched_import(statement, df, batch_size=1000):\n",
-    "    \"\"\"\n",
-    "    Import a dataframe into Neo4j using a batched approach.\n",
-    "\n",
-    "    Parameters: statement is the Cypher query to execute, df is the dataframe to import, and batch_size is the number of rows to import in each batch.\n",
-    "    \"\"\"\n",
-    "    total = len(df)\n",
-    "    start_s = time.time()\n",
-    "    for start in range(0, total, batch_size):\n",
-    "        batch = df.iloc[start : min(start + batch_size, total)]\n",
-    "        result = driver.execute_query(\n",
-    "            \"UNWIND $rows AS value \" + statement,\n",
-    "            rows=batch.to_dict(\"records\"),\n",
-    "            database_=NEO4J_DATABASE,\n",
-    "        )\n",
-    "        print(result.summary.counters)\n",
-    "    print(f\"{total} rows in {time.time() - start_s} s.\")\n",
-    "    return total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fb45f42",
-   "metadata": {},
-   "source": [
-    "## Indexes and Constraints\n",
-    "\n",
-    "Indexes in Neo4j are only used to find the starting points for graph queries, e.g. quickly finding two nodes to connect.\n",
-    "Constraints exist to avoid duplicates, we create them mostly on id's of Entity types.\n",
-    "\n",
-    "We use some Types as markers with two underscores before and after to distinguish them from the actual entity types.\n",
-    "\n",
-    "The default relationship type here is `RELATED` but we could also infer a real relationship-type from the description or the types of the start and end-nodes.\n",
-    "\n",
-    "* `__Entity__`\n",
-    "* `__Document__`\n",
-    "* `__Chunk__`\n",
-    "* `__Community__`\n",
-    "* `__Covariate__`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "ed7f212e-9148-424c-adc6-d81db9f8e5a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "create constraint chunk_id if not exists for (c:__Chunk__) require c.id is unique\n",
-      "\n",
-      "create constraint document_id if not exists for (d:__Document__) require d.id is unique\n",
-      "\n",
-      "create constraint entity_id if not exists for (c:__Community__) require c.community is unique\n",
-      "\n",
-      "create constraint entity_id if not exists for (e:__Entity__) require e.id is unique\n",
-      "\n",
-      "create constraint entity_title if not exists for (e:__Entity__) require e.name is unique\n",
-      "\n",
-      "create constraint entity_title if not exists for (e:__Covariate__) require e.title is unique\n",
-      "\n",
-      "create constraint related_id if not exists for ()-[rel:RELATED]->() require rel.id is unique\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create constraints, idempotent operation\n",
-    "\n",
-    "statements = [\n",
-    "    \"\\ncreate constraint chunk_id if not exists for (c:__Chunk__) require c.id is unique\",\n",
-    "    \"\\ncreate constraint document_id if not exists for (d:__Document__) require d.id is unique\",\n",
-    "    \"\\ncreate constraint entity_id if not exists for (c:__Community__) require c.community is unique\",\n",
-    "    \"\\ncreate constraint entity_id if not exists for (e:__Entity__) require e.id is unique\",\n",
-    "    \"\\ncreate constraint entity_title if not exists for (e:__Entity__) require e.name is unique\",\n",
-    "    \"\\ncreate constraint entity_title if not exists for (e:__Covariate__) require e.title is unique\",\n",
-    "    \"\\ncreate constraint related_id if not exists for ()-[rel:RELATED]->() require rel.id is unique\",\n",
-    "    \"\\n\",\n",
-    "]\n",
-    "\n",
-    "for statement in statements:\n",
-    "    if len((statement or \"\").strip()) > 0:\n",
-    "        print(statement)\n",
-    "        driver.execute_query(statement)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "beea073b",
-   "metadata": {},
-   "source": [
-    "## Import Process\n",
-    "\n",
-    "### Importing the Documents\n",
-    "\n",
-    "We're loading the parquet file for the documents and create nodes with their ids and add the title property.\n",
-    "We don't need to store text_unit_ids as we can create the relationships and the text content is also contained in the chunks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "1ba023e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>title</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>c305886e4aa2f6efcf64b57762777055</td>\n",
-       "      <td>book.txt</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 id     title\n",
-       "0  c305886e4aa2f6efcf64b57762777055  book.txt"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "doc_df = pd.read_parquet(\n",
-    "    f\"{GRAPHRAG_FOLDER}/documents.parquet\", columns=[\"id\", \"title\"]\n",
-    ")\n",
-    "doc_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "96391c15",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'labels_added': 1, 'nodes_created': 1, 'properties_set': 2}\n",
-      "1 rows in 0.05211496353149414 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import documents\n",
-    "statement = \"\"\"\n",
-    "MERGE (d:__Document__ {id:value.id})\n",
-    "SET d += value {.title}\n",
-    "\"\"\"\n",
-    "\n",
-    "batched_import(statement, doc_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f97bbadb",
-   "metadata": {},
-   "source": [
-    "### Loading Text Units\n",
-    "\n",
-    "We load the text units, create a node per id and set the text and number of tokens.\n",
-    "Then we connect them to the documents that we created before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "0d825626",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>text</th>\n",
-       "      <th>n_tokens</th>\n",
-       "      <th>document_ids</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>680dd6d2a970a49082fa4f34bf63a34e</td>\n",
-       "      <td>The Project Gutenberg eBook of A Christmas Ca...</td>\n",
-       "      <td>300</td>\n",
-       "      <td>[c305886e4aa2f6efcf64b57762777055]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>95f1f8f5bdbf0bee3a2c6f2f4a4907f6</td>\n",
-       "      <td>THE PROJECT GUTENBERG EBOOK A CHRISTMAS CAROL...</td>\n",
-       "      <td>300</td>\n",
-       "      <td>[c305886e4aa2f6efcf64b57762777055]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 id  \\\n",
-       "0  680dd6d2a970a49082fa4f34bf63a34e   \n",
-       "1  95f1f8f5bdbf0bee3a2c6f2f4a4907f6   \n",
-       "\n",
-       "                                                text  n_tokens  \\\n",
-       "0  The Project Gutenberg eBook of A Christmas Ca...       300   \n",
-       "1   THE PROJECT GUTENBERG EBOOK A CHRISTMAS CAROL...       300   \n",
-       "\n",
-       "                         document_ids  \n",
-       "0  [c305886e4aa2f6efcf64b57762777055]  \n",
-       "1  [c305886e4aa2f6efcf64b57762777055]  "
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text_df = pd.read_parquet(\n",
-    "    f\"{GRAPHRAG_FOLDER}/text_units.parquet\",\n",
-    "    columns=[\"id\", \"text\", \"n_tokens\", \"document_ids\"],\n",
-    ")\n",
-    "text_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "ffd3d380-8710-46f5-b90a-04ed8482192c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'relationships_created': 231, 'properties_set': 462}\n",
-      "231 rows in 0.05993008613586426 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "231"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "statement = \"\"\"\n",
-    "MERGE (c:__Chunk__ {id:value.id})\n",
-    "SET c += value {.text, .n_tokens}\n",
-    "WITH c, value\n",
-    "UNWIND value.document_ids AS document\n",
-    "MATCH (d:__Document__ {id:document})\n",
-    "MERGE (c)-[:PART_OF]->(d)\n",
-    "\"\"\"\n",
-    "\n",
-    "batched_import(statement, text_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f01b2094",
-   "metadata": {},
-   "source": [
-    "### Loading Nodes\n",
-    "\n",
-    "For the nodes we store id, name, description, embedding (if available), human readable id."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "2392f9e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>type</th>\n",
-       "      <th>description</th>\n",
-       "      <th>human_readable_id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>description_embedding</th>\n",
-       "      <th>text_unit_ids</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>\"PROJECT GUTENBERG\"</td>\n",
-       "      <td>\"ORGANIZATION\"</td>\n",
-       "      <td>Project Gutenberg is a pioneering organization...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>b45241d70f0e43fca764df95b2b81f77</td>\n",
-       "      <td>[-0.020793898031115532, 0.02951139025390148, 0...</td>\n",
-       "      <td>[01e84646075b255eab0a34d872336a89, 10bab8e9773...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>\"UNITED STATES\"</td>\n",
-       "      <td>\"GEO\"</td>\n",
-       "      <td>The United States is prominently recognized fo...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4119fd06010c494caa07f439b333f4c5</td>\n",
-       "      <td>[-0.009704762138426304, 0.013335365802049637, ...</td>\n",
-       "      <td>[01e84646075b255eab0a34d872336a89, 28f242c4515...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  name            type  \\\n",
-       "0  \"PROJECT GUTENBERG\"  \"ORGANIZATION\"   \n",
-       "1      \"UNITED STATES\"           \"GEO\"   \n",
-       "\n",
-       "                                         description  human_readable_id  \\\n",
-       "0  Project Gutenberg is a pioneering organization...                  0   \n",
-       "1  The United States is prominently recognized fo...                  1   \n",
-       "\n",
-       "                                 id  \\\n",
-       "0  b45241d70f0e43fca764df95b2b81f77   \n",
-       "1  4119fd06010c494caa07f439b333f4c5   \n",
-       "\n",
-       "                               description_embedding  \\\n",
-       "0  [-0.020793898031115532, 0.02951139025390148, 0...   \n",
-       "1  [-0.009704762138426304, 0.013335365802049637, ...   \n",
-       "\n",
-       "                                       text_unit_ids  \n",
-       "0  [01e84646075b255eab0a34d872336a89, 10bab8e9773...  \n",
-       "1  [01e84646075b255eab0a34d872336a89, 28f242c4515...  "
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "entity_df = pd.read_parquet(\n",
-    "    f\"{GRAPHRAG_FOLDER}/entities.parquet\",\n",
-    "    columns=[\n",
-    "        \"name\",\n",
-    "        \"type\",\n",
-    "        \"description\",\n",
-    "        \"human_readable_id\",\n",
-    "        \"id\",\n",
-    "        \"description_embedding\",\n",
-    "        \"text_unit_ids\",\n",
-    "    ],\n",
-    ")\n",
-    "entity_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1d038114-0714-48ee-a48a-c421cd539661",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'properties_set': 831}\n",
-      "277 rows in 0.6978070735931396 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "277"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "entity_statement = \"\"\"\n",
-    "MERGE (e:__Entity__ {id:value.id})\n",
-    "SET e += value {.human_readable_id, .description, name:replace(value.name,'\"','')}\n",
-    "WITH e, value\n",
-    "CALL db.create.setNodeVectorProperty(e, \"description_embedding\", value.description_embedding)\n",
-    "CALL apoc.create.addLabels(e, case when coalesce(value.type,\"\") = \"\" then [] else [apoc.text.upperCamelCase(replace(value.type,'\"',''))] end) yield node\n",
-    "UNWIND value.text_unit_ids AS text_unit\n",
-    "MATCH (c:__Chunk__ {id:text_unit})\n",
-    "MERGE (c)-[:HAS_ENTITY]->(e)\n",
-    "\"\"\"\n",
-    "\n",
-    "batched_import(entity_statement, entity_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "018d4f87",
-   "metadata": {},
-   "source": [
-    "### Import Relationships\n",
-    "\n",
-    "For the relationships we find the source and target node by name, using the base `__Entity__` type.\n",
-    "After creating the `RELATED` relationships, we set the description as attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "b347a047",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>source</th>\n",
-       "      <th>target</th>\n",
-       "      <th>id</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>human_readable_id</th>\n",
-       "      <th>description</th>\n",
-       "      <th>text_unit_ids</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>\"PROJECT GUTENBERG\"</td>\n",
-       "      <td>\"A CHRISTMAS CAROL\"</td>\n",
-       "      <td>b84d71ed9c3b45819eb3205fd28e13a0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>\"Project Gutenberg is responsible for releasin...</td>\n",
-       "      <td>[680dd6d2a970a49082fa4f34bf63a34e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>\"PROJECT GUTENBERG\"</td>\n",
-       "      <td>\"SUZANNE SHELL\"</td>\n",
-       "      <td>b0b464bc92a541e48547fe9738378dab</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>\"Suzanne Shell produced the eBook version of '...</td>\n",
-       "      <td>[680dd6d2a970a49082fa4f34bf63a34e]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                source               target                                id  \\\n",
-       "0  \"PROJECT GUTENBERG\"  \"A CHRISTMAS CAROL\"  b84d71ed9c3b45819eb3205fd28e13a0   \n",
-       "1  \"PROJECT GUTENBERG\"      \"SUZANNE SHELL\"  b0b464bc92a541e48547fe9738378dab   \n",
-       "\n",
-       "   rank  weight human_readable_id  \\\n",
-       "0    20     1.0                 0   \n",
-       "1    15     1.0                 1   \n",
-       "\n",
-       "                                         description  \\\n",
-       "0  \"Project Gutenberg is responsible for releasin...   \n",
-       "1  \"Suzanne Shell produced the eBook version of '...   \n",
-       "\n",
-       "                        text_unit_ids  \n",
-       "0  [680dd6d2a970a49082fa4f34bf63a34e]  \n",
-       "1  [680dd6d2a970a49082fa4f34bf63a34e]  "
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rel_df = pd.read_parquet(\n",
-    "    f\"{GRAPHRAG_FOLDER}/relationships.parquet\",\n",
-    "    columns=[\n",
-    "        \"source\",\n",
-    "        \"target\",\n",
-    "        \"id\",\n",
-    "        \"rank\",\n",
-    "        \"weight\",\n",
-    "        \"human_readable_id\",\n",
-    "        \"description\",\n",
-    "        \"text_unit_ids\",\n",
-    "    ],\n",
-    ")\n",
-    "rel_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "27900c01-89e1-4dec-9d5c-c07317c68baf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'properties_set': 1710}\n",
-      "342 rows in 0.14740705490112305 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "342"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rel_statement = \"\"\"\n",
-    "    MATCH (source:__Entity__ {name:replace(value.source,'\"','')})\n",
-    "    MATCH (target:__Entity__ {name:replace(value.target,'\"','')})\n",
-    "    // not necessary to merge on id as there is only one relationship per pair\n",
-    "    MERGE (source)-[rel:RELATED {id: value.id}]->(target)\n",
-    "    SET rel += value {.rank, .weight, .human_readable_id, .description, .text_unit_ids}\n",
-    "    RETURN count(*) as createdRels\n",
-    "\"\"\"\n",
-    "\n",
-    "batched_import(rel_statement, rel_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6365dd7",
-   "metadata": {},
-   "source": [
-    "### Importing Communities\n",
-    "\n",
-    "For communities we import their id, title, level.\n",
-    "We connect the `__Community__` nodes to the start and end nodes of the relationships they refer to.\n",
-    "\n",
-    "Connecting them to the chunks they orignate from is optional, as the entites are already connected to the chunks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "c2fab66c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>level</th>\n",
-       "      <th>title</th>\n",
-       "      <th>text_unit_ids</th>\n",
-       "      <th>relationship_ids</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Community 2</td>\n",
-       "      <td>[0546d296a4d3bb0486bd0c94c01dc9be,0d6bc6e701a0...</td>\n",
-       "      <td>[ba481175ee1d4329bf07757a30abd3a1, 8d8da35190b...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Community 4</td>\n",
-       "      <td>[054bdcba0a3690b43609d9226a47f84d,3a450ed2b7fb...</td>\n",
-       "      <td>[929f30875e1744b49e7b416eaf5a790c, 4920fda0318...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  id  level        title                                      text_unit_ids  \\\n",
-       "0  2      0  Community 2  [0546d296a4d3bb0486bd0c94c01dc9be,0d6bc6e701a0...   \n",
-       "1  4      0  Community 4  [054bdcba0a3690b43609d9226a47f84d,3a450ed2b7fb...   \n",
-       "\n",
-       "                                    relationship_ids  \n",
-       "0  [ba481175ee1d4329bf07757a30abd3a1, 8d8da35190b...  \n",
-       "1  [929f30875e1744b49e7b416eaf5a790c, 4920fda0318...  "
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "community_df = pd.read_parquet(\n",
-    "    f\"{GRAPHRAG_FOLDER}/communities.parquet\",\n",
-    "    columns=[\"id\", \"level\", \"title\", \"text_unit_ids\", \"relationship_ids\"],\n",
-    ")\n",
-    "\n",
-    "community_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "1351f7e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'properties_set': 94}\n",
-      "47 rows in 0.07877922058105469 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "47"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "statement = \"\"\"\n",
-    "MERGE (c:__Community__ {community:value.id})\n",
-    "SET c += value {.level, .title}\n",
-    "/*\n",
-    "UNWIND value.text_unit_ids as text_unit_id\n",
-    "MATCH (t:__Chunk__ {id:text_unit_id})\n",
-    "MERGE (c)-[:HAS_CHUNK]->(t)\n",
-    "WITH distinct c, value\n",
-    "*/\n",
-    "WITH *\n",
-    "UNWIND value.relationship_ids as rel_id\n",
-    "MATCH (start:__Entity__)-[:RELATED {id:rel_id}]->(end:__Entity__)\n",
-    "MERGE (start)-[:IN_COMMUNITY]->(c)\n",
-    "MERGE (end)-[:IN_COMMUNITY]->(c)\n",
-    "RETURn count(distinct c) as createdCommunities\n",
-    "\"\"\"\n",
-    "\n",
-    "batched_import(statement, community_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd9adf50",
-   "metadata": {},
-   "source": [
-    "### Importing Community Reports\n",
-    "\n",
-    "Fo the community reports we create nodes for each communitiy set the id, community, level, title, summary, rank, and rank_explanation and connect them to the entities they are about.\n",
-    "For the findings we create the findings in context of the communities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "1be9e7a9-69ee-406b-bce5-95a9c41ecffe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>community</th>\n",
-       "      <th>level</th>\n",
-       "      <th>title</th>\n",
-       "      <th>summary</th>\n",
-       "      <th>findings</th>\n",
-       "      <th>rank</th>\n",
-       "      <th>rank_explanation</th>\n",
-       "      <th>full_content</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>e7822326-4da8-4954-afa9-be7f4f5791a5</td>\n",
-       "      <td>42</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Scrooge's Supernatural Encounters: Marley's Gh...</td>\n",
-       "      <td>This report delves into the pivotal supernatur...</td>\n",
-       "      <td>[{'explanation': 'Marley's Ghost plays a cruci...</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>The impact severity rating is high due to the ...</td>\n",
-       "      <td># Scrooge's Supernatural Encounters: Marley's ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8a5afac1-99ef-4f01-a1b1-f044ce392ff9</td>\n",
-       "      <td>43</td>\n",
-       "      <td>2</td>\n",
-       "      <td>The Ghost's Influence on Scrooge's Transformation</td>\n",
-       "      <td>This report delves into the pivotal role of 'T...</td>\n",
-       "      <td>[{'explanation': 'The Ghost, identified at tim...</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>The impact severity rating is high due to the ...</td>\n",
-       "      <td># The Ghost's Influence on Scrooge's Transform...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     id community  level  \\\n",
-       "0  e7822326-4da8-4954-afa9-be7f4f5791a5        42      2   \n",
-       "1  8a5afac1-99ef-4f01-a1b1-f044ce392ff9        43      2   \n",
-       "\n",
-       "                                               title  \\\n",
-       "0  Scrooge's Supernatural Encounters: Marley's Gh...   \n",
-       "1  The Ghost's Influence on Scrooge's Transformation   \n",
-       "\n",
-       "                                             summary  \\\n",
-       "0  This report delves into the pivotal supernatur...   \n",
-       "1  This report delves into the pivotal role of 'T...   \n",
-       "\n",
-       "                                            findings  rank  \\\n",
-       "0  [{'explanation': 'Marley's Ghost plays a cruci...   8.0   \n",
-       "1  [{'explanation': 'The Ghost, identified at tim...   8.5   \n",
-       "\n",
-       "                                    rank_explanation  \\\n",
-       "0  The impact severity rating is high due to the ...   \n",
-       "1  The impact severity rating is high due to the ...   \n",
-       "\n",
-       "                                        full_content  \n",
-       "0  # Scrooge's Supernatural Encounters: Marley's ...  \n",
-       "1  # The Ghost's Influence on Scrooge's Transform...  "
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "community_report_df = pd.read_parquet(\n",
-    "    f\"{GRAPHRAG_FOLDER}/community_reports.parquet\",\n",
-    "    columns=[\n",
-    "        \"id\",\n",
-    "        \"community\",\n",
-    "        \"level\",\n",
-    "        \"title\",\n",
-    "        \"summary\",\n",
-    "        \"findings\",\n",
-    "        \"rank\",\n",
-    "        \"rank_explanation\",\n",
-    "        \"full_content\",\n",
-    "    ],\n",
-    ")\n",
-    "community_report_df.head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "5c6ed591-f98c-4403-9fde-8d4cb4c01cca",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'properties_set': 729}\n",
-      "47 rows in 0.02472519874572754 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "47"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import communities\n",
-    "community_statement = \"\"\"\n",
-    "MERGE (c:__Community__ {community:value.community})\n",
-    "SET c += value {.level, .title, .rank, .rank_explanation, .full_content, .summary}\n",
-    "WITH c, value\n",
-    "UNWIND range(0, size(value.findings)-1) AS finding_idx\n",
-    "WITH c, value, finding_idx, value.findings[finding_idx] as finding\n",
-    "MERGE (c)-[:HAS_FINDING]->(f:Finding {id:finding_idx})\n",
-    "SET f += finding\n",
-    "\"\"\"\n",
-    "batched_import(community_statement, community_report_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50a1a24a",
-   "metadata": {},
-   "source": [
-    "### Importing Covariates\n",
-    "\n",
-    "Covariates are for instance claims on entities, we connect them to the chunks where they originate from."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "523bed92-d12c-4fc4-aa44-6c62321b36bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cov_df = (pd.read_parquet(f\"{GRAPHRAG_FOLDER}/covariates.parquet\"),)\n",
-    "#                         columns=[\"id\",\"text_unit_id\"])\n",
-    "cov_df.head(2)\n",
-    "# Subject id do not match entity ids"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3e064234-5fce-448e-8bb4-ab2f35699049",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'_contains_updates': True, 'labels_added': 89, 'relationships_created': 89, 'nodes_created': 89, 'properties_set': 1061}\n",
-      "89 rows in 0.13370895385742188 s.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "89"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import covariates\n",
-    "cov_statement = \"\"\"\n",
-    "MERGE (c:__Covariate__ {id:value.id})\n",
-    "SET c += apoc.map.clean(value, [\"text_unit_id\", \"document_ids\", \"n_tokens\"], [NULL, \"\"])\n",
-    "WITH c, value\n",
-    "MATCH (ch:__Chunk__ {id: value.text_unit_id})\n",
-    "MERGE (ch)-[:HAS_COVARIATE]->(c)\n",
-    "\"\"\"\n",
-    "batched_import(cov_statement, cov_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00340bae",
-   "metadata": {},
-   "source": [
-    "### Visualize your data\n",
-    "\n",
-    "You can now [Open] Neo4j on Aura, you need to log in with either SSO or your credentials.\n",
-    "\n",
-    "Or open https://workspace-preview.neo4j.io and connect to your local instance, remember the URI is `neo4j://localhost` and `neo4j` as username and `password` as password.\n",
-    "\n",
-    "In \"Explore\" you can explore by using visual graph patterns and then explore and expand further.\n",
-    "\n",
-    "In \"Query\", you can open the left sidebar and explore by clicking on the nodes and relationships.\n",
-    "You can also use the co-pilot to generate Cypher queries for your, here are some examples.\n",
-    "\n",
-    "#### Show a few `__Entity__` nodes and their relationships (Entity Graph)\n",
-    "\n",
-    "```cypher\n",
-    "MATCH path = (:__Entity__)-[:RELATED]->(:__Entity__)\n",
-    "RETURN path LIMIT 200\n",
-    "```\n",
-    "\n",
-    "#### Show the Chunks and the Document (Lexical Graph)\n",
-    "\n",
-    "```cypher\n",
-    "MATCH (d:__Document__) WITH d LIMIT 1\n",
-    "MATCH path = (d)<-[:PART_OF]-(c:__Chunk__)\n",
-    "RETURN path LIMIT 100\n",
-    "```\n",
-    "\n",
-    "####  Show a Community and it's Entities\n",
-    "\n",
-    "```cypher\n",
-    "MATCH (c:__Community__) WITH c LIMIT 1\n",
-    "MATCH path = (c)<-[:IN_COMMUNITY]-()-[:RELATED]-(:__Entity__)\n",
-    "RETURN path LIMIT 100\n",
-    "```\n",
-    "\n",
-    "#### Show everything\n",
-    "\n",
-    "```cypher\n",
-    "MATCH (d:__Document__) WITH d LIMIT 1\n",
-    "MATCH path = (d)<-[:PART_OF]-(:__Chunk__)-[:HAS_ENTIY]->()-[:RELATED]-()-[:IN_COMMUNITY]->()\n",
-    "RETURN path LIMIT 250\n",
-    "```\n",
-    "\n",
-    "We showed the visualization of this last query at the beginning."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0aa8529",
-   "metadata": {},
-   "source": [
-    "If you have questions, feel free to reach out in the GraphRAG discord server: \n",
-    "https://discord.gg/graphrag"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb b/examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb
deleted file mode 100644
index fb53287b55..0000000000
--- a/examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb
+++ /dev/null
@@ -1,523 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualizing the knowledge graph with `yfiles-jupyter-graphs`\n",
-    "\n",
-    "This notebook is a partial copy of [local_search.ipynb](../../local_search.ipynb) that shows how to use `yfiles-jupyter-graphs` to add interactive graph visualizations of the parquet files  and how to visualize the result context of `graphrag` queries (see at the end of this notebook)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Copyright (c) 2024 Microsoft Corporation.\n",
-    "# Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import pandas as pd\n",
-    "import tiktoken\n",
-    "from graphrag.query.llm.oai.chat_openai import ChatOpenAI\n",
-    "from graphrag.query.llm.oai.embedding import OpenAIEmbedding\n",
-    "from graphrag.query.llm.oai.typing import OpenaiApiType\n",
-    "\n",
-    "from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey\n",
-    "from graphrag.query.indexer_adapters import (\n",
-    "    read_indexer_covariates,\n",
-    "    read_indexer_entities,\n",
-    "    read_indexer_relationships,\n",
-    "    read_indexer_reports,\n",
-    "    read_indexer_text_units,\n",
-    ")\n",
-    "from graphrag.query.structured_search.local_search.mixed_context import (\n",
-    "    LocalSearchMixedContext,\n",
-    ")\n",
-    "from graphrag.query.structured_search.local_search.search import LocalSearch\n",
-    "from graphrag.vector_stores.lancedb import LanceDBVectorStore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Local Search Example\n",
-    "\n",
-    "Local search method generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents (e.g. What are the healing properties of chamomile?)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load text units and graph data tables as context for local search\n",
-    "\n",
-    "- In this test we first load indexing outputs from parquet files to dataframes, then convert these dataframes into collections of data objects aligning with the knowledge model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load tables to dataframes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "INPUT_DIR = \"../../inputs/operation dulce\"\n",
-    "LANCEDB_URI = f\"{INPUT_DIR}/lancedb\"\n",
-    "\n",
-    "COMMUNITY_REPORT_TABLE = \"community_reports\"\n",
-    "COMMUNITY_TABLE = \"communities\"\n",
-    "ENTITY_TABLE = \"entities\"\n",
-    "RELATIONSHIP_TABLE = \"relationships\"\n",
-    "COVARIATE_TABLE = \"covariates\"\n",
-    "TEXT_UNIT_TABLE = \"text_units\"\n",
-    "COMMUNITY_LEVEL = 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Read entities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read nodes table to get community and degree data\n",
-    "entity_df = pd.read_parquet(f\"{INPUT_DIR}/{ENTITY_TABLE}.parquet\")\n",
-    "community_df = pd.read_parquet(f\"{INPUT_DIR}/{COMMUNITY_TABLE}.parquet\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Read relationships"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "relationship_df = pd.read_parquet(f\"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet\")\n",
-    "relationships = read_indexer_relationships(relationship_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualizing nodes and relationships with `yfiles-jupyter-graphs`\n",
-    "\n",
-    "`yfiles-jupyter-graphs` is a graph visualization extension that provides interactive and customizable visualizations for structured node and relationship data.\n",
-    "\n",
-    "In this case, we use it to provide an interactive visualization for the knowledge graph of the [local_search.ipynb](../../local_search.ipynb) sample by passing node and relationship lists converted from the given parquet files. The requirements for the input data is an `id` attribute for the nodes and `start`/`end` properties for the relationships that correspond to the node ids. Additional attributes can be added in the `properties` of each node/relationship dict:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install yfiles_jupyter_graphs --quiet\n",
-    "from yfiles_jupyter_graphs import GraphWidget\n",
-    "\n",
-    "\n",
-    "# converts the entities dataframe to a list of dicts for yfiles-jupyter-graphs\n",
-    "def convert_entities_to_dicts(df):\n",
-    "    \"\"\"Convert the entities dataframe to a list of dicts for yfiles-jupyter-graphs.\"\"\"\n",
-    "    nodes_dict = {}\n",
-    "    for _, row in df.iterrows():\n",
-    "        # Create a dictionary for each row and collect unique nodes\n",
-    "        node_id = row[\"title\"]\n",
-    "        if node_id not in nodes_dict:\n",
-    "            nodes_dict[node_id] = {\n",
-    "                \"id\": node_id,\n",
-    "                \"properties\": row.to_dict(),\n",
-    "            }\n",
-    "    return list(nodes_dict.values())\n",
-    "\n",
-    "\n",
-    "# converts the relationships dataframe to a list of dicts for yfiles-jupyter-graphs\n",
-    "def convert_relationships_to_dicts(df):\n",
-    "    \"\"\"Convert the relationships dataframe to a list of dicts for yfiles-jupyter-graphs.\"\"\"\n",
-    "    relationships = []\n",
-    "    for _, row in df.iterrows():\n",
-    "        # Create a dictionary for each row\n",
-    "        relationships.append({\n",
-    "            \"start\": row[\"source\"],\n",
-    "            \"end\": row[\"target\"],\n",
-    "            \"properties\": row.to_dict(),\n",
-    "        })\n",
-    "    return relationships\n",
-    "\n",
-    "\n",
-    "w = GraphWidget()\n",
-    "w.directed = True\n",
-    "w.nodes = convert_entities_to_dicts(entity_df)\n",
-    "w.edges = convert_relationships_to_dicts(relationship_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure data-driven visualization\n",
-    "\n",
-    "The additional properties can be used to configure the visualization for different use cases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# show title on the node\n",
-    "w.node_label_mapping = \"title\"\n",
-    "\n",
-    "\n",
-    "# map community to a color\n",
-    "def community_to_color(community):\n",
-    "    \"\"\"Map a community to a color.\"\"\"\n",
-    "    colors = [\n",
-    "        \"crimson\",\n",
-    "        \"darkorange\",\n",
-    "        \"indigo\",\n",
-    "        \"cornflowerblue\",\n",
-    "        \"cyan\",\n",
-    "        \"teal\",\n",
-    "        \"green\",\n",
-    "    ]\n",
-    "    return (\n",
-    "        colors[int(community) % len(colors)] if community is not None else \"lightgray\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def edge_to_source_community(edge):\n",
-    "    \"\"\"Get the community of the source node of an edge.\"\"\"\n",
-    "    source_node = next(\n",
-    "        (entry for entry in w.nodes if entry[\"properties\"][\"title\"] == edge[\"start\"]),\n",
-    "        None,\n",
-    "    )\n",
-    "    source_node_community = source_node[\"properties\"][\"community\"]\n",
-    "    return source_node_community if source_node_community is not None else None\n",
-    "\n",
-    "\n",
-    "w.node_color_mapping = lambda node: community_to_color(node[\"properties\"][\"community\"])\n",
-    "w.edge_color_mapping = lambda edge: community_to_color(edge_to_source_community(edge))\n",
-    "# map size data to a reasonable factor\n",
-    "w.node_scale_factor_mapping = lambda node: 0.5 + node[\"properties\"][\"size\"] * 1.5 / 20\n",
-    "# use weight for edge thickness\n",
-    "w.edge_thickness_factor_mapping = \"weight\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Automatic layouts\n",
-    "\n",
-    "The widget provides different automatic layouts that serve different purposes: `Circular`, `Hierarchic`, `Organic (interactiv or static)`, `Orthogonal`, `Radial`, `Tree`, `Geo-spatial`.\n",
-    "\n",
-    "For the knowledge graph, this sample uses the `Circular` layout, though `Hierarchic` or `Organic` are also suitable choices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use the circular layout for this visualization. For larger graphs, the default organic layout is often preferrable.\n",
-    "w.circular_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Display the graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(w)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualizing the result context of `graphrag` queries\n",
-    "\n",
-    "The result context of `graphrag` queries allow to inspect the context graph of the request. This data can similarly be visualized as graph with `yfiles-jupyter-graphs`.\n",
-    "\n",
-    "## Making the request\n",
-    "\n",
-    "The following cell recreates the sample queries from [local_search.ipynb](../../local_search.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# setup (see also ../../local_search.ipynb)\n",
-    "entities = read_indexer_entities(entity_df, community_df, COMMUNITY_LEVEL)\n",
-    "\n",
-    "description_embedding_store = LanceDBVectorStore(\n",
-    "    collection_name=\"default-entity-description\",\n",
-    ")\n",
-    "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
-    "covariate_df = pd.read_parquet(f\"{INPUT_DIR}/{COVARIATE_TABLE}.parquet\")\n",
-    "claims = read_indexer_covariates(covariate_df)\n",
-    "covariates = {\"claims\": claims}\n",
-    "report_df = pd.read_parquet(f\"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet\")\n",
-    "reports = read_indexer_reports(report_df, community_df, COMMUNITY_LEVEL)\n",
-    "text_unit_df = pd.read_parquet(f\"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet\")\n",
-    "text_units = read_indexer_text_units(text_unit_df)\n",
-    "\n",
-    "api_key = os.environ[\"GRAPHRAG_API_KEY\"]\n",
-    "llm_model = os.environ[\"GRAPHRAG_LLM_MODEL\"]\n",
-    "embedding_model = os.environ[\"GRAPHRAG_EMBEDDING_MODEL\"]\n",
-    "\n",
-    "llm = ChatOpenAI(\n",
-    "    api_key=api_key,\n",
-    "    model=llm_model,\n",
-    "    api_type=OpenaiApiType.OpenAI,  # OpenaiApiType.OpenAI or OpenaiApiType.AzureOpenAI\n",
-    "    max_retries=20,\n",
-    ")\n",
-    "\n",
-    "token_encoder = tiktoken.get_encoding(\"cl100k_base\")\n",
-    "\n",
-    "text_embedder = OpenAIEmbedding(\n",
-    "    api_key=api_key,\n",
-    "    api_base=None,\n",
-    "    api_type=OpenaiApiType.OpenAI,\n",
-    "    model=embedding_model,\n",
-    "    deployment_name=embedding_model,\n",
-    "    max_retries=20,\n",
-    ")\n",
-    "\n",
-    "context_builder = LocalSearchMixedContext(\n",
-    "    community_reports=reports,\n",
-    "    text_units=text_units,\n",
-    "    entities=entities,\n",
-    "    relationships=relationships,\n",
-    "    covariates=covariates,\n",
-    "    entity_text_embeddings=description_embedding_store,\n",
-    "    embedding_vectorstore_key=EntityVectorStoreKey.ID,  # if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE\n",
-    "    text_embedder=text_embedder,\n",
-    "    token_encoder=token_encoder,\n",
-    ")\n",
-    "\n",
-    "local_context_params = {\n",
-    "    \"text_unit_prop\": 0.5,\n",
-    "    \"community_prop\": 0.1,\n",
-    "    \"conversation_history_max_turns\": 5,\n",
-    "    \"conversation_history_user_turns_only\": True,\n",
-    "    \"top_k_mapped_entities\": 10,\n",
-    "    \"top_k_relationships\": 10,\n",
-    "    \"include_entity_rank\": True,\n",
-    "    \"include_relationship_weight\": True,\n",
-    "    \"include_community_rank\": False,\n",
-    "    \"return_candidate_context\": False,\n",
-    "    \"embedding_vectorstore_key\": EntityVectorStoreKey.ID,  # set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids\n",
-    "    \"max_tokens\": 12_000,  # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)\n",
-    "}\n",
-    "\n",
-    "llm_params = {\n",
-    "    \"max_tokens\": 2_000,  # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500)\n",
-    "    \"temperature\": 0.0,\n",
-    "}\n",
-    "\n",
-    "search_engine = LocalSearch(\n",
-    "    llm=llm,\n",
-    "    context_builder=context_builder,\n",
-    "    token_encoder=token_encoder,\n",
-    "    llm_params=llm_params,\n",
-    "    context_builder_params=local_context_params,\n",
-    "    response_type=\"multiple paragraphs\",  # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run local search on sample queries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = await search_engine.search(\"Tell me about Agent Mercer\")\n",
-    "print(result.response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Tell me about Dr. Jordan Hayes\"\n",
-    "result = await search_engine.search(question)\n",
-    "print(result.response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspecting the context data used to generate the response"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result.context_data[\"entities\"].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result.context_data[\"relationships\"].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing the result context as graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "Helper function to visualize the result context with `yfiles-jupyter-graphs`.\n",
-    "\n",
-    "The dataframes are converted into supported nodes and relationships lists and then passed to yfiles-jupyter-graphs.\n",
-    "Additionally, some values are mapped to visualization properties.\n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "def show_graph(result):\n",
-    "    \"\"\"Visualize the result context with yfiles-jupyter-graphs.\"\"\"\n",
-    "    from yfiles_jupyter_graphs import GraphWidget\n",
-    "\n",
-    "    if (\n",
-    "        \"entities\" not in result.context_data\n",
-    "        or \"relationships\" not in result.context_data\n",
-    "    ):\n",
-    "        msg = \"The passed results do not contain 'entities' or 'relationships'\"\n",
-    "        raise ValueError(msg)\n",
-    "\n",
-    "    # converts the entities dataframe to a list of dicts for yfiles-jupyter-graphs\n",
-    "    def convert_entities_to_dicts(df):\n",
-    "        \"\"\"Convert the entities dataframe to a list of dicts for yfiles-jupyter-graphs.\"\"\"\n",
-    "        nodes_dict = {}\n",
-    "        for _, row in df.iterrows():\n",
-    "            # Create a dictionary for each row and collect unique nodes\n",
-    "            node_id = row[\"entity\"]\n",
-    "            if node_id not in nodes_dict:\n",
-    "                nodes_dict[node_id] = {\n",
-    "                    \"id\": node_id,\n",
-    "                    \"properties\": row.to_dict(),\n",
-    "                }\n",
-    "        return list(nodes_dict.values())\n",
-    "\n",
-    "    # converts the relationships dataframe to a list of dicts for yfiles-jupyter-graphs\n",
-    "    def convert_relationships_to_dicts(df):\n",
-    "        \"\"\"Convert the relationships dataframe to a list of dicts for yfiles-jupyter-graphs.\"\"\"\n",
-    "        relationships = []\n",
-    "        for _, row in df.iterrows():\n",
-    "            # Create a dictionary for each row\n",
-    "            relationships.append({\n",
-    "                \"start\": row[\"source\"],\n",
-    "                \"end\": row[\"target\"],\n",
-    "                \"properties\": row.to_dict(),\n",
-    "            })\n",
-    "        return relationships\n",
-    "\n",
-    "    w = GraphWidget()\n",
-    "    # use the converted data to visualize the graph\n",
-    "    w.nodes = convert_entities_to_dicts(result.context_data[\"entities\"])\n",
-    "    w.edges = convert_relationships_to_dicts(result.context_data[\"relationships\"])\n",
-    "    w.directed = True\n",
-    "    # show title on the node\n",
-    "    w.node_label_mapping = \"entity\"\n",
-    "    # use weight for edge thickness\n",
-    "    w.edge_thickness_factor_mapping = \"weight\"\n",
-    "    display(w)\n",
-    "\n",
-    "\n",
-    "show_graph(result)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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deleted file mode 100644
index deb4cffeae..0000000000
--- a/examples_notebooks/inputs/operation dulce/lancedb/entity_description_embeddings.lance/_transactions/0-498c6e24-dd0a-42b9-8f7e-5e3d2ab258b0.txn	
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diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index e81c5ac31c..6f36169afb 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -125,6 +125,7 @@ class CommunityReportDefaults:
     max_length: int = 2000
     max_input_length: int = 8000
     model_id: str = DEFAULT_CHAT_MODEL_ID
+    model_instance_name: str = "community_reporting"
 
 
 @dataclass
@@ -161,6 +162,7 @@ class EmbedTextDefaults:
     """Default values for embedding text."""
 
     model_id: str = DEFAULT_EMBEDDING_MODEL_ID
+    model_instance_name: str = "text_embedding"
     batch_size: int = 16
     batch_max_tokens: int = 8191
     names: list[str] = field(default_factory=lambda: default_embeddings)
@@ -179,6 +181,7 @@ class ExtractClaimsDefaults:
     max_gleanings: int = 1
     strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
+    model_instance_name: str = "extract_claims"
 
 
 @dataclass
@@ -192,6 +195,7 @@ class ExtractGraphDefaults:
     max_gleanings: int = 1
     strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
+    model_instance_name: str = "extract_graph"
 
 
 @dataclass
@@ -382,6 +386,7 @@ class SummarizeDescriptionsDefaults:
     max_input_tokens: int = 4_000
     strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
+    model_instance_name: str = "summarize_descriptions"
 
 
 @dataclass
diff --git a/graphrag/config/models/community_reports_config.py b/graphrag/config/models/community_reports_config.py
index 5369dea232..1257124bde 100644
--- a/graphrag/config/models/community_reports_config.py
+++ b/graphrag/config/models/community_reports_config.py
@@ -30,6 +30,10 @@ class CommunityReportsConfig(BaseModel):
         description="The model ID to use for community reports.",
         default=graphrag_config_defaults.community_reports.model_id,
     )
+    model_instance_name: str = Field(
+        description="The model singleton instance name. This primarily affects the cache storage partitioning.",
+        default=graphrag_config_defaults.community_reports.model_instance_name,
+    )
     graph_prompt: str | None = Field(
         description="The community report extraction prompt to use for graph-based summarization.",
         default=graphrag_config_defaults.community_reports.graph_prompt,
diff --git a/graphrag/config/models/embed_text_config.py b/graphrag/config/models/embed_text_config.py
index 5e5963811c..c33409d2f5 100644
--- a/graphrag/config/models/embed_text_config.py
+++ b/graphrag/config/models/embed_text_config.py
@@ -15,6 +15,10 @@ class EmbedTextConfig(BaseModel):
         description="The model ID to use for text embeddings.",
         default=graphrag_config_defaults.embed_text.model_id,
     )
+    model_instance_name: str = Field(
+        description="The model singleton instance name. This primarily affects the cache storage partitioning.",
+        default=graphrag_config_defaults.embed_text.model_instance_name,
+    )
     batch_size: int = Field(
         description="The batch size to use.",
         default=graphrag_config_defaults.embed_text.batch_size,
diff --git a/graphrag/config/models/extract_claims_config.py b/graphrag/config/models/extract_claims_config.py
index 78fe926795..77a633b05f 100644
--- a/graphrag/config/models/extract_claims_config.py
+++ b/graphrag/config/models/extract_claims_config.py
@@ -30,6 +30,10 @@ class ExtractClaimsConfig(BaseModel):
         description="The model ID to use for claim extraction.",
         default=graphrag_config_defaults.extract_claims.model_id,
     )
+    model_instance_name: str = Field(
+        description="The model singleton instance name. This primarily affects the cache storage partitioning.",
+        default=graphrag_config_defaults.extract_claims.model_instance_name,
+    )
     prompt: str | None = Field(
         description="The claim extraction prompt to use.",
         default=graphrag_config_defaults.extract_claims.prompt,
diff --git a/graphrag/config/models/extract_graph_config.py b/graphrag/config/models/extract_graph_config.py
index b8dfce3e40..8a61585e5c 100644
--- a/graphrag/config/models/extract_graph_config.py
+++ b/graphrag/config/models/extract_graph_config.py
@@ -26,6 +26,10 @@ class ExtractGraphConfig(BaseModel):
         description="The model ID to use for text embeddings.",
         default=graphrag_config_defaults.extract_graph.model_id,
     )
+    model_instance_name: str = Field(
+        description="The model singleton instance name. This primarily affects the cache storage partitioning.",
+        default=graphrag_config_defaults.extract_graph.model_instance_name,
+    )
     prompt: str | None = Field(
         description="The entity extraction prompt to use.",
         default=graphrag_config_defaults.extract_graph.prompt,
diff --git a/graphrag/config/models/summarize_descriptions_config.py b/graphrag/config/models/summarize_descriptions_config.py
index 3ab1fdaec4..3414db71ec 100644
--- a/graphrag/config/models/summarize_descriptions_config.py
+++ b/graphrag/config/models/summarize_descriptions_config.py
@@ -26,6 +26,10 @@ class SummarizeDescriptionsConfig(BaseModel):
         description="The model ID to use for summarization.",
         default=graphrag_config_defaults.summarize_descriptions.model_id,
     )
+    model_instance_name: str = Field(
+        description="The model singleton instance name. This primarily affects the cache storage partitioning.",
+        default=graphrag_config_defaults.summarize_descriptions.model_instance_name,
+    )
     prompt: str | None = Field(
         description="The description summarization prompt to use.",
         default=graphrag_config_defaults.summarize_descriptions.prompt,
diff --git a/graphrag/index/workflows/create_community_reports.py b/graphrag/index/workflows/create_community_reports.py
index e9f19533ef..0415cb3b0b 100644
--- a/graphrag/index/workflows/create_community_reports.py
+++ b/graphrag/index/workflows/create_community_reports.py
@@ -58,7 +58,7 @@ async def run_workflow(
     prompts = config.community_reports.resolved_prompts(config.root_dir)
 
     model = ModelManager().get_or_create_chat_model(
-        name="community_reporting",
+        name=config.community_reports.model_instance_name,
         model_type=model_config.type,
         config=model_config,
         callbacks=context.callbacks,
diff --git a/graphrag/index/workflows/create_community_reports_text.py b/graphrag/index/workflows/create_community_reports_text.py
index 80b236890d..94d79ca572 100644
--- a/graphrag/index/workflows/create_community_reports_text.py
+++ b/graphrag/index/workflows/create_community_reports_text.py
@@ -47,7 +47,7 @@ async def run_workflow(
 
     model_config = config.get_language_model_config(config.community_reports.model_id)
     model = ModelManager().get_or_create_chat_model(
-        name="community_reporting",
+        name=config.community_reports.model_instance_name,
         model_type=model_config.type,
         config=model_config,
         callbacks=context.callbacks,
diff --git a/graphrag/index/workflows/extract_covariates.py b/graphrag/index/workflows/extract_covariates.py
index 76889a56db..52da07451e 100644
--- a/graphrag/index/workflows/extract_covariates.py
+++ b/graphrag/index/workflows/extract_covariates.py
@@ -38,7 +38,7 @@ async def run_workflow(
         model_config = config.get_language_model_config(config.extract_claims.model_id)
 
         model = ModelManager().get_or_create_chat_model(
-            name="extract_claims",
+            name=config.extract_claims.model_instance_name,
             model_type=model_config.type,
             config=model_config,
             callbacks=context.callbacks,
diff --git a/graphrag/index/workflows/extract_graph.py b/graphrag/index/workflows/extract_graph.py
index ce466379c7..1a28a3978a 100644
--- a/graphrag/index/workflows/extract_graph.py
+++ b/graphrag/index/workflows/extract_graph.py
@@ -38,7 +38,7 @@ async def run_workflow(
     )
     extraction_prompts = config.extract_graph.resolved_prompts(config.root_dir)
     extraction_model = ModelManager().get_or_create_chat_model(
-        name="extract_graph",
+        name=config.extract_graph.model_instance_name,
         model_type=extraction_model_config.type,
         config=extraction_model_config,
         cache=context.cache,
@@ -51,7 +51,7 @@ async def run_workflow(
         config.root_dir
     )
     summarization_model = ModelManager().get_or_create_chat_model(
-        name="summarize_descriptions",
+        name=config.summarize_descriptions.model_instance_name,
         model_type=summarization_model_config.type,
         config=summarization_model_config,
         cache=context.cache,
diff --git a/graphrag/index/workflows/generate_text_embeddings.py b/graphrag/index/workflows/generate_text_embeddings.py
index 848b57d292..c15ff3e07f 100644
--- a/graphrag/index/workflows/generate_text_embeddings.py
+++ b/graphrag/index/workflows/generate_text_embeddings.py
@@ -77,7 +77,7 @@ async def run_workflow(
     model_config = config.get_language_model_config(config.embed_text.model_id)
 
     model = ModelManager().get_or_create_embedding_model(
-        name="text_embedding",
+        name=config.embed_text.model_instance_name,
         model_type=model_config.type,
         config=model_config,
         callbacks=context.callbacks,
diff --git a/pyproject.toml b/pyproject.toml
index 96f01b5d9d..48342e06e7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -246,7 +246,7 @@ convention = "numpy"
 
 # https://github.com/microsoft/pyright/blob/9f81564a4685ff5c55edd3959f9b39030f590b2f/docs/configuration.md#sample-pyprojecttoml-file
 [tool.pyright]
-include = ["graphrag", "tests", "examples_notebooks"]
+include = ["graphrag", "tests"]
 exclude = ["**/node_modules", "**/__pycache__"]
 
 [tool.pytest.ini_options]
diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json
index 7b1b4d61e6..64f90a3f07 100644
--- a/tests/fixtures/min-csv/config.json
+++ b/tests/fixtures/min-csv/config.json
@@ -54,7 +54,7 @@
                 "period",
                 "size"
             ],
-            "max_runtime": 300,
+            "max_runtime": 360,
             "expected_artifacts": ["community_reports.parquet"]
         },
         "create_final_text_units": {
diff --git a/tests/fixtures/text/config.json b/tests/fixtures/text/config.json
index 5b5738b1c1..f7278a2308 100644
--- a/tests/fixtures/text/config.json
+++ b/tests/fixtures/text/config.json
@@ -40,7 +40,7 @@
                 "end_date",
                 "source_text"
             ],
-            "max_runtime": 300,
+            "max_runtime": 360,
             "expected_artifacts": ["covariates.parquet"]
         },
         "create_communities": {
@@ -67,7 +67,7 @@
                 "period",
                 "size"
             ],
-            "max_runtime": 300,
+            "max_runtime": 360,
             "expected_artifacts": ["community_reports.parquet"]
         },
         "create_final_text_units": {

From 0436405962bb7f7fe5056dd23b0ab84ff5080501 Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Thu, 16 Oct 2025 07:56:54 -0600
Subject: [PATCH 15/40] Remove document overwrite (#2101)

* remove document overwrite from vector store configuration

* remove document overwrite and refactor load documents method

* fix test

* fix test

* fix test

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 graphrag/config/models/vector_store_config.py |    5 -
 .../index/operations/embed_text/embed_text.py |   21 +-
 graphrag/vector_stores/azure_ai_search.py     |   97 +-
 graphrag/vector_stores/base.py                |    8 +-
 graphrag/vector_stores/cosmosdb.py            |   11 +-
 graphrag/vector_stores/lancedb.py             |   47 +-
 .../vector_stores/test_azure_ai_search.py     |    2 +
 .../vector_stores/test_cosmosdb.py            |    5 +
 .../integration/vector_stores/test_factory.py |    5 +-
 .../integration/vector_stores/test_lancedb.py |   14 +-
 tests/unit/config/utils.py                    |    1 -
 .../context_builder/test_entity_extraction.py |    7 +-
 uv.lock                                       | 3528 ++++++++---------
 13 files changed, 1878 insertions(+), 1873 deletions(-)

diff --git a/graphrag/config/models/vector_store_config.py b/graphrag/config/models/vector_store_config.py
index 983165dcf1..ac00679e46 100644
--- a/graphrag/config/models/vector_store_config.py
+++ b/graphrag/config/models/vector_store_config.py
@@ -82,11 +82,6 @@ def _validate_url(self) -> None:
         default=vector_store_defaults.database_name,
     )
 
-    overwrite: bool = Field(
-        description="Overwrite the existing data.",
-        default=vector_store_defaults.overwrite,
-    )
-
     embeddings_schema: dict[str, VectorStoreSchemaConfig] = {}
 
     def _validate_embeddings_schema(self) -> None:
diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py
index f01682604f..d8144f8c18 100644
--- a/graphrag/index/operations/embed_text/embed_text.py
+++ b/graphrag/index/operations/embed_text/embed_text.py
@@ -44,27 +44,21 @@ async def embed_text(
         msg = f"Column {id_column} not found in input dataframe with columns {input.columns}"
         raise ValueError(msg)
 
-    total_rows = 0
-    for row in input[embed_column]:
-        if isinstance(row, list):
-            total_rows += len(row)
-        else:
-            total_rows += 1
+    vector_store.create_index()
 
-    i = 0
-    starting_index = 0
+    index = 0
 
     all_results = []
 
     num_total_batches = (input.shape[0] + batch_size - 1) // batch_size
-    while batch_size * i < input.shape[0]:
+    while batch_size * index < input.shape[0]:
         logger.info(
             "uploading text embeddings batch %d/%d of size %d to vector store",
-            i + 1,
+            index + 1,
             num_total_batches,
             batch_size,
         )
-        batch = input.iloc[batch_size * i : batch_size * (i + 1)]
+        batch = input.iloc[batch_size * index : batch_size * (index + 1)]
         texts: list[str] = batch[embed_column].tolist()
         ids: list[str] = batch[id_column].tolist()
         result = await run_embed_text(
@@ -93,8 +87,7 @@ async def embed_text(
             )
             documents.append(document)
 
-        vector_store.load_documents(documents, True)
-        starting_index += len(documents)
-        i += 1
+        vector_store.load_documents(documents)
+        index += 1
 
     return all_results
diff --git a/graphrag/vector_stores/azure_ai_search.py b/graphrag/vector_stores/azure_ai_search.py
index c1eb063d64..3d08f2fc11 100644
--- a/graphrag/vector_stores/azure_ai_search.py
+++ b/graphrag/vector_stores/azure_ai_search.py
@@ -74,58 +74,57 @@ def connect(self, **kwargs: Any) -> Any:
             not_supported_error = "Azure AI Search expects `url`."
             raise ValueError(not_supported_error)
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
         """Load documents into an Azure AI Search index."""
-        if overwrite:
-            if (
-                self.index_name is not None
-                and self.index_name in self.index_client.list_index_names()
-            ):
-                self.index_client.delete_index(self.index_name)
-
-            # Configure vector search profile
-            vector_search = VectorSearch(
-                algorithms=[
-                    HnswAlgorithmConfiguration(
-                        name="HnswAlg",
-                        parameters=HnswParameters(
-                            metric=VectorSearchAlgorithmMetric.COSINE
-                        ),
-                    )
-                ],
-                profiles=[
-                    VectorSearchProfile(
-                        name=self.vector_search_profile_name,
-                        algorithm_configuration_name="HnswAlg",
-                    )
-                ],
-            )
-            # Configure the index
-            index = SearchIndex(
-                name=self.index_name if self.index_name else "",
-                fields=[
-                    SimpleField(
-                        name=self.id_field,
-                        type=SearchFieldDataType.String,
-                        key=True,
-                    ),
-                    SearchField(
-                        name=self.vector_field,
-                        type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
-                        searchable=True,
-                        hidden=False,  # DRIFT needs to return the vector for client-side similarity
-                        vector_search_dimensions=self.vector_size,
-                        vector_search_profile_name=self.vector_search_profile_name,
+        if (
+            self.index_name is not None
+            and self.index_name in self.index_client.list_index_names()
+        ):
+            self.index_client.delete_index(self.index_name)
+
+        # Configure vector search profile
+        vector_search = VectorSearch(
+            algorithms=[
+                HnswAlgorithmConfiguration(
+                    name="HnswAlg",
+                    parameters=HnswParameters(
+                        metric=VectorSearchAlgorithmMetric.COSINE
                     ),
-                ],
-                vector_search=vector_search,
-            )
-            self.index_client.create_or_update_index(
-                index,
-            )
+                )
+            ],
+            profiles=[
+                VectorSearchProfile(
+                    name=self.vector_search_profile_name,
+                    algorithm_configuration_name="HnswAlg",
+                )
+            ],
+        )
+        # Configure the index
+        index = SearchIndex(
+            name=self.index_name if self.index_name else "",
+            fields=[
+                SimpleField(
+                    name=self.id_field,
+                    type=SearchFieldDataType.String,
+                    key=True,
+                ),
+                SearchField(
+                    name=self.vector_field,
+                    type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
+                    searchable=True,
+                    hidden=False,  # DRIFT needs to return the vector for client-side similarity
+                    vector_search_dimensions=self.vector_size,
+                    vector_search_profile_name=self.vector_search_profile_name,
+                ),
+            ],
+            vector_search=vector_search,
+        )
+        self.index_client.create_or_update_index(
+            index,
+        )
 
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
+        """Load documents into an Azure AI Search index."""
         batch = [
             {
                 self.id_field: doc.id,
diff --git a/graphrag/vector_stores/base.py b/graphrag/vector_stores/base.py
index 1bee5910af..eda7e189b6 100644
--- a/graphrag/vector_stores/base.py
+++ b/graphrag/vector_stores/base.py
@@ -58,9 +58,11 @@ def connect(self, **kwargs: Any) -> None:
         """Connect to vector storage."""
 
     @abstractmethod
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
+        """Create index."""
+
+    @abstractmethod
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
         """Load documents into the vector-store."""
 
     @abstractmethod
diff --git a/graphrag/vector_stores/cosmosdb.py b/graphrag/vector_stores/cosmosdb.py
index c609aa30b2..3010e517ec 100644
--- a/graphrag/vector_stores/cosmosdb.py
+++ b/graphrag/vector_stores/cosmosdb.py
@@ -149,19 +149,18 @@ def _container_exists(self) -> bool:
         ]
         return self._container_name in existing_container_names
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
         """Load documents into CosmosDB."""
         # Create a CosmosDB container on overwrite
-        if overwrite:
-            self._delete_container()
-            self._create_container()
+        self._delete_container()
+        self._create_container()
 
         if self._container_client is None:
             msg = "Container client is not initialized."
             raise ValueError(msg)
 
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
+        """Load documents into CosmosDB."""
         # Upload documents to CosmosDB
         for doc in documents:
             if doc.vector is not None:
diff --git a/graphrag/vector_stores/lancedb.py b/graphrag/vector_stores/lancedb.py
index fe53ca7182..629df0d8f9 100644
--- a/graphrag/vector_stores/lancedb.py
+++ b/graphrag/vector_stores/lancedb.py
@@ -35,10 +35,33 @@ def connect(self, **kwargs: Any) -> Any:
         if self.index_name and self.index_name in self.db_connection.table_names():
             self.document_collection = self.db_connection.open_table(self.index_name)
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
+        """Create index."""
+        dummy_vector = np.zeros(self.vector_size, dtype=np.float32)
+        flat_array = pa.array(dummy_vector, type=pa.float32())
+        vector_column = pa.FixedSizeListArray.from_arrays(flat_array, self.vector_size)
+
+        data = pa.table({
+            self.id_field: pa.array(["__DUMMY__"], type=pa.string()),
+            self.vector_field: vector_column,
+        })
+
+        self.document_collection = self.db_connection.create_table(
+            self.index_name if self.index_name else "",
+            data=data,
+            mode="overwrite",
+            schema=data.schema,
+        )
+
+        # Step 5: Create index now that schema exists
+        self.document_collection.create_index(
+            vector_column_name=self.vector_field, index_type="IVF_FLAT"
+        )
+
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
         """Load documents into vector storage."""
+        self.document_collection.delete(f"{self.id_field} = '__DUMMY__'")
+
         # Step 1: Prepare data columns manually
         ids = []
         vectors = []
@@ -68,10 +91,6 @@ def load_documents(
                 self.vector_field: vector_column,
             })
 
-        # NOTE: If modifying the next section of code, ensure that the schema remains the same.
-        #       The pyarrow format of the 'vector' field may change if the order of operations is changed
-        #       and will break vector search.
-        if overwrite:
             if data:
                 self.document_collection = self.db_connection.create_table(
                     self.index_name if self.index_name else "",
@@ -79,20 +98,6 @@ def load_documents(
                     mode="overwrite",
                     schema=data.schema,
                 )
-            else:
-                self.document_collection = self.db_connection.create_table(
-                    self.index_name if self.index_name else "", mode="overwrite"
-                )
-            self.document_collection.create_index(
-                vector_column_name=self.vector_field, index_type="IVF_FLAT"
-            )
-        else:
-            # add data to existing table
-            self.document_collection = self.db_connection.open_table(
-                self.index_name if self.index_name else ""
-            )
-            if data:
-                self.document_collection.add(data)
 
     def similarity_search_by_vector(
         self, query_embedding: list[float] | np.ndarray, k: int = 10
diff --git a/tests/integration/vector_stores/test_azure_ai_search.py b/tests/integration/vector_stores/test_azure_ai_search.py
index aaf6f00374..f5b4bd2e3e 100644
--- a/tests/integration/vector_stores/test_azure_ai_search.py
+++ b/tests/integration/vector_stores/test_azure_ai_search.py
@@ -120,6 +120,7 @@ async def test_vector_store_operations(
             "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
         }
 
+        vector_store.create_index()
         vector_store.load_documents(sample_documents)
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
@@ -188,6 +189,7 @@ async def test_vector_store_customization(
             vector_store_custom.vector_field: [0.1, 0.2, 0.3, 0.4, 0.5],
         }
 
+        vector_store_custom.create_index()
         vector_store_custom.load_documents(sample_documents)
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
diff --git a/tests/integration/vector_stores/test_cosmosdb.py b/tests/integration/vector_stores/test_cosmosdb.py
index 364a3d85d8..529843c44b 100644
--- a/tests/integration/vector_stores/test_cosmosdb.py
+++ b/tests/integration/vector_stores/test_cosmosdb.py
@@ -44,6 +44,8 @@ def test_vector_store_operations():
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
             ),
         ]
+
+        vector_store.create_index()
         vector_store.load_documents(docs)
 
         doc = vector_store.search_by_id("doc1")
@@ -84,6 +86,7 @@ def test_clear():
             vector=[0.1, 0.2, 0.3, 0.4, 0.5],
         )
 
+        vector_store.create_index()
         vector_store.load_documents([doc])
         result = vector_store.search_by_id("test")
         assert result.id == "test"
@@ -122,6 +125,8 @@ def test_vector_store_customization():
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
             ),
         ]
+
+        vector_store.create_index()
         vector_store.load_documents(docs)
 
         doc = vector_store.search_by_id("doc1")
diff --git a/tests/integration/vector_stores/test_factory.py b/tests/integration/vector_stores/test_factory.py
index de819a6ab0..2a2c9c7173 100644
--- a/tests/integration/vector_stores/test_factory.py
+++ b/tests/integration/vector_stores/test_factory.py
@@ -131,7 +131,10 @@ def __init__(self, **kwargs):
         def connect(self, **kwargs):
             pass
 
-        def load_documents(self, documents, overwrite=True):
+        def create_index(self, **kwargs):
+            pass
+
+        def load_documents(self, documents):
             pass
 
         def similarity_search_by_vector(self, query_embedding, k=10, **kwargs):
diff --git a/tests/integration/vector_stores/test_lancedb.py b/tests/integration/vector_stores/test_lancedb.py
index d0fb19eaaa..e1a58c2413 100644
--- a/tests/integration/vector_stores/test_lancedb.py
+++ b/tests/integration/vector_stores/test_lancedb.py
@@ -64,6 +64,7 @@ def test_vector_store_operations(self, sample_documents):
                 )
             )
             vector_store.connect(db_uri=temp_dir)
+            vector_store.create_index()
             vector_store.load_documents(sample_documents[:2])
 
             if vector_store.index_name:
@@ -83,7 +84,8 @@ def test_vector_store_operations(self, sample_documents):
             assert isinstance(results[0].score, float)
 
             # Test append mode
-            vector_store.load_documents([sample_documents[2]], overwrite=False)
+            vector_store.create_index()
+            vector_store.load_documents([sample_documents[2]])
             result = vector_store.search_by_id("3")
             assert result.id == "3"
 
@@ -121,6 +123,7 @@ def test_empty_collection(self):
                 id="tmp",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
             )
+            vector_store.create_index()
             vector_store.load_documents([sample_doc])
             vector_store.db_connection.open_table(
                 vector_store.index_name if vector_store.index_name else ""
@@ -137,7 +140,8 @@ def test_empty_collection(self):
                 id="1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
             )
-            vector_store.load_documents([doc], overwrite=False)
+            vector_store.create_index()
+            vector_store.load_documents([doc])
 
             result = vector_store.search_by_id("1")
             assert result.id == "1"
@@ -157,7 +161,7 @@ def test_filter_search(self, sample_documents_categories):
             )
 
             vector_store.connect(db_uri=temp_dir)
-
+            vector_store.create_index()
             vector_store.load_documents(sample_documents_categories)
 
             # Filter to include only documents about animals
@@ -186,6 +190,7 @@ def test_vector_store_customization(self, sample_documents):
                 ),
             )
             vector_store.connect(db_uri=temp_dir)
+            vector_store.create_index()
             vector_store.load_documents(sample_documents[:2])
 
             if vector_store.index_name:
@@ -205,7 +210,8 @@ def test_vector_store_customization(self, sample_documents):
             assert isinstance(results[0].score, float)
 
             # Test append mode
-            vector_store.load_documents([sample_documents[2]], overwrite=False)
+            vector_store.create_index()
+            vector_store.load_documents([sample_documents[2]])
             result = vector_store.search_by_id("3")
             assert result.id == "3"
 
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 7588335a87..ecd489a517 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -115,7 +115,6 @@ def assert_vector_store_configs(
     assert actual.api_key == expected.api_key
     assert actual.audience == expected.audience
     assert actual.container_name == expected.container_name
-    assert actual.overwrite == expected.overwrite
     assert actual.database_name == expected.database_name
 
 
diff --git a/tests/unit/query/context_builder/test_entity_extraction.py b/tests/unit/query/context_builder/test_entity_extraction.py
index a0b7b77c52..1fd5e41474 100644
--- a/tests/unit/query/context_builder/test_entity_extraction.py
+++ b/tests/unit/query/context_builder/test_entity_extraction.py
@@ -28,9 +28,10 @@ def __init__(self, documents: list[VectorStoreDocument]) -> None:
     def connect(self, **kwargs: Any) -> None:
         raise NotImplementedError
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
+        raise NotImplementedError
+
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
         raise NotImplementedError
 
     def similarity_search_by_vector(
diff --git a/uv.lock b/uv.lock
index 0f3dbb7a7f..402e5d6b71 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 2
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -9,25 +9,25 @@ resolution-markers = [
 
 [[package]]
 name = "aiofiles"
-version = "24.1.0"
+version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
+sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload_time = "2025-10-09T20:51:04.358Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload_time = "2025-10-09T20:51:03.174Z" },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload_time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload_time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.12.15"
+version = "3.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -39,59 +39,59 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/47/dc/ef9394bde9080128ad401ac7ede185267ed637df03b51f05d14d1c99ad67/aiohttp-3.12.15-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b6fc902bff74d9b1879ad55f5404153e2b33a82e72a95c89cec5eb6cc9e92fbc", size = 703921 },
-    { url = "https://files.pythonhosted.org/packages/8f/42/63fccfc3a7ed97eb6e1a71722396f409c46b60a0552d8a56d7aad74e0df5/aiohttp-3.12.15-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:098e92835b8119b54c693f2f88a1dec690e20798ca5f5fe5f0520245253ee0af", size = 480288 },
-    { url = "https://files.pythonhosted.org/packages/9c/a2/7b8a020549f66ea2a68129db6960a762d2393248f1994499f8ba9728bbed/aiohttp-3.12.15-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:40b3fee496a47c3b4a39a731954c06f0bd9bd3e8258c059a4beb76ac23f8e421", size = 468063 },
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 version = "3.23.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547 }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547, upload_time = "2025-06-08T17:06:39.4Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 },
+    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload_time = "2025-06-08T17:06:38.034Z" },
 ]

From 1bb9fa8e1306f395175c74476d4f905715ea7a40 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Mon, 20 Oct 2025 12:05:27 -0700
Subject: [PATCH 16/40] Unified factory (#2105)

* Simplify Factory interface

* Migrate CacheFactory to standard base class

* Migrate LoggerFactory to standard base class

* Migrate StorageFactory to standard base class

* Migrate VectorStoreFactory to standard base class

* Update vector store example notebook

* Delete notebook outputs

* Move default providers into factories

* Move retry/limit tests into integ

* Split language model factories

* Set smoke test tpm/rpm

* Fix factory integ tests

* Add method to smoke test, switch text to 'fast'

* Fix text smoke config for fast workflow

* Add new workflows to text smoke test

* Convert input readers to a proper factory

* Remove covariates from fast smoke test

* Update docs for input factory

* Bump smoke runtime

* Even longer runtime

* min-csv timeout

* Remove unnecessary lambdas
---
 docs/config/models.md                         |  6 +-
 .../custom_vector_store.ipynb                 | 58 +++++-------
 docs/index/architecture.md                    |  1 +
 docs/index/inputs.md                          |  4 +
 graphrag/cache/factory.py                     | 67 ++------------
 graphrag/config/defaults.py                   | 31 -------
 graphrag/config/models/graph_rag_config.py    |  9 +-
 .../config/models/language_model_config.py    |  9 +-
 graphrag/factory/factory.py                   | 12 +--
 graphrag/index/input/csv.py                   | 35 +++----
 graphrag/index/input/factory.py               | 61 +++---------
 graphrag/index/input/input_reader.py          | 84 +++++++++++++++++
 graphrag/index/input/json.py                  | 32 ++++---
 graphrag/index/input/text.py                  | 28 +++---
 graphrag/index/input/util.py                  | 35 -------
 graphrag/index/workflows/finalize_graph.py    |  1 -
 .../workflows/generate_text_embeddings.py     | 10 +-
 .../index/workflows/load_input_documents.py   | 19 ++--
 .../index/workflows/load_update_documents.py  | 16 ++--
 graphrag/language_model/factory.py            | 92 ++-----------------
 graphrag/language_model/manager.py            | 10 +-
 .../request_wrappers/with_rate_limiter.py     |  2 +-
 .../litellm/request_wrappers/with_retries.py  |  6 +-
 .../rate_limiter/rate_limiter_factory.py      | 10 +-
 .../litellm/services/retry/retry_factory.py   | 19 +++-
 graphrag/logger/factory.py                    | 59 +-----------
 graphrag/logger/standard_logging.py           |  2 +-
 graphrag/prompt_tune/loader/input.py          |  8 +-
 graphrag/storage/factory.py                   | 67 ++------------
 graphrag/utils/api.py                         | 21 ++---
 graphrag/vector_stores/azure_ai_search.py     |  8 --
 graphrag/vector_stores/cosmosdb.py            |  8 --
 graphrag/vector_stores/factory.py             | 78 ++--------------
 graphrag/vector_stores/lancedb.py             |  8 --
 tests/__init__.py                             |  6 +-
 tests/fixtures/azure/config.json              |  1 +
 tests/fixtures/min-csv/config.json            |  7 +-
 tests/fixtures/min-csv/settings.yml           |  8 +-
 tests/fixtures/text/config.json               | 35 ++-----
 tests/fixtures/text/settings.yml              | 11 +--
 tests/integration/cache/test_factory.py       | 77 +++++++---------
 .../language_model/test_factory.py            |  6 +-
 .../language_model}/test_rate_limiter.py      | 37 +++++---
 .../language_model}/test_retries.py           | 12 ++-
 .../language_model}/utils.py                  |  0
 tests/integration/logging/test_factory.py     | 18 ++--
 tests/integration/storage/test_factory.py     | 37 ++++----
 .../integration/vector_stores/test_factory.py | 82 +++++++----------
 tests/smoke/test_fixtures.py                  |  6 +-
 tests/unit/indexing/input/test_csv_loader.py  | 27 +++++-
 tests/unit/indexing/input/test_json_loader.py | 32 +++++--
 tests/unit/indexing/input/test_txt_loader.py  | 20 +++-
 .../test_gi_entity_extraction.py              |  3 -
 tests/unit/litellm_services/__init__.py       |  2 -
 54 files changed, 520 insertions(+), 823 deletions(-)
 create mode 100644 graphrag/index/input/input_reader.py
 rename tests/{unit/litellm_services => integration/language_model}/test_rate_limiter.py (89%)
 rename tests/{unit/litellm_services => integration/language_model}/test_retries.py (95%)
 rename tests/{unit/litellm_services => integration/language_model}/utils.py (100%)
 delete mode 100644 tests/unit/litellm_services/__init__.py

diff --git a/docs/config/models.md b/docs/config/models.md
index 676cb4b2ff..ca3f044e3d 100644
--- a/docs/config/models.md
+++ b/docs/config/models.md
@@ -95,12 +95,12 @@ Many users have used platforms such as [ollama](https://ollama.com/) and [LiteLL
 
 ### Model Protocol
 
-As of GraphRAG 2.0.0, we support model injection through the use of a standard chat and embedding Protocol and an accompanying ModelFactory that you can use to register your model implementation. This is not supported with the CLI, so you'll need to use GraphRAG as a library.
+As of GraphRAG 2.0.0, we support model injection through the use of a standard chat and embedding Protocol and an accompanying factories that you can use to register your model implementation. This is not supported with the CLI, so you'll need to use GraphRAG as a library.
 
 - Our Protocol is [defined here](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/protocol/base.py)
 - We have a simple mock implementation in our tests that you can [reference here](https://github.com/microsoft/graphrag/blob/main/tests/mock_provider.py)
 
-Once you have a model implementation, you need to register it with our ModelFactory:
+Once you have a model implementation, you need to register it with our ChatModelFactory or EmbeddingModelFactory:
 
 ```python
 class MyCustomModel:
@@ -108,7 +108,7 @@ class MyCustomModel:
     # implementation
 
 # elsewhere...
-ModelFactory.register_chat("my-custom-chat-model", lambda **kwargs: MyCustomModel(**kwargs))
+ChatModelFactory.register("my-custom-chat-model", lambda **kwargs: MyCustomModel(**kwargs))
 ```
 
 Then in your config you can reference the type name you used:
diff --git a/docs/examples_notebooks/custom_vector_store.ipynb b/docs/examples_notebooks/custom_vector_store.ipynb
index bce47f8bac..fe8495bbae 100644
--- a/docs/examples_notebooks/custom_vector_store.ipynb
+++ b/docs/examples_notebooks/custom_vector_store.ipynb
@@ -155,18 +155,19 @@
     "        self.connected = True\n",
     "        print(f\"✅ Connected to in-memory vector store: {self.index_name}\")\n",
     "\n",
-    "    def load_documents(\n",
-    "        self, documents: list[VectorStoreDocument], overwrite: bool = True\n",
-    "    ) -> None:\n",
+    "    def create_index(self) -> None:\n",
+    "        \"\"\"Create index in the vector store (no-op for in-memory store).\"\"\"\n",
+    "        self.documents.clear()\n",
+    "        self.vectors.clear()\n",
+    "\n",
+    "        print(f\"✅ Index '{self.index_name}' is ready in in-memory vector store\")\n",
+    "\n",
+    "    def load_documents(self, documents: list[VectorStoreDocument]) -> None:\n",
     "        \"\"\"Load documents into the vector store.\"\"\"\n",
     "        if not self.connected:\n",
     "            msg = \"Vector store not connected. Call connect() first.\"\n",
     "            raise RuntimeError(msg)\n",
     "\n",
-    "        if overwrite:\n",
-    "            self.documents.clear()\n",
-    "            self.vectors.clear()\n",
-    "\n",
     "        loaded_count = 0\n",
     "        for doc in documents:\n",
     "            if doc.vector is not None:\n",
@@ -230,13 +231,6 @@
     "        # Use vector search with the embedding\n",
     "        return self.similarity_search_by_vector(query_embedding, k, **kwargs)\n",
     "\n",
-    "    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:\n",
-    "        \"\"\"Build a query filter to filter documents by id.\n",
-    "\n",
-    "        For this simple implementation, we return the list of IDs as the filter.\n",
-    "        \"\"\"\n",
-    "        return [str(id_) for id_ in include_ids]\n",
-    "\n",
     "    def search_by_id(self, id: str) -> VectorStoreDocument:\n",
     "        \"\"\"Search for a document by id.\"\"\"\n",
     "        doc_id = str(id)\n",
@@ -281,15 +275,15 @@
     "CUSTOM_VECTOR_STORE_TYPE = \"simple_memory\"\n",
     "\n",
     "# Register the vector store class\n",
-    "VectorStoreFactory.register(CUSTOM_VECTOR_STORE_TYPE, SimpleInMemoryVectorStore)\n",
+    "VectorStoreFactory().register(CUSTOM_VECTOR_STORE_TYPE, SimpleInMemoryVectorStore)\n",
     "\n",
     "print(f\"✅ Registered custom vector store with type: '{CUSTOM_VECTOR_STORE_TYPE}'\")\n",
     "\n",
     "# Verify registration\n",
-    "available_types = VectorStoreFactory.get_vector_store_types()\n",
+    "available_types = VectorStoreFactory().keys()\n",
     "print(f\"\\n📋 Available vector store types: {available_types}\")\n",
     "print(\n",
-    "    f\"� Is our custom type supported? {VectorStoreFactory.is_supported_type(CUSTOM_VECTOR_STORE_TYPE)}\"\n",
+    "    f\"� Is our custom type supported? {CUSTOM_VECTOR_STORE_TYPE in VectorStoreFactory()}\"\n",
     ")"
    ]
   },
@@ -347,8 +341,8 @@
     "schema = VectorStoreSchemaConfig(index_name=\"test_collection\")\n",
     "\n",
     "# Create vector store instance using factory\n",
-    "vector_store = VectorStoreFactory.create_vector_store(\n",
-    "    CUSTOM_VECTOR_STORE_TYPE, vector_store_schema_config=schema\n",
+    "vector_store = VectorStoreFactory().create(\n",
+    "    CUSTOM_VECTOR_STORE_TYPE, {\"vector_store_schema_config\": schema}\n",
     ")\n",
     "\n",
     "print(f\"✅ Created vector store instance: {type(vector_store).__name__}\")\n",
@@ -363,6 +357,7 @@
    "source": [
     "# Connect and load documents\n",
     "vector_store.connect()\n",
+    "vector_store.create_index()\n",
     "vector_store.load_documents(sample_documents)\n",
     "\n",
     "print(f\"📊 Updated stats: {vector_store.get_stats()}\")"
@@ -472,13 +467,12 @@
     "    # 1. GraphRAG creates vector store using factory\n",
     "    schema = VectorStoreSchemaConfig(index_name=\"graphrag_entities\")\n",
     "\n",
-    "    store = VectorStoreFactory.create_vector_store(\n",
+    "    store = VectorStoreFactory().create(\n",
     "        CUSTOM_VECTOR_STORE_TYPE,\n",
-    "        vector_store_schema_config=schema,\n",
-    "        similarity_threshold=0.3,\n",
+    "        {\"vector_store_schema_config\": schema, \"similarity_threshold\": 0.3},\n",
     "    )\n",
     "    store.connect()\n",
-    "\n",
+    "    store.create_index()\n",
     "    print(\"✅ Step 1: Vector store created and connected\")\n",
     "\n",
     "    # 2. During indexing, GraphRAG loads extracted entities\n",
@@ -534,12 +528,12 @@
     "\n",
     "    # Test 1: Basic functionality\n",
     "    print(\"Test 1: Basic functionality\")\n",
-    "    store = VectorStoreFactory.create_vector_store(\n",
+    "    store = VectorStoreFactory().create(\n",
     "        CUSTOM_VECTOR_STORE_TYPE,\n",
-    "        vector_store_schema_config=VectorStoreSchemaConfig(index_name=\"test\"),\n",
+    "        {\"vector_store_schema_config\": VectorStoreSchemaConfig(index_name=\"test\")},\n",
     "    )\n",
     "    store.connect()\n",
-    "\n",
+    "    store.create_index()\n",
     "    # Load test documents\n",
     "    test_docs = sample_documents[:2]\n",
     "    store.load_documents(test_docs)\n",
@@ -575,17 +569,11 @@
     "\n",
     "    print(\"✅ Search by ID test passed\")\n",
     "\n",
-    "    # Test 4: Filter functionality\n",
-    "    print(\"\\nTest 4: Filter functionality\")\n",
-    "    filter_result = store.filter_by_id([\"doc_1\", \"doc_2\"])\n",
-    "    assert filter_result == [\"doc_1\", \"doc_2\"], \"Should return filtered IDs\"\n",
-    "    print(\"✅ Filter functionality test passed\")\n",
-    "\n",
-    "    # Test 5: Error handling\n",
+    "    # Test 4: Error handling\n",
     "    print(\"\\nTest 5: Error handling\")\n",
-    "    disconnected_store = VectorStoreFactory.create_vector_store(\n",
+    "    disconnected_store = VectorStoreFactory().create(\n",
     "        CUSTOM_VECTOR_STORE_TYPE,\n",
-    "        vector_store_schema_config=VectorStoreSchemaConfig(index_name=\"test2\"),\n",
+    "        {\"vector_store_schema_config\": VectorStoreSchemaConfig(index_name=\"test2\")},\n",
     "    )\n",
     "\n",
     "    try:\n",
diff --git a/docs/index/architecture.md b/docs/index/architecture.md
index 199538a0c9..be73bae7ed 100644
--- a/docs/index/architecture.md
+++ b/docs/index/architecture.md
@@ -39,6 +39,7 @@ Several subsystems within GraphRAG use a factory pattern to register and retriev
 The following subsystems use a factory pattern that allows you to register your own implementations:
 
 - [language model](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/factory.py) - implement your own `chat` and `embed` methods to use a model provider of choice beyond the built-in OpenAI/Azure support
+- [input reader](https://github.com/microsoft/graphrag/blob/main/graphrag/index/input/factory.py) - implement your own input document reader to support file types other than text, CSV, and JSON
 - [cache](https://github.com/microsoft/graphrag/blob/main/graphrag/cache/factory.py) - create your own cache storage location in addition to the file, blob, and CosmosDB ones we provide
 - [logger](https://github.com/microsoft/graphrag/blob/main/graphrag/logger/factory.py) - create your own log writing location in addition to the built-in file and blob storage
 - [storage](https://github.com/microsoft/graphrag/blob/main/graphrag/storage/factory.py) - create your own storage provider (database, etc.) beyond the file, blob, and CosmosDB ones built in
diff --git a/docs/index/inputs.md b/docs/index/inputs.md
index af8a310825..4bc5ad620a 100644
--- a/docs/index/inputs.md
+++ b/docs/index/inputs.md
@@ -20,6 +20,10 @@ Also see the [outputs](outputs.md) documentation for the final documents table s
 
 As of version 2.6.0, GraphRAG's [indexing API method](https://github.com/microsoft/graphrag/blob/main/graphrag/api/index.py) allows you to pass in your own pandas DataFrame and bypass all of the input loading/parsing described in the next section. This is convenient if you have content in a format or storage location we don't support out-of-the-box. __You must ensure that your input DataFrame conforms to the schema described above.__ All of the chunking behavior described later will proceed exactly the same.
 
+## Custom File Handling
+
+As of version 3.0.0, we have migrated to using an injectable InputReader provider class. This means you can implement any input file handling you want in a class that extends InputReader and register it with the InputReaderFactory. See the [architecture page](https://microsoft.github.io/graphrag/index/architecture/) for more info on our standard provider pattern.
+
 ## Formats
 
 We support three file formats out-of-the-box. This covers the overwhelming majority of use cases we have encountered. If you have a different format, we recommend writing a script to convert to one of these, which are widely used and supported by many tools and libraries.
diff --git a/graphrag/cache/factory.py b/graphrag/cache/factory.py
index 331540260d..f406fc856d 100644
--- a/graphrag/cache/factory.py
+++ b/graphrag/cache/factory.py
@@ -5,23 +5,18 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, ClassVar
-
 from graphrag.cache.json_pipeline_cache import JsonPipelineCache
 from graphrag.cache.memory_pipeline_cache import InMemoryCache
 from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
+from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.enums import CacheType
+from graphrag.factory.factory import Factory
 from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
 from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
 from graphrag.storage.file_pipeline_storage import FilePipelineStorage
 
-if TYPE_CHECKING:
-    from collections.abc import Callable
-
-    from graphrag.cache.pipeline_cache import PipelineCache
 
-
-class CacheFactory:
+class CacheFactory(Factory[PipelineCache]):
     """A factory class for cache implementations.
 
     Includes a method for users to register a custom cache implementation.
@@ -30,51 +25,6 @@ class CacheFactory:
     for individual enforcement of required/optional arguments.
     """
 
-    _registry: ClassVar[dict[str, Callable[..., PipelineCache]]] = {}
-
-    @classmethod
-    def register(cls, cache_type: str, creator: Callable[..., PipelineCache]) -> None:
-        """Register a custom cache implementation.
-
-        Args:
-            cache_type: The type identifier for the cache.
-            creator: A class or callable that creates an instance of PipelineCache.
-        """
-        cls._registry[cache_type] = creator
-
-    @classmethod
-    def create_cache(cls, cache_type: str, kwargs: dict) -> PipelineCache:
-        """Create a cache object from the provided type.
-
-        Args:
-            cache_type: The type of cache to create.
-            root_dir: The root directory for file-based caches.
-            kwargs: Additional keyword arguments for the cache constructor.
-
-        Returns
-        -------
-            A PipelineCache instance.
-
-        Raises
-        ------
-            ValueError: If the cache type is not registered.
-        """
-        if cache_type not in cls._registry:
-            msg = f"Unknown cache type: {cache_type}"
-            raise ValueError(msg)
-
-        return cls._registry[cache_type](**kwargs)
-
-    @classmethod
-    def get_cache_types(cls) -> list[str]:
-        """Get the registered cache implementations."""
-        return list(cls._registry.keys())
-
-    @classmethod
-    def is_supported_type(cls, cache_type: str) -> bool:
-        """Check if the given cache type is supported."""
-        return cache_type in cls._registry
-
 
 # --- register built-in cache implementations ---
 def create_file_cache(root_dir: str, base_dir: str, **kwargs) -> PipelineCache:
@@ -108,8 +58,9 @@ def create_memory_cache(**kwargs) -> PipelineCache:
 
 
 # --- register built-in cache implementations ---
-CacheFactory.register(CacheType.none.value, create_noop_cache)
-CacheFactory.register(CacheType.memory.value, create_memory_cache)
-CacheFactory.register(CacheType.file.value, create_file_cache)
-CacheFactory.register(CacheType.blob.value, create_blob_cache)
-CacheFactory.register(CacheType.cosmosdb.value, create_cosmosdb_cache)
+cache_factory = CacheFactory()
+cache_factory.register(CacheType.none.value, create_noop_cache)
+cache_factory.register(CacheType.memory.value, create_memory_cache)
+cache_factory.register(CacheType.file.value, create_file_cache)
+cache_factory.register(CacheType.blob.value, create_blob_cache)
+cache_factory.register(CacheType.cosmosdb.value, create_cosmosdb_cache)
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index 6f36169afb..9c6a65008c 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -3,7 +3,6 @@
 
 """Common default configuration values."""
 
-from collections.abc import Callable
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import ClassVar
@@ -24,25 +23,6 @@
 from graphrag.index.operations.build_noun_graph.np_extractors.stop_words import (
     EN_STOP_WORDS,
 )
-from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter import (
-    RateLimiter,
-)
-from graphrag.language_model.providers.litellm.services.rate_limiter.static_rate_limiter import (
-    StaticRateLimiter,
-)
-from graphrag.language_model.providers.litellm.services.retry.exponential_retry import (
-    ExponentialRetry,
-)
-from graphrag.language_model.providers.litellm.services.retry.incremental_wait_retry import (
-    IncrementalWaitRetry,
-)
-from graphrag.language_model.providers.litellm.services.retry.native_wait_retry import (
-    NativeRetry,
-)
-from graphrag.language_model.providers.litellm.services.retry.random_wait_retry import (
-    RandomWaitRetry,
-)
-from graphrag.language_model.providers.litellm.services.retry.retry import Retry
 
 DEFAULT_OUTPUT_BASE_DIR = "output"
 DEFAULT_CHAT_MODEL_ID = "default_chat_model"
@@ -60,17 +40,6 @@
 
 DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event"]
 
-DEFAULT_RETRY_SERVICES: dict[str, Callable[..., Retry]] = {
-    "native": NativeRetry,
-    "exponential_backoff": ExponentialRetry,
-    "random_wait": RandomWaitRetry,
-    "incremental_wait": IncrementalWaitRetry,
-}
-
-DEFAULT_RATE_LIMITER_SERVICES: dict[str, Callable[..., RateLimiter]] = {
-    "static": StaticRateLimiter,
-}
-
 
 @dataclass
 class BasicSearchDefaults:
diff --git a/graphrag/config/models/graph_rag_config.py b/graphrag/config/models/graph_rag_config.py
index c8cdca819c..6a6a98e973 100644
--- a/graphrag/config/models/graph_rag_config.py
+++ b/graphrag/config/models/graph_rag_config.py
@@ -104,8 +104,10 @@ def _validate_retry_services(self) -> None:
 
                 _ = retry_factory.create(
                     strategy=model.retry_strategy,
-                    max_retries=model.max_retries,
-                    max_retry_wait=model.max_retry_wait,
+                    init_args={
+                        "max_retries": model.max_retries,
+                        "max_retry_wait": model.max_retry_wait,
+                    },
                 )
 
     def _validate_rate_limiter_services(self) -> None:
@@ -130,7 +132,8 @@ def _validate_rate_limiter_services(self) -> None:
                 )
                 if rpm is not None or tpm is not None:
                     _ = rate_limiter_factory.create(
-                        strategy=model.rate_limit_strategy, rpm=rpm, tpm=tpm
+                        strategy=model.rate_limit_strategy,
+                        init_args={"rpm": rpm, "tpm": tpm},
                     )
 
     input: InputConfig = Field(
diff --git a/graphrag/config/models/language_model_config.py b/graphrag/config/models/language_model_config.py
index 33aee99893..11c46d76c7 100644
--- a/graphrag/config/models/language_model_config.py
+++ b/graphrag/config/models/language_model_config.py
@@ -15,7 +15,7 @@
     AzureApiVersionMissingError,
     ConflictingSettingsError,
 )
-from graphrag.language_model.factory import ModelFactory
+from graphrag.language_model.factory import ChatModelFactory, EmbeddingModelFactory
 
 logger = logging.getLogger(__name__)
 
@@ -91,8 +91,11 @@ def _validate_type(self) -> None:
             If the model name is not recognized.
         """
         # Type should be contained by the registered models
-        if not ModelFactory.is_supported_model(self.type):
-            msg = f"Model type {self.type} is not recognized, must be one of {ModelFactory.get_chat_models() + ModelFactory.get_embedding_models()}."
+        if (
+            self.type not in ChatModelFactory()
+            and self.type not in EmbeddingModelFactory()
+        ):
+            msg = f"Model type {self.type} is not recognized, must be one of {ChatModelFactory().keys() + EmbeddingModelFactory().keys()}."
             raise KeyError(msg)
 
     model_provider: str | None = Field(
diff --git a/graphrag/factory/factory.py b/graphrag/factory/factory.py
index 0624e9bfa1..3116c538ef 100644
--- a/graphrag/factory/factory.py
+++ b/graphrag/factory/factory.py
@@ -34,25 +34,25 @@ def keys(self) -> list[str]:
         """Get a list of registered strategy names."""
         return list(self._services.keys())
 
-    def register(self, *, strategy: str, service_initializer: Callable[..., T]) -> None:
+    def register(self, strategy: str, initializer: Callable[..., T]) -> None:
         """
         Register a new service.
 
         Args
         ----
             strategy: The name of the strategy.
-            service_initializer: A callable that creates an instance of T.
+            initializer: A callable that creates an instance of T.
         """
-        self._services[strategy] = service_initializer
+        self._services[strategy] = initializer
 
-    def create(self, *, strategy: str, **kwargs: Any) -> T:
+    def create(self, strategy: str, init_args: dict[str, Any] | None = None) -> T:
         """
         Create a service instance based on the strategy.
 
         Args
         ----
             strategy: The name of the strategy.
-            **kwargs: Additional arguments to pass to the service initializer.
+            init_args: Dict of keyword arguments to pass to the service initializer.
 
         Returns
         -------
@@ -65,4 +65,4 @@ def create(self, *, strategy: str, **kwargs: Any) -> T:
         if strategy not in self._services:
             msg = f"Strategy '{strategy}' is not registered."
             raise ValueError(msg)
-        return self._services[strategy](**kwargs)
+        return self._services[strategy](**(init_args or {}))
diff --git a/graphrag/index/input/csv.py b/graphrag/index/input/csv.py
index 7db033debb..a70863e7ae 100644
--- a/graphrag/index/input/csv.py
+++ b/graphrag/index/input/csv.py
@@ -8,27 +8,28 @@
 
 import pandas as pd
 
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.util import load_files, process_data_columns
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag.index.input.input_reader import InputReader
+from graphrag.index.input.util import process_data_columns
 
 logger = logging.getLogger(__name__)
 
 
-async def load_csv(
-    config: InputConfig,
-    storage: PipelineStorage,
-) -> pd.DataFrame:
-    """Load csv inputs from a directory."""
-    logger.info("Loading csv files from %s", config.storage.base_dir)
+class CSVFileReader(InputReader):
+    """Reader implementation for csv files."""
 
-    async def load_file(path: str) -> pd.DataFrame:
-        buffer = BytesIO(await storage.get(path, as_bytes=True))
-        data = pd.read_csv(buffer, encoding=config.encoding)
-        data = process_data_columns(data, config, path)
-        creation_date = await storage.get_creation_date(path)
-        data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
+    async def read_file(self, path: str) -> pd.DataFrame:
+        """Read a csv file into a DataFrame of documents.
 
-        return data
+        Args:
+            - path - The path to read the file from.
 
-    return await load_files(load_file, config, storage)
+        Returns
+        -------
+            - output - DataFrame with a row for each document in the file.
+        """
+        buffer = BytesIO(await self._storage.get(path, as_bytes=True))
+        data = pd.read_csv(buffer, encoding=self._config.encoding)
+        data = process_data_columns(data, self._config, path)
+        creation_date = await self._storage.get_creation_date(path)
+        data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
+        return data
diff --git a/graphrag/index/input/factory.py b/graphrag/index/input/factory.py
index bc4da8c7a1..bdabd0d7c7 100644
--- a/graphrag/index/input/factory.py
+++ b/graphrag/index/input/factory.py
@@ -4,53 +4,22 @@
 """A module containing create_input method definition."""
 
 import logging
-from collections.abc import Awaitable, Callable
-from typing import cast
-
-import pandas as pd
 
 from graphrag.config.enums import InputFileType
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.csv import load_csv
-from graphrag.index.input.json import load_json
-from graphrag.index.input.text import load_text
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag.factory.factory import Factory
+from graphrag.index.input.csv import CSVFileReader
+from graphrag.index.input.input_reader import InputReader
+from graphrag.index.input.json import JSONFileReader
+from graphrag.index.input.text import TextFileReader
 
 logger = logging.getLogger(__name__)
-loaders: dict[str, Callable[..., Awaitable[pd.DataFrame]]] = {
-    InputFileType.text: load_text,
-    InputFileType.csv: load_csv,
-    InputFileType.json: load_json,
-}
-
-
-async def create_input(
-    config: InputConfig,
-    storage: PipelineStorage,
-) -> pd.DataFrame:
-    """Instantiate input data for a pipeline."""
-    logger.info("loading input from root_dir=%s", config.storage.base_dir)
-
-    if config.file_type in loaders:
-        logger.info("Loading Input %s", config.file_type)
-        loader = loaders[config.file_type]
-        result = await loader(config, storage)
-        # Convert metadata columns to strings and collapse them into a JSON object
-        if config.metadata:
-            if all(col in result.columns for col in config.metadata):
-                # Collapse the metadata columns into a single JSON object column
-                result["metadata"] = result[config.metadata].apply(
-                    lambda row: row.to_dict(), axis=1
-                )
-            else:
-                value_error_msg = (
-                    "One or more metadata columns not found in the DataFrame."
-                )
-                raise ValueError(value_error_msg)
-
-            result[config.metadata] = result[config.metadata].astype(str)
-
-        return cast("pd.DataFrame", result)
-
-    msg = f"Unknown input type {config.file_type}"
-    raise ValueError(msg)
+
+
+class InputReaderFactory(Factory[InputReader]):
+    """Factory for creating Input Reader instances."""
+
+
+input_reader_factory = InputReaderFactory()
+input_reader_factory.register(InputFileType.text, TextFileReader)
+input_reader_factory.register(InputFileType.csv, CSVFileReader)
+input_reader_factory.register(InputFileType.json, JSONFileReader)
diff --git a/graphrag/index/input/input_reader.py b/graphrag/index/input/input_reader.py
new file mode 100644
index 0000000000..ed0add9f97
--- /dev/null
+++ b/graphrag/index/input/input_reader.py
@@ -0,0 +1,84 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'PipelineCache' model."""
+
+from __future__ import annotations
+
+import logging
+import re
+from abc import ABCMeta, abstractmethod
+from typing import TYPE_CHECKING
+
+import pandas as pd
+
+if TYPE_CHECKING:
+    from graphrag.config.models.input_config import InputConfig
+    from graphrag.storage.pipeline_storage import PipelineStorage
+
+logger = logging.getLogger(__name__)
+
+
+class InputReader(metaclass=ABCMeta):
+    """Provide a cache interface for the pipeline."""
+
+    def __init__(self, storage: PipelineStorage, config: InputConfig, **kwargs):
+        self._storage = storage
+        self._config = config
+
+    async def read_files(self) -> pd.DataFrame:
+        """Load files from storage and apply a loader function based on file type. Process metadata on the results if needed."""
+        files = list(self._storage.find(re.compile(self._config.file_pattern)))
+
+        if len(files) == 0:
+            msg = f"No {self._config.file_type} files found in {self._config.storage.base_dir}"
+            raise ValueError(msg)
+
+        files_loaded = []
+
+        for file in files:
+            try:
+                files_loaded.append(await self.read_file(file))
+            except Exception as e:  # noqa: BLE001 (catching Exception is fine here)
+                logger.warning("Warning! Error loading file %s. Skipping...", file)
+                logger.warning("Error: %s", e)
+
+        logger.info(
+            "Found %d %s files, loading %d",
+            len(files),
+            self._config.file_type,
+            len(files_loaded),
+        )
+        result = pd.concat(files_loaded)
+        total_files_log = (
+            f"Total number of unfiltered {self._config.file_type} rows: {len(result)}"
+        )
+        logger.info(total_files_log)
+        # Convert metadata columns to strings and collapse them into a JSON object
+        if self._config.metadata:
+            if all(col in result.columns for col in self._config.metadata):
+                # Collapse the metadata columns into a single JSON object column
+                result["metadata"] = result[self._config.metadata].apply(
+                    lambda row: row.to_dict(), axis=1
+                )
+            else:
+                value_error_msg = (
+                    "One or more metadata columns not found in the DataFrame."
+                )
+                raise ValueError(value_error_msg)
+
+            result[self._config.metadata] = result[self._config.metadata].astype(str)
+
+        return result
+
+    @abstractmethod
+    async def read_file(self, path: str) -> pd.DataFrame:
+        """Read a file into a DataFrame of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - DataFrame with a row for each document in the file.
+        """
diff --git a/graphrag/index/input/json.py b/graphrag/index/input/json.py
index 41343fd2b2..cae7db5dbc 100644
--- a/graphrag/index/input/json.py
+++ b/graphrag/index/input/json.py
@@ -8,30 +8,32 @@
 
 import pandas as pd
 
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.util import load_files, process_data_columns
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag.index.input.input_reader import InputReader
+from graphrag.index.input.util import process_data_columns
 
 logger = logging.getLogger(__name__)
 
 
-async def load_json(
-    config: InputConfig,
-    storage: PipelineStorage,
-) -> pd.DataFrame:
-    """Load json inputs from a directory."""
-    logger.info("Loading json files from %s", config.storage.base_dir)
+class JSONFileReader(InputReader):
+    """Reader implementation for json files."""
 
-    async def load_file(path: str) -> pd.DataFrame:
-        text = await storage.get(path, encoding=config.encoding)
+    async def read_file(self, path: str) -> pd.DataFrame:
+        """Read a JSON file into a DataFrame of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - DataFrame with a row for each document in the file.
+        """
+        text = await self._storage.get(path, encoding=self._config.encoding)
         as_json = json.loads(text)
         # json file could just be a single object, or an array of objects
         rows = as_json if isinstance(as_json, list) else [as_json]
         data = pd.DataFrame(rows)
-        data = process_data_columns(data, config, path)
-        creation_date = await storage.get_creation_date(path)
+        data = process_data_columns(data, self._config, path)
+        creation_date = await self._storage.get_creation_date(path)
         data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
 
         return data
-
-    return await load_files(load_file, config, storage)
diff --git a/graphrag/index/input/text.py b/graphrag/index/input/text.py
index f1fc74352f..dac2c4c701 100644
--- a/graphrag/index/input/text.py
+++ b/graphrag/index/input/text.py
@@ -8,26 +8,28 @@
 
 import pandas as pd
 
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.util import load_files
+from graphrag.index.input.input_reader import InputReader
 from graphrag.index.utils.hashing import gen_sha512_hash
-from graphrag.storage.pipeline_storage import PipelineStorage
 
 logger = logging.getLogger(__name__)
 
 
-async def load_text(
-    config: InputConfig,
-    storage: PipelineStorage,
-) -> pd.DataFrame:
-    """Load text inputs from a directory."""
+class TextFileReader(InputReader):
+    """Reader implementation for text files."""
 
-    async def load_file(path: str) -> pd.DataFrame:
-        text = await storage.get(path, encoding=config.encoding)
+    async def read_file(self, path: str) -> pd.DataFrame:
+        """Read a text file into a DataFrame of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - DataFrame with a row for each document in the file.
+        """
+        text = await self._storage.get(path, encoding=self._config.encoding)
         new_item = {"text": text}
         new_item["id"] = gen_sha512_hash(new_item, new_item.keys())
         new_item["title"] = str(Path(path).name)
-        new_item["creation_date"] = await storage.get_creation_date(path)
+        new_item["creation_date"] = await self._storage.get_creation_date(path)
         return pd.DataFrame([new_item])
-
-    return await load_files(load_file, config, storage)
diff --git a/graphrag/index/input/util.py b/graphrag/index/input/util.py
index 457c1864a9..2780909167 100644
--- a/graphrag/index/input/util.py
+++ b/graphrag/index/input/util.py
@@ -4,50 +4,15 @@
 """Shared column processing for structured input files."""
 
 import logging
-import re
-from typing import Any
 
 import pandas as pd
 
 from graphrag.config.models.input_config import InputConfig
 from graphrag.index.utils.hashing import gen_sha512_hash
-from graphrag.storage.pipeline_storage import PipelineStorage
 
 logger = logging.getLogger(__name__)
 
 
-async def load_files(
-    loader: Any,
-    config: InputConfig,
-    storage: PipelineStorage,
-) -> pd.DataFrame:
-    """Load files from storage and apply a loader function."""
-    files = list(storage.find(re.compile(config.file_pattern)))
-
-    if len(files) == 0:
-        msg = f"No {config.file_type} files found in {config.storage.base_dir}"
-        raise ValueError(msg)
-
-    files_loaded = []
-
-    for file in files:
-        try:
-            files_loaded.append(await loader(file))
-        except Exception as e:  # noqa: BLE001 (catching Exception is fine here)
-            logger.warning("Warning! Error loading file %s. Skipping...", file)
-            logger.warning("Error: %s", e)
-
-    logger.info(
-        "Found %d %s files, loading %d", len(files), config.file_type, len(files_loaded)
-    )
-    result = pd.concat(files_loaded)
-    total_files_log = (
-        f"Total number of unfiltered {config.file_type} rows: {len(result)}"
-    )
-    logger.info(total_files_log)
-    return result
-
-
 def process_data_columns(
     documents: pd.DataFrame, config: InputConfig, path: str
 ) -> pd.DataFrame:
diff --git a/graphrag/index/workflows/finalize_graph.py b/graphrag/index/workflows/finalize_graph.py
index 5681173c59..49529aea3a 100644
--- a/graphrag/index/workflows/finalize_graph.py
+++ b/graphrag/index/workflows/finalize_graph.py
@@ -41,7 +41,6 @@ async def run_workflow(
     )
 
     if config.snapshots.graphml:
-        # todo: extract graphs at each level, and add in meta like descriptions
         graph = create_graph(final_relationships, edge_attr=["weight"])
 
         await snapshot_graphml(
diff --git a/graphrag/index/workflows/generate_text_embeddings.py b/graphrag/index/workflows/generate_text_embeddings.py
index c15ff3e07f..63859faa49 100644
--- a/graphrag/index/workflows/generate_text_embeddings.py
+++ b/graphrag/index/workflows/generate_text_embeddings.py
@@ -236,8 +236,6 @@ def _create_vector_store(
     index_name: str,
     embedding_name: str | None = None,
 ) -> BaseVectorStore:
-    vector_store_type: str = str(vector_store_config.type)
-
     embeddings_schema: dict[str, VectorStoreSchemaConfig] = (
         vector_store_config.embeddings_schema
     )
@@ -259,10 +257,10 @@ def _create_vector_store(
         single_embedding_config.index_name = index_name
 
     args = vector_store_config.model_dump()
-    vector_store = VectorStoreFactory().create_vector_store(
-        vector_store_schema_config=single_embedding_config,
-        vector_store_type=vector_store_type,
-        **args,
+    args["vector_store_schema_config"] = single_embedding_config
+    vector_store = VectorStoreFactory().create(
+        vector_store_config.type,
+        args,
     )
 
     vector_store.connect(**args)
diff --git a/graphrag/index/workflows/load_input_documents.py b/graphrag/index/workflows/load_input_documents.py
index 33e14d0cb2..228ca4d0c4 100644
--- a/graphrag/index/workflows/load_input_documents.py
+++ b/graphrag/index/workflows/load_input_documents.py
@@ -8,11 +8,10 @@
 import pandas as pd
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.factory import create_input
+from graphrag.index.input.factory import InputReaderFactory
+from graphrag.index.input.input_reader import InputReader
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -23,11 +22,13 @@ async def run_workflow(
     context: PipelineRunContext,
 ) -> WorkflowFunctionOutput:
     """Load and parse input documents into a standard format."""
-    output = await load_input_documents(
-        config.input,
-        context.input_storage,
+    input_reader = InputReaderFactory().create(
+        config.input.file_type,
+        {"storage": context.input_storage, "config": config.input},
     )
 
+    output = await load_input_documents(input_reader)
+
     logger.info("Final # of rows loaded: %s", len(output))
     context.stats.num_documents = len(output)
 
@@ -36,8 +37,6 @@ async def run_workflow(
     return WorkflowFunctionOutput(result=output)
 
 
-async def load_input_documents(
-    config: InputConfig, storage: PipelineStorage
-) -> pd.DataFrame:
+async def load_input_documents(input_reader: InputReader) -> pd.DataFrame:
     """Load and parse input documents into a standard format."""
-    return await create_input(config, storage)
+    return await input_reader.read_files()
diff --git a/graphrag/index/workflows/load_update_documents.py b/graphrag/index/workflows/load_update_documents.py
index fbe48b6419..7755091017 100644
--- a/graphrag/index/workflows/load_update_documents.py
+++ b/graphrag/index/workflows/load_update_documents.py
@@ -8,8 +8,8 @@
 import pandas as pd
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.factory import create_input
+from graphrag.index.input.factory import InputReaderFactory
+from graphrag.index.input.input_reader import InputReader
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.update.incremental_index import get_delta_docs
@@ -24,9 +24,12 @@ async def run_workflow(
     context: PipelineRunContext,
 ) -> WorkflowFunctionOutput:
     """Load and parse update-only input documents into a standard format."""
+    input_reader = InputReaderFactory().create(
+        config.input.file_type,
+        {"storage": context.input_storage, "config": config.input},
+    )
     output = await load_update_documents(
-        config.input,
-        context.input_storage,
+        input_reader,
         context.previous_storage,
     )
 
@@ -43,12 +46,11 @@ async def run_workflow(
 
 
 async def load_update_documents(
-    config: InputConfig,
-    input_storage: PipelineStorage,
+    input_reader: InputReader,
     previous_storage: PipelineStorage,
 ) -> pd.DataFrame:
     """Load and parse update-only input documents into a standard format."""
-    input_documents = await create_input(config, input_storage)
+    input_documents = await input_reader.read_files()
     # previous storage is the output of the previous run
     # we'll use this to diff the input from the prior
     delta_documents = await get_delta_docs(input_documents, previous_storage)
diff --git a/graphrag/language_model/factory.py b/graphrag/language_model/factory.py
index 840dada3dc..6ff32b530e 100644
--- a/graphrag/language_model/factory.py
+++ b/graphrag/language_model/factory.py
@@ -3,10 +3,8 @@
 
 """A package containing a factory for supported llm types."""
 
-from collections.abc import Callable
-from typing import Any, ClassVar
-
 from graphrag.config.enums import ModelType
+from graphrag.factory.factory import Factory
 from graphrag.language_model.protocol.base import ChatModel, EmbeddingModel
 from graphrag.language_model.providers.litellm.chat_model import LitellmChatModel
 from graphrag.language_model.providers.litellm.embedding_model import (
@@ -14,90 +12,14 @@
 )
 
 
-class ModelFactory:
-    """A factory for creating Model instances."""
-
-    _chat_registry: ClassVar[dict[str, Callable[..., ChatModel]]] = {}
-    _embedding_registry: ClassVar[dict[str, Callable[..., EmbeddingModel]]] = {}
-
-    @classmethod
-    def register_chat(cls, model_type: str, creator: Callable[..., ChatModel]) -> None:
-        """Register a ChatModel implementation."""
-        cls._chat_registry[model_type] = creator
-
-    @classmethod
-    def register_embedding(
-        cls, model_type: str, creator: Callable[..., EmbeddingModel]
-    ) -> None:
-        """Register an EmbeddingModel implementation."""
-        cls._embedding_registry[model_type] = creator
-
-    @classmethod
-    def create_chat_model(cls, model_type: str, **kwargs: Any) -> ChatModel:
-        """
-        Create a ChatModel instance.
-
-        Args:
-            model_type: The type of ChatModel to create.
-            **kwargs: Additional keyword arguments for the ChatModel constructor.
-
-        Returns
-        -------
-            A ChatModel instance.
-        """
-        if model_type not in cls._chat_registry:
-            msg = f"ChatMOdel implementation '{model_type}' is not registered."
-            raise ValueError(msg)
-        return cls._chat_registry[model_type](**kwargs)
+class ChatModelFactory(Factory[ChatModel]):
+    """Singleton factory for creating ChatModel instances."""
 
-    @classmethod
-    def create_embedding_model(cls, model_type: str, **kwargs: Any) -> EmbeddingModel:
-        """
-        Create an EmbeddingModel instance.
 
-        Args:
-            model_type: The type of EmbeddingModel to create.
-            **kwargs: Additional keyword arguments for the EmbeddingLLM constructor.
-
-        Returns
-        -------
-            An EmbeddingLLM instance.
-        """
-        if model_type not in cls._embedding_registry:
-            msg = f"EmbeddingModel implementation '{model_type}' is not registered."
-            raise ValueError(msg)
-        return cls._embedding_registry[model_type](**kwargs)
-
-    @classmethod
-    def get_chat_models(cls) -> list[str]:
-        """Get the registered ChatModel implementations."""
-        return list(cls._chat_registry.keys())
-
-    @classmethod
-    def get_embedding_models(cls) -> list[str]:
-        """Get the registered EmbeddingModel implementations."""
-        return list(cls._embedding_registry.keys())
-
-    @classmethod
-    def is_supported_chat_model(cls, model_type: str) -> bool:
-        """Check if the given model type is supported."""
-        return model_type in cls._chat_registry
-
-    @classmethod
-    def is_supported_embedding_model(cls, model_type: str) -> bool:
-        """Check if the given model type is supported."""
-        return model_type in cls._embedding_registry
-
-    @classmethod
-    def is_supported_model(cls, model_type: str) -> bool:
-        """Check if the given model type is supported."""
-        return cls.is_supported_chat_model(
-            model_type
-        ) or cls.is_supported_embedding_model(model_type)
+class EmbeddingModelFactory(Factory[EmbeddingModel]):
+    """Singleton factory for creating EmbeddingModel instances."""
 
 
 # --- Register default implementations ---
-ModelFactory.register_chat(ModelType.Chat, lambda **kwargs: LitellmChatModel(**kwargs))
-ModelFactory.register_embedding(
-    ModelType.Embedding, lambda **kwargs: LitellmEmbeddingModel(**kwargs)
-)
+ChatModelFactory().register(ModelType.Chat, LitellmChatModel)
+EmbeddingModelFactory().register(ModelType.Embedding, LitellmEmbeddingModel)
diff --git a/graphrag/language_model/manager.py b/graphrag/language_model/manager.py
index bc41235dda..29349cb278 100644
--- a/graphrag/language_model/manager.py
+++ b/graphrag/language_model/manager.py
@@ -13,7 +13,7 @@
 
 from typing_extensions import Self
 
-from graphrag.language_model.factory import ModelFactory
+from graphrag.language_model.factory import ChatModelFactory, EmbeddingModelFactory
 
 if TYPE_CHECKING:
     from graphrag.language_model.protocol.base import ChatModel, EmbeddingModel
@@ -54,9 +54,7 @@ def register_chat(
             **chat_kwargs: Additional parameters for instantiation.
         """
         chat_kwargs["name"] = name
-        self.chat_models[name] = ModelFactory.create_chat_model(
-            model_type, **chat_kwargs
-        )
+        self.chat_models[name] = ChatModelFactory().create(model_type, chat_kwargs)
         return self.chat_models[name]
 
     def register_embedding(
@@ -71,8 +69,8 @@ def register_embedding(
             **embedding_kwargs: Additional parameters for instantiation.
         """
         embedding_kwargs["name"] = name
-        self.embedding_models[name] = ModelFactory.create_embedding_model(
-            model_type, **embedding_kwargs
+        self.embedding_models[name] = EmbeddingModelFactory().create(
+            model_type, embedding_kwargs
         )
         return self.embedding_models[name]
 
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py b/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py
index c0e0728f2e..108369444f 100644
--- a/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py
+++ b/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py
@@ -55,7 +55,7 @@ def with_rate_limiter(
         raise ValueError(msg)
 
     rate_limiter_service = rate_limiter_factory.create(
-        strategy=model_config.rate_limit_strategy, rpm=rpm, tpm=tpm
+        strategy=model_config.rate_limit_strategy, init_args={"rpm": rpm, "tpm": tpm}
     )
 
     max_tokens = model_config.max_completion_tokens or model_config.max_tokens or 0
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py b/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py
index 1279f9e820..53e13f3fe9 100644
--- a/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py
+++ b/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py
@@ -39,8 +39,10 @@ def with_retries(
     retry_factory = RetryFactory()
     retry_service = retry_factory.create(
         strategy=model_config.retry_strategy,
-        max_retries=model_config.max_retries,
-        max_retry_wait=model_config.max_retry_wait,
+        init_args={
+            "max_retries": model_config.max_retries,
+            "max_retry_wait": model_config.max_retry_wait,
+        },
     )
 
     def _wrapped_with_retries(**kwargs: Any) -> Any:
diff --git a/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py b/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
index a6ef6880ef..5904be0b56 100644
--- a/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
+++ b/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
@@ -3,11 +3,13 @@
 
 """LiteLLM Rate Limiter Factory."""
 
-from graphrag.config.defaults import DEFAULT_RATE_LIMITER_SERVICES
 from graphrag.factory.factory import Factory
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter import (
     RateLimiter,
 )
+from graphrag.language_model.providers.litellm.services.rate_limiter.static_rate_limiter import (
+    StaticRateLimiter,
+)
 
 
 class RateLimiterFactory(Factory[RateLimiter]):
@@ -15,8 +17,4 @@ class RateLimiterFactory(Factory[RateLimiter]):
 
 
 rate_limiter_factory = RateLimiterFactory()
-
-for service_name, service_cls in DEFAULT_RATE_LIMITER_SERVICES.items():
-    rate_limiter_factory.register(
-        strategy=service_name, service_initializer=service_cls
-    )
+rate_limiter_factory.register("static", StaticRateLimiter)
diff --git a/graphrag/language_model/providers/litellm/services/retry/retry_factory.py b/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
index 15b3318630..9acdf21425 100644
--- a/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
+++ b/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
@@ -3,8 +3,19 @@
 
 """LiteLLM Retry Factory."""
 
-from graphrag.config.defaults import DEFAULT_RETRY_SERVICES
 from graphrag.factory.factory import Factory
+from graphrag.language_model.providers.litellm.services.retry.exponential_retry import (
+    ExponentialRetry,
+)
+from graphrag.language_model.providers.litellm.services.retry.incremental_wait_retry import (
+    IncrementalWaitRetry,
+)
+from graphrag.language_model.providers.litellm.services.retry.native_wait_retry import (
+    NativeRetry,
+)
+from graphrag.language_model.providers.litellm.services.retry.random_wait_retry import (
+    RandomWaitRetry,
+)
 from graphrag.language_model.providers.litellm.services.retry.retry import Retry
 
 
@@ -14,5 +25,7 @@ class RetryFactory(Factory[Retry]):
 
 retry_factory = RetryFactory()
 
-for service_name, service_cls in DEFAULT_RETRY_SERVICES.items():
-    retry_factory.register(strategy=service_name, service_initializer=service_cls)
+retry_factory.register("native", NativeRetry)
+retry_factory.register("exponential_backoff", ExponentialRetry)
+retry_factory.register("random_wait", RandomWaitRetry)
+retry_factory.register("incremental_wait", IncrementalWaitRetry)
diff --git a/graphrag/logger/factory.py b/graphrag/logger/factory.py
index 03245d1bb0..56800733f5 100644
--- a/graphrag/logger/factory.py
+++ b/graphrag/logger/factory.py
@@ -7,18 +7,15 @@
 
 import logging
 from pathlib import Path
-from typing import TYPE_CHECKING, ClassVar
 
 from graphrag.config.enums import ReportingType
-
-if TYPE_CHECKING:
-    from collections.abc import Callable
+from graphrag.factory.factory import Factory
 
 LOG_FORMAT = "%(asctime)s.%(msecs)04d - %(levelname)s - %(name)s - %(message)s"
 DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
 
 
-class LoggerFactory:
+class LoggerFactory(Factory[logging.Handler]):
     """A factory class for logger implementations.
 
     Includes a method for users to register a custom logger implementation.
@@ -30,53 +27,6 @@ class LoggerFactory:
     it merely configures the logger to your specified storage location.
     """
 
-    _registry: ClassVar[dict[str, Callable[..., logging.Handler]]] = {}
-
-    @classmethod
-    def register(
-        cls, reporting_type: str, creator: Callable[..., logging.Handler]
-    ) -> None:
-        """Register a custom logger implementation.
-
-        Args:
-            reporting_type: The type identifier for the logger.
-            creator: A class or callable that initializes logging.
-        """
-        cls._registry[reporting_type] = creator
-
-    @classmethod
-    def create_logger(cls, reporting_type: str, kwargs: dict) -> logging.Handler:
-        """Create a logger from the provided type.
-
-        Args:
-            reporting_type: The type of logger to create.
-            logger: The logger instance for the application.
-            kwargs: Additional keyword arguments for the constructor.
-
-        Returns
-        -------
-            A logger instance.
-
-        Raises
-        ------
-            ValueError: If the logger type is not registered.
-        """
-        if reporting_type not in cls._registry:
-            msg = f"Unknown reporting type: {reporting_type}"
-            raise ValueError(msg)
-
-        return cls._registry[reporting_type](**kwargs)
-
-    @classmethod
-    def get_logger_types(cls) -> list[str]:
-        """Get the registered logger implementations."""
-        return list(cls._registry.keys())
-
-    @classmethod
-    def is_supported_type(cls, reporting_type: str) -> bool:
-        """Check if the given logger type is supported."""
-        return reporting_type in cls._registry
-
 
 # --- register built-in logger implementations ---
 def create_file_logger(**kwargs) -> logging.Handler:
@@ -109,5 +59,6 @@ def create_blob_logger(**kwargs) -> logging.Handler:
 
 
 # --- register built-in implementations ---
-LoggerFactory.register(ReportingType.file.value, create_file_logger)
-LoggerFactory.register(ReportingType.blob.value, create_blob_logger)
+logger_factory = LoggerFactory()
+logger_factory.register(ReportingType.file.value, create_file_logger)
+logger_factory.register(ReportingType.blob.value, create_blob_logger)
diff --git a/graphrag/logger/standard_logging.py b/graphrag/logger/standard_logging.py
index fea504c02f..31296e12d9 100644
--- a/graphrag/logger/standard_logging.py
+++ b/graphrag/logger/standard_logging.py
@@ -79,5 +79,5 @@ def init_loggers(
     config_dict = reporting_config.model_dump()
     args = {**config_dict, "root_dir": config.root_dir, "filename": filename}
 
-    handler = LoggerFactory.create_logger(reporting_config.type, args)
+    handler = LoggerFactory().create(reporting_config.type, args)
     logger.addHandler(handler)
diff --git a/graphrag/prompt_tune/loader/input.py b/graphrag/prompt_tune/loader/input.py
index bdc42e5b5c..5e9fccb440 100644
--- a/graphrag/prompt_tune/loader/input.py
+++ b/graphrag/prompt_tune/loader/input.py
@@ -12,7 +12,7 @@
 from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.index.input.factory import create_input
+from graphrag.index.input.factory import InputReaderFactory
 from graphrag.index.operations.embed_text.run_embed_text import (
     run_embed_text,
 )
@@ -64,7 +64,11 @@ async def load_docs_in_chunks(
     )
     tokenizer = get_tokenizer(embeddings_llm_settings)
     input_storage = create_storage_from_config(config.input.storage)
-    dataset = await create_input(config.input, input_storage)
+    input_reader = InputReaderFactory().create(
+        config.input.file_type,
+        {"storage": input_storage, "config": config.input},
+    )
+    dataset = await input_reader.read_files()
     chunk_config = config.chunks
     chunks_df = create_base_text_units(
         documents=dataset,
diff --git a/graphrag/storage/factory.py b/graphrag/storage/factory.py
index 89c4eeee0d..d2dfa6645c 100644
--- a/graphrag/storage/factory.py
+++ b/graphrag/storage/factory.py
@@ -5,21 +5,16 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, ClassVar
-
 from graphrag.config.enums import StorageType
+from graphrag.factory.factory import Factory
 from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
 from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
 from graphrag.storage.file_pipeline_storage import FilePipelineStorage
 from graphrag.storage.memory_pipeline_storage import MemoryPipelineStorage
+from graphrag.storage.pipeline_storage import PipelineStorage
 
-if TYPE_CHECKING:
-    from collections.abc import Callable
-
-    from graphrag.storage.pipeline_storage import PipelineStorage
 
-
-class StorageFactory:
+class StorageFactory(Factory[PipelineStorage]):
     """A factory class for storage implementations.
 
     Includes a method for users to register a custom storage implementation.
@@ -28,56 +23,10 @@ class StorageFactory:
     for individual enforcement of required/optional arguments.
     """
 
-    _registry: ClassVar[dict[str, Callable[..., PipelineStorage]]] = {}
-
-    @classmethod
-    def register(
-        cls, storage_type: str, creator: Callable[..., PipelineStorage]
-    ) -> None:
-        """Register a custom storage implementation.
-
-        Args:
-            storage_type: The type identifier for the storage.
-            creator: A class or callable that creates an instance of PipelineStorage.
-
-        """
-        cls._registry[storage_type] = creator
-
-    @classmethod
-    def create_storage(cls, storage_type: str, kwargs: dict) -> PipelineStorage:
-        """Create a storage object from the provided type.
-
-        Args:
-            storage_type: The type of storage to create.
-            kwargs: Additional keyword arguments for the storage constructor.
-
-        Returns
-        -------
-            A PipelineStorage instance.
-
-        Raises
-        ------
-            ValueError: If the storage type is not registered.
-        """
-        if storage_type not in cls._registry:
-            msg = f"Unknown storage type: {storage_type}"
-            raise ValueError(msg)
-
-        return cls._registry[storage_type](**kwargs)
-
-    @classmethod
-    def get_storage_types(cls) -> list[str]:
-        """Get the registered storage implementations."""
-        return list(cls._registry.keys())
-
-    @classmethod
-    def is_supported_type(cls, storage_type: str) -> bool:
-        """Check if the given storage type is supported."""
-        return storage_type in cls._registry
-
 
 # --- register built-in storage implementations ---
-StorageFactory.register(StorageType.blob.value, BlobPipelineStorage)
-StorageFactory.register(StorageType.cosmosdb.value, CosmosDBPipelineStorage)
-StorageFactory.register(StorageType.file.value, FilePipelineStorage)
-StorageFactory.register(StorageType.memory.value, MemoryPipelineStorage)
+storage_factory = StorageFactory()
+storage_factory.register(StorageType.blob.value, BlobPipelineStorage)
+storage_factory.register(StorageType.cosmosdb.value, CosmosDBPipelineStorage)
+storage_factory.register(StorageType.file.value, FilePipelineStorage)
+storage_factory.register(StorageType.memory.value, MemoryPipelineStorage)
diff --git a/graphrag/utils/api.py b/graphrag/utils/api.py
index 16a5f9ed52..db4a90a3a5 100644
--- a/graphrag/utils/api.py
+++ b/graphrag/utils/api.py
@@ -49,10 +49,9 @@ def get_embedding_store(
     if embedding_config.index_name is None:
         embedding_config.index_name = index_name
 
-    embedding_store = VectorStoreFactory().create_vector_store(
-        vector_store_type=vector_store_type,
-        vector_store_schema_config=embedding_config,
-        **store,
+    embedding_store = VectorStoreFactory().create(
+        vector_store_type,
+        {**store, "vector_store_schema_config": embedding_config},
     )
     embedding_store.connect(**store)
 
@@ -107,19 +106,19 @@ def load_search_prompt(root_dir: str, prompt_config: str | None) -> str | None:
 def create_storage_from_config(output: StorageConfig) -> PipelineStorage:
     """Create a storage object from the config."""
     storage_config = output.model_dump()
-    return StorageFactory().create_storage(
-        storage_type=storage_config["type"],
-        kwargs=storage_config,
+    return StorageFactory().create(
+        storage_config["type"],
+        storage_config,
     )
 
 
 def create_cache_from_config(cache: CacheConfig, root_dir: str) -> PipelineCache:
     """Create a cache object from the config."""
     cache_config = cache.model_dump()
-    kwargs = {**cache_config, "root_dir": root_dir}
-    return CacheFactory().create_cache(
-        cache_type=cache_config["type"],
-        kwargs=kwargs,
+    args = {**cache_config, "root_dir": root_dir}
+    return CacheFactory().create(
+        strategy=cache_config["type"],
+        init_args=args,
     )
 
 
diff --git a/graphrag/vector_stores/azure_ai_search.py b/graphrag/vector_stores/azure_ai_search.py
index 3d08f2fc11..e6193e09d9 100644
--- a/graphrag/vector_stores/azure_ai_search.py
+++ b/graphrag/vector_stores/azure_ai_search.py
@@ -22,7 +22,6 @@
 )
 from azure.search.documents.models import VectorizedQuery
 
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.data_model.types import TextEmbedder
 from graphrag.vector_stores.base import (
     BaseVectorStore,
@@ -36,13 +35,6 @@ class AzureAISearchVectorStore(BaseVectorStore):
 
     index_client: SearchIndexClient
 
-    def __init__(
-        self, vector_store_schema_config: VectorStoreSchemaConfig, **kwargs: Any
-    ) -> None:
-        super().__init__(
-            vector_store_schema_config=vector_store_schema_config, **kwargs
-        )
-
     def connect(self, **kwargs: Any) -> Any:
         """Connect to AI search vector storage."""
         url = kwargs["url"]
diff --git a/graphrag/vector_stores/cosmosdb.py b/graphrag/vector_stores/cosmosdb.py
index 3010e517ec..23b2c8f821 100644
--- a/graphrag/vector_stores/cosmosdb.py
+++ b/graphrag/vector_stores/cosmosdb.py
@@ -10,7 +10,6 @@
 from azure.cosmos.partition_key import PartitionKey
 from azure.identity import DefaultAzureCredential
 
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.data_model.types import TextEmbedder
 from graphrag.vector_stores.base import (
     BaseVectorStore,
@@ -26,13 +25,6 @@ class CosmosDBVectorStore(BaseVectorStore):
     _database_client: DatabaseProxy
     _container_client: ContainerProxy
 
-    def __init__(
-        self, vector_store_schema_config: VectorStoreSchemaConfig, **kwargs: Any
-    ) -> None:
-        super().__init__(
-            vector_store_schema_config=vector_store_schema_config, **kwargs
-        )
-
     def connect(self, **kwargs: Any) -> Any:
         """Connect to CosmosDB vector storage."""
         connection_string = kwargs.get("connection_string")
diff --git a/graphrag/vector_stores/factory.py b/graphrag/vector_stores/factory.py
index 8e4f7baa30..90b004cb52 100644
--- a/graphrag/vector_stores/factory.py
+++ b/graphrag/vector_stores/factory.py
@@ -5,23 +5,15 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, ClassVar
-
 from graphrag.config.enums import VectorStoreType
+from graphrag.factory.factory import Factory
 from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
+from graphrag.vector_stores.base import BaseVectorStore
 from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
 from graphrag.vector_stores.lancedb import LanceDBVectorStore
 
-if TYPE_CHECKING:
-    from collections.abc import Callable
-
-    from graphrag.config.models.vector_store_schema_config import (
-        VectorStoreSchemaConfig,
-    )
-    from graphrag.vector_stores.base import BaseVectorStore
 
-
-class VectorStoreFactory:
+class VectorStoreFactory(Factory[BaseVectorStore]):
     """A factory for vector stores.
 
     Includes a method for users to register a custom vector store implementation.
@@ -30,67 +22,11 @@ class VectorStoreFactory:
     for individual enforcement of required/optional arguments.
     """
 
-    _registry: ClassVar[dict[str, Callable[..., BaseVectorStore]]] = {}
-
-    @classmethod
-    def register(
-        cls, vector_store_type: str, creator: Callable[..., BaseVectorStore]
-    ) -> None:
-        """Register a custom vector store implementation.
-
-        Args:
-            vector_store_type: The type identifier for the vector store.
-            creator: A class or callable that creates an instance of BaseVectorStore.
-
-        Raises
-        ------
-            TypeError: If creator is a class type instead of a factory function.
-        """
-        cls._registry[vector_store_type] = creator
-
-    @classmethod
-    def create_vector_store(
-        cls,
-        vector_store_type: str,
-        vector_store_schema_config: VectorStoreSchemaConfig,
-        **kwargs: dict,
-    ) -> BaseVectorStore:
-        """Create a vector store object from the provided type.
-
-        Args:
-            vector_store_type: The type of vector store to create.
-            kwargs: Additional keyword arguments for the vector store constructor.
-
-        Returns
-        -------
-            A BaseVectorStore instance.
-
-        Raises
-        ------
-            ValueError: If the vector store type is not registered.
-        """
-        if vector_store_type not in cls._registry:
-            msg = f"Unknown vector store type: {vector_store_type}"
-            raise ValueError(msg)
-
-        return cls._registry[vector_store_type](
-            vector_store_schema_config=vector_store_schema_config, **kwargs
-        )
-
-    @classmethod
-    def get_vector_store_types(cls) -> list[str]:
-        """Get the registered vector store implementations."""
-        return list(cls._registry.keys())
-
-    @classmethod
-    def is_supported_type(cls, vector_store_type: str) -> bool:
-        """Check if the given vector store type is supported."""
-        return vector_store_type in cls._registry
-
 
 # --- register built-in vector store implementations ---
-VectorStoreFactory.register(VectorStoreType.LanceDB.value, LanceDBVectorStore)
-VectorStoreFactory.register(
+vector_store_factory = VectorStoreFactory()
+vector_store_factory.register(VectorStoreType.LanceDB.value, LanceDBVectorStore)
+vector_store_factory.register(
     VectorStoreType.AzureAISearch.value, AzureAISearchVectorStore
 )
-VectorStoreFactory.register(VectorStoreType.CosmosDB.value, CosmosDBVectorStore)
+vector_store_factory.register(VectorStoreType.CosmosDB.value, CosmosDBVectorStore)
diff --git a/graphrag/vector_stores/lancedb.py b/graphrag/vector_stores/lancedb.py
index 629df0d8f9..2b589d5bb0 100644
--- a/graphrag/vector_stores/lancedb.py
+++ b/graphrag/vector_stores/lancedb.py
@@ -9,7 +9,6 @@
 import numpy as np
 import pyarrow as pa
 
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.data_model.types import TextEmbedder
 from graphrag.vector_stores.base import (
     BaseVectorStore,
@@ -21,13 +20,6 @@
 class LanceDBVectorStore(BaseVectorStore):
     """LanceDB vector storage implementation."""
 
-    def __init__(
-        self, vector_store_schema_config: VectorStoreSchemaConfig, **kwargs: Any
-    ) -> None:
-        super().__init__(
-            vector_store_schema_config=vector_store_schema_config, **kwargs
-        )
-
     def connect(self, **kwargs: Any) -> Any:
         """Connect to the vector storage."""
         self.db_connection = lancedb.connect(kwargs["db_uri"])
diff --git a/tests/__init__.py b/tests/__init__.py
index cbb9376b53..e5c93e3503 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -6,10 +6,10 @@
 
 # Register MOCK providers
 from graphrag.config.enums import ModelType
-from graphrag.language_model.factory import ModelFactory
+from graphrag.language_model.factory import ChatModelFactory, EmbeddingModelFactory
 from tests.mock_provider import MockChatLLM, MockEmbeddingLLM
 
-ModelFactory.register_chat(ModelType.MockChat, lambda **kwargs: MockChatLLM(**kwargs))
-ModelFactory.register_embedding(
+ChatModelFactory().register(ModelType.MockChat, lambda **kwargs: MockChatLLM(**kwargs))
+EmbeddingModelFactory().register(
     ModelType.MockEmbedding, lambda **kwargs: MockEmbeddingLLM(**kwargs)
 )
diff --git a/tests/fixtures/azure/config.json b/tests/fixtures/azure/config.json
index 8adced2c08..91d9fc51d0 100644
--- a/tests/fixtures/azure/config.json
+++ b/tests/fixtures/azure/config.json
@@ -1,6 +1,7 @@
 {
     "input_path": "./tests/fixtures/azure",
     "input_file_type": "text",
+    "index_method": "standard",
     "workflow_config": {
         "skip_assert": true,
         "azure": {
diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json
index 64f90a3f07..ca2bd7d823 100644
--- a/tests/fixtures/min-csv/config.json
+++ b/tests/fixtures/min-csv/config.json
@@ -1,6 +1,7 @@
 {
     "input_path": "./tests/fixtures/min-csv",
     "input_file_type": "text",
+    "index_method": "standard",
     "workflow_config": {
         "load_input_documents": {
             "max_runtime": 30
@@ -20,10 +21,6 @@
                 100,
                 750
             ],
-            "nan_allowed_columns": [
-                "x",
-                "y"
-            ],
             "max_runtime": 30,
             "expected_artifacts": [
                 "entities.parquet",
@@ -54,7 +51,7 @@
                 "period",
                 "size"
             ],
-            "max_runtime": 360,
+            "max_runtime": 1200,
             "expected_artifacts": ["community_reports.parquet"]
         },
         "create_final_text_units": {
diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml
index 26143840e4..88f23ad061 100644
--- a/tests/fixtures/min-csv/settings.yml
+++ b/tests/fixtures/min-csv/settings.yml
@@ -8,8 +8,8 @@ models:
     api_version: "2025-04-01-preview"
     model: gpt-4.1
     retry_strategy: exponential_backoff
-    tokens_per_minute: null
-    requests_per_minute: null
+    tokens_per_minute: 250_000
+    requests_per_minute: 250
     model_supports_json: true
     concurrent_requests: 25
   default_embedding_model:
@@ -21,8 +21,8 @@ models:
     api_version: "2025-04-01-preview"
     model: text-embedding-3-large
     retry_strategy: exponential_backoff
-    tokens_per_minute: null
-    requests_per_minute: null
+    tokens_per_minute: 250_000
+    requests_per_minute: 250
     concurrent_requests: 25
 
 vector_store:
diff --git a/tests/fixtures/text/config.json b/tests/fixtures/text/config.json
index f7278a2308..eef957da2f 100644
--- a/tests/fixtures/text/config.json
+++ b/tests/fixtures/text/config.json
@@ -1,6 +1,7 @@
 {
     "input_path": "./tests/fixtures/text",
     "input_file_type": "text",
+    "index_method": "fast",
     "workflow_config": {
         "load_input_documents": {
             "max_runtime": 30
@@ -8,17 +9,16 @@
         "create_base_text_units": {
             "max_runtime": 30
         },
-        "extract_graph": {
-            "max_runtime": 500
+        "extract_graph_nlp": {
+            "max_runtime": 30
+        },
+        "prune_graph": {
+            "max_runtime": 30
         },
         "finalize_graph": {
             "row_range": [
                 10,
-                200
-            ],
-            "nan_allowed_columns": [
-                "x",
-                "y"
+                300
             ],
             "max_runtime": 30,
             "expected_artifacts": [
@@ -26,23 +26,6 @@
                 "relationships.parquet"
             ]
         },
-        "extract_covariates": {
-            "row_range": [
-                10,
-                100
-            ],
-            "nan_allowed_columns": [
-                "type",
-                "description",
-                "object_id",
-                "status",
-                "start_date",
-                "end_date",
-                "source_text"
-            ],
-            "max_runtime": 360,
-            "expected_artifacts": ["covariates.parquet"]
-        },
         "create_communities": {
             "row_range": [
                 1,
@@ -51,7 +34,7 @@
             "max_runtime": 30,
             "expected_artifacts": ["communities.parquet"]
         },
-        "create_community_reports": {
+        "create_community_reports_text": {
             "row_range": [
                 1,
                 30
@@ -67,7 +50,7 @@
                 "period",
                 "size"
             ],
-            "max_runtime": 360,
+            "max_runtime": 1200,
             "expected_artifacts": ["community_reports.parquet"]
         },
         "create_final_text_units": {
diff --git a/tests/fixtures/text/settings.yml b/tests/fixtures/text/settings.yml
index 633c12a03f..f7602ccb73 100644
--- a/tests/fixtures/text/settings.yml
+++ b/tests/fixtures/text/settings.yml
@@ -8,8 +8,8 @@ models:
     api_version: "2025-04-01-preview"
     model: gpt-4.1
     retry_strategy: exponential_backoff
-    tokens_per_minute: null
-    requests_per_minute: null
+    tokens_per_minute: 250_000
+    requests_per_minute: 250
     model_supports_json: true
     concurrent_requests: 25
   default_embedding_model:
@@ -21,8 +21,8 @@ models:
     api_version: "2025-04-01-preview"
     model: text-embedding-3-large
     retry_strategy: exponential_backoff
-    tokens_per_minute: null
-    requests_per_minute: null
+    tokens_per_minute: 250_000
+    requests_per_minute: 250
     concurrent_requests: 25
 
 vector_store:
@@ -32,9 +32,6 @@ vector_store:
     api_key: ${AZURE_AI_SEARCH_API_KEY}
     container_name: "simple_text_ci"
 
-extract_claims:
-  enabled: true
-
 community_reports:
   prompt: "prompts/community_report.txt"
   max_length: 2000
diff --git a/tests/integration/cache/test_factory.py b/tests/integration/cache/test_factory.py
index 5dde4e2635..c7a7964471 100644
--- a/tests/integration/cache/test_factory.py
+++ b/tests/integration/cache/test_factory.py
@@ -2,7 +2,7 @@
 # Licensed under the MIT License
 """CacheFactory Tests.
 
-These tests will test the CacheFactory class and the creation of each cache type that is natively supported.
+These tests will test the CacheFactory() class and the creation of each cache type that is natively supported.
 """
 
 import sys
@@ -23,30 +23,30 @@
 
 
 def test_create_noop_cache():
-    kwargs = {}
-    cache = CacheFactory.create_cache(CacheType.none.value, kwargs)
+    cache = CacheFactory().create(strategy=CacheType.none.value)
     assert isinstance(cache, NoopPipelineCache)
 
 
 def test_create_memory_cache():
-    kwargs = {}
-    cache = CacheFactory.create_cache(CacheType.memory.value, kwargs)
+    cache = CacheFactory().create(strategy=CacheType.memory.value)
     assert isinstance(cache, InMemoryCache)
 
 
 def test_create_file_cache():
-    kwargs = {"root_dir": "/tmp", "base_dir": "testcache"}
-    cache = CacheFactory.create_cache(CacheType.file.value, kwargs)
+    cache = CacheFactory().create(
+        strategy=CacheType.file.value,
+        init_args={"root_dir": "/tmp", "base_dir": "testcache"},
+    )
     assert isinstance(cache, JsonPipelineCache)
 
 
 def test_create_blob_cache():
-    kwargs = {
+    init_args = {
         "connection_string": WELL_KNOWN_BLOB_STORAGE_KEY,
         "container_name": "testcontainer",
         "base_dir": "testcache",
     }
-    cache = CacheFactory.create_cache(CacheType.blob.value, kwargs)
+    cache = CacheFactory().create(strategy=CacheType.blob.value, init_args=init_args)
     assert isinstance(cache, JsonPipelineCache)
 
 
@@ -55,12 +55,14 @@ def test_create_blob_cache():
     reason="cosmosdb emulator is only available on windows runners at this time",
 )
 def test_create_cosmosdb_cache():
-    kwargs = {
+    init_args = {
         "connection_string": WELL_KNOWN_COSMOS_CONNECTION_STRING,
         "base_dir": "testdatabase",
         "container_name": "testcontainer",
     }
-    cache = CacheFactory.create_cache(CacheType.cosmosdb.value, kwargs)
+    cache = CacheFactory().create(
+        strategy=CacheType.cosmosdb.value, init_args=init_args
+    )
     assert isinstance(cache, JsonPipelineCache)
 
 
@@ -75,8 +77,11 @@ def test_register_and_create_custom_cache():
     instance.initialized = True
     custom_cache_class.return_value = instance
 
-    CacheFactory.register("custom", lambda **kwargs: custom_cache_class(**kwargs))
-    cache = CacheFactory.create_cache("custom", {})
+    CacheFactory().register(
+        strategy="custom",
+        initializer=lambda **kwargs: custom_cache_class(**kwargs),
+    )
+    cache = CacheFactory().create(strategy="custom")
 
     assert custom_cache_class.called
     assert cache is instance
@@ -84,47 +89,34 @@ def test_register_and_create_custom_cache():
     assert cache.initialized is True  # type: ignore # Attribute only exists on our mock
 
     # Check if it's in the list of registered cache types
-    assert "custom" in CacheFactory.get_cache_types()
-    assert CacheFactory.is_supported_type("custom")
-
-
-def test_get_cache_types():
-    cache_types = CacheFactory.get_cache_types()
-    # Check that built-in types are registered
-    assert CacheType.none.value in cache_types
-    assert CacheType.memory.value in cache_types
-    assert CacheType.file.value in cache_types
-    assert CacheType.blob.value in cache_types
-    assert CacheType.cosmosdb.value in cache_types
+    assert "custom" in CacheFactory()
 
 
 def test_create_unknown_cache():
-    with pytest.raises(ValueError, match="Unknown cache type: unknown"):
-        CacheFactory.create_cache("unknown", {})
+    with pytest.raises(ValueError, match="Strategy 'unknown' is not registered\\."):
+        CacheFactory().create(strategy="unknown")
 
 
 def test_is_supported_type():
     # Test built-in types
-    assert CacheFactory.is_supported_type(CacheType.none.value)
-    assert CacheFactory.is_supported_type(CacheType.memory.value)
-    assert CacheFactory.is_supported_type(CacheType.file.value)
-    assert CacheFactory.is_supported_type(CacheType.blob.value)
-    assert CacheFactory.is_supported_type(CacheType.cosmosdb.value)
+    assert CacheType.none.value in CacheFactory()
+    assert CacheType.memory.value in CacheFactory()
+    assert CacheType.file.value in CacheFactory()
+    assert CacheType.blob.value in CacheFactory()
+    assert CacheType.cosmosdb.value in CacheFactory()
 
     # Test unknown type
-    assert not CacheFactory.is_supported_type("unknown")
+    assert "unknown" not in CacheFactory()
 
 
 def test_enum_and_string_compatibility():
     """Test that both enum and string types work for cache creation."""
-    kwargs = {}
-
     # Test with enum
-    cache_enum = CacheFactory.create_cache(CacheType.memory, kwargs)
+    cache_enum = CacheFactory().create(strategy=CacheType.memory)
     assert isinstance(cache_enum, InMemoryCache)
 
     # Test with string
-    cache_str = CacheFactory.create_cache("memory", kwargs)
+    cache_str = CacheFactory().create(strategy="memory")
     assert isinstance(cache_str, InMemoryCache)
 
     # Both should create the same type
@@ -132,7 +124,7 @@ def test_enum_and_string_compatibility():
 
 
 def test_register_class_directly_works():
-    """Test that registering a class directly works (CacheFactory allows this)."""
+    """Test that registering a class directly works (CacheFactory() allows this)."""
     from graphrag.cache.pipeline_cache import PipelineCache
 
     class CustomCache(PipelineCache):
@@ -157,13 +149,12 @@ async def clear(self):
         def child(self, name: str):
             return self
 
-    # CacheFactory allows registering classes directly (no TypeError)
-    CacheFactory.register("custom_class", CustomCache)
+    # CacheFactory() allows registering classes directly (no TypeError)
+    CacheFactory().register("custom_class", CustomCache)
 
     # Verify it was registered
-    assert "custom_class" in CacheFactory.get_cache_types()
-    assert CacheFactory.is_supported_type("custom_class")
+    assert "custom_class" in CacheFactory()
 
     # Test creating an instance
-    cache = CacheFactory.create_cache("custom_class", {})
+    cache = CacheFactory().create(strategy="custom_class")
     assert isinstance(cache, CustomCache)
diff --git a/tests/integration/language_model/test_factory.py b/tests/integration/language_model/test_factory.py
index af503265d6..9757c68d99 100644
--- a/tests/integration/language_model/test_factory.py
+++ b/tests/integration/language_model/test_factory.py
@@ -9,7 +9,7 @@
 from collections.abc import AsyncGenerator, Generator
 from typing import Any
 
-from graphrag.language_model.factory import ModelFactory
+from graphrag.language_model.factory import ChatModelFactory, EmbeddingModelFactory
 from graphrag.language_model.manager import ModelManager
 from graphrag.language_model.response.base import (
     BaseModelOutput,
@@ -48,7 +48,7 @@ def chat_stream(
             self, prompt: str, history: list | None = None, **kwargs: Any
         ) -> Generator[str, None]: ...
 
-    ModelFactory.register_chat("custom_chat", CustomChatModel)
+    ChatModelFactory().register("custom_chat", CustomChatModel)
     model = ModelManager().get_or_create_chat_model("custom", "custom_chat")
     assert isinstance(model, CustomChatModel)
     response = await model.achat("prompt")
@@ -81,7 +81,7 @@ async def aembed_batch(
         def embed_batch(self, text_list: list[str], **kwargs) -> list[list[float]]:
             return [[1.0]]
 
-    ModelFactory.register_embedding("custom_embedding", CustomEmbeddingModel)
+    EmbeddingModelFactory().register("custom_embedding", CustomEmbeddingModel)
     llm = ModelManager().get_or_create_embedding_model("custom", "custom_embedding")
     assert isinstance(llm, CustomEmbeddingModel)
     response = await llm.aembed("text")
diff --git a/tests/unit/litellm_services/test_rate_limiter.py b/tests/integration/language_model/test_rate_limiter.py
similarity index 89%
rename from tests/unit/litellm_services/test_rate_limiter.py
rename to tests/integration/language_model/test_rate_limiter.py
index ffe144212d..ec482b7240 100644
--- a/tests/unit/litellm_services/test_rate_limiter.py
+++ b/tests/integration/language_model/test_rate_limiter.py
@@ -16,7 +16,7 @@
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter_factory import (
     RateLimiterFactory,
 )
-from tests.unit.litellm_services.utils import (
+from tests.integration.language_model.utils import (
     assert_max_num_values_per_period,
     assert_stagger,
     bin_time_intervals,
@@ -51,7 +51,7 @@ def test_rate_limiter_validation():
 
     # Valid parameters
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", rpm=60, tpm=10000, period_in_seconds=60
+        strategy="static", init_args={"rpm": 60, "tpm": 10000, "period_in_seconds": 60}
     )
     assert rate_limiter is not None
 
@@ -60,7 +60,9 @@ def test_rate_limiter_validation():
         ValueError,
         match=r"Strategy 'invalid_strategy' is not registered.",
     ):
-        rate_limiter_factory.create(strategy="invalid_strategy", rpm=60, tpm=10000)
+        rate_limiter_factory.create(
+            strategy="invalid_strategy", init_args={"rpm": 60, "tpm": 10000}
+        )
 
     # Both rpm and tpm are None
     with pytest.raises(
@@ -74,26 +76,29 @@ def test_rate_limiter_validation():
         ValueError,
         match=r"RPM and TPM must be either None \(disabled\) or positive integers.",
     ):
-        rate_limiter_factory.create(strategy="static", rpm=-10)
+        rate_limiter_factory.create(strategy="static", init_args={"rpm": -10})
 
     # Invalid tpm
     with pytest.raises(
         ValueError,
         match=r"RPM and TPM must be either None \(disabled\) or positive integers.",
     ):
-        rate_limiter_factory.create(strategy="static", tpm=-10)
+        rate_limiter_factory.create(strategy="static", init_args={"tpm": -10})
 
     # Invalid period_in_seconds
     with pytest.raises(
         ValueError, match=r"Period in seconds must be a positive integer."
     ):
-        rate_limiter_factory.create(strategy="static", rpm=10, period_in_seconds=-10)
+        rate_limiter_factory.create(
+            strategy="static", init_args={"rpm": 10, "period_in_seconds": -10}
+        )
 
 
 def test_rpm():
     """Test that the rate limiter enforces RPM limits."""
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", rpm=_rpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"rpm": _rpm, "period_in_seconds": _period_in_seconds},
     )
 
     time_values: list[float] = []
@@ -121,7 +126,8 @@ def test_rpm():
 def test_tpm():
     """Test that the rate limiter enforces TPM limits."""
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", tpm=_tpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"tpm": _tpm, "period_in_seconds": _period_in_seconds},
     )
 
     time_values: list[float] = []
@@ -154,7 +160,8 @@ def test_token_in_request_exceeds_tpm():
     In this case, the request should still be allowed to proceed but may take up its own rate limit bin.
     """
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", tpm=_tpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"tpm": _tpm, "period_in_seconds": _period_in_seconds},
     )
 
     time_values: list[float] = []
@@ -178,7 +185,8 @@ def test_token_in_request_exceeds_tpm():
 def test_rpm_and_tpm_with_rpm_as_limiting_factor():
     """Test that the rate limiter enforces RPM and TPM limits."""
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", rpm=_rpm, tpm=_tpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"rpm": _rpm, "tpm": _tpm, "period_in_seconds": _period_in_seconds},
     )
 
     time_values: list[float] = []
@@ -207,7 +215,8 @@ def test_rpm_and_tpm_with_rpm_as_limiting_factor():
 def test_rpm_and_tpm_with_tpm_as_limiting_factor():
     """Test that the rate limiter enforces TPM limits."""
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", rpm=_rpm, tpm=_tpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"rpm": _rpm, "tpm": _tpm, "period_in_seconds": _period_in_seconds},
     )
 
     time_values: list[float] = []
@@ -251,7 +260,8 @@ def _run_rate_limiter(
 def test_rpm_threaded():
     """Test that the rate limiter enforces RPM limits in a threaded environment."""
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", rpm=_rpm, tpm=_tpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"rpm": _rpm, "tpm": _tpm, "period_in_seconds": _period_in_seconds},
     )
 
     input_queue: Queue[int | None] = Queue()
@@ -311,7 +321,8 @@ def test_rpm_threaded():
 def test_tpm_threaded():
     """Test that the rate limiter enforces TPM limits in a threaded environment."""
     rate_limiter = rate_limiter_factory.create(
-        strategy="static", rpm=_rpm, tpm=_tpm, period_in_seconds=_period_in_seconds
+        strategy="static",
+        init_args={"rpm": _rpm, "tpm": _tpm, "period_in_seconds": _period_in_seconds},
     )
 
     input_queue: Queue[int | None] = Queue()
diff --git a/tests/unit/litellm_services/test_retries.py b/tests/integration/language_model/test_retries.py
similarity index 95%
rename from tests/unit/litellm_services/test_retries.py
rename to tests/integration/language_model/test_retries.py
index 3db2a39027..617e11a8cb 100644
--- a/tests/unit/litellm_services/test_retries.py
+++ b/tests/integration/language_model/test_retries.py
@@ -57,8 +57,10 @@ def test_retries(
     """
     retry_service = retry_factory.create(
         strategy=strategy,
-        max_retries=max_retries,
-        max_retry_wait=max_retry_wait,
+        init_args={
+            "max_retries": max_retries,
+            "max_retry_wait": max_retry_wait,
+        },
     )
 
     retries = 0
@@ -124,8 +126,10 @@ async def test_retries_async(
     """
     retry_service = retry_factory.create(
         strategy=strategy,
-        max_retries=max_retries,
-        max_retry_wait=max_retry_wait,
+        init_args={
+            "max_retries": max_retries,
+            "max_retry_wait": max_retry_wait,
+        },
     )
 
     retries = 0
diff --git a/tests/unit/litellm_services/utils.py b/tests/integration/language_model/utils.py
similarity index 100%
rename from tests/unit/litellm_services/utils.py
rename to tests/integration/language_model/utils.py
diff --git a/tests/integration/logging/test_factory.py b/tests/integration/logging/test_factory.py
index bacb4f0712..cb22ca8674 100644
--- a/tests/integration/logging/test_factory.py
+++ b/tests/integration/logging/test_factory.py
@@ -27,7 +27,7 @@ def test_create_blob_logger():
         "base_dir": "testbasedir",
         "container_name": "testcontainer",
     }
-    logger = LoggerFactory.create_logger(ReportingType.blob.value, kwargs)
+    logger = LoggerFactory().create(ReportingType.blob.value, kwargs)
     assert isinstance(logger, BlobWorkflowLogger)
 
 
@@ -40,8 +40,8 @@ def test_register_and_create_custom_logger():
     instance.initialized = True
     custom_logger_class.return_value = instance
 
-    LoggerFactory.register("custom", lambda **kwargs: custom_logger_class(**kwargs))
-    logger = LoggerFactory.create_logger("custom", {})
+    LoggerFactory().register("custom", lambda **kwargs: custom_logger_class(**kwargs))
+    logger = LoggerFactory().create("custom")
 
     assert custom_logger_class.called
     assert logger is instance
@@ -49,17 +49,15 @@ def test_register_and_create_custom_logger():
     assert logger.initialized is True  # type: ignore # Attribute only exists on our mock
 
     # Check if it's in the list of registered logger types
-    assert "custom" in LoggerFactory.get_logger_types()
-    assert LoggerFactory.is_supported_type("custom")
+    assert "custom" in LoggerFactory()
 
 
 def test_get_logger_types():
-    logger_types = LoggerFactory.get_logger_types()
     # Check that built-in types are registered
-    assert ReportingType.file.value in logger_types
-    assert ReportingType.blob.value in logger_types
+    assert ReportingType.file.value in LoggerFactory()
+    assert ReportingType.blob.value in LoggerFactory()
 
 
 def test_create_unknown_logger():
-    with pytest.raises(ValueError, match="Unknown reporting type: unknown"):
-        LoggerFactory.create_logger("unknown", {})
+    with pytest.raises(ValueError, match="Strategy 'unknown' is not registered\\."):
+        LoggerFactory().create("unknown")
diff --git a/tests/integration/storage/test_factory.py b/tests/integration/storage/test_factory.py
index 0b59366ef9..8817b25036 100644
--- a/tests/integration/storage/test_factory.py
+++ b/tests/integration/storage/test_factory.py
@@ -31,7 +31,7 @@ def test_create_blob_storage():
         "base_dir": "testbasedir",
         "container_name": "testcontainer",
     }
-    storage = StorageFactory.create_storage(StorageType.blob.value, kwargs)
+    storage = StorageFactory().create(StorageType.blob.value, kwargs)
     assert isinstance(storage, BlobPipelineStorage)
 
 
@@ -46,19 +46,19 @@ def test_create_cosmosdb_storage():
         "base_dir": "testdatabase",
         "container_name": "testcontainer",
     }
-    storage = StorageFactory.create_storage(StorageType.cosmosdb.value, kwargs)
+    storage = StorageFactory().create(StorageType.cosmosdb.value, kwargs)
     assert isinstance(storage, CosmosDBPipelineStorage)
 
 
-def test_create_file_storage():
+def test_create_file():
     kwargs = {"type": "file", "base_dir": "/tmp/teststorage"}
-    storage = StorageFactory.create_storage(StorageType.file.value, kwargs)
+    storage = StorageFactory().create(StorageType.file.value, kwargs)
     assert isinstance(storage, FilePipelineStorage)
 
 
 def test_create_memory_storage():
     kwargs = {}  # MemoryPipelineStorage doesn't accept any constructor parameters
-    storage = StorageFactory.create_storage(StorageType.memory.value, kwargs)
+    storage = StorageFactory().create(StorageType.memory.value, kwargs)
     assert isinstance(storage, MemoryPipelineStorage)
 
 
@@ -74,8 +74,8 @@ def test_register_and_create_custom_storage():
     instance.initialized = True
     custom_storage_class.return_value = instance
 
-    StorageFactory.register("custom", lambda **kwargs: custom_storage_class(**kwargs))
-    storage = StorageFactory.create_storage("custom", {})
+    StorageFactory().register("custom", lambda **kwargs: custom_storage_class(**kwargs))
+    storage = StorageFactory().create("custom", {})
 
     assert custom_storage_class.called
     assert storage is instance
@@ -83,22 +83,20 @@ def test_register_and_create_custom_storage():
     assert storage.initialized is True  # type: ignore # Attribute only exists on our mock
 
     # Check if it's in the list of registered storage types
-    assert "custom" in StorageFactory.get_storage_types()
-    assert StorageFactory.is_supported_type("custom")
+    assert "custom" in StorageFactory()
 
 
 def test_get_storage_types():
-    storage_types = StorageFactory.get_storage_types()
     # Check that built-in types are registered
-    assert StorageType.file.value in storage_types
-    assert StorageType.memory.value in storage_types
-    assert StorageType.blob.value in storage_types
-    assert StorageType.cosmosdb.value in storage_types
+    assert StorageType.file.value in StorageFactory()
+    assert StorageType.memory.value in StorageFactory()
+    assert StorageType.blob.value in StorageFactory()
+    assert StorageType.cosmosdb.value in StorageFactory()
 
 
 def test_create_unknown_storage():
-    with pytest.raises(ValueError, match="Unknown storage type: unknown"):
-        StorageFactory.create_storage("unknown", {})
+    with pytest.raises(ValueError, match="Strategy 'unknown' is not registered\\."):
+        StorageFactory().create("unknown")
 
 
 def test_register_class_directly_works():
@@ -148,12 +146,11 @@ async def get_creation_date(self, key: str) -> str:
             return "2024-01-01 00:00:00 +0000"
 
     # StorageFactory allows registering classes directly (no TypeError)
-    StorageFactory.register("custom_class", CustomStorage)
+    StorageFactory().register("custom_class", CustomStorage)
 
     # Verify it was registered
-    assert "custom_class" in StorageFactory.get_storage_types()
-    assert StorageFactory.is_supported_type("custom_class")
+    assert "custom_class" in StorageFactory()
 
     # Test creating an instance
-    storage = StorageFactory.create_storage("custom_class", {})
+    storage = StorageFactory().create("custom_class")
     assert isinstance(storage, CustomStorage)
diff --git a/tests/integration/vector_stores/test_factory.py b/tests/integration/vector_stores/test_factory.py
index 2a2c9c7173..69720c9664 100644
--- a/tests/integration/vector_stores/test_factory.py
+++ b/tests/integration/vector_stores/test_factory.py
@@ -19,13 +19,13 @@
 def test_create_lancedb_vector_store():
     kwargs = {
         "db_uri": "/tmp/lancedb",
-    }
-    vector_store = VectorStoreFactory.create_vector_store(
-        vector_store_type=VectorStoreType.LanceDB.value,
-        vector_store_schema_config=VectorStoreSchemaConfig(
+        "vector_store_schema_config": VectorStoreSchemaConfig(
             index_name="test_collection"
         ),
-        kwargs=kwargs,
+    }
+    vector_store = VectorStoreFactory().create(
+        VectorStoreType.LanceDB.value,
+        kwargs,
     )
     assert isinstance(vector_store, LanceDBVectorStore)
     assert vector_store.index_name == "test_collection"
@@ -36,13 +36,13 @@ def test_create_azure_ai_search_vector_store():
     kwargs = {
         "url": "https://test.search.windows.net",
         "api_key": "test_key",
-    }
-    vector_store = VectorStoreFactory.create_vector_store(
-        vector_store_type=VectorStoreType.AzureAISearch.value,
-        vector_store_schema_config=VectorStoreSchemaConfig(
+        "vector_store_schema_config": VectorStoreSchemaConfig(
             index_name="test_collection"
         ),
-        kwargs=kwargs,
+    }
+    vector_store = VectorStoreFactory().create(
+        VectorStoreType.AzureAISearch.value,
+        kwargs,
     )
     assert isinstance(vector_store, AzureAISearchVectorStore)
 
@@ -52,14 +52,14 @@ def test_create_cosmosdb_vector_store():
     kwargs = {
         "connection_string": "AccountEndpoint=https://test.documents.azure.com:443/;AccountKey=test_key==",
         "database_name": "test_db",
-    }
-
-    vector_store = VectorStoreFactory.create_vector_store(
-        vector_store_type=VectorStoreType.CosmosDB.value,
-        vector_store_schema_config=VectorStoreSchemaConfig(
+        "vector_store_schema_config": VectorStoreSchemaConfig(
             index_name="test_collection"
         ),
-        kwargs=kwargs,
+    }
+
+    vector_store = VectorStoreFactory().create(
+        VectorStoreType.CosmosDB.value,
+        kwargs,
     )
 
     assert isinstance(vector_store, CosmosDBVectorStore)
@@ -76,12 +76,12 @@ def test_register_and_create_custom_vector_store():
     instance.initialized = True
     custom_vector_store_class.return_value = instance
 
-    VectorStoreFactory.register(
+    VectorStoreFactory().register(
         "custom", lambda **kwargs: custom_vector_store_class(**kwargs)
     )
 
-    vector_store = VectorStoreFactory.create_vector_store(
-        vector_store_type="custom", vector_store_schema_config=VectorStoreSchemaConfig()
+    vector_store = VectorStoreFactory().create(
+        "custom", {"vector_store_schema_config": VectorStoreSchemaConfig()}
     )
 
     assert custom_vector_store_class.called
@@ -90,38 +90,26 @@ def test_register_and_create_custom_vector_store():
     assert vector_store.initialized is True  # type: ignore # Attribute only exists on our mock
 
     # Check if it's in the list of registered vector store types
-    assert "custom" in VectorStoreFactory.get_vector_store_types()
-    assert VectorStoreFactory.is_supported_type("custom")
-
-
-def test_get_vector_store_types():
-    vector_store_types = VectorStoreFactory.get_vector_store_types()
-    # Check that built-in types are registered
-    assert VectorStoreType.LanceDB.value in vector_store_types
-    assert VectorStoreType.AzureAISearch.value in vector_store_types
-    assert VectorStoreType.CosmosDB.value in vector_store_types
+    assert "custom" in VectorStoreFactory()
 
 
 def test_create_unknown_vector_store():
-    with pytest.raises(ValueError, match="Unknown vector store type: unknown"):
-        VectorStoreFactory.create_vector_store(
-            vector_store_type="unknown",
-            vector_store_schema_config=VectorStoreSchemaConfig(),
-        )
+    with pytest.raises(ValueError, match="Strategy 'unknown' is not registered\\."):
+        VectorStoreFactory().create("unknown")
 
 
 def test_is_supported_type():
     # Test built-in types
-    assert VectorStoreFactory.is_supported_type(VectorStoreType.LanceDB.value)
-    assert VectorStoreFactory.is_supported_type(VectorStoreType.AzureAISearch.value)
-    assert VectorStoreFactory.is_supported_type(VectorStoreType.CosmosDB.value)
+    assert VectorStoreType.LanceDB.value in VectorStoreFactory()
+    assert VectorStoreType.AzureAISearch.value in VectorStoreFactory()
+    assert VectorStoreType.CosmosDB.value in VectorStoreFactory()
 
     # Test unknown type
-    assert not VectorStoreFactory.is_supported_type("unknown")
+    assert "unknown" not in VectorStoreFactory()
 
 
 def test_register_class_directly_works():
-    """Test that registering a class directly works (VectorStoreFactory allows this)."""
+    """Test that registering a class directly works."""
     from graphrag.vector_stores.base import BaseVectorStore
 
     class CustomVectorStore(BaseVectorStore):
@@ -143,25 +131,21 @@ def similarity_search_by_vector(self, query_embedding, k=10, **kwargs):
         def similarity_search_by_text(self, text, text_embedder, k=10, **kwargs):
             return []
 
-        def filter_by_id(self, include_ids):
-            return {}
-
         def search_by_id(self, id):
             from graphrag.vector_stores.base import VectorStoreDocument
 
             return VectorStoreDocument(id=id, vector=None)
 
-    # VectorStoreFactory allows registering classes directly (no TypeError)
-    VectorStoreFactory.register("custom_class", CustomVectorStore)
+    # VectorStoreFactory() allows registering classes directly (no TypeError)
+    VectorStoreFactory().register("custom_class", CustomVectorStore)
 
     # Verify it was registered
-    assert "custom_class" in VectorStoreFactory.get_vector_store_types()
-    assert VectorStoreFactory.is_supported_type("custom_class")
+    assert "custom_class" in VectorStoreFactory()
 
     # Test creating an instance
-    vector_store = VectorStoreFactory.create_vector_store(
-        vector_store_type="custom_class",
-        vector_store_schema_config=VectorStoreSchemaConfig(),
+    vector_store = VectorStoreFactory().create(
+        "custom_class",
+        {"vector_store_schema_config": VectorStoreSchemaConfig()},
     )
 
     assert isinstance(vector_store, CustomVectorStore)
diff --git a/tests/smoke/test_fixtures.py b/tests/smoke/test_fixtures.py
index 7a81bf2d16..e0dd6b7b37 100644
--- a/tests/smoke/test_fixtures.py
+++ b/tests/smoke/test_fixtures.py
@@ -128,6 +128,7 @@ def __run_indexer(
         self,
         root: Path,
         input_file_type: str,
+        index_method: str,
     ):
         command = [
             "uv",
@@ -138,7 +139,7 @@ def __run_indexer(
             "--root",
             root.resolve().as_posix(),
             "--method",
-            "standard",
+            index_method,
         ]
         command = [arg for arg in command if arg]
         logger.info("running command ", " ".join(command))
@@ -234,6 +235,7 @@ def test_fixture(
         self,
         input_path: str,
         input_file_type: str,
+        index_method: str,
         workflow_config: dict[str, dict[str, Any]],
         query_config: list[dict[str, str]],
     ):
@@ -248,7 +250,7 @@ def test_fixture(
             dispose = asyncio.run(prepare_azurite_data(input_path, azure))
 
         print("running indexer")
-        self.__run_indexer(root, input_file_type)
+        self.__run_indexer(root, input_file_type, index_method)
         print("indexer complete")
 
         if dispose is not None:
diff --git a/tests/unit/indexing/input/test_csv_loader.py b/tests/unit/indexing/input/test_csv_loader.py
index 965f836676..8a6b0e351d 100644
--- a/tests/unit/indexing/input/test_csv_loader.py
+++ b/tests/unit/indexing/input/test_csv_loader.py
@@ -4,7 +4,7 @@
 from graphrag.config.enums import InputFileType
 from graphrag.config.models.input_config import InputConfig
 from graphrag.config.models.storage_config import StorageConfig
-from graphrag.index.input.factory import create_input
+from graphrag.index.input.factory import InputReaderFactory
 from graphrag.utils.api import create_storage_from_config
 
 
@@ -17,7 +17,11 @@ async def test_csv_loader_one_file():
         file_pattern=".*\\.csv$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (2, 4)
     assert documents["title"].iloc[0] == "input.csv"
 
@@ -32,7 +36,11 @@ async def test_csv_loader_one_file_with_title():
         title_column="title",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (2, 4)
     assert documents["title"].iloc[0] == "Hello"
 
@@ -48,7 +56,12 @@ async def test_csv_loader_one_file_with_metadata():
         metadata=["title"],
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
+    print(documents)
     assert documents.shape == (2, 5)
     assert documents["metadata"][0] == {"title": "Hello"}
 
@@ -62,5 +75,9 @@ async def test_csv_loader_multiple_files():
         file_pattern=".*\\.csv$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (4, 4)
diff --git a/tests/unit/indexing/input/test_json_loader.py b/tests/unit/indexing/input/test_json_loader.py
index c97d38d4a0..1ce7001aab 100644
--- a/tests/unit/indexing/input/test_json_loader.py
+++ b/tests/unit/indexing/input/test_json_loader.py
@@ -4,7 +4,7 @@
 from graphrag.config.enums import InputFileType
 from graphrag.config.models.input_config import InputConfig
 from graphrag.config.models.storage_config import StorageConfig
-from graphrag.index.input.factory import create_input
+from graphrag.index.input.factory import InputReaderFactory
 from graphrag.utils.api import create_storage_from_config
 
 
@@ -17,7 +17,11 @@ async def test_json_loader_one_file_one_object():
         file_pattern=".*\\.json$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (1, 4)
     assert documents["title"].iloc[0] == "input.json"
 
@@ -31,7 +35,11 @@ async def test_json_loader_one_file_multiple_objects():
         file_pattern=".*\\.json$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     print(documents)
     assert documents.shape == (3, 4)
     assert documents["title"].iloc[0] == "input.json"
@@ -47,7 +55,11 @@ async def test_json_loader_one_file_with_title():
         title_column="title",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (1, 4)
     assert documents["title"].iloc[0] == "Hello"
 
@@ -63,7 +75,11 @@ async def test_json_loader_one_file_with_metadata():
         metadata=["title"],
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (1, 5)
     assert documents["metadata"][0] == {"title": "Hello"}
 
@@ -77,5 +93,9 @@ async def test_json_loader_multiple_files():
         file_pattern=".*\\.json$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (4, 4)
diff --git a/tests/unit/indexing/input/test_txt_loader.py b/tests/unit/indexing/input/test_txt_loader.py
index 6b82a408fb..239f622d72 100644
--- a/tests/unit/indexing/input/test_txt_loader.py
+++ b/tests/unit/indexing/input/test_txt_loader.py
@@ -4,7 +4,7 @@
 from graphrag.config.enums import InputFileType
 from graphrag.config.models.input_config import InputConfig
 from graphrag.config.models.storage_config import StorageConfig
-from graphrag.index.input.factory import create_input
+from graphrag.index.input.factory import InputReaderFactory
 from graphrag.utils.api import create_storage_from_config
 
 
@@ -17,7 +17,11 @@ async def test_txt_loader_one_file():
         file_pattern=".*\\.txt$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (1, 4)
     assert documents["title"].iloc[0] == "input.txt"
 
@@ -32,7 +36,11 @@ async def test_txt_loader_one_file_with_metadata():
         metadata=["title"],
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (1, 5)
     # unlike csv, we cannot set the title to anything other than the filename
     assert documents["metadata"][0] == {"title": "input.txt"}
@@ -47,5 +55,9 @@ async def test_txt_loader_multiple_files():
         file_pattern=".*\\.txt$",
     )
     storage = create_storage_from_config(config.storage)
-    documents = await create_input(config=config, storage=storage)
+    documents = (
+        await InputReaderFactory()
+        .create(config.file_type, {"storage": storage, "config": config})
+        .read_files()
+    )
     assert documents.shape == (2, 4)
diff --git a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
index 39f041b10c..8390bfe8aa 100644
--- a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
+++ b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
@@ -2,7 +2,6 @@
 # Licensed under the MIT License
 import unittest
 
-from graphrag.cache.factory import CacheFactory
 from graphrag.index.operations.extract_graph.extract_graph import run_extract_graph
 from graphrag.index.operations.extract_graph.typing import (
     Document,
@@ -10,8 +9,6 @@
 from graphrag.prompts.index.extract_graph import GRAPH_EXTRACTION_PROMPT
 from tests.unit.indexing.verbs.helpers.mock_llm import create_mock_llm
 
-_cache = CacheFactory.create_cache("none", kwargs={})
-
 
 class TestRunChain(unittest.IsolatedAsyncioTestCase):
     async def test_run_extract_graph_single_document_correct_entities_returned(self):
diff --git a/tests/unit/litellm_services/__init__.py b/tests/unit/litellm_services/__init__.py
deleted file mode 100644
index 0a3e38adfb..0000000000
--- a/tests/unit/litellm_services/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License

From 542d3db2a908d1673abdc251c5a20b446a7dd014 Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Tue, 21 Oct 2025 15:30:50 -0600
Subject: [PATCH 17/40] Prefix vector store (#2106)

* add prefix to vector store configuration and removal of container name

* docs updated

* change prefix property name

* change prefix property name

* feedback implemented

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 docs/config/yaml.md                           | 40 ++++++++++++++++++-
 graphrag/config/defaults.py                   |  2 +-
 graphrag/config/embeddings.py                 |  9 +++--
 graphrag/config/init_content.py               |  1 -
 graphrag/config/models/vector_store_config.py |  6 +--
 .../workflows/generate_text_embeddings.py     | 14 +++++--
 tests/unit/config/utils.py                    |  2 +-
 7 files changed, 60 insertions(+), 14 deletions(-)

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index ae4d42790d..2c2ba7eb52 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -171,9 +171,45 @@ Where to put all vectors for the system. Configured for lancedb by default. This
 - `url` **str** (only for AI Search) - AI Search endpoint
 - `api_key` **str** (optional - only for AI Search) - The AI Search api key to use.
 - `audience` **str** (only for AI Search) - Audience for managed identity token if managed identity authentication is used.
-- `container_name` **str** - The name of a vector container. This stores all indexes (tables) for a given dataset ingest. Default=`default`
+- `index_prefix` **str** - (optional) A prefix for the indexes you will create for embeddings. This stores all indexes (tables) for a given dataset ingest.
 - `database_name` **str** - (cosmosdb only) Name of the database.
-- `overwrite` **bool** (only used at index creation time) - Overwrite collection if it exist. Default=`True`
+- `embeddings_schema` **list[dict[str, str]]** (optional) - Enables customization for each of your embeddings. 
+  - `<supported_embedding>`: 
+    - `index_name` **str**: (optional) - Name for the specific embedding index table.
+    - `id_field` **str**: (optional) - Field name to be used as id. Default=`id`
+    - `vector_field` **str**: (optional) - Field name to be used as vector. Default=`vector`
+    - `vector_size` **int**: (optional) - Vector size for the embeddings. Default=`3072`
+
+The supported embeddings are:
+
+- `text_unit.text`
+- `document.text`
+- `entity.title`
+- `entity.description`
+- `relationship.description`
+- `community.title`
+- `community.summary`
+- `community.full_content`
+
+For example:
+
+```yaml
+vector_store:
+  type: lancedb
+  db_uri: output/lancedb
+  container_name: christmas-carol
+  index_prefix: "christmas-carol"
+  embeddings_schema:
+    text_unit.text:
+      index_name: "text-unit-embeddings"
+      id_field: "id_custom"
+      vector_field: "vector_custom"
+      vector_size: 3072
+    entity.description:
+      id_field: "id_custom"
+
+```
+  
 
 ## Workflow Configurations
 
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index 9c6a65008c..fdeb241604 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -371,8 +371,8 @@ class VectorStoreDefaults:
 
     type: ClassVar[str] = VectorStoreType.LanceDB.value
     db_uri: str = str(Path(DEFAULT_OUTPUT_BASE_DIR) / "lancedb")
-    container_name: str = "default"
     overwrite: bool = True
+    index_prefix: None = None
     url: None = None
     api_key: None = None
     audience: None = None
diff --git a/graphrag/config/embeddings.py b/graphrag/config/embeddings.py
index f150238576..60711b8aa6 100644
--- a/graphrag/config/embeddings.py
+++ b/graphrag/config/embeddings.py
@@ -30,13 +30,13 @@
 
 
 def create_index_name(
-    container_name: str, embedding_name: str, validate: bool = True
+    index_prefix: str, embedding_name: str, validate: bool = True
 ) -> str:
     """
     Create a index name for the embedding store.
 
     Within any given vector store, we can have multiple sets of embeddings organized into projects.
-    The `container` param is used for this partitioning, and is added as a prefix to the index name for differentiation.
+    The `container` param is used for this partitioning, and is added as a index_prefix to the index name for differentiation.
 
     The embedding name is fixed, with the available list defined in graphrag.index.config.embeddings
 
@@ -45,4 +45,7 @@ def create_index_name(
     if validate and embedding_name not in all_embeddings:
         msg = f"Invalid embedding name: {embedding_name}"
         raise KeyError(msg)
-    return f"{container_name}-{embedding_name}".replace(".", "-")
+
+    if index_prefix:
+        return f"{index_prefix}-{embedding_name}".replace(".", "-")
+    return embedding_name.replace(".", "-")
diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py
index 69b05015fb..1cbccf74df 100644
--- a/graphrag/config/init_content.py
+++ b/graphrag/config/init_content.py
@@ -77,7 +77,6 @@
 vector_store:
   type: {vector_store_defaults.type}
   db_uri: {vector_store_defaults.db_uri}
-  container_name: {vector_store_defaults.container_name}
 
 ### Workflow settings ###
 
diff --git a/graphrag/config/models/vector_store_config.py b/graphrag/config/models/vector_store_config.py
index ac00679e46..c2b3e61de1 100644
--- a/graphrag/config/models/vector_store_config.py
+++ b/graphrag/config/models/vector_store_config.py
@@ -72,9 +72,9 @@ def _validate_url(self) -> None:
         default=vector_store_defaults.audience,
     )
 
-    container_name: str = Field(
-        description="The container name to use.",
-        default=vector_store_defaults.container_name,
+    index_prefix: str | None = Field(
+        description="The index prefix to use.",
+        default=vector_store_defaults.index_prefix,
     )
 
     database_name: str | None = Field(
diff --git a/graphrag/index/workflows/generate_text_embeddings.py b/graphrag/index/workflows/generate_text_embeddings.py
index 63859faa49..6e474437f5 100644
--- a/graphrag/index/workflows/generate_text_embeddings.py
+++ b/graphrag/index/workflows/generate_text_embeddings.py
@@ -253,6 +253,14 @@ def _create_vector_store(
         else:
             single_embedding_config = raw_config
 
+    if (
+        single_embedding_config.index_name is not None
+        and vector_store_config.index_prefix
+    ):
+        single_embedding_config.index_name = (
+            f"{vector_store_config.index_prefix}-{single_embedding_config.index_name}"
+        )
+
     if single_embedding_config.index_name is None:
         single_embedding_config.index_name = index_name
 
@@ -268,9 +276,9 @@ def _create_vector_store(
 
 
 def _get_index_name(vector_store_config: VectorStoreConfig, embedding_name: str) -> str:
-    container_name = vector_store_config.container_name
-    index_name = create_index_name(container_name, embedding_name)
+    index_prefix = vector_store_config.index_prefix or ""
+    index_name = create_index_name(index_prefix, embedding_name)
 
-    msg = f"using vector store {vector_store_config.type} with container_name {container_name} for embedding {embedding_name}: {index_name}"
+    msg = f"using vector store {vector_store_config.type} with index prefix {index_prefix} for embedding {embedding_name}: {index_name}"
     logger.info(msg)
     return index_name
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index ecd489a517..9769cf99b2 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -114,7 +114,7 @@ def assert_vector_store_configs(
     assert actual.url == expected.url
     assert actual.api_key == expected.api_key
     assert actual.audience == expected.audience
-    assert actual.container_name == expected.container_name
+    assert actual.index_prefix == expected.index_prefix
     assert actual.database_name == expected.database_name
 
 

From c43a58c3531e1e9981bf8dfcc340b373d9e7dcea Mon Sep 17 00:00:00 2001
From: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
Date: Thu, 23 Oct 2025 10:40:21 -0600
Subject: [PATCH 18/40] fix for container name

---
 docs/config/yaml.md         | 1 -
 graphrag/config/defaults.py | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 2c2ba7eb52..58dfb1779b 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -197,7 +197,6 @@ For example:
 vector_store:
   type: lancedb
   db_uri: output/lancedb
-  container_name: christmas-carol
   index_prefix: "christmas-carol"
   embeddings_schema:
     text_unit.text:
diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py
index fdeb241604..8a04851682 100644
--- a/graphrag/config/defaults.py
+++ b/graphrag/config/defaults.py
@@ -372,7 +372,7 @@ class VectorStoreDefaults:
     type: ClassVar[str] = VectorStoreType.LanceDB.value
     db_uri: str = str(Path(DEFAULT_OUTPUT_BASE_DIR) / "lancedb")
     overwrite: bool = True
-    index_prefix: None = None
+    index_prefix: str = ""
     url: None = None
     api_key: None = None
     audience: None = None

From 619269243d97610601a7494d1dd6d408fb0b02dc Mon Sep 17 00:00:00 2001
From: Derek Worthen <worthend.derek@gmail.com>
Date: Tue, 4 Nov 2025 09:51:56 -0800
Subject: [PATCH 19/40] Restructure project as monorepo. (#2111)

* Restructure project as monorepo.
---
 .github/workflows/gh-pages.yml                |    2 +-
 .github/workflows/python-ci.yml               |    4 +-
 .../workflows/python-integration-tests.yml    |    4 +-
 .github/workflows/python-notebook-tests.yml   |    2 +-
 .github/workflows/python-publish.yml          |   11 +-
 .github/workflows/python-smoke-tests.yml      |    4 +-
 .gitignore                                    |    3 +
 .vscode/launch.json                           |   29 +-
 docs/examples_notebooks/api_overview.ipynb    |    3 +-
 .../custom_vector_store.ipynb                 |    1 -
 docs/examples_notebooks/drift_search.ipynb    |    1 -
 docs/examples_notebooks/global_search.ipynb   |    1 -
 ...rch_with_dynamic_community_selection.ipynb |    1 -
 .../index_migration_to_v2.ipynb               |    1 -
 docs/examples_notebooks/input_documents.ipynb |    3 +-
 docs/examples_notebooks/local_search.ipynb    |    1 -
 packages/graphrag-common/README.md            |   51 +
 .../graphrag_common/__init__.py               |    4 +
 .../graphrag_common/factory/__init__.py       |    8 +
 .../graphrag_common}/factory/factory.py       |   47 +-
 .../graphrag-common/graphrag_common/py.typed  |    0
 packages/graphrag-common/pyproject.toml       |   41 +
 packages/graphrag/README.md                   |   76 +
 .../graphrag/graphrag}/__init__.py            |    0
 .../graphrag/graphrag}/__main__.py            |    0
 .../graphrag/graphrag}/api/__init__.py        |    0
 .../graphrag/graphrag}/api/index.py           |    0
 .../graphrag/graphrag}/api/prompt_tune.py     |    0
 .../graphrag/graphrag}/api/query.py           |    0
 .../graphrag/graphrag}/cache/__init__.py      |    0
 .../graphrag/graphrag}/cache/factory.py       |    3 +-
 .../graphrag}/cache/json_pipeline_cache.py    |    0
 .../graphrag}/cache/memory_pipeline_cache.py  |    0
 .../graphrag}/cache/noop_pipeline_cache.py    |    0
 .../graphrag}/cache/pipeline_cache.py         |    0
 .../graphrag/graphrag}/callbacks/__init__.py  |    0
 .../callbacks/console_workflow_callbacks.py   |    0
 .../graphrag}/callbacks/llm_callbacks.py      |    0
 .../callbacks/noop_query_callbacks.py         |    0
 .../callbacks/noop_workflow_callbacks.py      |    0
 .../graphrag}/callbacks/query_callbacks.py    |    0
 .../graphrag}/callbacks/workflow_callbacks.py |    0
 .../callbacks/workflow_callbacks_manager.py   |    0
 .../graphrag/graphrag}/cli/__init__.py        |    0
 .../graphrag/graphrag}/cli/index.py           |    0
 .../graphrag/graphrag}/cli/initialize.py      |    0
 .../graphrag/graphrag}/cli/main.py            |    0
 .../graphrag/graphrag}/cli/prompt_tune.py     |    0
 .../graphrag/graphrag}/cli/query.py           |    0
 .../graphrag/graphrag}/config/__init__.py     |    0
 .../config/create_graphrag_config.py          |    0
 .../graphrag/graphrag}/config/defaults.py     |    0
 .../graphrag/graphrag}/config/embeddings.py   |    0
 .../graphrag/graphrag}/config/enums.py        |    0
 .../graphrag}/config/environment_reader.py    |    0
 .../graphrag/graphrag}/config/errors.py       |    0
 .../graphrag/graphrag}/config/init_content.py |    0
 .../graphrag/graphrag}/config/load_config.py  |    0
 .../graphrag}/config/models/__init__.py       |    0
 .../config/models/basic_search_config.py      |    0
 .../graphrag}/config/models/cache_config.py   |    0
 .../config/models/chunking_config.py          |    0
 .../config/models/cluster_graph_config.py     |    0
 .../config/models/community_reports_config.py |    0
 .../config/models/drift_search_config.py      |    0
 .../config/models/embed_text_config.py        |    0
 .../config/models/extract_claims_config.py    |    0
 .../config/models/extract_graph_config.py     |    0
 .../config/models/extract_graph_nlp_config.py |    0
 .../config/models/global_search_config.py     |    0
 .../config/models/graph_rag_config.py         |    0
 .../graphrag}/config/models/input_config.py   |    0
 .../config/models/language_model_config.py    |    0
 .../config/models/local_search_config.py      |    0
 .../config/models/prune_graph_config.py       |    0
 .../config/models/reporting_config.py         |    0
 .../config/models/snapshots_config.py         |    0
 .../graphrag}/config/models/storage_config.py |    0
 .../models/summarize_descriptions_config.py   |    0
 .../config/models/vector_store_config.py      |    0
 .../models/vector_store_schema_config.py      |    0
 .../graphrag/graphrag}/config/read_dotenv.py  |    0
 .../graphrag/graphrag}/data_model/__init__.py |    0
 .../graphrag}/data_model/community.py         |    0
 .../graphrag}/data_model/community_report.py  |    0
 .../graphrag}/data_model/covariate.py         |    0
 .../graphrag/graphrag}/data_model/document.py |    0
 .../graphrag/graphrag}/data_model/entity.py   |    0
 .../graphrag}/data_model/identified.py        |    0
 .../graphrag/graphrag}/data_model/named.py    |    0
 .../graphrag}/data_model/relationship.py      |    0
 .../graphrag/graphrag}/data_model/schemas.py  |    0
 .../graphrag}/data_model/text_unit.py         |    0
 .../graphrag/graphrag}/data_model/types.py    |    0
 .../graphrag/graphrag}/index/__init__.py      |    0
 .../graphrag}/index/input/__init__.py         |    0
 .../graphrag/graphrag}/index/input/csv.py     |    0
 .../graphrag/graphrag}/index/input/factory.py |    3 +-
 .../graphrag}/index/input/input_reader.py     |    0
 .../graphrag/graphrag}/index/input/json.py    |    0
 .../graphrag/graphrag}/index/input/text.py    |    0
 .../graphrag/graphrag}/index/input/util.py    |    0
 .../graphrag}/index/operations/__init__.py    |    0
 .../operations/build_noun_graph/__init__.py   |    0
 .../build_noun_graph/build_noun_graph.py      |    0
 .../np_extractors/__init__.py                 |    0
 .../build_noun_graph/np_extractors/base.py    |    0
 .../np_extractors/cfg_extractor.py            |    0
 .../build_noun_graph/np_extractors/factory.py |    0
 .../np_extractors/np_validator.py             |    0
 .../np_extractors/regex_extractor.py          |    0
 .../np_extractors/resource_loader.py          |    0
 .../np_extractors/stop_words.py               |    0
 .../syntactic_parsing_extractor.py            |    0
 .../index/operations/chunk_text/__init__.py   |    0
 .../index/operations/chunk_text/bootstrap.py  |    0
 .../index/operations/chunk_text/chunk_text.py |    0
 .../index/operations/chunk_text/strategies.py |    0
 .../index/operations/chunk_text/typing.py     |    0
 .../index/operations/cluster_graph.py         |    0
 .../index/operations/compute_degree.py        |    0
 .../compute_edge_combined_degree.py           |    0
 .../index/operations/create_graph.py          |    0
 .../index/operations/embed_text/__init__.py   |    0
 .../index/operations/embed_text/embed_text.py |    0
 .../operations/embed_text/run_embed_text.py   |    0
 .../operations/extract_covariates/__init__.py |    0
 .../extract_covariates/claim_extractor.py     |    0
 .../extract_covariates/extract_covariates.py  |    0
 .../operations/extract_covariates/typing.py   |    0
 .../operations/extract_graph/__init__.py      |    0
 .../operations/extract_graph/extract_graph.py |    0
 .../extract_graph/graph_extractor.py          |    0
 .../index/operations/extract_graph/typing.py  |    0
 .../operations/finalize_community_reports.py  |    0
 .../index/operations/finalize_entities.py     |    0
 .../operations/finalize_relationships.py      |    0
 .../index/operations/graph_to_dataframes.py   |    0
 .../graphrag}/index/operations/prune_graph.py |    0
 .../index/operations/snapshot_graphml.py      |    0
 .../summarize_communities/__init__.py         |    0
 .../build_mixed_context.py                    |    0
 .../community_reports_extractor.py            |    0
 .../explode_communities.py                    |    0
 .../graph_context/__init__.py                 |    0
 .../graph_context/context_builder.py          |    0
 .../graph_context/sort_context.py             |    0
 .../summarize_communities.py                  |    0
 .../text_unit_context/__init__.py             |    0
 .../text_unit_context/context_builder.py      |    0
 .../text_unit_context/prep_text_units.py      |    0
 .../text_unit_context/sort_context.py         |    0
 .../summarize_communities/typing.py           |    0
 .../operations/summarize_communities/utils.py |    0
 .../summarize_descriptions/__init__.py        |    0
 .../description_summary_extractor.py          |    0
 .../summarize_descriptions.py                 |    0
 .../summarize_descriptions/typing.py          |    0
 .../graphrag/graphrag}/index/run/__init__.py  |    0
 .../graphrag}/index/run/run_pipeline.py       |    0
 .../graphrag/graphrag}/index/run/utils.py     |    0
 .../index/text_splitting/__init__.py          |    0
 .../index/text_splitting/check_token_limit.py |    0
 .../index/text_splitting/text_splitting.py    |    0
 .../graphrag}/index/typing/__init__.py        |    0
 .../graphrag}/index/typing/context.py         |    0
 .../graphrag}/index/typing/error_handler.py   |    0
 .../graphrag}/index/typing/pipeline.py        |    0
 .../index/typing/pipeline_run_result.py       |    0
 .../graphrag/graphrag}/index/typing/state.py  |    0
 .../graphrag/graphrag}/index/typing/stats.py  |    0
 .../graphrag}/index/typing/workflow.py        |    0
 .../graphrag}/index/update/__init__.py        |    0
 .../graphrag}/index/update/communities.py     |    0
 .../graphrag}/index/update/entities.py        |    0
 .../index/update/incremental_index.py         |    0
 .../graphrag}/index/update/relationships.py   |    0
 .../graphrag}/index/utils/__init__.py         |    0
 .../graphrag}/index/utils/dataframes.py       |    0
 .../graphrag}/index/utils/derive_from_rows.py |    0
 .../graphrag/graphrag}/index/utils/dicts.py   |    0
 .../graphrag/graphrag}/index/utils/graphs.py  |    0
 .../graphrag/graphrag}/index/utils/hashing.py |    0
 .../graphrag/graphrag}/index/utils/is_null.py |    0
 .../graphrag}/index/utils/stable_lcc.py       |    0
 .../graphrag/graphrag}/index/utils/string.py  |    0
 .../graphrag/graphrag}/index/utils/uuid.py    |    0
 .../graphrag}/index/validate_config.py        |    0
 .../graphrag}/index/workflows/__init__.py     |    0
 .../index/workflows/create_base_text_units.py |    0
 .../index/workflows/create_communities.py     |    0
 .../workflows/create_community_reports.py     |    0
 .../create_community_reports_text.py          |    0
 .../index/workflows/create_final_documents.py |    0
 .../workflows/create_final_text_units.py      |    0
 .../index/workflows/extract_covariates.py     |    0
 .../index/workflows/extract_graph.py          |    0
 .../index/workflows/extract_graph_nlp.py      |    0
 .../graphrag}/index/workflows/factory.py      |    0
 .../index/workflows/finalize_graph.py         |    0
 .../workflows/generate_text_embeddings.py     |    0
 .../index/workflows/load_input_documents.py   |    0
 .../index/workflows/load_update_documents.py  |    0
 .../graphrag}/index/workflows/prune_graph.py  |    0
 .../index/workflows/update_clean_state.py     |    0
 .../index/workflows/update_communities.py     |    0
 .../workflows/update_community_reports.py     |    0
 .../index/workflows/update_covariates.py      |    0
 .../update_entities_relationships.py          |    0
 .../index/workflows/update_final_documents.py |    0
 .../index/workflows/update_text_embeddings.py |    0
 .../index/workflows/update_text_units.py      |    0
 .../graphrag}/language_model/__init__.py      |    0
 .../language_model/cache/__init__.py          |    0
 .../graphrag}/language_model/cache/base.py    |    0
 .../language_model/events/__init__.py         |    0
 .../graphrag}/language_model/events/base.py   |    0
 .../graphrag}/language_model/factory.py       |    3 +-
 .../graphrag}/language_model/manager.py       |    0
 .../language_model/protocol/__init__.py       |    0
 .../graphrag}/language_model/protocol/base.py |    0
 .../language_model/providers/__init__.py      |    0
 .../providers/litellm/__init__.py             |    0
 .../providers/litellm/chat_model.py           |    0
 .../providers/litellm/embedding_model.py      |    0
 .../providers/litellm/get_cache_key.py        |    0
 .../litellm/request_wrappers/__init__.py      |    0
 .../litellm/request_wrappers/with_cache.py    |    0
 .../litellm/request_wrappers/with_logging.py  |    0
 .../request_wrappers/with_rate_limiter.py     |    0
 .../litellm/request_wrappers/with_retries.py  |    0
 .../providers/litellm/services/__init__.py    |    0
 .../litellm/services/rate_limiter/__init__.py |    0
 .../services/rate_limiter/rate_limiter.py     |    0
 .../rate_limiter/rate_limiter_factory.py      |    3 +-
 .../rate_limiter/static_rate_limiter.py       |    0
 .../litellm/services/retry/__init__.py        |    0
 .../services/retry/exponential_retry.py       |    0
 .../services/retry/incremental_wait_retry.py  |    0
 .../services/retry/native_wait_retry.py       |    0
 .../services/retry/random_wait_retry.py       |    0
 .../providers/litellm/services/retry/retry.py |    0
 .../litellm/services/retry/retry_factory.py   |    3 +-
 .../language_model/providers/litellm/types.py |    0
 .../language_model/response/__init__.py       |    0
 .../graphrag}/language_model/response/base.py |    0
 .../language_model/response/base.pyi          |    0
 .../graphrag/graphrag}/language_model/util.py |    0
 .../graphrag/graphrag}/logger/__init__.py     |    0
 .../graphrag}/logger/blob_workflow_logger.py  |    0
 .../graphrag/graphrag}/logger/factory.py      |    3 +-
 .../graphrag/graphrag}/logger/progress.py     |    0
 .../graphrag}/logger/standard_logging.py      |    0
 .../graphrag}/prompt_tune/__init__.py         |    0
 .../graphrag}/prompt_tune/defaults.py         |    0
 .../prompt_tune/generator/__init__.py         |    0
 .../generator/community_report_rating.py      |    0
 .../community_report_summarization.py         |    0
 .../generator/community_reporter_role.py      |    0
 .../graphrag}/prompt_tune/generator/domain.py |    0
 .../generator/entity_relationship.py          |    0
 .../generator/entity_summarization_prompt.py  |    0
 .../prompt_tune/generator/entity_types.py     |    0
 .../generator/extract_graph_prompt.py         |    0
 .../prompt_tune/generator/language.py         |    0
 .../prompt_tune/generator/persona.py          |    0
 .../graphrag}/prompt_tune/loader/__init__.py  |    0
 .../graphrag}/prompt_tune/loader/input.py     |    0
 .../graphrag}/prompt_tune/prompt/__init__.py  |    0
 .../prompt/community_report_rating.py         |    0
 .../prompt/community_reporter_role.py         |    0
 .../graphrag}/prompt_tune/prompt/domain.py    |    0
 .../prompt_tune/prompt/entity_relationship.py |    0
 .../prompt_tune/prompt/entity_types.py        |    0
 .../graphrag}/prompt_tune/prompt/language.py  |    0
 .../graphrag}/prompt_tune/prompt/persona.py   |    0
 .../prompt_tune/template/__init__.py          |    0
 .../community_report_summarization.py         |    0
 .../template/entity_summarization.py          |    0
 .../prompt_tune/template/extract_graph.py     |    0
 .../graphrag/graphrag}/prompt_tune/types.py   |    0
 .../graphrag/graphrag}/prompts/__init__.py    |    0
 .../graphrag}/prompts/index/__init__.py       |    0
 .../prompts/index/community_report.py         |    0
 .../index/community_report_text_units.py      |    0
 .../graphrag}/prompts/index/extract_claims.py |    0
 .../graphrag}/prompts/index/extract_graph.py  |    0
 .../prompts/index/summarize_descriptions.py   |    0
 .../graphrag}/prompts/query/__init__.py       |    0
 .../query/basic_search_system_prompt.py       |    0
 .../query/drift_search_system_prompt.py       |    0
 .../global_search_knowledge_system_prompt.py  |    0
 .../query/global_search_map_system_prompt.py  |    0
 .../global_search_reduce_system_prompt.py     |    0
 .../query/local_search_system_prompt.py       |    0
 .../query/question_gen_system_prompt.py       |    0
 .../graphrag/graphrag}/py.typed               |    0
 .../graphrag/graphrag}/query/__init__.py      |    0
 .../query/context_builder/__init__.py         |    0
 .../query/context_builder/builders.py         |    0
 .../context_builder/community_context.py      |    0
 .../context_builder/conversation_history.py   |    0
 .../dynamic_community_selection.py            |    0
 .../context_builder/entity_extraction.py      |    0
 .../query/context_builder/local_context.py    |    0
 .../query/context_builder/rate_prompt.py      |    0
 .../query/context_builder/rate_relevancy.py   |    0
 .../query/context_builder/source_context.py   |    0
 .../graphrag/graphrag}/query/factory.py       |    0
 .../graphrag}/query/indexer_adapters.py       |    0
 .../graphrag}/query/input/__init__.py         |    0
 .../graphrag}/query/input/loaders/__init__.py |    0
 .../graphrag}/query/input/loaders/dfs.py      |    0
 .../graphrag}/query/input/loaders/utils.py    |    0
 .../query/input/retrieval/__init__.py         |    0
 .../input/retrieval/community_reports.py      |    0
 .../query/input/retrieval/covariates.py       |    0
 .../query/input/retrieval/entities.py         |    0
 .../query/input/retrieval/relationships.py    |    0
 .../query/input/retrieval/text_units.py       |    0
 .../graphrag/graphrag}/query/llm/__init__.py  |    0
 .../graphrag}/query/llm/text_utils.py         |    0
 .../graphrag}/query/question_gen/__init__.py  |    0
 .../graphrag}/query/question_gen/base.py      |    0
 .../graphrag}/query/question_gen/local_gen.py |    0
 .../query/structured_search/__init__.py       |    0
 .../graphrag}/query/structured_search/base.py |    0
 .../basic_search/__init__.py                  |    0
 .../basic_search/basic_context.py             |    0
 .../structured_search/basic_search/search.py  |    0
 .../drift_search/__init__.py                  |    0
 .../structured_search/drift_search/action.py  |    0
 .../drift_search/drift_context.py             |    0
 .../structured_search/drift_search/primer.py  |    0
 .../structured_search/drift_search/search.py  |    0
 .../structured_search/drift_search/state.py   |    0
 .../global_search/__init__.py                 |    0
 .../global_search/community_context.py        |    0
 .../structured_search/global_search/search.py |    0
 .../local_search/__init__.py                  |    0
 .../local_search/mixed_context.py             |    0
 .../structured_search/local_search/search.py  |    0
 .../graphrag/graphrag}/storage/__init__.py    |    0
 .../storage/blob_pipeline_storage.py          |    0
 .../storage/cosmosdb_pipeline_storage.py      |    0
 .../graphrag/graphrag}/storage/factory.py     |    3 +-
 .../storage/file_pipeline_storage.py          |    0
 .../storage/memory_pipeline_storage.py        |    0
 .../graphrag}/storage/pipeline_storage.py     |    0
 .../graphrag/graphrag}/tokenizer/__init__.py  |    0
 .../graphrag}/tokenizer/get_tokenizer.py      |    0
 .../graphrag}/tokenizer/litellm_tokenizer.py  |    0
 .../graphrag}/tokenizer/tiktoken_tokenizer.py |    0
 .../graphrag/graphrag}/tokenizer/tokenizer.py |    0
 .../graphrag/graphrag}/utils/__init__.py      |    0
 .../graphrag/graphrag}/utils/api.py           |    4 +-
 .../graphrag/graphrag}/utils/cli.py           |    0
 .../graphrag/graphrag}/utils/storage.py       |    0
 .../graphrag}/vector_stores/__init__.py       |    0
 .../vector_stores/azure_ai_search.py          |    0
 .../graphrag/graphrag}/vector_stores/base.py  |    0
 .../graphrag}/vector_stores/cosmosdb.py       |    0
 .../graphrag}/vector_stores/factory.py        |    3 +-
 .../graphrag}/vector_stores/lancedb.py        |    0
 packages/graphrag/pyproject.toml              |   74 +
 pyproject.toml                                |   79 +-
 {graphrag/factory => scripts}/__init__.py     |    3 +-
 scripts/copy_build_assets.py                  |   25 +
 .../update_workspace_dependency_versions.py   |   58 +
 tests/__init__.py                             |    1 +
 tests/fixtures/min-csv/settings.yml           |    2 +
 tests/fixtures/text/settings.yml              |    2 +
 tests/integration/cache/test_factory.py       |    1 -
 .../language_model/test_rate_limiter.py       |    2 +-
 .../language_model/test_retries.py            |    1 -
 tests/integration/logging/test_factory.py     |    1 -
 .../logging/test_standard_logging.py          |    1 +
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 uv.lock                                       | 3517 +++++++++--------
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 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/prompt/domain.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/prompt/entity_relationship.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/prompt/entity_types.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/prompt/language.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/prompt/persona.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/template/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/template/community_report_summarization.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/template/entity_summarization.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/template/extract_graph.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompt_tune/types.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/index/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/index/community_report.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/index/community_report_text_units.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/index/extract_claims.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/index/extract_graph.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/index/summarize_descriptions.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/basic_search_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/drift_search_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/global_search_knowledge_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/global_search_map_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/global_search_reduce_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/local_search_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/prompts/query/question_gen_system_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/py.typed (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/builders.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/community_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/conversation_history.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/dynamic_community_selection.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/entity_extraction.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/local_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/rate_prompt.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/rate_relevancy.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/context_builder/source_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/factory.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/indexer_adapters.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/loaders/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/loaders/dfs.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/loaders/utils.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/retrieval/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/retrieval/community_reports.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/retrieval/covariates.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/retrieval/entities.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/retrieval/relationships.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/input/retrieval/text_units.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/llm/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/llm/text_utils.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/question_gen/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/question_gen/base.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/question_gen/local_gen.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/base.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/basic_search/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/basic_search/basic_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/basic_search/search.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/drift_search/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/drift_search/action.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/drift_search/drift_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/drift_search/primer.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/drift_search/search.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/drift_search/state.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/global_search/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/global_search/community_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/global_search/search.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/local_search/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/local_search/mixed_context.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/query/structured_search/local_search/search.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/storage/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/storage/blob_pipeline_storage.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/storage/cosmosdb_pipeline_storage.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/storage/factory.py (96%)
 rename {graphrag => packages/graphrag/graphrag}/storage/file_pipeline_storage.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/storage/memory_pipeline_storage.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/storage/pipeline_storage.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/tokenizer/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/tokenizer/get_tokenizer.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/tokenizer/litellm_tokenizer.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/tokenizer/tiktoken_tokenizer.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/tokenizer/tokenizer.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/utils/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/utils/api.py (97%)
 rename {graphrag => packages/graphrag/graphrag}/utils/cli.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/utils/storage.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/vector_stores/__init__.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/vector_stores/azure_ai_search.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/vector_stores/base.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/vector_stores/cosmosdb.py (100%)
 rename {graphrag => packages/graphrag/graphrag}/vector_stores/factory.py (96%)
 rename {graphrag => packages/graphrag/graphrag}/vector_stores/lancedb.py (100%)
 create mode 100644 packages/graphrag/pyproject.toml
 rename {graphrag/factory => scripts}/__init__.py (70%)
 create mode 100644 scripts/copy_build_assets.py
 create mode 100644 scripts/update_workspace_dependency_versions.py
 create mode 100644 tests/unit/graphrag_factory/__init__.py
 create mode 100644 tests/unit/graphrag_factory/test_factory.py

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index b41be1ef03..d8a507657b 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -31,7 +31,7 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: uv sync
+        run: uv sync --all-packages
   
       - name: mkdocs build
         shell: bash
diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml
index 37651589e8..1848b4f138 100644
--- a/.github/workflows/python-ci.yml
+++ b/.github/workflows/python-ci.yml
@@ -67,7 +67,7 @@ jobs:
       - name: Install dependencies
         shell: bash
         run: |
-          uv sync
+          uv sync --all-packages
           uv pip install gensim
 
       - name: Check
@@ -76,7 +76,7 @@ jobs:
 
       - name: Build
         run: |
-          uv build
+          uv build --all-packages
 
       - name: Unit Test
         run: |
diff --git a/.github/workflows/python-integration-tests.yml b/.github/workflows/python-integration-tests.yml
index 48634f7b06..d7ded02619 100644
--- a/.github/workflows/python-integration-tests.yml
+++ b/.github/workflows/python-integration-tests.yml
@@ -67,12 +67,12 @@ jobs:
       - name: Install dependencies
         shell: bash
         run: |
-          uv sync
+          uv sync --all-packages
           uv pip install gensim
 
       - name: Build
         run: |
-          uv build
+          uv build --all-packages
 
       - name: Install and start Azurite
         shell: bash
diff --git a/.github/workflows/python-notebook-tests.yml b/.github/workflows/python-notebook-tests.yml
index aae1f5c3a2..335442df23 100644
--- a/.github/workflows/python-notebook-tests.yml
+++ b/.github/workflows/python-notebook-tests.yml
@@ -67,7 +67,7 @@ jobs:
       - name: Install dependencies
         shell: bash
         run: |
-          uv sync
+          uv sync --all-packages
           uv pip install gensim
 
       - name: Notebook Test
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
index b738225090..53a59c3f98 100644
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -15,7 +15,6 @@ jobs:
     runs-on: ubuntu-latest
     environment:
       name: pypi
-      url: https://pypi.org/p/graphrag
     permissions:
       id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
 
@@ -35,18 +34,14 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: uv sync
+        run: uv sync --all-packages
 
       - name: Export Publication Version
         run: echo "version=$(uv version --short)" >> $GITHUB_OUTPUT
 
       - name: Build Distributable
         shell: bash
-        run: uv build
+        run: uv run poe build
 
       - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          packages-dir: dist
-          skip-existing: true
-          verbose: true
+        run: uv publish
diff --git a/.github/workflows/python-smoke-tests.yml b/.github/workflows/python-smoke-tests.yml
index ec420d310b..46a3641c22 100644
--- a/.github/workflows/python-smoke-tests.yml
+++ b/.github/workflows/python-smoke-tests.yml
@@ -72,12 +72,12 @@ jobs:
       - name: Install dependencies
         shell: bash
         run: |
-          uv sync
+          uv sync --all-packages
           uv pip install gensim
 
       - name: Build
         run: |
-          uv build
+          uv build --all-packages
 
       - name: Install and start Azurite
         shell: bash
diff --git a/.gitignore b/.gitignore
index f050557b3a..92652fcb71 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,3 +58,6 @@ docsite/
 
 # Jupyter notebook
 .ipynb_checkpoints/
+
+# Root build assets
+packages/*/LICENSE
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 9434ab0ef5..9f949cb12f 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -10,7 +10,7 @@
 			"args": [
 				"index",
 				"--root", 
-				"<path_to_index_folder>"
+				"${input:root_folder}"
 			],
 			"console": "integratedTerminal"
 		},
@@ -22,9 +22,9 @@
 			"args": [
 				"query",
 				"--root", 
-				"<path_to_index_folder>",
-				"--method", "basic",
-				"--query", "What are the top themes in this story",
+				"${input:root_folder}",
+				"--method", "${input:query_method}",
+				"--query", "${input:query}"
 			]
 		},
 		{
@@ -35,7 +35,7 @@
 			"args": [
 				"poe", "prompt-tune",
 				"--config",
-				"<path_to_ragtest_root_demo>/settings.yaml",
+				"${input:root_folder}/settings.yaml",
 			]
 		},
 		{
@@ -74,5 +74,22 @@
 			"console": "integratedTerminal",
     		"justMyCode": false 
 		},
-	]
+	],
+    "inputs": [
+        {
+            "id": "root_folder",
+            "type": "promptString",
+            "description": "Enter the root folder path"
+        },
+        {
+            "id": "query_method",
+            "type": "promptString",
+            "description": "Enter the query method (e.g., 'global', 'local')"
+        },
+        {
+            "id": "query",
+            "type": "promptString",
+            "description": "Enter the query text"
+        }
+    ]
 }
\ No newline at end of file
diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index 7e219e7688..86b0184b29 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,9 +28,8 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import pandas as pd\n",
-    "\n",
     "import graphrag.api as api\n",
+    "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
     "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
diff --git a/docs/examples_notebooks/custom_vector_store.ipynb b/docs/examples_notebooks/custom_vector_store.ipynb
index fe8495bbae..60be6c1fb5 100644
--- a/docs/examples_notebooks/custom_vector_store.ipynb
+++ b/docs/examples_notebooks/custom_vector_store.ipynb
@@ -60,7 +60,6 @@
     "\n",
     "import numpy as np\n",
     "import yaml\n",
-    "\n",
     "from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig\n",
     "from graphrag.data_model.types import TextEmbedder\n",
     "\n",
diff --git a/docs/examples_notebooks/drift_search.ipynb b/docs/examples_notebooks/drift_search.ipynb
index 1283018fb0..e33e8bc94f 100644
--- a/docs/examples_notebooks/drift_search.ipynb
+++ b/docs/examples_notebooks/drift_search.ipynb
@@ -20,7 +20,6 @@
     "from pathlib import Path\n",
     "\n",
     "import pandas as pd\n",
-    "\n",
     "from graphrag.config.enums import ModelType\n",
     "from graphrag.config.models.drift_search_config import DRIFTSearchConfig\n",
     "from graphrag.config.models.language_model_config import LanguageModelConfig\n",
diff --git a/docs/examples_notebooks/global_search.ipynb b/docs/examples_notebooks/global_search.ipynb
index c95a037df0..605f704bd2 100644
--- a/docs/examples_notebooks/global_search.ipynb
+++ b/docs/examples_notebooks/global_search.ipynb
@@ -19,7 +19,6 @@
     "import os\n",
     "\n",
     "import pandas as pd\n",
-    "\n",
     "from graphrag.config.enums import ModelType\n",
     "from graphrag.config.models.language_model_config import LanguageModelConfig\n",
     "from graphrag.language_model.manager import ModelManager\n",
diff --git a/docs/examples_notebooks/global_search_with_dynamic_community_selection.ipynb b/docs/examples_notebooks/global_search_with_dynamic_community_selection.ipynb
index bb0e0f0975..6b3763d73b 100644
--- a/docs/examples_notebooks/global_search_with_dynamic_community_selection.ipynb
+++ b/docs/examples_notebooks/global_search_with_dynamic_community_selection.ipynb
@@ -19,7 +19,6 @@
     "import os\n",
     "\n",
     "import pandas as pd\n",
-    "\n",
     "from graphrag.config.enums import ModelType\n",
     "from graphrag.config.models.language_model_config import LanguageModelConfig\n",
     "from graphrag.language_model.manager import ModelManager\n",
diff --git a/docs/examples_notebooks/index_migration_to_v2.ipynb b/docs/examples_notebooks/index_migration_to_v2.ipynb
index dad9011696..0681d1a0b2 100644
--- a/docs/examples_notebooks/index_migration_to_v2.ipynb
+++ b/docs/examples_notebooks/index_migration_to_v2.ipynb
@@ -70,7 +70,6 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "\n",
     "from graphrag.utils.storage import (\n",
     "    delete_table_from_storage,\n",
     "    load_table_from_storage,\n",
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 1135cb9eb0..73e51780d8 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,9 +30,8 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import pandas as pd\n",
-    "\n",
     "import graphrag.api as api\n",
+    "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
     "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
diff --git a/docs/examples_notebooks/local_search.ipynb b/docs/examples_notebooks/local_search.ipynb
index 77a74c544b..4a5497a824 100644
--- a/docs/examples_notebooks/local_search.ipynb
+++ b/docs/examples_notebooks/local_search.ipynb
@@ -19,7 +19,6 @@
     "import os\n",
     "\n",
     "import pandas as pd\n",
-    "\n",
     "from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig\n",
     "from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey\n",
     "from graphrag.query.indexer_adapters import (\n",
diff --git a/packages/graphrag-common/README.md b/packages/graphrag-common/README.md
new file mode 100644
index 0000000000..4447dc7219
--- /dev/null
+++ b/packages/graphrag-common/README.md
@@ -0,0 +1,51 @@
+# GraphRAG Common
+
+## Factory module
+
+```python
+from abc import ABC, abstractmethod
+
+from graphrag_common.factory import Factory
+
+class SampleABC(ABC):
+
+    @abstractmethod
+    def get_value(self) -> str:
+        msg = "Subclasses must implement the get_value method."
+        raise NotImplementedError(msg)
+
+
+class ConcreteClass(SampleABC):
+    def __init__(self, value: str):
+        self._value = value
+
+    def get_value(self) -> str:
+        return self._value
+
+class SampleFactory(Factory[SampleABC]):
+"""A Factory for SampleABC classes."""
+
+factory = SampleFactory()
+
+# Registering transient services
+# A new one is created for every request
+factory.register("some_strategy", ConcreteTestClass)
+
+trans1 = factory.create("some_strategy", {"value": "test1"})
+trans2 = factory.create("some_strategy", {"value": "test2"})
+
+assert trans1 is not trans2
+assert trans1.get_value() == "test1"
+assert trans2.get_value() == "test2"
+
+# Registering singleton services
+# After first creation, the same one is returned every time
+factory.register("some_other_strategy", ConcreteTestClass, scope="singleton")
+
+single1 = factory.create("some_other_strategy", {"value": "singleton"})
+single2 = factory.create("some_other_strategy", {"value": "ignored"})
+
+assert single1 is single2
+assert single1.get_value() == "singleton"
+assert single2.get_value() == "singleton"
+```
\ No newline at end of file
diff --git a/packages/graphrag-common/graphrag_common/__init__.py b/packages/graphrag-common/graphrag_common/__init__.py
new file mode 100644
index 0000000000..e6f83e0260
--- /dev/null
+++ b/packages/graphrag-common/graphrag_common/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""GraphRAG Common package."""
diff --git a/packages/graphrag-common/graphrag_common/factory/__init__.py b/packages/graphrag-common/graphrag_common/factory/__init__.py
new file mode 100644
index 0000000000..ff9d0dce40
--- /dev/null
+++ b/packages/graphrag-common/graphrag_common/factory/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The GraphRAG factory module."""
+
+from graphrag_common.factory.factory import Factory
+
+__all__ = ["Factory"]
diff --git a/graphrag/factory/factory.py b/packages/graphrag-common/graphrag_common/factory/factory.py
similarity index 51%
rename from graphrag/factory/factory.py
rename to packages/graphrag-common/graphrag_common/factory/factory.py
index 3116c538ef..d9aa236cc4 100644
--- a/graphrag/factory/factory.py
+++ b/packages/graphrag-common/graphrag_common/factory/factory.py
@@ -5,10 +5,21 @@
 
 from abc import ABC
 from collections.abc import Callable
-from typing import Any, ClassVar, Generic, TypeVar
+from dataclasses import dataclass
+from typing import Any, ClassVar, Generic, Literal, TypeVar
 
 T = TypeVar("T", covariant=True)
 
+ServiceScope = Literal["singleton", "transient"]
+
+
+@dataclass
+class _ServiceDescriptor(Generic[T]):
+    """Descriptor for a service."""
+
+    scope: ServiceScope
+    initializer: Callable[..., T]
+
 
 class Factory(ABC, Generic[T]):
     """Abstract base class for factories."""
@@ -23,18 +34,24 @@ def __new__(cls, *args: Any, **kwargs: Any) -> "Factory":
 
     def __init__(self):
         if not hasattr(self, "_initialized"):
-            self._services: dict[str, Callable[..., T]] = {}
+            self._service_initializers: dict[str, _ServiceDescriptor[T]] = {}
+            self._initialized_services: dict[str, T] = {}
             self._initialized = True
 
     def __contains__(self, strategy: str) -> bool:
         """Check if a strategy is registered."""
-        return strategy in self._services
+        return strategy in self._service_initializers
 
     def keys(self) -> list[str]:
         """Get a list of registered strategy names."""
-        return list(self._services.keys())
-
-    def register(self, strategy: str, initializer: Callable[..., T]) -> None:
+        return list(self._service_initializers.keys())
+
+    def register(
+        self,
+        strategy: str,
+        initializer: Callable[..., T],
+        scope: ServiceScope = "transient",
+    ) -> None:
         """
         Register a new service.
 
@@ -42,8 +59,9 @@ def register(self, strategy: str, initializer: Callable[..., T]) -> None:
         ----
             strategy: The name of the strategy.
             initializer: A callable that creates an instance of T.
+            scope: The service scope, either 'singleton' or 'transient'.
         """
-        self._services[strategy] = initializer
+        self._service_initializers[strategy] = _ServiceDescriptor(scope, initializer)
 
     def create(self, strategy: str, init_args: dict[str, Any] | None = None) -> T:
         """
@@ -62,7 +80,16 @@ def create(self, strategy: str, init_args: dict[str, Any] | None = None) -> T:
         ------
             ValueError: If the strategy is not registered.
         """
-        if strategy not in self._services:
-            msg = f"Strategy '{strategy}' is not registered."
+        if strategy not in self._service_initializers:
+            msg = f"Strategy '{strategy}' is not registered. Registered strategies are: {', '.join(list(self._service_initializers.keys()))}"
             raise ValueError(msg)
-        return self._services[strategy](**(init_args or {}))
+
+        service_descriptor = self._service_initializers[strategy]
+        if service_descriptor.scope == "singleton":
+            if strategy not in self._initialized_services:
+                self._initialized_services[strategy] = service_descriptor.initializer(
+                    **(init_args or {})
+                )
+            return self._initialized_services[strategy]
+
+        return service_descriptor.initializer(**(init_args or {}))
diff --git a/packages/graphrag-common/graphrag_common/py.typed b/packages/graphrag-common/graphrag_common/py.typed
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/packages/graphrag-common/pyproject.toml b/packages/graphrag-common/pyproject.toml
new file mode 100644
index 0000000000..e8396e2cdb
--- /dev/null
+++ b/packages/graphrag-common/pyproject.toml
@@ -0,0 +1,41 @@
+[project]
+name = "graphrag-common"
+version = "2.7.0"
+description = "Common utilities and types for GraphRAG"
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = "MIT"
+readme = "README.md"
+license-files = ["LICENSE"]
+requires-python = ">=3.10,<3.13"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+dependencies = []
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
+
diff --git a/packages/graphrag/README.md b/packages/graphrag/README.md
new file mode 100644
index 0000000000..b17d46fdaf
--- /dev/null
+++ b/packages/graphrag/README.md
@@ -0,0 +1,76 @@
+# GraphRAG
+
+👉 [Microsoft Research Blog Post](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)<br/>
+👉 [Read the docs](https://microsoft.github.io/graphrag)<br/>
+👉 [GraphRAG Arxiv](https://arxiv.org/pdf/2404.16130)
+
+<div align="left">
+  <a href="https://pypi.org/project/graphrag/">
+    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/graphrag">
+  </a>
+  <a href="https://pypi.org/project/graphrag/">
+    <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/graphrag">
+  </a>
+  <a href="https://github.com/microsoft/graphrag/issues">
+    <img alt="GitHub Issues" src="https://img.shields.io/github/issues/microsoft/graphrag">
+  </a>
+  <a href="https://github.com/microsoft/graphrag/discussions">
+    <img alt="GitHub Discussions" src="https://img.shields.io/github/discussions/microsoft/graphrag">
+  </a>
+</div>
+
+## Overview
+
+The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.
+
+To learn more about GraphRAG and how it can be used to enhance your LLM's ability to reason about your private data, please visit the <a href="https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/" target="_blank">Microsoft Research Blog Post.</a>
+
+## Quickstart
+
+To get started with the GraphRAG system we recommend trying the [command line quickstart](https://microsoft.github.io/graphrag/get_started/).
+
+## Repository Guidance
+
+This repository presents a methodology for using knowledge graph memory structures to enhance LLM outputs. Please note that the provided code serves as a demonstration and is not an officially supported Microsoft offering.
+
+⚠� *Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.*
+
+## Diving Deeper
+
+- To learn about our contribution guidelines, see [CONTRIBUTING.md](./CONTRIBUTING.md)
+- To start developing _GraphRAG_, see [DEVELOPING.md](./DEVELOPING.md)
+- Join the conversation and provide feedback in the [GitHub Discussions tab!](https://github.com/microsoft/graphrag/discussions)
+
+## Prompt Tuning
+
+Using _GraphRAG_ with your data out of the box may not yield the best possible results.
+We strongly recommend to fine-tune your prompts following the [Prompt Tuning Guide](https://microsoft.github.io/graphrag/prompt_tuning/overview/) in our documentation.
+
+## Versioning
+
+Please see the [breaking changes](./breaking-changes.md) document for notes on our approach to versioning the project.
+
+*Always run `graphrag init --root [path] --force` between minor version bumps to ensure you have the latest config format. Run the provided migration notebook between major version bumps if you want to avoid re-indexing prior datasets. Note that this will overwrite your configuration and prompts, so backup if necessary.*
+
+## Responsible AI FAQ
+
+See [RAI_TRANSPARENCY.md](./RAI_TRANSPARENCY.md)
+
+- [What is GraphRAG?](./RAI_TRANSPARENCY.md#what-is-graphrag)
+- [What can GraphRAG do?](./RAI_TRANSPARENCY.md#what-can-graphrag-do)
+- [What are GraphRAG’s intended use(s)?](./RAI_TRANSPARENCY.md#what-are-graphrags-intended-uses)
+- [How was GraphRAG evaluated? What metrics are used to measure performance?](./RAI_TRANSPARENCY.md#how-was-graphrag-evaluated-what-metrics-are-used-to-measure-performance)
+- [What are the limitations of GraphRAG? How can users minimize the impact of GraphRAG’s limitations when using the system?](./RAI_TRANSPARENCY.md#what-are-the-limitations-of-graphrag-how-can-users-minimize-the-impact-of-graphrags-limitations-when-using-the-system)
+- [What operational factors and settings allow for effective and responsible use of GraphRAG?](./RAI_TRANSPARENCY.md#what-operational-factors-and-settings-allow-for-effective-and-responsible-use-of-graphrag)
+
+## Trademarks
+
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
+trademarks or logos is subject to and must follow
+[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
+Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
+Any use of third-party trademarks or logos are subject to those third-party's policies.
+
+## Privacy
+
+[Microsoft Privacy Statement](https://privacy.microsoft.com/en-us/privacystatement)
diff --git a/graphrag/__init__.py b/packages/graphrag/graphrag/__init__.py
similarity index 100%
rename from graphrag/__init__.py
rename to packages/graphrag/graphrag/__init__.py
diff --git a/graphrag/__main__.py b/packages/graphrag/graphrag/__main__.py
similarity index 100%
rename from graphrag/__main__.py
rename to packages/graphrag/graphrag/__main__.py
diff --git a/graphrag/api/__init__.py b/packages/graphrag/graphrag/api/__init__.py
similarity index 100%
rename from graphrag/api/__init__.py
rename to packages/graphrag/graphrag/api/__init__.py
diff --git a/graphrag/api/index.py b/packages/graphrag/graphrag/api/index.py
similarity index 100%
rename from graphrag/api/index.py
rename to packages/graphrag/graphrag/api/index.py
diff --git a/graphrag/api/prompt_tune.py b/packages/graphrag/graphrag/api/prompt_tune.py
similarity index 100%
rename from graphrag/api/prompt_tune.py
rename to packages/graphrag/graphrag/api/prompt_tune.py
diff --git a/graphrag/api/query.py b/packages/graphrag/graphrag/api/query.py
similarity index 100%
rename from graphrag/api/query.py
rename to packages/graphrag/graphrag/api/query.py
diff --git a/graphrag/cache/__init__.py b/packages/graphrag/graphrag/cache/__init__.py
similarity index 100%
rename from graphrag/cache/__init__.py
rename to packages/graphrag/graphrag/cache/__init__.py
diff --git a/graphrag/cache/factory.py b/packages/graphrag/graphrag/cache/factory.py
similarity index 98%
rename from graphrag/cache/factory.py
rename to packages/graphrag/graphrag/cache/factory.py
index f406fc856d..1f2c700a4e 100644
--- a/graphrag/cache/factory.py
+++ b/packages/graphrag/graphrag/cache/factory.py
@@ -5,12 +5,13 @@
 
 from __future__ import annotations
 
+from graphrag_common.factory import Factory
+
 from graphrag.cache.json_pipeline_cache import JsonPipelineCache
 from graphrag.cache.memory_pipeline_cache import InMemoryCache
 from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.enums import CacheType
-from graphrag.factory.factory import Factory
 from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
 from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
 from graphrag.storage.file_pipeline_storage import FilePipelineStorage
diff --git a/graphrag/cache/json_pipeline_cache.py b/packages/graphrag/graphrag/cache/json_pipeline_cache.py
similarity index 100%
rename from graphrag/cache/json_pipeline_cache.py
rename to packages/graphrag/graphrag/cache/json_pipeline_cache.py
diff --git a/graphrag/cache/memory_pipeline_cache.py b/packages/graphrag/graphrag/cache/memory_pipeline_cache.py
similarity index 100%
rename from graphrag/cache/memory_pipeline_cache.py
rename to packages/graphrag/graphrag/cache/memory_pipeline_cache.py
diff --git a/graphrag/cache/noop_pipeline_cache.py b/packages/graphrag/graphrag/cache/noop_pipeline_cache.py
similarity index 100%
rename from graphrag/cache/noop_pipeline_cache.py
rename to packages/graphrag/graphrag/cache/noop_pipeline_cache.py
diff --git a/graphrag/cache/pipeline_cache.py b/packages/graphrag/graphrag/cache/pipeline_cache.py
similarity index 100%
rename from graphrag/cache/pipeline_cache.py
rename to packages/graphrag/graphrag/cache/pipeline_cache.py
diff --git a/graphrag/callbacks/__init__.py b/packages/graphrag/graphrag/callbacks/__init__.py
similarity index 100%
rename from graphrag/callbacks/__init__.py
rename to packages/graphrag/graphrag/callbacks/__init__.py
diff --git a/graphrag/callbacks/console_workflow_callbacks.py b/packages/graphrag/graphrag/callbacks/console_workflow_callbacks.py
similarity index 100%
rename from graphrag/callbacks/console_workflow_callbacks.py
rename to packages/graphrag/graphrag/callbacks/console_workflow_callbacks.py
diff --git a/graphrag/callbacks/llm_callbacks.py b/packages/graphrag/graphrag/callbacks/llm_callbacks.py
similarity index 100%
rename from graphrag/callbacks/llm_callbacks.py
rename to packages/graphrag/graphrag/callbacks/llm_callbacks.py
diff --git a/graphrag/callbacks/noop_query_callbacks.py b/packages/graphrag/graphrag/callbacks/noop_query_callbacks.py
similarity index 100%
rename from graphrag/callbacks/noop_query_callbacks.py
rename to packages/graphrag/graphrag/callbacks/noop_query_callbacks.py
diff --git a/graphrag/callbacks/noop_workflow_callbacks.py b/packages/graphrag/graphrag/callbacks/noop_workflow_callbacks.py
similarity index 100%
rename from graphrag/callbacks/noop_workflow_callbacks.py
rename to packages/graphrag/graphrag/callbacks/noop_workflow_callbacks.py
diff --git a/graphrag/callbacks/query_callbacks.py b/packages/graphrag/graphrag/callbacks/query_callbacks.py
similarity index 100%
rename from graphrag/callbacks/query_callbacks.py
rename to packages/graphrag/graphrag/callbacks/query_callbacks.py
diff --git a/graphrag/callbacks/workflow_callbacks.py b/packages/graphrag/graphrag/callbacks/workflow_callbacks.py
similarity index 100%
rename from graphrag/callbacks/workflow_callbacks.py
rename to packages/graphrag/graphrag/callbacks/workflow_callbacks.py
diff --git a/graphrag/callbacks/workflow_callbacks_manager.py b/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
similarity index 100%
rename from graphrag/callbacks/workflow_callbacks_manager.py
rename to packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
diff --git a/graphrag/cli/__init__.py b/packages/graphrag/graphrag/cli/__init__.py
similarity index 100%
rename from graphrag/cli/__init__.py
rename to packages/graphrag/graphrag/cli/__init__.py
diff --git a/graphrag/cli/index.py b/packages/graphrag/graphrag/cli/index.py
similarity index 100%
rename from graphrag/cli/index.py
rename to packages/graphrag/graphrag/cli/index.py
diff --git a/graphrag/cli/initialize.py b/packages/graphrag/graphrag/cli/initialize.py
similarity index 100%
rename from graphrag/cli/initialize.py
rename to packages/graphrag/graphrag/cli/initialize.py
diff --git a/graphrag/cli/main.py b/packages/graphrag/graphrag/cli/main.py
similarity index 100%
rename from graphrag/cli/main.py
rename to packages/graphrag/graphrag/cli/main.py
diff --git a/graphrag/cli/prompt_tune.py b/packages/graphrag/graphrag/cli/prompt_tune.py
similarity index 100%
rename from graphrag/cli/prompt_tune.py
rename to packages/graphrag/graphrag/cli/prompt_tune.py
diff --git a/graphrag/cli/query.py b/packages/graphrag/graphrag/cli/query.py
similarity index 100%
rename from graphrag/cli/query.py
rename to packages/graphrag/graphrag/cli/query.py
diff --git a/graphrag/config/__init__.py b/packages/graphrag/graphrag/config/__init__.py
similarity index 100%
rename from graphrag/config/__init__.py
rename to packages/graphrag/graphrag/config/__init__.py
diff --git a/graphrag/config/create_graphrag_config.py b/packages/graphrag/graphrag/config/create_graphrag_config.py
similarity index 100%
rename from graphrag/config/create_graphrag_config.py
rename to packages/graphrag/graphrag/config/create_graphrag_config.py
diff --git a/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
similarity index 100%
rename from graphrag/config/defaults.py
rename to packages/graphrag/graphrag/config/defaults.py
diff --git a/graphrag/config/embeddings.py b/packages/graphrag/graphrag/config/embeddings.py
similarity index 100%
rename from graphrag/config/embeddings.py
rename to packages/graphrag/graphrag/config/embeddings.py
diff --git a/graphrag/config/enums.py b/packages/graphrag/graphrag/config/enums.py
similarity index 100%
rename from graphrag/config/enums.py
rename to packages/graphrag/graphrag/config/enums.py
diff --git a/graphrag/config/environment_reader.py b/packages/graphrag/graphrag/config/environment_reader.py
similarity index 100%
rename from graphrag/config/environment_reader.py
rename to packages/graphrag/graphrag/config/environment_reader.py
diff --git a/graphrag/config/errors.py b/packages/graphrag/graphrag/config/errors.py
similarity index 100%
rename from graphrag/config/errors.py
rename to packages/graphrag/graphrag/config/errors.py
diff --git a/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py
similarity index 100%
rename from graphrag/config/init_content.py
rename to packages/graphrag/graphrag/config/init_content.py
diff --git a/graphrag/config/load_config.py b/packages/graphrag/graphrag/config/load_config.py
similarity index 100%
rename from graphrag/config/load_config.py
rename to packages/graphrag/graphrag/config/load_config.py
diff --git a/graphrag/config/models/__init__.py b/packages/graphrag/graphrag/config/models/__init__.py
similarity index 100%
rename from graphrag/config/models/__init__.py
rename to packages/graphrag/graphrag/config/models/__init__.py
diff --git a/graphrag/config/models/basic_search_config.py b/packages/graphrag/graphrag/config/models/basic_search_config.py
similarity index 100%
rename from graphrag/config/models/basic_search_config.py
rename to packages/graphrag/graphrag/config/models/basic_search_config.py
diff --git a/graphrag/config/models/cache_config.py b/packages/graphrag/graphrag/config/models/cache_config.py
similarity index 100%
rename from graphrag/config/models/cache_config.py
rename to packages/graphrag/graphrag/config/models/cache_config.py
diff --git a/graphrag/config/models/chunking_config.py b/packages/graphrag/graphrag/config/models/chunking_config.py
similarity index 100%
rename from graphrag/config/models/chunking_config.py
rename to packages/graphrag/graphrag/config/models/chunking_config.py
diff --git a/graphrag/config/models/cluster_graph_config.py b/packages/graphrag/graphrag/config/models/cluster_graph_config.py
similarity index 100%
rename from graphrag/config/models/cluster_graph_config.py
rename to packages/graphrag/graphrag/config/models/cluster_graph_config.py
diff --git a/graphrag/config/models/community_reports_config.py b/packages/graphrag/graphrag/config/models/community_reports_config.py
similarity index 100%
rename from graphrag/config/models/community_reports_config.py
rename to packages/graphrag/graphrag/config/models/community_reports_config.py
diff --git a/graphrag/config/models/drift_search_config.py b/packages/graphrag/graphrag/config/models/drift_search_config.py
similarity index 100%
rename from graphrag/config/models/drift_search_config.py
rename to packages/graphrag/graphrag/config/models/drift_search_config.py
diff --git a/graphrag/config/models/embed_text_config.py b/packages/graphrag/graphrag/config/models/embed_text_config.py
similarity index 100%
rename from graphrag/config/models/embed_text_config.py
rename to packages/graphrag/graphrag/config/models/embed_text_config.py
diff --git a/graphrag/config/models/extract_claims_config.py b/packages/graphrag/graphrag/config/models/extract_claims_config.py
similarity index 100%
rename from graphrag/config/models/extract_claims_config.py
rename to packages/graphrag/graphrag/config/models/extract_claims_config.py
diff --git a/graphrag/config/models/extract_graph_config.py b/packages/graphrag/graphrag/config/models/extract_graph_config.py
similarity index 100%
rename from graphrag/config/models/extract_graph_config.py
rename to packages/graphrag/graphrag/config/models/extract_graph_config.py
diff --git a/graphrag/config/models/extract_graph_nlp_config.py b/packages/graphrag/graphrag/config/models/extract_graph_nlp_config.py
similarity index 100%
rename from graphrag/config/models/extract_graph_nlp_config.py
rename to packages/graphrag/graphrag/config/models/extract_graph_nlp_config.py
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similarity index 100%
rename from graphrag/config/models/global_search_config.py
rename to packages/graphrag/graphrag/config/models/global_search_config.py
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similarity index 100%
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rename to packages/graphrag/graphrag/config/models/graph_rag_config.py
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similarity index 100%
rename from graphrag/config/models/input_config.py
rename to packages/graphrag/graphrag/config/models/input_config.py
diff --git a/graphrag/config/models/language_model_config.py b/packages/graphrag/graphrag/config/models/language_model_config.py
similarity index 100%
rename from graphrag/config/models/language_model_config.py
rename to packages/graphrag/graphrag/config/models/language_model_config.py
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similarity index 100%
rename from graphrag/config/models/local_search_config.py
rename to packages/graphrag/graphrag/config/models/local_search_config.py
diff --git a/graphrag/config/models/prune_graph_config.py b/packages/graphrag/graphrag/config/models/prune_graph_config.py
similarity index 100%
rename from graphrag/config/models/prune_graph_config.py
rename to packages/graphrag/graphrag/config/models/prune_graph_config.py
diff --git a/graphrag/config/models/reporting_config.py b/packages/graphrag/graphrag/config/models/reporting_config.py
similarity index 100%
rename from graphrag/config/models/reporting_config.py
rename to packages/graphrag/graphrag/config/models/reporting_config.py
diff --git a/graphrag/config/models/snapshots_config.py b/packages/graphrag/graphrag/config/models/snapshots_config.py
similarity index 100%
rename from graphrag/config/models/snapshots_config.py
rename to packages/graphrag/graphrag/config/models/snapshots_config.py
diff --git a/graphrag/config/models/storage_config.py b/packages/graphrag/graphrag/config/models/storage_config.py
similarity index 100%
rename from graphrag/config/models/storage_config.py
rename to packages/graphrag/graphrag/config/models/storage_config.py
diff --git a/graphrag/config/models/summarize_descriptions_config.py b/packages/graphrag/graphrag/config/models/summarize_descriptions_config.py
similarity index 100%
rename from graphrag/config/models/summarize_descriptions_config.py
rename to packages/graphrag/graphrag/config/models/summarize_descriptions_config.py
diff --git a/graphrag/config/models/vector_store_config.py b/packages/graphrag/graphrag/config/models/vector_store_config.py
similarity index 100%
rename from graphrag/config/models/vector_store_config.py
rename to packages/graphrag/graphrag/config/models/vector_store_config.py
diff --git a/graphrag/config/models/vector_store_schema_config.py b/packages/graphrag/graphrag/config/models/vector_store_schema_config.py
similarity index 100%
rename from graphrag/config/models/vector_store_schema_config.py
rename to packages/graphrag/graphrag/config/models/vector_store_schema_config.py
diff --git a/graphrag/config/read_dotenv.py b/packages/graphrag/graphrag/config/read_dotenv.py
similarity index 100%
rename from graphrag/config/read_dotenv.py
rename to packages/graphrag/graphrag/config/read_dotenv.py
diff --git a/graphrag/data_model/__init__.py b/packages/graphrag/graphrag/data_model/__init__.py
similarity index 100%
rename from graphrag/data_model/__init__.py
rename to packages/graphrag/graphrag/data_model/__init__.py
diff --git a/graphrag/data_model/community.py b/packages/graphrag/graphrag/data_model/community.py
similarity index 100%
rename from graphrag/data_model/community.py
rename to packages/graphrag/graphrag/data_model/community.py
diff --git a/graphrag/data_model/community_report.py b/packages/graphrag/graphrag/data_model/community_report.py
similarity index 100%
rename from graphrag/data_model/community_report.py
rename to packages/graphrag/graphrag/data_model/community_report.py
diff --git a/graphrag/data_model/covariate.py b/packages/graphrag/graphrag/data_model/covariate.py
similarity index 100%
rename from graphrag/data_model/covariate.py
rename to packages/graphrag/graphrag/data_model/covariate.py
diff --git a/graphrag/data_model/document.py b/packages/graphrag/graphrag/data_model/document.py
similarity index 100%
rename from graphrag/data_model/document.py
rename to packages/graphrag/graphrag/data_model/document.py
diff --git a/graphrag/data_model/entity.py b/packages/graphrag/graphrag/data_model/entity.py
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rename from graphrag/data_model/entity.py
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diff --git a/graphrag/data_model/identified.py b/packages/graphrag/graphrag/data_model/identified.py
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rename from graphrag/data_model/identified.py
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diff --git a/graphrag/data_model/named.py b/packages/graphrag/graphrag/data_model/named.py
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diff --git a/graphrag/data_model/relationship.py b/packages/graphrag/graphrag/data_model/relationship.py
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rename from graphrag/data_model/relationship.py
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diff --git a/graphrag/data_model/schemas.py b/packages/graphrag/graphrag/data_model/schemas.py
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rename from graphrag/data_model/schemas.py
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diff --git a/graphrag/data_model/text_unit.py b/packages/graphrag/graphrag/data_model/text_unit.py
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rename to packages/graphrag/graphrag/data_model/text_unit.py
diff --git a/graphrag/data_model/types.py b/packages/graphrag/graphrag/data_model/types.py
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rename from graphrag/data_model/types.py
rename to packages/graphrag/graphrag/data_model/types.py
diff --git a/graphrag/index/__init__.py b/packages/graphrag/graphrag/index/__init__.py
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rename from graphrag/index/__init__.py
rename to packages/graphrag/graphrag/index/__init__.py
diff --git a/graphrag/index/input/__init__.py b/packages/graphrag/graphrag/index/input/__init__.py
similarity index 100%
rename from graphrag/index/input/__init__.py
rename to packages/graphrag/graphrag/index/input/__init__.py
diff --git a/graphrag/index/input/csv.py b/packages/graphrag/graphrag/index/input/csv.py
similarity index 100%
rename from graphrag/index/input/csv.py
rename to packages/graphrag/graphrag/index/input/csv.py
diff --git a/graphrag/index/input/factory.py b/packages/graphrag/graphrag/index/input/factory.py
similarity index 94%
rename from graphrag/index/input/factory.py
rename to packages/graphrag/graphrag/index/input/factory.py
index bdabd0d7c7..c938c6e4ed 100644
--- a/graphrag/index/input/factory.py
+++ b/packages/graphrag/graphrag/index/input/factory.py
@@ -5,8 +5,9 @@
 
 import logging
 
+from graphrag_common.factory import Factory
+
 from graphrag.config.enums import InputFileType
-from graphrag.factory.factory import Factory
 from graphrag.index.input.csv import CSVFileReader
 from graphrag.index.input.input_reader import InputReader
 from graphrag.index.input.json import JSONFileReader
diff --git a/graphrag/index/input/input_reader.py b/packages/graphrag/graphrag/index/input/input_reader.py
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rename from graphrag/index/input/input_reader.py
rename to packages/graphrag/graphrag/index/input/input_reader.py
diff --git a/graphrag/index/input/json.py b/packages/graphrag/graphrag/index/input/json.py
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rename from graphrag/index/input/json.py
rename to packages/graphrag/graphrag/index/input/json.py
diff --git a/graphrag/index/input/text.py b/packages/graphrag/graphrag/index/input/text.py
similarity index 100%
rename from graphrag/index/input/text.py
rename to packages/graphrag/graphrag/index/input/text.py
diff --git a/graphrag/index/input/util.py b/packages/graphrag/graphrag/index/input/util.py
similarity index 100%
rename from graphrag/index/input/util.py
rename to packages/graphrag/graphrag/index/input/util.py
diff --git a/graphrag/index/operations/__init__.py b/packages/graphrag/graphrag/index/operations/__init__.py
similarity index 100%
rename from graphrag/index/operations/__init__.py
rename to packages/graphrag/graphrag/index/operations/__init__.py
diff --git a/graphrag/index/operations/build_noun_graph/__init__.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/__init__.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/__init__.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/__init__.py
diff --git a/graphrag/index/operations/build_noun_graph/build_noun_graph.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/build_noun_graph.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/__init__.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/__init__.py
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rename from graphrag/index/operations/build_noun_graph/np_extractors/__init__.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/__init__.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/base.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/base.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/base.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/base.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/cfg_extractor.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/cfg_extractor.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/cfg_extractor.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/cfg_extractor.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/factory.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/factory.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/factory.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/factory.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/np_validator.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/np_validator.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/np_validator.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/np_validator.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/regex_extractor.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/regex_extractor.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/regex_extractor.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/regex_extractor.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/resource_loader.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/resource_loader.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/resource_loader.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/resource_loader.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/stop_words.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/stop_words.py
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rename from graphrag/index/operations/build_noun_graph/np_extractors/stop_words.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/stop_words.py
diff --git a/graphrag/index/operations/build_noun_graph/np_extractors/syntactic_parsing_extractor.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/syntactic_parsing_extractor.py
similarity index 100%
rename from graphrag/index/operations/build_noun_graph/np_extractors/syntactic_parsing_extractor.py
rename to packages/graphrag/graphrag/index/operations/build_noun_graph/np_extractors/syntactic_parsing_extractor.py
diff --git a/graphrag/index/operations/chunk_text/__init__.py b/packages/graphrag/graphrag/index/operations/chunk_text/__init__.py
similarity index 100%
rename from graphrag/index/operations/chunk_text/__init__.py
rename to packages/graphrag/graphrag/index/operations/chunk_text/__init__.py
diff --git a/graphrag/index/operations/chunk_text/bootstrap.py b/packages/graphrag/graphrag/index/operations/chunk_text/bootstrap.py
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rename from graphrag/index/operations/chunk_text/bootstrap.py
rename to packages/graphrag/graphrag/index/operations/chunk_text/bootstrap.py
diff --git a/graphrag/index/operations/chunk_text/chunk_text.py b/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
similarity index 100%
rename from graphrag/index/operations/chunk_text/chunk_text.py
rename to packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
diff --git a/graphrag/index/operations/chunk_text/strategies.py b/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
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rename from graphrag/index/operations/chunk_text/strategies.py
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diff --git a/graphrag/index/operations/chunk_text/typing.py b/packages/graphrag/graphrag/index/operations/chunk_text/typing.py
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diff --git a/graphrag/index/operations/cluster_graph.py b/packages/graphrag/graphrag/index/operations/cluster_graph.py
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rename from graphrag/index/operations/cluster_graph.py
rename to packages/graphrag/graphrag/index/operations/cluster_graph.py
diff --git a/graphrag/index/operations/compute_degree.py b/packages/graphrag/graphrag/index/operations/compute_degree.py
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rename from graphrag/index/operations/compute_degree.py
rename to packages/graphrag/graphrag/index/operations/compute_degree.py
diff --git a/graphrag/index/operations/compute_edge_combined_degree.py b/packages/graphrag/graphrag/index/operations/compute_edge_combined_degree.py
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rename from graphrag/index/operations/compute_edge_combined_degree.py
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diff --git a/graphrag/index/operations/create_graph.py b/packages/graphrag/graphrag/index/operations/create_graph.py
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rename from graphrag/index/operations/create_graph.py
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diff --git a/graphrag/index/operations/embed_text/__init__.py b/packages/graphrag/graphrag/index/operations/embed_text/__init__.py
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rename from graphrag/index/operations/embed_text/__init__.py
rename to packages/graphrag/graphrag/index/operations/embed_text/__init__.py
diff --git a/graphrag/index/operations/embed_text/embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
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rename from graphrag/index/operations/embed_text/embed_text.py
rename to packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
diff --git a/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
similarity index 100%
rename from graphrag/index/operations/embed_text/run_embed_text.py
rename to packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
diff --git a/graphrag/index/operations/extract_covariates/__init__.py b/packages/graphrag/graphrag/index/operations/extract_covariates/__init__.py
similarity index 100%
rename from graphrag/index/operations/extract_covariates/__init__.py
rename to packages/graphrag/graphrag/index/operations/extract_covariates/__init__.py
diff --git a/graphrag/index/operations/extract_covariates/claim_extractor.py b/packages/graphrag/graphrag/index/operations/extract_covariates/claim_extractor.py
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rename from graphrag/index/operations/extract_covariates/claim_extractor.py
rename to packages/graphrag/graphrag/index/operations/extract_covariates/claim_extractor.py
diff --git a/graphrag/index/operations/extract_covariates/extract_covariates.py b/packages/graphrag/graphrag/index/operations/extract_covariates/extract_covariates.py
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diff --git a/graphrag/index/operations/extract_graph/__init__.py b/packages/graphrag/graphrag/index/operations/extract_graph/__init__.py
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diff --git a/graphrag/index/operations/extract_graph/extract_graph.py b/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
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rename from graphrag/index/operations/extract_graph/extract_graph.py
rename to packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
diff --git a/graphrag/index/operations/extract_graph/graph_extractor.py b/packages/graphrag/graphrag/index/operations/extract_graph/graph_extractor.py
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rename from graphrag/index/operations/extract_graph/graph_extractor.py
rename to packages/graphrag/graphrag/index/operations/extract_graph/graph_extractor.py
diff --git a/graphrag/index/operations/extract_graph/typing.py b/packages/graphrag/graphrag/index/operations/extract_graph/typing.py
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rename from graphrag/index/operations/extract_graph/typing.py
rename to packages/graphrag/graphrag/index/operations/extract_graph/typing.py
diff --git a/graphrag/index/operations/finalize_community_reports.py b/packages/graphrag/graphrag/index/operations/finalize_community_reports.py
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rename from graphrag/index/operations/finalize_community_reports.py
rename to packages/graphrag/graphrag/index/operations/finalize_community_reports.py
diff --git a/graphrag/index/operations/finalize_entities.py b/packages/graphrag/graphrag/index/operations/finalize_entities.py
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rename from graphrag/index/operations/finalize_entities.py
rename to packages/graphrag/graphrag/index/operations/finalize_entities.py
diff --git a/graphrag/index/operations/finalize_relationships.py b/packages/graphrag/graphrag/index/operations/finalize_relationships.py
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rename from graphrag/index/operations/finalize_relationships.py
rename to packages/graphrag/graphrag/index/operations/finalize_relationships.py
diff --git a/graphrag/index/operations/graph_to_dataframes.py b/packages/graphrag/graphrag/index/operations/graph_to_dataframes.py
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rename from graphrag/index/operations/graph_to_dataframes.py
rename to packages/graphrag/graphrag/index/operations/graph_to_dataframes.py
diff --git a/graphrag/index/operations/prune_graph.py b/packages/graphrag/graphrag/index/operations/prune_graph.py
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rename from graphrag/index/operations/prune_graph.py
rename to packages/graphrag/graphrag/index/operations/prune_graph.py
diff --git a/graphrag/index/operations/snapshot_graphml.py b/packages/graphrag/graphrag/index/operations/snapshot_graphml.py
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rename from graphrag/index/operations/snapshot_graphml.py
rename to packages/graphrag/graphrag/index/operations/snapshot_graphml.py
diff --git a/graphrag/index/operations/summarize_communities/__init__.py b/packages/graphrag/graphrag/index/operations/summarize_communities/__init__.py
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diff --git a/graphrag/index/operations/summarize_communities/build_mixed_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py
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diff --git a/graphrag/index/operations/summarize_communities/community_reports_extractor.py b/packages/graphrag/graphrag/index/operations/summarize_communities/community_reports_extractor.py
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diff --git a/graphrag/index/operations/summarize_communities/explode_communities.py b/packages/graphrag/graphrag/index/operations/summarize_communities/explode_communities.py
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diff --git a/graphrag/index/operations/summarize_communities/graph_context/__init__.py b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/__init__.py
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diff --git a/graphrag/index/operations/summarize_communities/text_unit_context/__init__.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/__init__.py
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diff --git a/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py
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diff --git a/graphrag/index/operations/summarize_communities/text_unit_context/prep_text_units.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/prep_text_units.py
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diff --git a/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py
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diff --git a/graphrag/index/operations/summarize_communities/typing.py b/packages/graphrag/graphrag/index/operations/summarize_communities/typing.py
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diff --git a/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py b/packages/graphrag/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
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diff --git a/graphrag/index/utils/dataframes.py b/packages/graphrag/graphrag/index/utils/dataframes.py
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diff --git a/graphrag/index/utils/derive_from_rows.py b/packages/graphrag/graphrag/index/utils/derive_from_rows.py
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rename from graphrag/index/utils/derive_from_rows.py
rename to packages/graphrag/graphrag/index/utils/derive_from_rows.py
diff --git a/graphrag/index/utils/dicts.py b/packages/graphrag/graphrag/index/utils/dicts.py
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rename from graphrag/index/utils/dicts.py
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rename from graphrag/index/utils/string.py
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diff --git a/graphrag/index/utils/uuid.py b/packages/graphrag/graphrag/index/utils/uuid.py
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rename from graphrag/index/utils/uuid.py
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diff --git a/graphrag/index/validate_config.py b/packages/graphrag/graphrag/index/validate_config.py
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rename from graphrag/index/validate_config.py
rename to packages/graphrag/graphrag/index/validate_config.py
diff --git a/graphrag/index/workflows/__init__.py b/packages/graphrag/graphrag/index/workflows/__init__.py
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rename from graphrag/index/workflows/__init__.py
rename to packages/graphrag/graphrag/index/workflows/__init__.py
diff --git a/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
similarity index 100%
rename from graphrag/index/workflows/create_base_text_units.py
rename to packages/graphrag/graphrag/index/workflows/create_base_text_units.py
diff --git a/graphrag/index/workflows/create_communities.py b/packages/graphrag/graphrag/index/workflows/create_communities.py
similarity index 100%
rename from graphrag/index/workflows/create_communities.py
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diff --git a/graphrag/index/workflows/create_community_reports.py b/packages/graphrag/graphrag/index/workflows/create_community_reports.py
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rename from graphrag/index/workflows/create_community_reports.py
rename to packages/graphrag/graphrag/index/workflows/create_community_reports.py
diff --git a/graphrag/index/workflows/create_community_reports_text.py b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py
similarity index 100%
rename from graphrag/index/workflows/create_community_reports_text.py
rename to packages/graphrag/graphrag/index/workflows/create_community_reports_text.py
diff --git a/graphrag/index/workflows/create_final_documents.py b/packages/graphrag/graphrag/index/workflows/create_final_documents.py
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rename from graphrag/index/workflows/create_final_documents.py
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diff --git a/graphrag/index/workflows/create_final_text_units.py b/packages/graphrag/graphrag/index/workflows/create_final_text_units.py
similarity index 100%
rename from graphrag/index/workflows/create_final_text_units.py
rename to packages/graphrag/graphrag/index/workflows/create_final_text_units.py
diff --git a/graphrag/index/workflows/extract_covariates.py b/packages/graphrag/graphrag/index/workflows/extract_covariates.py
similarity index 100%
rename from graphrag/index/workflows/extract_covariates.py
rename to packages/graphrag/graphrag/index/workflows/extract_covariates.py
diff --git a/graphrag/index/workflows/extract_graph.py b/packages/graphrag/graphrag/index/workflows/extract_graph.py
similarity index 100%
rename from graphrag/index/workflows/extract_graph.py
rename to packages/graphrag/graphrag/index/workflows/extract_graph.py
diff --git a/graphrag/index/workflows/extract_graph_nlp.py b/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
similarity index 100%
rename from graphrag/index/workflows/extract_graph_nlp.py
rename to packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
diff --git a/graphrag/index/workflows/factory.py b/packages/graphrag/graphrag/index/workflows/factory.py
similarity index 100%
rename from graphrag/index/workflows/factory.py
rename to packages/graphrag/graphrag/index/workflows/factory.py
diff --git a/graphrag/index/workflows/finalize_graph.py b/packages/graphrag/graphrag/index/workflows/finalize_graph.py
similarity index 100%
rename from graphrag/index/workflows/finalize_graph.py
rename to packages/graphrag/graphrag/index/workflows/finalize_graph.py
diff --git a/graphrag/index/workflows/generate_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
similarity index 100%
rename from graphrag/index/workflows/generate_text_embeddings.py
rename to packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
diff --git a/graphrag/index/workflows/load_input_documents.py b/packages/graphrag/graphrag/index/workflows/load_input_documents.py
similarity index 100%
rename from graphrag/index/workflows/load_input_documents.py
rename to packages/graphrag/graphrag/index/workflows/load_input_documents.py
diff --git a/graphrag/index/workflows/load_update_documents.py b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
similarity index 100%
rename from graphrag/index/workflows/load_update_documents.py
rename to packages/graphrag/graphrag/index/workflows/load_update_documents.py
diff --git a/graphrag/index/workflows/prune_graph.py b/packages/graphrag/graphrag/index/workflows/prune_graph.py
similarity index 100%
rename from graphrag/index/workflows/prune_graph.py
rename to packages/graphrag/graphrag/index/workflows/prune_graph.py
diff --git a/graphrag/index/workflows/update_clean_state.py b/packages/graphrag/graphrag/index/workflows/update_clean_state.py
similarity index 100%
rename from graphrag/index/workflows/update_clean_state.py
rename to packages/graphrag/graphrag/index/workflows/update_clean_state.py
diff --git a/graphrag/index/workflows/update_communities.py b/packages/graphrag/graphrag/index/workflows/update_communities.py
similarity index 100%
rename from graphrag/index/workflows/update_communities.py
rename to packages/graphrag/graphrag/index/workflows/update_communities.py
diff --git a/graphrag/index/workflows/update_community_reports.py b/packages/graphrag/graphrag/index/workflows/update_community_reports.py
similarity index 100%
rename from graphrag/index/workflows/update_community_reports.py
rename to packages/graphrag/graphrag/index/workflows/update_community_reports.py
diff --git a/graphrag/index/workflows/update_covariates.py b/packages/graphrag/graphrag/index/workflows/update_covariates.py
similarity index 100%
rename from graphrag/index/workflows/update_covariates.py
rename to packages/graphrag/graphrag/index/workflows/update_covariates.py
diff --git a/graphrag/index/workflows/update_entities_relationships.py b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
similarity index 100%
rename from graphrag/index/workflows/update_entities_relationships.py
rename to packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
diff --git a/graphrag/index/workflows/update_final_documents.py b/packages/graphrag/graphrag/index/workflows/update_final_documents.py
similarity index 100%
rename from graphrag/index/workflows/update_final_documents.py
rename to packages/graphrag/graphrag/index/workflows/update_final_documents.py
diff --git a/graphrag/index/workflows/update_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/update_text_embeddings.py
similarity index 100%
rename from graphrag/index/workflows/update_text_embeddings.py
rename to packages/graphrag/graphrag/index/workflows/update_text_embeddings.py
diff --git a/graphrag/index/workflows/update_text_units.py b/packages/graphrag/graphrag/index/workflows/update_text_units.py
similarity index 100%
rename from graphrag/index/workflows/update_text_units.py
rename to packages/graphrag/graphrag/index/workflows/update_text_units.py
diff --git a/graphrag/language_model/__init__.py b/packages/graphrag/graphrag/language_model/__init__.py
similarity index 100%
rename from graphrag/language_model/__init__.py
rename to packages/graphrag/graphrag/language_model/__init__.py
diff --git a/graphrag/language_model/cache/__init__.py b/packages/graphrag/graphrag/language_model/cache/__init__.py
similarity index 100%
rename from graphrag/language_model/cache/__init__.py
rename to packages/graphrag/graphrag/language_model/cache/__init__.py
diff --git a/graphrag/language_model/cache/base.py b/packages/graphrag/graphrag/language_model/cache/base.py
similarity index 100%
rename from graphrag/language_model/cache/base.py
rename to packages/graphrag/graphrag/language_model/cache/base.py
diff --git a/graphrag/language_model/events/__init__.py b/packages/graphrag/graphrag/language_model/events/__init__.py
similarity index 100%
rename from graphrag/language_model/events/__init__.py
rename to packages/graphrag/graphrag/language_model/events/__init__.py
diff --git a/graphrag/language_model/events/base.py b/packages/graphrag/graphrag/language_model/events/base.py
similarity index 100%
rename from graphrag/language_model/events/base.py
rename to packages/graphrag/graphrag/language_model/events/base.py
diff --git a/graphrag/language_model/factory.py b/packages/graphrag/graphrag/language_model/factory.py
similarity index 95%
rename from graphrag/language_model/factory.py
rename to packages/graphrag/graphrag/language_model/factory.py
index 6ff32b530e..2836d07d12 100644
--- a/graphrag/language_model/factory.py
+++ b/packages/graphrag/graphrag/language_model/factory.py
@@ -3,8 +3,9 @@
 
 """A package containing a factory for supported llm types."""
 
+from graphrag_common.factory import Factory
+
 from graphrag.config.enums import ModelType
-from graphrag.factory.factory import Factory
 from graphrag.language_model.protocol.base import ChatModel, EmbeddingModel
 from graphrag.language_model.providers.litellm.chat_model import LitellmChatModel
 from graphrag.language_model.providers.litellm.embedding_model import (
diff --git a/graphrag/language_model/manager.py b/packages/graphrag/graphrag/language_model/manager.py
similarity index 100%
rename from graphrag/language_model/manager.py
rename to packages/graphrag/graphrag/language_model/manager.py
diff --git a/graphrag/language_model/protocol/__init__.py b/packages/graphrag/graphrag/language_model/protocol/__init__.py
similarity index 100%
rename from graphrag/language_model/protocol/__init__.py
rename to packages/graphrag/graphrag/language_model/protocol/__init__.py
diff --git a/graphrag/language_model/protocol/base.py b/packages/graphrag/graphrag/language_model/protocol/base.py
similarity index 100%
rename from graphrag/language_model/protocol/base.py
rename to packages/graphrag/graphrag/language_model/protocol/base.py
diff --git a/graphrag/language_model/providers/__init__.py b/packages/graphrag/graphrag/language_model/providers/__init__.py
similarity index 100%
rename from graphrag/language_model/providers/__init__.py
rename to packages/graphrag/graphrag/language_model/providers/__init__.py
diff --git a/graphrag/language_model/providers/litellm/__init__.py b/packages/graphrag/graphrag/language_model/providers/litellm/__init__.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/__init__.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/__init__.py
diff --git a/graphrag/language_model/providers/litellm/chat_model.py b/packages/graphrag/graphrag/language_model/providers/litellm/chat_model.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/chat_model.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/chat_model.py
diff --git a/graphrag/language_model/providers/litellm/embedding_model.py b/packages/graphrag/graphrag/language_model/providers/litellm/embedding_model.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/embedding_model.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/embedding_model.py
diff --git a/graphrag/language_model/providers/litellm/get_cache_key.py b/packages/graphrag/graphrag/language_model/providers/litellm/get_cache_key.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/get_cache_key.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/get_cache_key.py
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/__init__.py b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/__init__.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/request_wrappers/__init__.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/__init__.py
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/request_wrappers/with_cache.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/with_logging.py b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_logging.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/request_wrappers/with_logging.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_logging.py
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_rate_limiter.py
diff --git a/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/request_wrappers/with_retries.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_retries.py
diff --git a/graphrag/language_model/providers/litellm/services/__init__.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/__init__.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/__init__.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/__init__.py
diff --git a/graphrag/language_model/providers/litellm/services/rate_limiter/__init__.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/__init__.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/rate_limiter/__init__.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/__init__.py
diff --git a/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter.py
diff --git a/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
similarity index 92%
rename from graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
index 5904be0b56..084f0bd0d1 100644
--- a/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
+++ b/packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/rate_limiter_factory.py
@@ -3,7 +3,8 @@
 
 """LiteLLM Rate Limiter Factory."""
 
-from graphrag.factory.factory import Factory
+from graphrag_common.factory import Factory
+
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter import (
     RateLimiter,
 )
diff --git a/graphrag/language_model/providers/litellm/services/rate_limiter/static_rate_limiter.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/static_rate_limiter.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/rate_limiter/static_rate_limiter.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/rate_limiter/static_rate_limiter.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/__init__.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/__init__.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/retry/__init__.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/__init__.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/exponential_retry.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/exponential_retry.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/retry/exponential_retry.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/exponential_retry.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/incremental_wait_retry.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/incremental_wait_retry.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/retry/incremental_wait_retry.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/incremental_wait_retry.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/native_wait_retry.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/native_wait_retry.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/retry/native_wait_retry.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/native_wait_retry.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/random_wait_retry.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/random_wait_retry.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/retry/random_wait_retry.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/random_wait_retry.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/retry.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/retry.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/services/retry/retry.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/retry.py
diff --git a/graphrag/language_model/providers/litellm/services/retry/retry_factory.py b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
similarity index 95%
rename from graphrag/language_model/providers/litellm/services/retry/retry_factory.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
index 9acdf21425..692463b0ca 100644
--- a/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
+++ b/packages/graphrag/graphrag/language_model/providers/litellm/services/retry/retry_factory.py
@@ -3,7 +3,8 @@
 
 """LiteLLM Retry Factory."""
 
-from graphrag.factory.factory import Factory
+from graphrag_common.factory import Factory
+
 from graphrag.language_model.providers.litellm.services.retry.exponential_retry import (
     ExponentialRetry,
 )
diff --git a/graphrag/language_model/providers/litellm/types.py b/packages/graphrag/graphrag/language_model/providers/litellm/types.py
similarity index 100%
rename from graphrag/language_model/providers/litellm/types.py
rename to packages/graphrag/graphrag/language_model/providers/litellm/types.py
diff --git a/graphrag/language_model/response/__init__.py b/packages/graphrag/graphrag/language_model/response/__init__.py
similarity index 100%
rename from graphrag/language_model/response/__init__.py
rename to packages/graphrag/graphrag/language_model/response/__init__.py
diff --git a/graphrag/language_model/response/base.py b/packages/graphrag/graphrag/language_model/response/base.py
similarity index 100%
rename from graphrag/language_model/response/base.py
rename to packages/graphrag/graphrag/language_model/response/base.py
diff --git a/graphrag/language_model/response/base.pyi b/packages/graphrag/graphrag/language_model/response/base.pyi
similarity index 100%
rename from graphrag/language_model/response/base.pyi
rename to packages/graphrag/graphrag/language_model/response/base.pyi
diff --git a/graphrag/language_model/util.py b/packages/graphrag/graphrag/language_model/util.py
similarity index 100%
rename from graphrag/language_model/util.py
rename to packages/graphrag/graphrag/language_model/util.py
diff --git a/graphrag/logger/__init__.py b/packages/graphrag/graphrag/logger/__init__.py
similarity index 100%
rename from graphrag/logger/__init__.py
rename to packages/graphrag/graphrag/logger/__init__.py
diff --git a/graphrag/logger/blob_workflow_logger.py b/packages/graphrag/graphrag/logger/blob_workflow_logger.py
similarity index 100%
rename from graphrag/logger/blob_workflow_logger.py
rename to packages/graphrag/graphrag/logger/blob_workflow_logger.py
diff --git a/graphrag/logger/factory.py b/packages/graphrag/graphrag/logger/factory.py
similarity index 97%
rename from graphrag/logger/factory.py
rename to packages/graphrag/graphrag/logger/factory.py
index 56800733f5..744a563272 100644
--- a/graphrag/logger/factory.py
+++ b/packages/graphrag/graphrag/logger/factory.py
@@ -8,8 +8,9 @@
 import logging
 from pathlib import Path
 
+from graphrag_common.factory import Factory
+
 from graphrag.config.enums import ReportingType
-from graphrag.factory.factory import Factory
 
 LOG_FORMAT = "%(asctime)s.%(msecs)04d - %(levelname)s - %(name)s - %(message)s"
 DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
diff --git a/graphrag/logger/progress.py b/packages/graphrag/graphrag/logger/progress.py
similarity index 100%
rename from graphrag/logger/progress.py
rename to packages/graphrag/graphrag/logger/progress.py
diff --git a/graphrag/logger/standard_logging.py b/packages/graphrag/graphrag/logger/standard_logging.py
similarity index 100%
rename from graphrag/logger/standard_logging.py
rename to packages/graphrag/graphrag/logger/standard_logging.py
diff --git a/graphrag/prompt_tune/__init__.py b/packages/graphrag/graphrag/prompt_tune/__init__.py
similarity index 100%
rename from graphrag/prompt_tune/__init__.py
rename to packages/graphrag/graphrag/prompt_tune/__init__.py
diff --git a/graphrag/prompt_tune/defaults.py b/packages/graphrag/graphrag/prompt_tune/defaults.py
similarity index 100%
rename from graphrag/prompt_tune/defaults.py
rename to packages/graphrag/graphrag/prompt_tune/defaults.py
diff --git a/graphrag/prompt_tune/generator/__init__.py b/packages/graphrag/graphrag/prompt_tune/generator/__init__.py
similarity index 100%
rename from graphrag/prompt_tune/generator/__init__.py
rename to packages/graphrag/graphrag/prompt_tune/generator/__init__.py
diff --git a/graphrag/prompt_tune/generator/community_report_rating.py b/packages/graphrag/graphrag/prompt_tune/generator/community_report_rating.py
similarity index 100%
rename from graphrag/prompt_tune/generator/community_report_rating.py
rename to packages/graphrag/graphrag/prompt_tune/generator/community_report_rating.py
diff --git a/graphrag/prompt_tune/generator/community_report_summarization.py b/packages/graphrag/graphrag/prompt_tune/generator/community_report_summarization.py
similarity index 100%
rename from graphrag/prompt_tune/generator/community_report_summarization.py
rename to packages/graphrag/graphrag/prompt_tune/generator/community_report_summarization.py
diff --git a/graphrag/prompt_tune/generator/community_reporter_role.py b/packages/graphrag/graphrag/prompt_tune/generator/community_reporter_role.py
similarity index 100%
rename from graphrag/prompt_tune/generator/community_reporter_role.py
rename to packages/graphrag/graphrag/prompt_tune/generator/community_reporter_role.py
diff --git a/graphrag/prompt_tune/generator/domain.py b/packages/graphrag/graphrag/prompt_tune/generator/domain.py
similarity index 100%
rename from graphrag/prompt_tune/generator/domain.py
rename to packages/graphrag/graphrag/prompt_tune/generator/domain.py
diff --git a/graphrag/prompt_tune/generator/entity_relationship.py b/packages/graphrag/graphrag/prompt_tune/generator/entity_relationship.py
similarity index 100%
rename from graphrag/prompt_tune/generator/entity_relationship.py
rename to packages/graphrag/graphrag/prompt_tune/generator/entity_relationship.py
diff --git a/graphrag/prompt_tune/generator/entity_summarization_prompt.py b/packages/graphrag/graphrag/prompt_tune/generator/entity_summarization_prompt.py
similarity index 100%
rename from graphrag/prompt_tune/generator/entity_summarization_prompt.py
rename to packages/graphrag/graphrag/prompt_tune/generator/entity_summarization_prompt.py
diff --git a/graphrag/prompt_tune/generator/entity_types.py b/packages/graphrag/graphrag/prompt_tune/generator/entity_types.py
similarity index 100%
rename from graphrag/prompt_tune/generator/entity_types.py
rename to packages/graphrag/graphrag/prompt_tune/generator/entity_types.py
diff --git a/graphrag/prompt_tune/generator/extract_graph_prompt.py b/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py
similarity index 100%
rename from graphrag/prompt_tune/generator/extract_graph_prompt.py
rename to packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py
diff --git a/graphrag/prompt_tune/generator/language.py b/packages/graphrag/graphrag/prompt_tune/generator/language.py
similarity index 100%
rename from graphrag/prompt_tune/generator/language.py
rename to packages/graphrag/graphrag/prompt_tune/generator/language.py
diff --git a/graphrag/prompt_tune/generator/persona.py b/packages/graphrag/graphrag/prompt_tune/generator/persona.py
similarity index 100%
rename from graphrag/prompt_tune/generator/persona.py
rename to packages/graphrag/graphrag/prompt_tune/generator/persona.py
diff --git a/graphrag/prompt_tune/loader/__init__.py b/packages/graphrag/graphrag/prompt_tune/loader/__init__.py
similarity index 100%
rename from graphrag/prompt_tune/loader/__init__.py
rename to packages/graphrag/graphrag/prompt_tune/loader/__init__.py
diff --git a/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py
similarity index 100%
rename from graphrag/prompt_tune/loader/input.py
rename to packages/graphrag/graphrag/prompt_tune/loader/input.py
diff --git a/graphrag/prompt_tune/prompt/__init__.py b/packages/graphrag/graphrag/prompt_tune/prompt/__init__.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/__init__.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/__init__.py
diff --git a/graphrag/prompt_tune/prompt/community_report_rating.py b/packages/graphrag/graphrag/prompt_tune/prompt/community_report_rating.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/community_report_rating.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/community_report_rating.py
diff --git a/graphrag/prompt_tune/prompt/community_reporter_role.py b/packages/graphrag/graphrag/prompt_tune/prompt/community_reporter_role.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/community_reporter_role.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/community_reporter_role.py
diff --git a/graphrag/prompt_tune/prompt/domain.py b/packages/graphrag/graphrag/prompt_tune/prompt/domain.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/domain.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/domain.py
diff --git a/graphrag/prompt_tune/prompt/entity_relationship.py b/packages/graphrag/graphrag/prompt_tune/prompt/entity_relationship.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/entity_relationship.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/entity_relationship.py
diff --git a/graphrag/prompt_tune/prompt/entity_types.py b/packages/graphrag/graphrag/prompt_tune/prompt/entity_types.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/entity_types.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/entity_types.py
diff --git a/graphrag/prompt_tune/prompt/language.py b/packages/graphrag/graphrag/prompt_tune/prompt/language.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/language.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/language.py
diff --git a/graphrag/prompt_tune/prompt/persona.py b/packages/graphrag/graphrag/prompt_tune/prompt/persona.py
similarity index 100%
rename from graphrag/prompt_tune/prompt/persona.py
rename to packages/graphrag/graphrag/prompt_tune/prompt/persona.py
diff --git a/graphrag/prompt_tune/template/__init__.py b/packages/graphrag/graphrag/prompt_tune/template/__init__.py
similarity index 100%
rename from graphrag/prompt_tune/template/__init__.py
rename to packages/graphrag/graphrag/prompt_tune/template/__init__.py
diff --git a/graphrag/prompt_tune/template/community_report_summarization.py b/packages/graphrag/graphrag/prompt_tune/template/community_report_summarization.py
similarity index 100%
rename from graphrag/prompt_tune/template/community_report_summarization.py
rename to packages/graphrag/graphrag/prompt_tune/template/community_report_summarization.py
diff --git a/graphrag/prompt_tune/template/entity_summarization.py b/packages/graphrag/graphrag/prompt_tune/template/entity_summarization.py
similarity index 100%
rename from graphrag/prompt_tune/template/entity_summarization.py
rename to packages/graphrag/graphrag/prompt_tune/template/entity_summarization.py
diff --git a/graphrag/prompt_tune/template/extract_graph.py b/packages/graphrag/graphrag/prompt_tune/template/extract_graph.py
similarity index 100%
rename from graphrag/prompt_tune/template/extract_graph.py
rename to packages/graphrag/graphrag/prompt_tune/template/extract_graph.py
diff --git a/graphrag/prompt_tune/types.py b/packages/graphrag/graphrag/prompt_tune/types.py
similarity index 100%
rename from graphrag/prompt_tune/types.py
rename to packages/graphrag/graphrag/prompt_tune/types.py
diff --git a/graphrag/prompts/__init__.py b/packages/graphrag/graphrag/prompts/__init__.py
similarity index 100%
rename from graphrag/prompts/__init__.py
rename to packages/graphrag/graphrag/prompts/__init__.py
diff --git a/graphrag/prompts/index/__init__.py b/packages/graphrag/graphrag/prompts/index/__init__.py
similarity index 100%
rename from graphrag/prompts/index/__init__.py
rename to packages/graphrag/graphrag/prompts/index/__init__.py
diff --git a/graphrag/prompts/index/community_report.py b/packages/graphrag/graphrag/prompts/index/community_report.py
similarity index 100%
rename from graphrag/prompts/index/community_report.py
rename to packages/graphrag/graphrag/prompts/index/community_report.py
diff --git a/graphrag/prompts/index/community_report_text_units.py b/packages/graphrag/graphrag/prompts/index/community_report_text_units.py
similarity index 100%
rename from graphrag/prompts/index/community_report_text_units.py
rename to packages/graphrag/graphrag/prompts/index/community_report_text_units.py
diff --git a/graphrag/prompts/index/extract_claims.py b/packages/graphrag/graphrag/prompts/index/extract_claims.py
similarity index 100%
rename from graphrag/prompts/index/extract_claims.py
rename to packages/graphrag/graphrag/prompts/index/extract_claims.py
diff --git a/graphrag/prompts/index/extract_graph.py b/packages/graphrag/graphrag/prompts/index/extract_graph.py
similarity index 100%
rename from graphrag/prompts/index/extract_graph.py
rename to packages/graphrag/graphrag/prompts/index/extract_graph.py
diff --git a/graphrag/prompts/index/summarize_descriptions.py b/packages/graphrag/graphrag/prompts/index/summarize_descriptions.py
similarity index 100%
rename from graphrag/prompts/index/summarize_descriptions.py
rename to packages/graphrag/graphrag/prompts/index/summarize_descriptions.py
diff --git a/graphrag/prompts/query/__init__.py b/packages/graphrag/graphrag/prompts/query/__init__.py
similarity index 100%
rename from graphrag/prompts/query/__init__.py
rename to packages/graphrag/graphrag/prompts/query/__init__.py
diff --git a/graphrag/prompts/query/basic_search_system_prompt.py b/packages/graphrag/graphrag/prompts/query/basic_search_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/basic_search_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/basic_search_system_prompt.py
diff --git a/graphrag/prompts/query/drift_search_system_prompt.py b/packages/graphrag/graphrag/prompts/query/drift_search_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/drift_search_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/drift_search_system_prompt.py
diff --git a/graphrag/prompts/query/global_search_knowledge_system_prompt.py b/packages/graphrag/graphrag/prompts/query/global_search_knowledge_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/global_search_knowledge_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/global_search_knowledge_system_prompt.py
diff --git a/graphrag/prompts/query/global_search_map_system_prompt.py b/packages/graphrag/graphrag/prompts/query/global_search_map_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/global_search_map_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/global_search_map_system_prompt.py
diff --git a/graphrag/prompts/query/global_search_reduce_system_prompt.py b/packages/graphrag/graphrag/prompts/query/global_search_reduce_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/global_search_reduce_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/global_search_reduce_system_prompt.py
diff --git a/graphrag/prompts/query/local_search_system_prompt.py b/packages/graphrag/graphrag/prompts/query/local_search_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/local_search_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/local_search_system_prompt.py
diff --git a/graphrag/prompts/query/question_gen_system_prompt.py b/packages/graphrag/graphrag/prompts/query/question_gen_system_prompt.py
similarity index 100%
rename from graphrag/prompts/query/question_gen_system_prompt.py
rename to packages/graphrag/graphrag/prompts/query/question_gen_system_prompt.py
diff --git a/graphrag/py.typed b/packages/graphrag/graphrag/py.typed
similarity index 100%
rename from graphrag/py.typed
rename to packages/graphrag/graphrag/py.typed
diff --git a/graphrag/query/__init__.py b/packages/graphrag/graphrag/query/__init__.py
similarity index 100%
rename from graphrag/query/__init__.py
rename to packages/graphrag/graphrag/query/__init__.py
diff --git a/graphrag/query/context_builder/__init__.py b/packages/graphrag/graphrag/query/context_builder/__init__.py
similarity index 100%
rename from graphrag/query/context_builder/__init__.py
rename to packages/graphrag/graphrag/query/context_builder/__init__.py
diff --git a/graphrag/query/context_builder/builders.py b/packages/graphrag/graphrag/query/context_builder/builders.py
similarity index 100%
rename from graphrag/query/context_builder/builders.py
rename to packages/graphrag/graphrag/query/context_builder/builders.py
diff --git a/graphrag/query/context_builder/community_context.py b/packages/graphrag/graphrag/query/context_builder/community_context.py
similarity index 100%
rename from graphrag/query/context_builder/community_context.py
rename to packages/graphrag/graphrag/query/context_builder/community_context.py
diff --git a/graphrag/query/context_builder/conversation_history.py b/packages/graphrag/graphrag/query/context_builder/conversation_history.py
similarity index 100%
rename from graphrag/query/context_builder/conversation_history.py
rename to packages/graphrag/graphrag/query/context_builder/conversation_history.py
diff --git a/graphrag/query/context_builder/dynamic_community_selection.py b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py
similarity index 100%
rename from graphrag/query/context_builder/dynamic_community_selection.py
rename to packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py
diff --git a/graphrag/query/context_builder/entity_extraction.py b/packages/graphrag/graphrag/query/context_builder/entity_extraction.py
similarity index 100%
rename from graphrag/query/context_builder/entity_extraction.py
rename to packages/graphrag/graphrag/query/context_builder/entity_extraction.py
diff --git a/graphrag/query/context_builder/local_context.py b/packages/graphrag/graphrag/query/context_builder/local_context.py
similarity index 100%
rename from graphrag/query/context_builder/local_context.py
rename to packages/graphrag/graphrag/query/context_builder/local_context.py
diff --git a/graphrag/query/context_builder/rate_prompt.py b/packages/graphrag/graphrag/query/context_builder/rate_prompt.py
similarity index 100%
rename from graphrag/query/context_builder/rate_prompt.py
rename to packages/graphrag/graphrag/query/context_builder/rate_prompt.py
diff --git a/graphrag/query/context_builder/rate_relevancy.py b/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py
similarity index 100%
rename from graphrag/query/context_builder/rate_relevancy.py
rename to packages/graphrag/graphrag/query/context_builder/rate_relevancy.py
diff --git a/graphrag/query/context_builder/source_context.py b/packages/graphrag/graphrag/query/context_builder/source_context.py
similarity index 100%
rename from graphrag/query/context_builder/source_context.py
rename to packages/graphrag/graphrag/query/context_builder/source_context.py
diff --git a/graphrag/query/factory.py b/packages/graphrag/graphrag/query/factory.py
similarity index 100%
rename from graphrag/query/factory.py
rename to packages/graphrag/graphrag/query/factory.py
diff --git a/graphrag/query/indexer_adapters.py b/packages/graphrag/graphrag/query/indexer_adapters.py
similarity index 100%
rename from graphrag/query/indexer_adapters.py
rename to packages/graphrag/graphrag/query/indexer_adapters.py
diff --git a/graphrag/query/input/__init__.py b/packages/graphrag/graphrag/query/input/__init__.py
similarity index 100%
rename from graphrag/query/input/__init__.py
rename to packages/graphrag/graphrag/query/input/__init__.py
diff --git a/graphrag/query/input/loaders/__init__.py b/packages/graphrag/graphrag/query/input/loaders/__init__.py
similarity index 100%
rename from graphrag/query/input/loaders/__init__.py
rename to packages/graphrag/graphrag/query/input/loaders/__init__.py
diff --git a/graphrag/query/input/loaders/dfs.py b/packages/graphrag/graphrag/query/input/loaders/dfs.py
similarity index 100%
rename from graphrag/query/input/loaders/dfs.py
rename to packages/graphrag/graphrag/query/input/loaders/dfs.py
diff --git a/graphrag/query/input/loaders/utils.py b/packages/graphrag/graphrag/query/input/loaders/utils.py
similarity index 100%
rename from graphrag/query/input/loaders/utils.py
rename to packages/graphrag/graphrag/query/input/loaders/utils.py
diff --git a/graphrag/query/input/retrieval/__init__.py b/packages/graphrag/graphrag/query/input/retrieval/__init__.py
similarity index 100%
rename from graphrag/query/input/retrieval/__init__.py
rename to packages/graphrag/graphrag/query/input/retrieval/__init__.py
diff --git a/graphrag/query/input/retrieval/community_reports.py b/packages/graphrag/graphrag/query/input/retrieval/community_reports.py
similarity index 100%
rename from graphrag/query/input/retrieval/community_reports.py
rename to packages/graphrag/graphrag/query/input/retrieval/community_reports.py
diff --git a/graphrag/query/input/retrieval/covariates.py b/packages/graphrag/graphrag/query/input/retrieval/covariates.py
similarity index 100%
rename from graphrag/query/input/retrieval/covariates.py
rename to packages/graphrag/graphrag/query/input/retrieval/covariates.py
diff --git a/graphrag/query/input/retrieval/entities.py b/packages/graphrag/graphrag/query/input/retrieval/entities.py
similarity index 100%
rename from graphrag/query/input/retrieval/entities.py
rename to packages/graphrag/graphrag/query/input/retrieval/entities.py
diff --git a/graphrag/query/input/retrieval/relationships.py b/packages/graphrag/graphrag/query/input/retrieval/relationships.py
similarity index 100%
rename from graphrag/query/input/retrieval/relationships.py
rename to packages/graphrag/graphrag/query/input/retrieval/relationships.py
diff --git a/graphrag/query/input/retrieval/text_units.py b/packages/graphrag/graphrag/query/input/retrieval/text_units.py
similarity index 100%
rename from graphrag/query/input/retrieval/text_units.py
rename to packages/graphrag/graphrag/query/input/retrieval/text_units.py
diff --git a/graphrag/query/llm/__init__.py b/packages/graphrag/graphrag/query/llm/__init__.py
similarity index 100%
rename from graphrag/query/llm/__init__.py
rename to packages/graphrag/graphrag/query/llm/__init__.py
diff --git a/graphrag/query/llm/text_utils.py b/packages/graphrag/graphrag/query/llm/text_utils.py
similarity index 100%
rename from graphrag/query/llm/text_utils.py
rename to packages/graphrag/graphrag/query/llm/text_utils.py
diff --git a/graphrag/query/question_gen/__init__.py b/packages/graphrag/graphrag/query/question_gen/__init__.py
similarity index 100%
rename from graphrag/query/question_gen/__init__.py
rename to packages/graphrag/graphrag/query/question_gen/__init__.py
diff --git a/graphrag/query/question_gen/base.py b/packages/graphrag/graphrag/query/question_gen/base.py
similarity index 100%
rename from graphrag/query/question_gen/base.py
rename to packages/graphrag/graphrag/query/question_gen/base.py
diff --git a/graphrag/query/question_gen/local_gen.py b/packages/graphrag/graphrag/query/question_gen/local_gen.py
similarity index 100%
rename from graphrag/query/question_gen/local_gen.py
rename to packages/graphrag/graphrag/query/question_gen/local_gen.py
diff --git a/graphrag/query/structured_search/__init__.py b/packages/graphrag/graphrag/query/structured_search/__init__.py
similarity index 100%
rename from graphrag/query/structured_search/__init__.py
rename to packages/graphrag/graphrag/query/structured_search/__init__.py
diff --git a/graphrag/query/structured_search/base.py b/packages/graphrag/graphrag/query/structured_search/base.py
similarity index 100%
rename from graphrag/query/structured_search/base.py
rename to packages/graphrag/graphrag/query/structured_search/base.py
diff --git a/graphrag/query/structured_search/basic_search/__init__.py b/packages/graphrag/graphrag/query/structured_search/basic_search/__init__.py
similarity index 100%
rename from graphrag/query/structured_search/basic_search/__init__.py
rename to packages/graphrag/graphrag/query/structured_search/basic_search/__init__.py
diff --git a/graphrag/query/structured_search/basic_search/basic_context.py b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py
similarity index 100%
rename from graphrag/query/structured_search/basic_search/basic_context.py
rename to packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py
diff --git a/graphrag/query/structured_search/basic_search/search.py b/packages/graphrag/graphrag/query/structured_search/basic_search/search.py
similarity index 100%
rename from graphrag/query/structured_search/basic_search/search.py
rename to packages/graphrag/graphrag/query/structured_search/basic_search/search.py
diff --git a/graphrag/query/structured_search/drift_search/__init__.py b/packages/graphrag/graphrag/query/structured_search/drift_search/__init__.py
similarity index 100%
rename from graphrag/query/structured_search/drift_search/__init__.py
rename to packages/graphrag/graphrag/query/structured_search/drift_search/__init__.py
diff --git a/graphrag/query/structured_search/drift_search/action.py b/packages/graphrag/graphrag/query/structured_search/drift_search/action.py
similarity index 100%
rename from graphrag/query/structured_search/drift_search/action.py
rename to packages/graphrag/graphrag/query/structured_search/drift_search/action.py
diff --git a/graphrag/query/structured_search/drift_search/drift_context.py b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
similarity index 100%
rename from graphrag/query/structured_search/drift_search/drift_context.py
rename to packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
diff --git a/graphrag/query/structured_search/drift_search/primer.py b/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py
similarity index 100%
rename from graphrag/query/structured_search/drift_search/primer.py
rename to packages/graphrag/graphrag/query/structured_search/drift_search/primer.py
diff --git a/graphrag/query/structured_search/drift_search/search.py b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py
similarity index 100%
rename from graphrag/query/structured_search/drift_search/search.py
rename to packages/graphrag/graphrag/query/structured_search/drift_search/search.py
diff --git a/graphrag/query/structured_search/drift_search/state.py b/packages/graphrag/graphrag/query/structured_search/drift_search/state.py
similarity index 100%
rename from graphrag/query/structured_search/drift_search/state.py
rename to packages/graphrag/graphrag/query/structured_search/drift_search/state.py
diff --git a/graphrag/query/structured_search/global_search/__init__.py b/packages/graphrag/graphrag/query/structured_search/global_search/__init__.py
similarity index 100%
rename from graphrag/query/structured_search/global_search/__init__.py
rename to packages/graphrag/graphrag/query/structured_search/global_search/__init__.py
diff --git a/graphrag/query/structured_search/global_search/community_context.py b/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py
similarity index 100%
rename from graphrag/query/structured_search/global_search/community_context.py
rename to packages/graphrag/graphrag/query/structured_search/global_search/community_context.py
diff --git a/graphrag/query/structured_search/global_search/search.py b/packages/graphrag/graphrag/query/structured_search/global_search/search.py
similarity index 100%
rename from graphrag/query/structured_search/global_search/search.py
rename to packages/graphrag/graphrag/query/structured_search/global_search/search.py
diff --git a/graphrag/query/structured_search/local_search/__init__.py b/packages/graphrag/graphrag/query/structured_search/local_search/__init__.py
similarity index 100%
rename from graphrag/query/structured_search/local_search/__init__.py
rename to packages/graphrag/graphrag/query/structured_search/local_search/__init__.py
diff --git a/graphrag/query/structured_search/local_search/mixed_context.py b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py
similarity index 100%
rename from graphrag/query/structured_search/local_search/mixed_context.py
rename to packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py
diff --git a/graphrag/query/structured_search/local_search/search.py b/packages/graphrag/graphrag/query/structured_search/local_search/search.py
similarity index 100%
rename from graphrag/query/structured_search/local_search/search.py
rename to packages/graphrag/graphrag/query/structured_search/local_search/search.py
diff --git a/graphrag/storage/__init__.py b/packages/graphrag/graphrag/storage/__init__.py
similarity index 100%
rename from graphrag/storage/__init__.py
rename to packages/graphrag/graphrag/storage/__init__.py
diff --git a/graphrag/storage/blob_pipeline_storage.py b/packages/graphrag/graphrag/storage/blob_pipeline_storage.py
similarity index 100%
rename from graphrag/storage/blob_pipeline_storage.py
rename to packages/graphrag/graphrag/storage/blob_pipeline_storage.py
diff --git a/graphrag/storage/cosmosdb_pipeline_storage.py b/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
similarity index 100%
rename from graphrag/storage/cosmosdb_pipeline_storage.py
rename to packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
diff --git a/graphrag/storage/factory.py b/packages/graphrag/graphrag/storage/factory.py
similarity index 96%
rename from graphrag/storage/factory.py
rename to packages/graphrag/graphrag/storage/factory.py
index d2dfa6645c..738a46420b 100644
--- a/graphrag/storage/factory.py
+++ b/packages/graphrag/graphrag/storage/factory.py
@@ -5,8 +5,9 @@
 
 from __future__ import annotations
 
+from graphrag_common.factory import Factory
+
 from graphrag.config.enums import StorageType
-from graphrag.factory.factory import Factory
 from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
 from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
 from graphrag.storage.file_pipeline_storage import FilePipelineStorage
diff --git a/graphrag/storage/file_pipeline_storage.py b/packages/graphrag/graphrag/storage/file_pipeline_storage.py
similarity index 100%
rename from graphrag/storage/file_pipeline_storage.py
rename to packages/graphrag/graphrag/storage/file_pipeline_storage.py
diff --git a/graphrag/storage/memory_pipeline_storage.py b/packages/graphrag/graphrag/storage/memory_pipeline_storage.py
similarity index 100%
rename from graphrag/storage/memory_pipeline_storage.py
rename to packages/graphrag/graphrag/storage/memory_pipeline_storage.py
diff --git a/graphrag/storage/pipeline_storage.py b/packages/graphrag/graphrag/storage/pipeline_storage.py
similarity index 100%
rename from graphrag/storage/pipeline_storage.py
rename to packages/graphrag/graphrag/storage/pipeline_storage.py
diff --git a/graphrag/tokenizer/__init__.py b/packages/graphrag/graphrag/tokenizer/__init__.py
similarity index 100%
rename from graphrag/tokenizer/__init__.py
rename to packages/graphrag/graphrag/tokenizer/__init__.py
diff --git a/graphrag/tokenizer/get_tokenizer.py b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py
similarity index 100%
rename from graphrag/tokenizer/get_tokenizer.py
rename to packages/graphrag/graphrag/tokenizer/get_tokenizer.py
diff --git a/graphrag/tokenizer/litellm_tokenizer.py b/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py
similarity index 100%
rename from graphrag/tokenizer/litellm_tokenizer.py
rename to packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py
diff --git a/graphrag/tokenizer/tiktoken_tokenizer.py b/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py
similarity index 100%
rename from graphrag/tokenizer/tiktoken_tokenizer.py
rename to packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py
diff --git a/graphrag/tokenizer/tokenizer.py b/packages/graphrag/graphrag/tokenizer/tokenizer.py
similarity index 100%
rename from graphrag/tokenizer/tokenizer.py
rename to packages/graphrag/graphrag/tokenizer/tokenizer.py
diff --git a/graphrag/utils/__init__.py b/packages/graphrag/graphrag/utils/__init__.py
similarity index 100%
rename from graphrag/utils/__init__.py
rename to packages/graphrag/graphrag/utils/__init__.py
diff --git a/graphrag/utils/api.py b/packages/graphrag/graphrag/utils/api.py
similarity index 97%
rename from graphrag/utils/api.py
rename to packages/graphrag/graphrag/utils/api.py
index db4a90a3a5..a972d21db9 100644
--- a/graphrag/utils/api.py
+++ b/packages/graphrag/graphrag/utils/api.py
@@ -26,9 +26,7 @@ def get_embedding_store(
 ) -> BaseVectorStore:
     """Get the embedding description store."""
     vector_store_type = store["type"]
-    index_name = create_index_name(
-        store.get("container_name", "default"), embedding_name
-    )
+    index_name = create_index_name(store.get("index_prefix", ""), embedding_name)
 
     embeddings_schema: dict[str, VectorStoreSchemaConfig] = store.get(
         "embeddings_schema", {}
diff --git a/graphrag/utils/cli.py b/packages/graphrag/graphrag/utils/cli.py
similarity index 100%
rename from graphrag/utils/cli.py
rename to packages/graphrag/graphrag/utils/cli.py
diff --git a/graphrag/utils/storage.py b/packages/graphrag/graphrag/utils/storage.py
similarity index 100%
rename from graphrag/utils/storage.py
rename to packages/graphrag/graphrag/utils/storage.py
diff --git a/graphrag/vector_stores/__init__.py b/packages/graphrag/graphrag/vector_stores/__init__.py
similarity index 100%
rename from graphrag/vector_stores/__init__.py
rename to packages/graphrag/graphrag/vector_stores/__init__.py
diff --git a/graphrag/vector_stores/azure_ai_search.py b/packages/graphrag/graphrag/vector_stores/azure_ai_search.py
similarity index 100%
rename from graphrag/vector_stores/azure_ai_search.py
rename to packages/graphrag/graphrag/vector_stores/azure_ai_search.py
diff --git a/graphrag/vector_stores/base.py b/packages/graphrag/graphrag/vector_stores/base.py
similarity index 100%
rename from graphrag/vector_stores/base.py
rename to packages/graphrag/graphrag/vector_stores/base.py
diff --git a/graphrag/vector_stores/cosmosdb.py b/packages/graphrag/graphrag/vector_stores/cosmosdb.py
similarity index 100%
rename from graphrag/vector_stores/cosmosdb.py
rename to packages/graphrag/graphrag/vector_stores/cosmosdb.py
diff --git a/graphrag/vector_stores/factory.py b/packages/graphrag/graphrag/vector_stores/factory.py
similarity index 96%
rename from graphrag/vector_stores/factory.py
rename to packages/graphrag/graphrag/vector_stores/factory.py
index 90b004cb52..00a451771c 100644
--- a/graphrag/vector_stores/factory.py
+++ b/packages/graphrag/graphrag/vector_stores/factory.py
@@ -5,8 +5,9 @@
 
 from __future__ import annotations
 
+from graphrag_common.factory import Factory
+
 from graphrag.config.enums import VectorStoreType
-from graphrag.factory.factory import Factory
 from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
 from graphrag.vector_stores.base import BaseVectorStore
 from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
diff --git a/graphrag/vector_stores/lancedb.py b/packages/graphrag/graphrag/vector_stores/lancedb.py
similarity index 100%
rename from graphrag/vector_stores/lancedb.py
rename to packages/graphrag/graphrag/vector_stores/lancedb.py
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
new file mode 100644
index 0000000000..a3167a8045
--- /dev/null
+++ b/packages/graphrag/pyproject.toml
@@ -0,0 +1,74 @@
+[project]
+name = "graphrag"
+# Maintainers: do not change the version here manually
+version = "2.7.0"
+description = "GraphRAG: A graph-based retrieval-augmented generation (RAG) system."
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = "MIT"
+readme = "README.md"
+license-files = ["LICENSE"]
+requires-python = ">=3.10,<3.13"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+
+dependencies = [
+    "aiofiles>=24.1.0",
+    "azure-cosmos>=4.9.0",
+    "azure-identity>=1.19.0",
+    "azure-search-documents>=11.5.2",
+    "azure-storage-blob>=12.24.0",
+    "devtools>=0.12.2",
+    "environs>=11.0.0",
+    "graphrag-common==2.7.0",
+    "graspologic-native>=1.2.5",
+    "json-repair>=0.30.3",
+    "lancedb>=0.17.0",
+    "litellm>=1.77.1",
+    "networkx>=3.4.2",
+    "nltk==3.9.1",
+    "numpy>=1.25.2",
+    "openai>=1.68.0",
+    "pandas>=2.2.3",
+    "pyarrow>=17.0.0",
+    "pydantic>=2.10.3",
+    "python-dotenv>=1.0.1",
+    "pyyaml>=6.0.2",
+    "spacy>=3.8.4",
+    "textblob>=0.18.0.post0",
+    "tiktoken>=0.11.0",
+    "tqdm>=4.67.1",
+    "typing-extensions>=4.12.2",
+    "typer>=0.16.0",
+]
+
+[project.scripts]
+graphrag = "graphrag.cli.main:app"
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
+
diff --git a/pyproject.toml b/pyproject.toml
index 48342e06e7..199fd6a514 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
-name = "graphrag"
-# Maintainers: do not change the version here manually, use ./scripts/release.sh
-version = "2.7.0"
+name = "graphrag-monorepo"
+classifiers = ["Private :: Do Not Upload"]
+version = "0.0.0"
 description = "GraphRAG: A graph-based retrieval-augmented generation (RAG) system."
 authors = [
     {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
@@ -21,44 +21,7 @@ authors = [
     {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
     {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
 ]
-license = "MIT"
-readme = "README.md"
 requires-python = ">=3.10,<3.13"
-classifiers = [
-    "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-]
-
-dependencies = [
-    "aiofiles>=24.1.0",
-    "azure-cosmos>=4.9.0",
-    "azure-identity>=1.19.0",
-    "azure-search-documents>=11.5.2",
-    "azure-storage-blob>=12.24.0",
-    "devtools>=0.12.2",
-    "environs>=11.0.0",
-    "graspologic-native>=1.2.5",
-    "json-repair>=0.30.3",
-    "lancedb>=0.17.0",
-    "litellm>=1.77.1",
-    "networkx>=3.4.2",
-    "nltk==3.9.1",
-    "numpy>=1.25.2",
-    "openai>=1.68.0",
-    "pandas>=2.2.3",
-    "pyarrow>=17.0.0",
-    "pydantic>=2.10.3",
-    "python-dotenv>=1.0.1",
-    "pyyaml>=6.0.2",
-    "spacy>=3.8.4",
-    "textblob>=0.18.0.post0",
-    "tiktoken>=0.11.0",
-    "tqdm>=4.67.1",
-    "typing-extensions>=4.12.2",
-    "typer>=0.16.0",
-]
 
 [dependency-groups]
 dev = [
@@ -83,19 +46,14 @@ dev = [
     "update-toml>=0.2.1",
 ]
 
-[project.scripts]
-graphrag = "graphrag.cli.main:app"
+[tool.uv]
+package = false
 
-[project.urls]
-Source = "https://github.com/microsoft/graphrag"
+[tool.uv.workspace]
+members = ["packages/*"]
 
-[build-system]
-requires = ["setuptools>=64", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[tool.setuptools.packages.find]
-include = ["graphrag*"]
-exclude = ["examples_notebooks*", "tests*"]
+[tool.uv.sources]
+graphrag-common = { workspace = true }
 
 # Keep poethepoet for task management to minimize changes
 [tool.poe.tasks]
@@ -105,9 +63,13 @@ _ruff_check = 'ruff check .'
 _pyright = "pyright"
 _convert_local_search_nb = 'jupyter nbconvert --output-dir=docsite/posts/query/notebooks/ --output="{notebook_name}_nb" --template=docsite/nbdocsite_template --to markdown examples_notebooks/local_search.ipynb'
 _convert_global_search_nb = 'jupyter nbconvert --output-dir=docsite/posts/query/notebooks/ --output="{notebook_name}_nb" --template=docsite/nbdocsite_template --to markdown examples_notebooks/global_search.ipynb'
+_copy_build_assets = "python -m scripts.copy_build_assets"
 _semversioner_release = "semversioner release"
 _semversioner_changelog = "semversioner changelog > CHANGELOG.md"
-_semversioner_update_toml_version = "update-toml update --path project.version --value $(uv run semversioner current-version)"
+# Add more update toml tasks as packages are added
+_semversioner_update_graphrag_toml_version = "update-toml update --file packages/graphrag/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_graphrag_common_toml_version = "update-toml update --file packages/graphrag-common/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_workspace_dependency_versions = "python -m scripts.update_workspace_dependency_versions"
 semversioner_add = "semversioner add-change"
 coverage_report = 'coverage report --omit "**/tests/**" --show-missing'
 check_format = 'ruff format . --check'
@@ -128,12 +90,21 @@ prompt_tune = "python -m graphrag prompt-tune"
 test_only = "pytest -s -k"
 serve_docs = "mkdocs serve"
 build_docs = "mkdocs build"
+_build_packages = "uv build --all-packages"
+_sync = "uv sync"
+
+[[tool.poe.tasks.build]]
+sequence = ['_copy_build_assets', '_build_packages']
 
 [[tool.poe.tasks.release]]
 sequence = [
     '_semversioner_release',
     '_semversioner_changelog',
-    '_semversioner_update_toml_version',
+    # Add more update toml tasks as packages are added
+    '_semversioner_update_graphrag_toml_version',
+    '_semversioner_update_graphrag_common_toml_version',
+    '_semversioner_update_workspace_dependency_versions',
+    '_sync',
 ]
 ignore_fail = 'return_non_zero'
 
@@ -246,7 +217,7 @@ convention = "numpy"
 
 # https://github.com/microsoft/pyright/blob/9f81564a4685ff5c55edd3959f9b39030f590b2f/docs/configuration.md#sample-pyprojecttoml-file
 [tool.pyright]
-include = ["graphrag", "tests"]
+include = ["packages/graphrag/graphrag", "packages/graphrag-common/graphrag_common", "tests"]
 exclude = ["**/node_modules", "**/__pycache__"]
 
 [tool.pytest.ini_options]
diff --git a/graphrag/factory/__init__.py b/scripts/__init__.py
similarity index 70%
rename from graphrag/factory/__init__.py
rename to scripts/__init__.py
index 55bd738251..4adce34aa7 100644
--- a/graphrag/factory/__init__.py
+++ b/scripts/__init__.py
@@ -1,4 +1,5 @@
 # Copyright (c) 2025 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""Factory module."""
+
+"""GraphRAG Scripts module."""
diff --git a/scripts/copy_build_assets.py b/scripts/copy_build_assets.py
new file mode 100644
index 0000000000..d1ac1d90e1
--- /dev/null
+++ b/scripts/copy_build_assets.py
@@ -0,0 +1,25 @@
+# Copyright (c) 2025 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Copy root build assets to package directories."""
+
+import shutil
+from pathlib import Path
+
+
+def copy_build_assets():
+    """Copy root build assets to package build directories so files are included in pypi distributions."""
+    root_dir = Path(__file__).parent.parent
+    build_assets = ["LICENSE"]
+
+    for package_dir in root_dir.glob("packages/*"):
+        if package_dir.is_dir():
+            for asset in build_assets:
+                src = root_dir / asset
+                dest = package_dir / asset
+                if src.exists():
+                    shutil.copy(src, dest)
+
+
+if __name__ == "__main__":
+    copy_build_assets()
diff --git a/scripts/update_workspace_dependency_versions.py b/scripts/update_workspace_dependency_versions.py
new file mode 100644
index 0000000000..199cf01173
--- /dev/null
+++ b/scripts/update_workspace_dependency_versions.py
@@ -0,0 +1,58 @@
+# Copyright (c) 2025 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Update workspace dependency versions."""
+
+import os
+import re
+import subprocess  # noqa: S404
+from pathlib import Path
+
+
+def _get_version() -> str:
+    command = ["uv", "run", "semversioner", "current-version"]
+    completion = subprocess.run(command, env=os.environ, capture_output=True, text=True)  # noqa: S603
+    if completion.returncode != 0:
+        msg = f"Failed to get current version with return code: {completion.returncode}"
+        raise RuntimeError(msg)
+    return completion.stdout.strip()
+
+
+def _get_package_paths() -> list[Path]:
+    root_dir = Path(__file__).parent.parent
+    return [p.resolve() for p in root_dir.glob("packages/*") if p.is_dir()]
+
+
+def update_workspace_dependency_versions():
+    """Update dependency versions across workspace packages.
+
+    Iterate through all the workspace packages and update cross-package
+    dependency versions to match the current version of the workspace.
+    """
+    version = _get_version()
+    package_paths = _get_package_paths()
+    for package_path in package_paths:
+        current_package_name = package_path.name
+        toml_path = package_path / "pyproject.toml"
+        if not toml_path.exists() or not toml_path.is_file():
+            continue
+        toml_contents = toml_path.read_text(encoding="utf-8")
+
+        for other_package_path in package_paths:
+            other_package_name = other_package_path.name
+            if other_package_name == current_package_name:
+                continue
+            dep_pattern = rf"{other_package_name}\s*==\s*\d+\.\d+\.\d+"
+
+            if re.search(dep_pattern, toml_contents):
+                toml_contents = re.sub(
+                    dep_pattern,
+                    f"{other_package_name}=={version}",
+                    toml_contents,
+                )
+
+        toml_path.write_text(toml_contents, encoding="utf-8", newline="\n")
+
+
+if __name__ == "__main__":
+    update_workspace_dependency_versions()
diff --git a/tests/__init__.py b/tests/__init__.py
index e5c93e3503..5f525950fb 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -7,6 +7,7 @@
 # Register MOCK providers
 from graphrag.config.enums import ModelType
 from graphrag.language_model.factory import ChatModelFactory, EmbeddingModelFactory
+
 from tests.mock_provider import MockChatLLM, MockEmbeddingLLM
 
 ChatModelFactory().register(ModelType.MockChat, lambda **kwargs: MockChatLLM(**kwargs))
diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml
index 88f23ad061..82066e753c 100644
--- a/tests/fixtures/min-csv/settings.yml
+++ b/tests/fixtures/min-csv/settings.yml
@@ -8,6 +8,7 @@ models:
     api_version: "2025-04-01-preview"
     model: gpt-4.1
     retry_strategy: exponential_backoff
+    rate_limit_strategy: static
     tokens_per_minute: 250_000
     requests_per_minute: 250
     model_supports_json: true
@@ -21,6 +22,7 @@ models:
     api_version: "2025-04-01-preview"
     model: text-embedding-3-large
     retry_strategy: exponential_backoff
+    rate_limit_strategy: static
     tokens_per_minute: 250_000
     requests_per_minute: 250
     concurrent_requests: 25
diff --git a/tests/fixtures/text/settings.yml b/tests/fixtures/text/settings.yml
index f7602ccb73..24e59b5942 100644
--- a/tests/fixtures/text/settings.yml
+++ b/tests/fixtures/text/settings.yml
@@ -8,6 +8,7 @@ models:
     api_version: "2025-04-01-preview"
     model: gpt-4.1
     retry_strategy: exponential_backoff
+    rate_limit_strategy: static
     tokens_per_minute: 250_000
     requests_per_minute: 250
     model_supports_json: true
@@ -21,6 +22,7 @@ models:
     api_version: "2025-04-01-preview"
     model: text-embedding-3-large
     retry_strategy: exponential_backoff
+    rate_limit_strategy: static
     tokens_per_minute: 250_000
     requests_per_minute: 250
     concurrent_requests: 25
diff --git a/tests/integration/cache/test_factory.py b/tests/integration/cache/test_factory.py
index c7a7964471..53ec3cba56 100644
--- a/tests/integration/cache/test_factory.py
+++ b/tests/integration/cache/test_factory.py
@@ -8,7 +8,6 @@
 import sys
 
 import pytest
-
 from graphrag.cache.factory import CacheFactory
 from graphrag.cache.json_pipeline_cache import JsonPipelineCache
 from graphrag.cache.memory_pipeline_cache import InMemoryCache
diff --git a/tests/integration/language_model/test_rate_limiter.py b/tests/integration/language_model/test_rate_limiter.py
index ec482b7240..a2e2f882b8 100644
--- a/tests/integration/language_model/test_rate_limiter.py
+++ b/tests/integration/language_model/test_rate_limiter.py
@@ -9,13 +9,13 @@
 from queue import Queue
 
 import pytest
-
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter import (
     RateLimiter,
 )
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter_factory import (
     RateLimiterFactory,
 )
+
 from tests.integration.language_model.utils import (
     assert_max_num_values_per_period,
     assert_stagger,
diff --git a/tests/integration/language_model/test_retries.py b/tests/integration/language_model/test_retries.py
index 617e11a8cb..912eb00e4b 100644
--- a/tests/integration/language_model/test_retries.py
+++ b/tests/integration/language_model/test_retries.py
@@ -6,7 +6,6 @@
 import time
 
 import pytest
-
 from graphrag.language_model.providers.litellm.services.retry.retry_factory import (
     RetryFactory,
 )
diff --git a/tests/integration/logging/test_factory.py b/tests/integration/logging/test_factory.py
index cb22ca8674..3da8f7965b 100644
--- a/tests/integration/logging/test_factory.py
+++ b/tests/integration/logging/test_factory.py
@@ -8,7 +8,6 @@
 import logging
 
 import pytest
-
 from graphrag.config.enums import ReportingType
 from graphrag.logger.blob_workflow_logger import BlobWorkflowLogger
 from graphrag.logger.factory import LoggerFactory
diff --git a/tests/integration/logging/test_standard_logging.py b/tests/integration/logging/test_standard_logging.py
index c0b485b5f2..106e57f9fa 100644
--- a/tests/integration/logging/test_standard_logging.py
+++ b/tests/integration/logging/test_standard_logging.py
@@ -8,6 +8,7 @@
 from pathlib import Path
 
 from graphrag.logger.standard_logging import DEFAULT_LOG_FILENAME, init_loggers
+
 from tests.unit.config.utils import get_default_graphrag_config
 
 
diff --git a/tests/integration/storage/test_cosmosdb_storage.py b/tests/integration/storage/test_cosmosdb_storage.py
index af08972fb3..3d6128872f 100644
--- a/tests/integration/storage/test_cosmosdb_storage.py
+++ b/tests/integration/storage/test_cosmosdb_storage.py
@@ -8,7 +8,6 @@
 from datetime import datetime
 
 import pytest
-
 from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
 
 # cspell:disable-next-line well-known-key
diff --git a/tests/integration/storage/test_factory.py b/tests/integration/storage/test_factory.py
index 8817b25036..5cab7187b8 100644
--- a/tests/integration/storage/test_factory.py
+++ b/tests/integration/storage/test_factory.py
@@ -8,7 +8,6 @@
 import sys
 
 import pytest
-
 from graphrag.config.enums import StorageType
 from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
 from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
diff --git a/tests/integration/vector_stores/test_azure_ai_search.py b/tests/integration/vector_stores/test_azure_ai_search.py
index f5b4bd2e3e..9f35f62fbd 100644
--- a/tests/integration/vector_stores/test_azure_ai_search.py
+++ b/tests/integration/vector_stores/test_azure_ai_search.py
@@ -7,7 +7,6 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
-
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
 from graphrag.vector_stores.base import VectorStoreDocument
diff --git a/tests/integration/vector_stores/test_cosmosdb.py b/tests/integration/vector_stores/test_cosmosdb.py
index 529843c44b..331ef1f297 100644
--- a/tests/integration/vector_stores/test_cosmosdb.py
+++ b/tests/integration/vector_stores/test_cosmosdb.py
@@ -7,7 +7,6 @@
 
 import numpy as np
 import pytest
-
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.vector_stores.base import VectorStoreDocument
 from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
diff --git a/tests/integration/vector_stores/test_factory.py b/tests/integration/vector_stores/test_factory.py
index 69720c9664..a8b797dbb2 100644
--- a/tests/integration/vector_stores/test_factory.py
+++ b/tests/integration/vector_stores/test_factory.py
@@ -6,7 +6,6 @@
 """
 
 import pytest
-
 from graphrag.config.enums import VectorStoreType
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
diff --git a/tests/integration/vector_stores/test_lancedb.py b/tests/integration/vector_stores/test_lancedb.py
index e1a58c2413..628c89f012 100644
--- a/tests/integration/vector_stores/test_lancedb.py
+++ b/tests/integration/vector_stores/test_lancedb.py
@@ -8,7 +8,6 @@
 
 import numpy as np
 import pytest
-
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.vector_stores.base import VectorStoreDocument
 from graphrag.vector_stores.lancedb import LanceDBVectorStore
diff --git a/tests/mock_provider.py b/tests/mock_provider.py
index 2c15ee347f..524222dc8f 100644
--- a/tests/mock_provider.py
+++ b/tests/mock_provider.py
@@ -6,8 +6,6 @@
 from collections.abc import AsyncGenerator, Generator
 from typing import Any
 
-from pydantic import BaseModel
-
 from graphrag.config.enums import ModelType
 from graphrag.config.models.language_model_config import LanguageModelConfig
 from graphrag.language_model.response.base import (
@@ -15,6 +13,7 @@
     BaseModelResponse,
     ModelResponse,
 )
+from pydantic import BaseModel
 
 
 class MockChatLLM:
diff --git a/tests/smoke/test_fixtures.py b/tests/smoke/test_fixtures.py
index e0dd6b7b37..b6966930e7 100644
--- a/tests/smoke/test_fixtures.py
+++ b/tests/smoke/test_fixtures.py
@@ -14,7 +14,6 @@
 
 import pandas as pd
 import pytest
-
 from graphrag.query.context_builder.community_context import (
     NO_COMMUNITY_RECORDS_WARNING,
 )
diff --git a/tests/unit/config/test_config.py b/tests/unit/config/test_config.py
index 0f67f451ec..ddf1d17a5f 100644
--- a/tests/unit/config/test_config.py
+++ b/tests/unit/config/test_config.py
@@ -5,13 +5,13 @@
 from pathlib import Path
 from unittest import mock
 
-import pytest
-from pydantic import ValidationError
-
 import graphrag.config.defaults as defs
+import pytest
 from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.enums import AuthType, ModelType
 from graphrag.config.load_config import load_config
+from pydantic import ValidationError
+
 from tests.unit.config.utils import (
     DEFAULT_EMBEDDING_MODEL_CONFIG,
     DEFAULT_MODEL_CONFIG,
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 9769cf99b2..c7dbb4cd5c 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -3,8 +3,6 @@
 
 from dataclasses import asdict
 
-from pydantic import BaseModel
-
 import graphrag.config.defaults as defs
 from graphrag.config.models.basic_search_config import BasicSearchConfig
 from graphrag.config.models.cache_config import CacheConfig
@@ -32,6 +30,7 @@
     SummarizeDescriptionsConfig,
 )
 from graphrag.config.models.vector_store_config import VectorStoreConfig
+from pydantic import BaseModel
 
 FAKE_API_KEY = "NOT_AN_API_KEY"
 
diff --git a/tests/unit/graphrag_factory/__init__.py b/tests/unit/graphrag_factory/__init__.py
new file mode 100644
index 0000000000..0a3e38adfb
--- /dev/null
+++ b/tests/unit/graphrag_factory/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
diff --git a/tests/unit/graphrag_factory/test_factory.py b/tests/unit/graphrag_factory/test_factory.py
new file mode 100644
index 0000000000..5ddfdd8d1a
--- /dev/null
+++ b/tests/unit/graphrag_factory/test_factory.py
@@ -0,0 +1,66 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Unit tests for graphrag_factory package."""
+
+from abc import ABC, abstractmethod
+
+from graphrag_common.factory import Factory
+
+
+class TestABC(ABC):
+    """Test abstract base class."""
+
+    @abstractmethod
+    def get_value(self) -> str:
+        """
+        Get a string value.
+
+        Returns
+        -------
+            str: A string value.
+        """
+        msg = "Subclasses must implement the get_value method."
+        raise NotImplementedError(msg)
+
+
+class ConcreteTestClass(TestABC):
+    """Concrete implementation of TestABC."""
+
+    def __init__(self, value: str):
+        """Initialize with a string value."""
+        self._value = value
+
+    def get_value(self) -> str:
+        """Get a string value.
+
+        Returns
+        -------
+            str: A string value.
+        """
+        return self._value
+
+
+def test_factory() -> None:
+    """Test the factory behavior."""
+
+    class TestFactory(Factory[TestABC]):
+        """Test factory for TestABC implementations."""
+
+    factory = TestFactory()
+    factory.register("transient_strategy", ConcreteTestClass)
+    factory.register("singleton_strategy", ConcreteTestClass, scope="singleton")
+
+    trans1 = factory.create("transient_strategy", {"value": "test1"})
+    trans2 = factory.create("transient_strategy", {"value": "test2"})
+
+    assert trans1 is not trans2
+    assert trans1.get_value() == "test1"
+    assert trans2.get_value() == "test2"
+
+    single1 = factory.create("singleton_strategy", {"value": "singleton"})
+    single2 = factory.create("singleton_strategy", {"value": "ignored"})
+
+    assert single1 is single2
+    assert single1.get_value() == "singleton"
+    assert single2.get_value() == "singleton"
diff --git a/tests/unit/indexing/graph/utils/test_stable_lcc.py b/tests/unit/indexing/graph/utils/test_stable_lcc.py
index 1bea2a9185..c4e17e54ee 100644
--- a/tests/unit/indexing/graph/utils/test_stable_lcc.py
+++ b/tests/unit/indexing/graph/utils/test_stable_lcc.py
@@ -3,7 +3,6 @@
 import unittest
 
 import networkx as nx
-
 from graphrag.index.utils.stable_lcc import stable_largest_connected_component
 
 
diff --git a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py b/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
index 7b6c54923f..33e5b7ccde 100644
--- a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
+++ b/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
@@ -7,7 +7,6 @@
 
 import pandas as pd
 import pytest
-
 from graphrag.config.enums import ChunkStrategyType
 from graphrag.index.operations.chunk_text.chunk_text import (
     _get_num_total,
diff --git a/tests/unit/indexing/test_init_content.py b/tests/unit/indexing/test_init_content.py
index 58791a905c..cfd59cd9ad 100644
--- a/tests/unit/indexing/test_init_content.py
+++ b/tests/unit/indexing/test_init_content.py
@@ -5,7 +5,6 @@
 from typing import Any, cast
 
 import yaml
-
 from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.init_content import INIT_YAML
 from graphrag.config.models.graph_rag_config import GraphRagConfig
diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/indexing/text_splitting/test_text_splitting.py
index 4ea8b25e5d..eb5e72d649 100644
--- a/tests/unit/indexing/text_splitting/test_text_splitting.py
+++ b/tests/unit/indexing/text_splitting/test_text_splitting.py
@@ -6,7 +6,6 @@
 
 import pytest
 import tiktoken
-
 from graphrag.index.text_splitting.text_splitting import (
     NoopTextSplitter,
     TokenChunkerOptions,
diff --git a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
index 8390bfe8aa..c27d905c70 100644
--- a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
+++ b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
@@ -7,6 +7,7 @@
     Document,
 )
 from graphrag.prompts.index.extract_graph import GRAPH_EXTRACTION_PROMPT
+
 from tests.unit.indexing.verbs.helpers.mock_llm import create_mock_llm
 
 
diff --git a/tests/unit/indexing/verbs/helpers/mock_llm.py b/tests/unit/indexing/verbs/helpers/mock_llm.py
index 161d5d56df..efb89158b3 100644
--- a/tests/unit/indexing/verbs/helpers/mock_llm.py
+++ b/tests/unit/indexing/verbs/helpers/mock_llm.py
@@ -1,9 +1,8 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
-from pydantic import BaseModel
-
 from graphrag.language_model.manager import ModelManager
 from graphrag.language_model.protocol.base import ChatModel
+from pydantic import BaseModel
 
 
 def create_mock_llm(responses: list[str | BaseModel], name: str = "mock") -> ChatModel:
diff --git a/tests/unit/utils/test_embeddings.py b/tests/unit/utils/test_embeddings.py
index 9349f0c813..343ee812b5 100644
--- a/tests/unit/utils/test_embeddings.py
+++ b/tests/unit/utils/test_embeddings.py
@@ -2,7 +2,6 @@
 # Licensed under the MIT License
 
 import pytest
-
 from graphrag.config.embeddings import create_index_name
 
 
diff --git a/tests/verbs/test_extract_covariates.py b/tests/verbs/test_extract_covariates.py
index c9c74f0fa9..d4645a34f9 100644
--- a/tests/verbs/test_extract_covariates.py
+++ b/tests/verbs/test_extract_covariates.py
@@ -1,8 +1,6 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from pandas.testing import assert_series_equal
-
 from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.enums import ModelType
 from graphrag.data_model.schemas import COVARIATES_FINAL_COLUMNS
@@ -10,6 +8,7 @@
     run_workflow,
 )
 from graphrag.utils.storage import load_table_from_storage
+from pandas.testing import assert_series_equal
 
 from .util import (
     DEFAULT_MODEL_CONFIG,
diff --git a/tests/verbs/test_pipeline_state.py b/tests/verbs/test_pipeline_state.py
index cac06e6293..306cd84256 100644
--- a/tests/verbs/test_pipeline_state.py
+++ b/tests/verbs/test_pipeline_state.py
@@ -9,6 +9,7 @@
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.workflows.factory import PipelineFactory
+
 from tests.verbs.util import DEFAULT_MODEL_CONFIG
 
 
diff --git a/tests/verbs/util.py b/tests/verbs/util.py
index 4e7871b0c2..6dc6667210 100644
--- a/tests/verbs/util.py
+++ b/tests/verbs/util.py
@@ -1,13 +1,12 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-import pandas as pd
-from pandas.testing import assert_series_equal
-
 import graphrag.config.defaults as defs
+import pandas as pd
 from graphrag.index.run.utils import create_run_context
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
+from pandas.testing import assert_series_equal
 
 pd.set_option("display.max_columns", None)
 
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index a573b9daa5..dc64e0e77c 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,6 +7,7 @@
 import logging
 from typing import TYPE_CHECKING
 
+import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -17,8 +18,6 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
-import graphrag.api as api
-
 if TYPE_CHECKING:
     import pandas as pd
 
diff --git a/unified-search-app/app/knowledge_loader/data_sources/blob_source.py b/unified-search-app/app/knowledge_loader/data_sources/blob_source.py
index 8f86b8dfa7..f54ed71854 100644
--- a/unified-search-app/app/knowledge_loader/data_sources/blob_source.py
+++ b/unified-search-app/app/knowledge_loader/data_sources/blob_source.py
@@ -13,10 +13,9 @@
 import yaml
 from azure.identity import DefaultAzureCredential
 from azure.storage.blob import BlobServiceClient, ContainerClient
-from knowledge_loader.data_sources.typing import Datasource
-
 from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
+from knowledge_loader.data_sources.typing import Datasource
 
 from .default import blob_account_name, blob_container_name
 
diff --git a/unified-search-app/app/knowledge_loader/data_sources/local_source.py b/unified-search-app/app/knowledge_loader/data_sources/local_source.py
index f68f616858..bd6370ba23 100644
--- a/unified-search-app/app/knowledge_loader/data_sources/local_source.py
+++ b/unified-search-app/app/knowledge_loader/data_sources/local_source.py
@@ -8,10 +8,9 @@
 from pathlib import Path
 
 import pandas as pd
-from knowledge_loader.data_sources.typing import Datasource
-
 from graphrag.config.load_config import load_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
+from knowledge_loader.data_sources.typing import Datasource
 
 logging.basicConfig(level=logging.INFO)
 logging.getLogger("azure").setLevel(logging.WARNING)
diff --git a/unified-search-app/app/knowledge_loader/data_sources/typing.py b/unified-search-app/app/knowledge_loader/data_sources/typing.py
index 95fc2f120b..2f595f00ec 100644
--- a/unified-search-app/app/knowledge_loader/data_sources/typing.py
+++ b/unified-search-app/app/knowledge_loader/data_sources/typing.py
@@ -8,7 +8,6 @@
 from enum import Enum
 
 import pandas as pd
-
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 
 
diff --git a/uv.lock b/uv.lock
index 402e5d6b71..761cd308f0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -7,22 +7,29 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 
+[manifest]
+members = [
+    "graphrag",
+    "graphrag-common",
+    "graphrag-monorepo",
+]
+
 [[package]]
 name = "aiofiles"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
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+    { name = "graphrag-common" },
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 [[package]]
@@ -2481,36 +2502,36 @@ dependencies = [
     { name = "tqdm" },
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+    { url = "https://files.pythonhosted.org/packages/44/c5/c21b562d1680a77634d748e30c653c3ca918beb35555cff24986fff54598/yarl-1.22.0-cp312-cp312-win_arm64.whl", hash = "sha256:ea70f61a47f3cc93bdf8b2f368ed359ef02a01ca6393916bc8ff877427181e74", size = 81330, upload-time = "2025-10-06T14:10:13.112Z" },
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 version = "3.23.0"
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 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" },
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From 6b03af6277426179c56d55984e9ae8bf747aeaf5 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 4 Nov 2025 10:48:09 -0800
Subject: [PATCH 20/40] Fix formatting

---
 docs/examples_notebooks/api_overview.ipynb    | 5 +++--
 docs/examples_notebooks/input_documents.ipynb | 5 +++--
 unified-search-app/app/app_logic.py           | 3 ++-
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index 86b0184b29..a42740d5e5 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,10 +28,11 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
+    "\n",
+    "import graphrag.api as api"
    ]
   },
   {
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 73e51780d8..07373d1a72 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,10 +30,11 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
+    "\n",
+    "import graphrag.api as api"
    ]
   },
   {
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index dc64e0e77c..a573b9daa5 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,7 +7,6 @@
 import logging
 from typing import TYPE_CHECKING
 
-import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -18,6 +17,8 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
+import graphrag.api as api
+
 if TYPE_CHECKING:
     import pandas as pd
 

From 6033e4ffa226405de7644070581993a362c624df Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Wed, 5 Nov 2025 13:06:09 -0800
Subject: [PATCH 21/40] Storage fixes and cleanup (#2118)

* Fix pipeline recursion

* Remove base_dir from storage.find

* Remove max_count from storage.find

* Remove prefix on storage integ test

* Add base_dir in creation_date test

* Wrap base_dir in Path

* Use constants for input/update directories
---
 packages/graphrag/graphrag/config/defaults.py |  10 +-
 .../config/models/graph_rag_config.py         |  10 +-
 .../graphrag/config/models/storage_config.py  |   2 +-
 .../graphrag/storage/blob_pipeline_storage.py | 108 +++++-------------
 .../storage/cosmosdb_pipeline_storage.py      |  30 ++---
 .../graphrag/storage/file_pipeline_storage.py |  52 ++++-----
 .../graphrag/storage/pipeline_storage.py      |   2 -
 .../storage/test_blob_pipeline_storage.py     |   8 +-
 tests/integration/storage/test_factory.py     |   2 -
 .../storage/test_file_pipeline_storage.py     |  20 ++--
 10 files changed, 86 insertions(+), 158 deletions(-)

diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index 8a04851682..f57bf610f9 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -24,7 +24,9 @@
     EN_STOP_WORDS,
 )
 
+DEFAULT_INPUT_BASE_DIR = "input"
 DEFAULT_OUTPUT_BASE_DIR = "output"
+DEFAULT_UPDATE_OUTPUT_BASE_DIR = "update_output"
 DEFAULT_CHAT_MODEL_ID = "default_chat_model"
 DEFAULT_CHAT_MODEL_TYPE = ModelType.Chat
 DEFAULT_CHAT_MODEL_AUTH_TYPE = AuthType.APIKey
@@ -229,7 +231,7 @@ class StorageDefaults:
     """Default values for storage."""
 
     type: ClassVar[StorageType] = StorageType.file
-    base_dir: str = DEFAULT_OUTPUT_BASE_DIR
+    base_dir: str | None = None
     connection_string: None = None
     container_name: None = None
     storage_account_blob_url: None = None
@@ -240,7 +242,7 @@ class StorageDefaults:
 class InputStorageDefaults(StorageDefaults):
     """Default values for input storage."""
 
-    base_dir: str = "input"
+    base_dir: str | None = DEFAULT_INPUT_BASE_DIR
 
 
 @dataclass
@@ -310,7 +312,7 @@ class LocalSearchDefaults:
 class OutputDefaults(StorageDefaults):
     """Default values for output."""
 
-    base_dir: str = DEFAULT_OUTPUT_BASE_DIR
+    base_dir: str | None = DEFAULT_OUTPUT_BASE_DIR
 
 
 @dataclass
@@ -362,7 +364,7 @@ class SummarizeDescriptionsDefaults:
 class UpdateIndexOutputDefaults(StorageDefaults):
     """Default values for update index output."""
 
-    base_dir: str = "update_output"
+    base_dir: str | None = DEFAULT_UPDATE_OUTPUT_BASE_DIR
 
 
 @dataclass
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 6a6a98e973..71509f0176 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -152,7 +152,7 @@ def _validate_input_pattern(self) -> None:
     def _validate_input_base_dir(self) -> None:
         """Validate the input base directory."""
         if self.input.storage.type == defs.StorageType.file:
-            if self.input.storage.base_dir.strip() == "":
+            if not self.input.storage.base_dir:
                 msg = "input storage base directory is required for file input storage. Please rerun `graphrag init` and set the input storage configuration."
                 raise ValueError(msg)
             self.input.storage.base_dir = str(
@@ -167,14 +167,16 @@ def _validate_input_base_dir(self) -> None:
 
     output: StorageConfig = Field(
         description="The output configuration.",
-        default=StorageConfig(),
+        default=StorageConfig(
+            base_dir=graphrag_config_defaults.output.base_dir,
+        ),
     )
     """The output configuration."""
 
     def _validate_output_base_dir(self) -> None:
         """Validate the output base directory."""
         if self.output.type == defs.StorageType.file:
-            if self.output.base_dir.strip() == "":
+            if not self.output.base_dir:
                 msg = "output base directory is required for file output. Please rerun `graphrag init` and set the output configuration."
                 raise ValueError(msg)
             self.output.base_dir = str(
@@ -192,7 +194,7 @@ def _validate_output_base_dir(self) -> None:
     def _validate_update_index_output_base_dir(self) -> None:
         """Validate the update index output base directory."""
         if self.update_index_output.type == defs.StorageType.file:
-            if self.update_index_output.base_dir.strip() == "":
+            if not self.update_index_output.base_dir:
                 msg = "update_index_output base directory is required for file output. Please rerun `graphrag init` and set the update_index_output configuration."
                 raise ValueError(msg)
             self.update_index_output.base_dir = str(
diff --git a/packages/graphrag/graphrag/config/models/storage_config.py b/packages/graphrag/graphrag/config/models/storage_config.py
index 3f01448c66..7491454c0a 100644
--- a/packages/graphrag/graphrag/config/models/storage_config.py
+++ b/packages/graphrag/graphrag/config/models/storage_config.py
@@ -18,7 +18,7 @@ class StorageConfig(BaseModel):
         description="The storage type to use.",
         default=graphrag_config_defaults.storage.type,
     )
-    base_dir: str = Field(
+    base_dir: str | None = Field(
         description="The base directory for the output.",
         default=graphrag_config_defaults.storage.base_dir,
     )
diff --git a/packages/graphrag/graphrag/storage/blob_pipeline_storage.py b/packages/graphrag/graphrag/storage/blob_pipeline_storage.py
index 5a00af85b5..1435cb387d 100644
--- a/packages/graphrag/graphrag/storage/blob_pipeline_storage.py
+++ b/packages/graphrag/graphrag/storage/blob_pipeline_storage.py
@@ -25,7 +25,7 @@ class BlobPipelineStorage(PipelineStorage):
 
     _connection_string: str | None
     _container_name: str
-    _path_prefix: str
+    _base_dir: str | None
     _encoding: str
     _storage_account_blob_url: str | None
 
@@ -33,7 +33,7 @@ def __init__(self, **kwargs: Any) -> None:
         """Create a new BlobStorage instance."""
         connection_string = kwargs.get("connection_string")
         storage_account_blob_url = kwargs.get("storage_account_blob_url")
-        path_prefix = kwargs.get("base_dir")
+        base_dir = kwargs.get("base_dir")
         container_name = kwargs["container_name"]
         if container_name is None:
             msg = "No container name provided for blob storage."
@@ -42,7 +42,9 @@ def __init__(self, **kwargs: Any) -> None:
             msg = "No storage account blob url provided for blob storage."
             raise ValueError(msg)
 
-        logger.info("Creating blob storage at %s", container_name)
+        logger.info(
+            "Creating blob storage at [%s] and base_dir [%s]", container_name, base_dir
+        )
         if connection_string:
             self._blob_service_client = BlobServiceClient.from_connection_string(
                 connection_string
@@ -59,18 +61,13 @@ def __init__(self, **kwargs: Any) -> None:
         self._encoding = kwargs.get("encoding", "utf-8")
         self._container_name = container_name
         self._connection_string = connection_string
-        self._path_prefix = path_prefix or ""
+        self._base_dir = base_dir
         self._storage_account_blob_url = storage_account_blob_url
         self._storage_account_name = (
             storage_account_blob_url.split("//")[1].split(".")[0]
             if storage_account_blob_url
             else None
         )
-        logger.debug(
-            "creating blob storage at container=%s, path=%s",
-            self._container_name,
-            self._path_prefix,
-        )
         self._create_container()
 
     def _create_container(self) -> None:
@@ -82,6 +79,7 @@ def _create_container(self) -> None:
                 for container in self._blob_service_client.list_containers()
             ]
             if container_name not in container_names:
+                logger.debug("Creating new container [%s]", container_name)
                 self._blob_service_client.create_container(container_name)
 
     def _delete_container(self) -> None:
@@ -100,31 +98,26 @@ def _container_exists(self) -> bool:
     def find(
         self,
         file_pattern: re.Pattern[str],
-        base_dir: str | None = None,
-        max_count=-1,
     ) -> Iterator[str]:
         """Find blobs in a container using a file pattern.
 
         Params:
-            base_dir: The name of the base container.
             file_pattern: The file pattern to use.
-            max_count: The maximum number of blobs to return. If -1, all blobs are returned.
 
         Returns
         -------
                 An iterator of blob names and their corresponding regex matches.
         """
-        base_dir = base_dir or ""
-
         logger.info(
-            "search container %s for files matching %s",
+            "Search container [%s] in base_dir [%s] for files matching [%s]",
             self._container_name,
+            self._base_dir,
             file_pattern.pattern,
         )
 
         def _blobname(blob_name: str) -> str:
-            if blob_name.startswith(self._path_prefix):
-                blob_name = blob_name.replace(self._path_prefix, "", 1)
+            if self._base_dir and blob_name.startswith(self._base_dir):
+                blob_name = blob_name.replace(self._base_dir, "", 1)
             if blob_name.startswith("/"):
                 blob_name = blob_name[1:]
             return blob_name
@@ -133,37 +126,35 @@ def _blobname(blob_name: str) -> str:
             container_client = self._blob_service_client.get_container_client(
                 self._container_name
             )
-            all_blobs = list(container_client.list_blobs())
-
+            all_blobs = list(container_client.list_blobs(self._base_dir))
+            logger.debug("All blobs: %s", [blob.name for blob in all_blobs])
             num_loaded = 0
             num_total = len(list(all_blobs))
             num_filtered = 0
             for blob in all_blobs:
                 match = file_pattern.search(blob.name)
-                if match and blob.name.startswith(base_dir):
+                if match:
                     yield _blobname(blob.name)
                     num_loaded += 1
-                    if max_count > 0 and num_loaded >= max_count:
-                        break
                 else:
                     num_filtered += 1
-                logger.debug(
-                    "Blobs loaded: %d, filtered: %d, total: %d",
-                    num_loaded,
-                    num_filtered,
-                    num_total,
-                )
+            logger.debug(
+                "Blobs loaded: %d, filtered: %d, total: %d",
+                num_loaded,
+                num_filtered,
+                num_total,
+            )
         except Exception:  # noqa: BLE001
             logger.warning(
                 "Error finding blobs: base_dir=%s, file_pattern=%s",
-                base_dir,
+                self._base_dir,
                 file_pattern,
             )
 
     async def get(
         self, key: str, as_bytes: bool | None = False, encoding: str | None = None
     ) -> Any:
-        """Get a value from the cache."""
+        """Get a value from the blob."""
         try:
             key = self._keyname(key)
             container_client = self._blob_service_client.get_container_client(
@@ -181,7 +172,7 @@ async def get(
             return blob_data
 
     async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
-        """Set a value in the cache."""
+        """Set a value in the blob."""
         try:
             key = self._keyname(key)
             container_client = self._blob_service_client.get_container_client(
@@ -196,46 +187,8 @@ async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
         except Exception:
             logger.exception("Error setting key %s: %s", key)
 
-    def _set_df_json(self, key: str, dataframe: Any) -> None:
-        """Set a json dataframe."""
-        if self._connection_string is None and self._storage_account_name:
-            dataframe.to_json(
-                self._abfs_url(key),
-                storage_options={
-                    "account_name": self._storage_account_name,
-                    "credential": DefaultAzureCredential(),
-                },
-                orient="records",
-                lines=True,
-                force_ascii=False,
-            )
-        else:
-            dataframe.to_json(
-                self._abfs_url(key),
-                storage_options={"connection_string": self._connection_string},
-                orient="records",
-                lines=True,
-                force_ascii=False,
-            )
-
-    def _set_df_parquet(self, key: str, dataframe: Any) -> None:
-        """Set a parquet dataframe."""
-        if self._connection_string is None and self._storage_account_name:
-            dataframe.to_parquet(
-                self._abfs_url(key),
-                storage_options={
-                    "account_name": self._storage_account_name,
-                    "credential": DefaultAzureCredential(),
-                },
-            )
-        else:
-            dataframe.to_parquet(
-                self._abfs_url(key),
-                storage_options={"connection_string": self._connection_string},
-            )
-
     async def has(self, key: str) -> bool:
-        """Check if a key exists in the cache."""
+        """Check if a key exists in the blob."""
         key = self._keyname(key)
         container_client = self._blob_service_client.get_container_client(
             self._container_name
@@ -244,7 +197,7 @@ async def has(self, key: str) -> bool:
         return blob_client.exists()
 
     async def delete(self, key: str) -> None:
-        """Delete a key from the cache."""
+        """Delete a key from the blob."""
         key = self._keyname(key)
         container_client = self._blob_service_client.get_container_client(
             self._container_name
@@ -259,7 +212,7 @@ def child(self, name: str | None) -> "PipelineStorage":
         """Create a child storage instance."""
         if name is None:
             return self
-        path = str(Path(self._path_prefix) / name)
+        path = str(Path(self._base_dir) / name) if self._base_dir else name
         return BlobPipelineStorage(
             connection_string=self._connection_string,
             container_name=self._container_name,
@@ -275,15 +228,10 @@ def keys(self) -> list[str]:
 
     def _keyname(self, key: str) -> str:
         """Get the key name."""
-        return str(Path(self._path_prefix) / key)
-
-    def _abfs_url(self, key: str) -> str:
-        """Get the ABFS URL."""
-        path = str(Path(self._container_name) / self._path_prefix / key)
-        return f"abfs://{path}"
+        return str(Path(self._base_dir) / key) if self._base_dir else key
 
     async def get_creation_date(self, key: str) -> str:
-        """Get a value from the cache."""
+        """Get creation date for the blob."""
         try:
             key = self._keyname(key)
             container_client = self._blob_service_client.get_container_client(
diff --git a/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py b/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
index 8d2673e89b..a12da0ee5f 100644
--- a/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
+++ b/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
@@ -77,7 +77,7 @@ def __init__(self, **kwargs: Any) -> None:
         )
         self._no_id_prefixes = []
         logger.debug(
-            "creating cosmosdb storage with account: %s and database: %s and container: %s",
+            "Creating cosmosdb storage with account [%s] and database [%s] and container [%s]",
             self._cosmosdb_account_name,
             self._database_name,
             self._container_name,
@@ -120,23 +120,18 @@ def _delete_container(self) -> None:
     def find(
         self,
         file_pattern: re.Pattern[str],
-        base_dir: str | None = None,
-        max_count=-1,
     ) -> Iterator[str]:
         """Find documents in a Cosmos DB container using a file pattern regex.
 
         Params:
-            base_dir: The name of the base directory (not used in Cosmos DB context).
             file_pattern: The file pattern to use.
-            max_count: The maximum number of documents to return. If -1, all documents are returned.
 
         Returns
         -------
             An iterator of document IDs and their corresponding regex matches.
         """
-        base_dir = base_dir or ""
         logger.info(
-            "search container %s for documents matching %s",
+            "Search container [%s] for documents matching [%s]",
             self._container_name,
             file_pattern.pattern,
         )
@@ -156,6 +151,7 @@ def find(
                     enable_cross_partition_query=True,
                 )
             )
+            logger.debug("All items: %s", [item["id"] for item in items])
             num_loaded = 0
             num_total = len(items)
             if num_total == 0:
@@ -166,20 +162,18 @@ def find(
                 if match:
                     yield item["id"]
                     num_loaded += 1
-                    if max_count > 0 and num_loaded >= max_count:
-                        break
                 else:
                     num_filtered += 1
 
-                progress_status = _create_progress_status(
-                    num_loaded, num_filtered, num_total
-                )
-                logger.debug(
-                    "Progress: %s (%d/%d completed)",
-                    progress_status.description,
-                    progress_status.completed_items,
-                    progress_status.total_items,
-                )
+            progress_status = _create_progress_status(
+                num_loaded, num_filtered, num_total
+            )
+            logger.debug(
+                "Progress: %s (%d/%d completed)",
+                progress_status.description,
+                progress_status.completed_items,
+                progress_status.total_items,
+            )
         except Exception:  # noqa: BLE001
             logger.warning(
                 "An error occurred while searching for documents in Cosmos DB."
diff --git a/packages/graphrag/graphrag/storage/file_pipeline_storage.py b/packages/graphrag/graphrag/storage/file_pipeline_storage.py
index 15445b0d3a..52402c8bd6 100644
--- a/packages/graphrag/graphrag/storage/file_pipeline_storage.py
+++ b/packages/graphrag/graphrag/storage/file_pipeline_storage.py
@@ -27,62 +27,54 @@
 class FilePipelineStorage(PipelineStorage):
     """File storage class definition."""
 
-    _root_dir: str
+    _base_dir: str
     _encoding: str
 
     def __init__(self, **kwargs: Any) -> None:
         """Create a file based storage."""
-        self._root_dir = kwargs.get("base_dir", "")
+        self._base_dir = kwargs.get("base_dir", "")
         self._encoding = kwargs.get("encoding", "utf-8")
-        logger.info("Creating file storage at %s", self._root_dir)
-        Path(self._root_dir).mkdir(parents=True, exist_ok=True)
+        logger.info("Creating file storage at [%s]", self._base_dir)
+        Path(self._base_dir).mkdir(parents=True, exist_ok=True)
 
     def find(
         self,
         file_pattern: re.Pattern[str],
-        base_dir: str | None = None,
-        max_count=-1,
     ) -> Iterator[str]:
         """Find files in the storage using a file pattern."""
-        search_path = Path(self._root_dir) / (base_dir or "")
         logger.info(
-            "search %s for files matching %s", search_path, file_pattern.pattern
+            "Search [%s] for files matching [%s]", self._base_dir, file_pattern.pattern
         )
-        all_files = list(search_path.rglob("**/*"))
+        all_files = list(Path(self._base_dir).rglob("**/*"))
+        logger.debug("All files and folders: %s", [file.name for file in all_files])
         num_loaded = 0
         num_total = len(all_files)
         num_filtered = 0
         for file in all_files:
             match = file_pattern.search(f"{file}")
             if match:
-                filename = f"{file}".replace(self._root_dir, "")
+                filename = f"{file}".replace(str(Path(self._base_dir)), "", 1)
                 if filename.startswith(os.sep):
                     filename = filename[1:]
                 yield filename
                 num_loaded += 1
-                if max_count > 0 and num_loaded >= max_count:
-                    break
             else:
                 num_filtered += 1
-            logger.debug(
-                "Files loaded: %d, filtered: %d, total: %d",
-                num_loaded,
-                num_filtered,
-                num_total,
-            )
+        logger.debug(
+            "Files loaded: %d, filtered: %d, total: %d",
+            num_loaded,
+            num_filtered,
+            num_total,
+        )
 
     async def get(
         self, key: str, as_bytes: bool | None = False, encoding: str | None = None
     ) -> Any:
         """Get method definition."""
-        file_path = join_path(self._root_dir, key)
+        file_path = join_path(self._base_dir, key)
 
         if await self.has(key):
             return await self._read_file(file_path, as_bytes, encoding)
-        if await exists(key):
-            # Lookup for key, as it is pressumably a new file loaded from inputs
-            # and not yet written to storage
-            return await self._read_file(key, as_bytes, encoding)
 
         return None
 
@@ -109,7 +101,7 @@ async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
         write_type = "wb" if is_bytes else "w"
         encoding = None if is_bytes else encoding or self._encoding
         async with aiofiles.open(
-            join_path(self._root_dir, key),
+            join_path(self._base_dir, key),
             cast("Any", write_type),
             encoding=encoding,
         ) as f:
@@ -117,16 +109,16 @@ async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
 
     async def has(self, key: str) -> bool:
         """Has method definition."""
-        return await exists(join_path(self._root_dir, key))
+        return await exists(join_path(self._base_dir, key))
 
     async def delete(self, key: str) -> None:
         """Delete method definition."""
         if await self.has(key):
-            await remove(join_path(self._root_dir, key))
+            await remove(join_path(self._base_dir, key))
 
     async def clear(self) -> None:
         """Clear method definition."""
-        for file in Path(self._root_dir).glob("*"):
+        for file in Path(self._base_dir).glob("*"):
             if file.is_dir():
                 shutil.rmtree(file)
             else:
@@ -136,16 +128,16 @@ def child(self, name: str | None) -> "PipelineStorage":
         """Create a child storage instance."""
         if name is None:
             return self
-        child_path = str(Path(self._root_dir) / Path(name))
+        child_path = str(Path(self._base_dir) / Path(name))
         return FilePipelineStorage(base_dir=child_path, encoding=self._encoding)
 
     def keys(self) -> list[str]:
         """Return the keys in the storage."""
-        return [item.name for item in Path(self._root_dir).iterdir() if item.is_file()]
+        return [item.name for item in Path(self._base_dir).iterdir() if item.is_file()]
 
     async def get_creation_date(self, key: str) -> str:
         """Get the creation date of a file."""
-        file_path = Path(join_path(self._root_dir, key))
+        file_path = Path(join_path(self._base_dir, key))
 
         creation_timestamp = file_path.stat().st_ctime
         creation_time_utc = datetime.fromtimestamp(creation_timestamp, tz=timezone.utc)
diff --git a/packages/graphrag/graphrag/storage/pipeline_storage.py b/packages/graphrag/graphrag/storage/pipeline_storage.py
index ba3ab86e97..5c79921736 100644
--- a/packages/graphrag/graphrag/storage/pipeline_storage.py
+++ b/packages/graphrag/graphrag/storage/pipeline_storage.py
@@ -17,8 +17,6 @@ class PipelineStorage(metaclass=ABCMeta):
     def find(
         self,
         file_pattern: re.Pattern[str],
-        base_dir: str | None = None,
-        max_count=-1,
     ) -> Iterator[str]:
         """Find files in the storage using a file pattern."""
 
diff --git a/tests/integration/storage/test_blob_pipeline_storage.py b/tests/integration/storage/test_blob_pipeline_storage.py
index f99a74ff74..818b588bd6 100644
--- a/tests/integration/storage/test_blob_pipeline_storage.py
+++ b/tests/integration/storage/test_blob_pipeline_storage.py
@@ -18,17 +18,13 @@ async def test_find():
     )
     try:
         try:
-            items = list(
-                storage.find(base_dir="input", file_pattern=re.compile(r".*\.txt$"))
-            )
+            items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
             assert items == []
 
             await storage.set(
                 "input/christmas.txt", "Merry Christmas!", encoding="utf-8"
             )
-            items = list(
-                storage.find(base_dir="input", file_pattern=re.compile(r".*\.txt$"))
-            )
+            items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
             assert items == ["input/christmas.txt"]
 
             await storage.set("test.txt", "Hello, World!", encoding="utf-8")
diff --git a/tests/integration/storage/test_factory.py b/tests/integration/storage/test_factory.py
index 5cab7187b8..87a2960dbc 100644
--- a/tests/integration/storage/test_factory.py
+++ b/tests/integration/storage/test_factory.py
@@ -113,8 +113,6 @@ def __init__(self, **kwargs):
         def find(
             self,
             file_pattern: re.Pattern[str],
-            base_dir: str | None = None,
-            max_count=-1,
         ) -> Iterator[str]:
             return iter([])
 
diff --git a/tests/integration/storage/test_file_pipeline_storage.py b/tests/integration/storage/test_file_pipeline_storage.py
index cc5b3f7c83..95e329b6bf 100644
--- a/tests/integration/storage/test_file_pipeline_storage.py
+++ b/tests/integration/storage/test_file_pipeline_storage.py
@@ -15,14 +15,12 @@
 
 
 async def test_find():
-    storage = FilePipelineStorage()
-    items = list(
-        storage.find(
-            base_dir="tests/fixtures/text/input", file_pattern=re.compile(r".*\.txt$")
-        )
+    storage = FilePipelineStorage(
+        base_dir="tests/fixtures/text/input",
     )
-    assert items == [str(Path("tests/fixtures/text/input/dulce.txt"))]
-    output = await storage.get("tests/fixtures/text/input/dulce.txt")
+    items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
+    assert items == [str(Path("dulce.txt"))]
+    output = await storage.get("dulce.txt")
     assert len(output) > 0
 
     await storage.set("test.txt", "Hello, World!", encoding="utf-8")
@@ -34,12 +32,12 @@ async def test_find():
 
 
 async def test_get_creation_date():
-    storage = FilePipelineStorage()
-
-    creation_date = await storage.get_creation_date(
-        "tests/fixtures/text/input/dulce.txt"
+    storage = FilePipelineStorage(
+        base_dir="tests/fixtures/text/input",
     )
 
+    creation_date = await storage.get_creation_date("dulce.txt")
+
     datetime_format = "%Y-%m-%d %H:%M:%S %z"
     parsed_datetime = datetime.strptime(creation_date, datetime_format).astimezone()
 

From ae1f5e1811a9ea2d49974c774f12e60e8ecbd40e Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Thu, 6 Nov 2025 10:03:22 -0800
Subject: [PATCH 22/40] Nov 2025 housekeeping (#2120)

* Remove gensim sideload

* Split CI build/type checks from unit tests

* Thorough review of docs to align with v3

* Format

* Fix version

* Fix type
---
 .github/workflows/gh-pages.yml                |  2 +-
 .github/workflows/python-checks.yml           | 78 +++++++++++++++++
 .../workflows/python-integration-tests.yml    |  3 +-
 .github/workflows/python-notebook-tests.yml   |  3 +-
 .github/workflows/python-publish.yml          |  2 +-
 .github/workflows/python-smoke-tests.yml      |  3 +-
 .../{python-ci.yml => python-unit-tests.yml}  | 13 +--
 docs/config/models.md                         | 16 ++--
 docs/config/yaml.md                           |  6 +-
 docs/developing.md                            |  2 +-
 docs/examples_notebooks/api_overview.ipynb    |  5 +-
 docs/examples_notebooks/input_documents.ipynb |  5 +-
 docs/get_started.md                           | 13 +--
 docs/index/architecture.md                    | 22 ++---
 docs/index/byog.md                            |  2 -
 docs/index/default_dataflow.md                | 85 +++++++++----------
 docs/index/inputs.md                          | 12 +--
 docs/index/methods.md                         |  6 +-
 docs/index/overview.md                        |  3 +-
 docs/prompt_tuning/auto_prompt_tuning.md      | 14 +--
 docs/prompt_tuning/overview.md                |  4 +-
 docs/query/drift_search.md                    |  2 +-
 docs/query/global_search.md                   |  2 +-
 docs/query/local_search.md                    |  2 +-
 docs/query/overview.md                        | 17 ++--
 docs/query/question_generation.md             |  2 +-
 unified-search-app/README.md                  |  4 +-
 unified-search-app/app/app_logic.py           |  3 +-
 28 files changed, 190 insertions(+), 141 deletions(-)
 create mode 100644 .github/workflows/python-checks.yml
 rename .github/workflows/{python-ci.yml => python-unit-tests.yml} (83%)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index d8a507657b..35980e3787 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -6,7 +6,7 @@ permissions:
   contents: write
 
 env:
-  PYTHON_VERSION: "3.11"
+  PYTHON_VERSION: "3.12"
 
 jobs:
   build:
diff --git a/.github/workflows/python-checks.yml b/.github/workflows/python-checks.yml
new file mode 100644
index 0000000000..fdc952102f
--- /dev/null
+++ b/.github/workflows/python-checks.yml
@@ -0,0 +1,78 @@
+name: Python Build and Type Check
+on:
+  push:
+    branches:
+      - "**/main" # match branches like feature/main
+      - "main"    # match the main branch
+  pull_request:
+    types:
+      - opened
+      - reopened
+      - synchronize
+      - ready_for_review
+    branches:
+      - "**/main"
+      - "main"
+    paths-ignore:
+      - "**/*.md"
+      - ".semversioner/**"
+
+permissions:
+  contents: read
+  pull-requests: read
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  # Only run the for the latest commit
+  cancel-in-progress: true
+
+jobs:
+  python-ci:
+    # skip draft PRs
+    if: github.event.pull_request.draft == false
+    strategy:
+      matrix:
+        python-version: ["3.11", "3.12"]
+        os: [ubuntu-latest, windows-latest]
+      fail-fast: false # Continue running all jobs even if one fails
+    env:
+      DEBUG: 1
+
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: dorny/paths-filter@v3
+        id: changes
+        with:
+          filters: |
+            python:
+              - 'graphrag/**/*'
+              - 'uv.lock'
+              - 'pyproject.toml'
+              - '**/*.py'
+              - '**/*.toml'
+              - '**/*.ipynb'
+              - '.github/workflows/python*.yml'
+              - 'tests/**/*'
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          uv sync --all-packages
+
+      - name: Check
+        run: |
+          uv run poe check
+
+      - name: Build
+        run: |
+          uv build --all-packages
\ No newline at end of file
diff --git a/.github/workflows/python-integration-tests.yml b/.github/workflows/python-integration-tests.yml
index d7ded02619..ecfac41b7b 100644
--- a/.github/workflows/python-integration-tests.yml
+++ b/.github/workflows/python-integration-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.12"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # continue running all jobs even if one fails
     env:
@@ -68,7 +68,6 @@ jobs:
         shell: bash
         run: |
           uv sync --all-packages
-          uv pip install gensim
 
       - name: Build
         run: |
diff --git a/.github/workflows/python-notebook-tests.yml b/.github/workflows/python-notebook-tests.yml
index 335442df23..2053a8f19d 100644
--- a/.github/workflows/python-notebook-tests.yml
+++ b/.github/workflows/python-notebook-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.12"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
@@ -68,7 +68,6 @@ jobs:
         shell: bash
         run: |
           uv sync --all-packages
-          uv pip install gensim
 
       - name: Notebook Test
         run: |
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
index 53a59c3f98..a81a5d7747 100644
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -6,7 +6,7 @@ on:
     branches: [main]
 
 env:
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
 
 jobs:
   publish:
diff --git a/.github/workflows/python-smoke-tests.yml b/.github/workflows/python-smoke-tests.yml
index 46a3641c22..3d51439388 100644
--- a/.github/workflows/python-smoke-tests.yml
+++ b/.github/workflows/python-smoke-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.12"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
@@ -73,7 +73,6 @@ jobs:
         shell: bash
         run: |
           uv sync --all-packages
-          uv pip install gensim
 
       - name: Build
         run: |
diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-unit-tests.yml
similarity index 83%
rename from .github/workflows/python-ci.yml
rename to .github/workflows/python-unit-tests.yml
index 1848b4f138..7cf80f9481 100644
--- a/.github/workflows/python-ci.yml
+++ b/.github/workflows/python-unit-tests.yml
@@ -1,4 +1,4 @@
-name: Python CI
+name: Python Unit Tests
 on:
   push:
     branches:
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"] # add 3.12 once gensim supports it. TODO: watch this issue - https://github.com/piskvorky/gensim/issues/3510
+        python-version: ["3.12"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
@@ -68,15 +68,6 @@ jobs:
         shell: bash
         run: |
           uv sync --all-packages
-          uv pip install gensim
-
-      - name: Check
-        run: |
-          uv run poe check
-
-      - name: Build
-        run: |
-          uv build --all-packages
 
       - name: Unit Test
         run: |
diff --git a/docs/config/models.md b/docs/config/models.md
index ca3f044e3d..a65bde3f3d 100644
--- a/docs/config/models.md
+++ b/docs/config/models.md
@@ -6,9 +6,9 @@ This page contains information on selecting a model to use and options to supply
 
 GraphRAG was built and tested using OpenAI models, so this is the default model set we support. This is not intended to be a limiter or statement of quality or fitness for your use case, only that it's the set we are most familiar with for prompting, tuning, and debugging.
 
-Starting with version 2.6.0, GraphRAG supports using [LiteLLM](https://docs.litellm.ai/) for calling language models. LiteLLM provides support for 100+ models though it is important to note that when choosing a model it must support returning [structured outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/) adhering to a [JSON schema](https://docs.litellm.ai/docs/completion/json_mode). 
+GraphRAG uses [LiteLLM](https://docs.litellm.ai/) for calling language models. LiteLLM provides support for 100+ models though it is important to note that when choosing a model it must support returning [structured outputs](https://openai.com/index/introducing-structured-outputs-in-the-api/) adhering to a [JSON schema](https://docs.litellm.ai/docs/completion/json_mode). 
 
-Example using LiteLLm as the language model tool for GraphRAG:
+Example using LiteLLM as the language model manager for GraphRAG:
 
 ```yaml
 models:
@@ -37,7 +37,7 @@ See [Detailed Configuration](yaml.md) for more details on configuration. [View L
 
 ## Model Selection Considerations
 
-GraphRAG has been most thoroughly tested with the gpt-4 series of models from OpenAI, including gpt-4 gpt-4-turbo, gpt-4o, and gpt-4o-mini. Our [arXiv paper](https://arxiv.org/abs/2404.16130), for example, performed quality evaluation using gpt-4-turbo. As stated above, non-OpenAI models are now supported with GraphRAG 2.6.0 and onwards through the use of LiteLLM but the suite of gpt-4 series of models from OpenAI remain the most tested and supported suite of models for GraphRAG.
+GraphRAG has been most thoroughly tested with the gpt-4 series of models from OpenAI, including gpt-4 gpt-4-turbo, gpt-4o, and gpt-4o-mini. Our [arXiv paper](https://arxiv.org/abs/2404.16130), for example, performed quality evaluation using gpt-4-turbo. As stated above, non-OpenAI models are supported through the use of LiteLLM but the suite of gpt-4 series of models from OpenAI remain the most tested and supported suite of models for GraphRAG – in other words, these are the models we know best and can help resolve issues with.
 
 Versions of GraphRAG before 2.2.0 made extensive use of `max_tokens` and `logit_bias` to control generated response length or content. The introduction of the o-series of models added new, non-compatible parameters because these models include a reasoning component that has different consumption patterns and response generation attributes than non-reasoning models. GraphRAG 2.2.0 now supports these models, but there are important differences that need to be understood before you switch.
 
@@ -85,9 +85,9 @@ global_search:
 
 Another option would be to avoid using a language model at all for the graph extraction, instead using the `fast` [indexing method](../index/methods.md) that uses NLP for portions of the indexing phase in lieu of LLM APIs.
 
-## Using Non-OpenAI Models
+## Using Custom Models
 
-As shown above, non-OpenAI models may be used via LiteLLM starting with GraphRAG version 2.6.0 but cases may still exist in which some users wish to use models not supported by LiteLLM. There are two approaches one can use to connect to unsupported models:
+LiteLLM supports hundreds of models, but cases may still exist in which some users wish to use models not supported by LiteLLM. There are two approaches one can use to connect to unsupported models:
 
 ### Proxy APIs
 
@@ -95,7 +95,7 @@ Many users have used platforms such as [ollama](https://ollama.com/) and [LiteLL
 
 ### Model Protocol
 
-As of GraphRAG 2.0.0, we support model injection through the use of a standard chat and embedding Protocol and an accompanying factories that you can use to register your model implementation. This is not supported with the CLI, so you'll need to use GraphRAG as a library.
+We support model injection through the use of a standard chat and embedding Protocol and accompanying factories that you can use to register your model implementation. This is not supported with the CLI, so you'll need to use GraphRAG as a library.
 
 - Our Protocol is [defined here](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/protocol/base.py)
 - We have a simple mock implementation in our tests that you can [reference here](https://github.com/microsoft/graphrag/blob/main/tests/mock_provider.py)
@@ -103,12 +103,12 @@ As of GraphRAG 2.0.0, we support model injection through the use of a standard c
 Once you have a model implementation, you need to register it with our ChatModelFactory or EmbeddingModelFactory:
 
 ```python
-class MyCustomModel:
+class MyCustomChatModel:
     ...
     # implementation
 
 # elsewhere...
-ChatModelFactory.register("my-custom-chat-model", lambda **kwargs: MyCustomModel(**kwargs))
+ChatModelFactory.register("my-custom-chat-model", MyCustomChatModel)
 ```
 
 Then in your config you can reference the type name you used:
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 58dfb1779b..ace57e3b1c 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -21,7 +21,7 @@ default_chat_model:
 
 ### models
 
-This is a dict of model configurations. The dict key is used to reference this configuration elsewhere when a model instance is desired. In this way, you can specify as many different models as you need, and reference them differentially in the workflow steps.
+This is a dict of model configurations. The dict key is used to reference this configuration elsewhere when a model instance is desired. In this way, you can specify as many different models as you need, and reference them independently in the workflow steps.
 
 For example:
 ```yml
@@ -173,7 +173,7 @@ Where to put all vectors for the system. Configured for lancedb by default. This
 - `audience` **str** (only for AI Search) - Audience for managed identity token if managed identity authentication is used.
 - `index_prefix` **str** - (optional) A prefix for the indexes you will create for embeddings. This stores all indexes (tables) for a given dataset ingest.
 - `database_name` **str** - (cosmosdb only) Name of the database.
-- `embeddings_schema` **list[dict[str, str]]** (optional) - Enables customization for each of your embeddings. 
+- `embeddings_schema` **dict[str, dict[str, str]]** (optional) - Enables customization for each of your embeddings. 
   - `<supported_embedding>`: 
     - `index_name` **str**: (optional) - Name for the specific embedding index table.
     - `id_field` **str**: (optional) - Field name to be used as id. Default=`id`
@@ -332,7 +332,7 @@ These are the settings used for Leiden hierarchical clustering of the graph to c
 #### Fields
 
 - `embeddings` **bool** - Export embeddings snapshots to parquet.
-- `graphml` **bool** - Export graph snapshots to GraphML.
+- `graphml` **bool** - Export graph snapshot to GraphML.
 
 ## Query
 
diff --git a/docs/developing.md b/docs/developing.md
index 5e7b6671a3..eea14d00b0 100644
--- a/docs/developing.md
+++ b/docs/developing.md
@@ -13,7 +13,7 @@
 
 ```sh
 # install python dependencies
-uv sync
+uv sync --all-packages
 ```
 
 ## Execute the Indexing Engine
diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index a42740d5e5..86b0184b29 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,11 +28,10 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
+    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
-    "\n",
-    "import graphrag.api as api"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
   },
   {
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 07373d1a72..73e51780d8 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,11 +30,10 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
+    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
-    "\n",
-    "import graphrag.api as api"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
   },
   {
diff --git a/docs/get_started.md b/docs/get_started.md
index 4af43b8160..5de773924d 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -6,12 +6,7 @@
 
 [Python 3.10-3.12](https://www.python.org/downloads/)
 
-To get started with the GraphRAG system, you have a few options:
-
-👉 [Install from pypi](https://pypi.org/project/graphrag/). <br/>
-👉 [Use it from source](developing.md)<br/>
-
-The following is a simple end-to-end example for using the GraphRAG system, using the install from pypi option.
+The following is a simple end-to-end example for using GraphRAG on the command line after installing from [pypi](https://pypi.org/project/graphrag/).
 
 It shows how to use the system to index some text, and then use the indexed data to answer questions about the documents.
 
@@ -22,7 +17,6 @@ pip install graphrag
 ```
 
 # Running the Indexer
-
 We need to set up a data project and some initial configuration. First let's get a sample dataset ready:
 
 ```sh
@@ -79,7 +73,7 @@ You will also need to login with [az login](https://learn.microsoft.com/en-us/cl
 
 ## Running the Indexing pipeline
 
-Finally we'll run the pipeline!
+Now we're ready to run the pipeline!
 
 ```sh
 graphrag index --root ./christmas
@@ -87,8 +81,7 @@ graphrag index --root ./christmas
 
 ![pipeline executing from the CLI](img/pipeline-running.png)
 
-This process will take some time to run. This depends on the size of your input data, what model you're using, and the text chunk size being used (these can be configured in your `settings.yaml` file).
-Once the pipeline is complete, you should see a new folder called `./christmas/output` with a series of parquet files.
+This process will usually take a few minutes to run. Once the pipeline is complete, you should see a new folder called `./christmas/output` with a series of parquet files.
 
 # Using the Query Engine
 
diff --git a/docs/index/architecture.md b/docs/index/architecture.md
index be73bae7ed..7c3e1ed2bc 100644
--- a/docs/index/architecture.md
+++ b/docs/index/architecture.md
@@ -6,23 +6,25 @@
 
 In order to support the GraphRAG system, the outputs of the indexing engine (in the Default Configuration Mode) are aligned to a knowledge model we call the _GraphRAG Knowledge Model_.
 This model is designed to be an abstraction over the underlying data storage technology, and to provide a common interface for the GraphRAG system to interact with.
-In normal use-cases the outputs of the GraphRAG Indexer would be loaded into a database system, and the GraphRAG's Query Engine would interact with the database using the knowledge model data-store types.
 
 ### Workflows
 
-Because of the complexity of our data indexing tasks, we needed to be able to express our data pipeline as series of multiple, interdependent workflows.
+Below is the core GraphRAG indexing pipeline. Individual workflows are described in detail in the [dataflow](./default_dataflow.md) page.
 
 ```mermaid
 ---
-title: Sample Workflow DAG
+title: Basic GraphRAG
 ---
 stateDiagram-v2
-    [*] --> Prepare
-    Prepare --> Chunk
-    Chunk --> ExtractGraph
-    Chunk --> EmbedDocuments
-    ExtractGraph --> GenerateReports
+    [*] --> LoadDocuments
+    LoadDocuments --> ChunkDocuments
+    ChunkDocuments --> ExtractGraph
+    ChunkDocuments --> ExtractClaims
+    ChunkDocuments --> EmbedChunks
+    ExtractGraph --> DetectCommunities
     ExtractGraph --> EmbedEntities
+    DetectCommunities --> GenerateReports
+    GenerateReports --> EmbedReports
 ```
 
 ### LLM Caching
@@ -34,11 +36,11 @@ This allows our indexer to be more resilient to network issues, to act idempoten
 
 ### Providers & Factories
 
-Several subsystems within GraphRAG use a factory pattern to register and retrieve provider implementations. This allows deep customization to support models, storage, and so on that you may use but isn't built directly into GraphRAG.
+Several subsystems within GraphRAG use a factory pattern to register and retrieve provider implementations. This allows deep customization to support your own implementations of models, storage, and so on that we haven't built into the core library.
 
 The following subsystems use a factory pattern that allows you to register your own implementations:
 
-- [language model](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/factory.py) - implement your own `chat` and `embed` methods to use a model provider of choice beyond the built-in OpenAI/Azure support
+- [language model](https://github.com/microsoft/graphrag/blob/main/graphrag/language_model/factory.py) - implement your own `chat` and `embed` methods to use a model provider of choice beyond the built-in LiteLLM wrapper
 - [input reader](https://github.com/microsoft/graphrag/blob/main/graphrag/index/input/factory.py) - implement your own input document reader to support file types other than text, CSV, and JSON
 - [cache](https://github.com/microsoft/graphrag/blob/main/graphrag/cache/factory.py) - create your own cache storage location in addition to the file, blob, and CosmosDB ones we provide
 - [logger](https://github.com/microsoft/graphrag/blob/main/graphrag/logger/factory.py) - create your own log writing location in addition to the built-in file and blob storage
diff --git a/docs/index/byog.md b/docs/index/byog.md
index 781b922466..7866e4b8f6 100644
--- a/docs/index/byog.md
+++ b/docs/index/byog.md
@@ -16,8 +16,6 @@ The approach described here will be to run a custom GraphRAG workflow pipeline t
 
 See the full entities [table schema](./outputs.md#entities). For graph summarization purposes, you only need id, title, description, and the list of text_unit_ids.
 
-The additional properties are used for optional graph visualization purposes.
-
 ### Relationships
 
 See the full relationships [table schema](./outputs.md#relationships). For graph summarization purposes, you only need id, source, target, description, weight, and the list of text_unit_ids.
diff --git a/docs/index/default_dataflow.md b/docs/index/default_dataflow.md
index 19f59dfe8a..9a810accb0 100644
--- a/docs/index/default_dataflow.md
+++ b/docs/index/default_dataflow.md
@@ -4,8 +4,8 @@
 
 The knowledge model is a specification for data outputs that conform to our data-model definition. You can find these definitions in the python/graphrag/graphrag/model folder within the GraphRAG repository. The following entity types are provided. The fields here represent the fields that are text-embedded by default.
 
-- `Document` - An input document into the system. These either represent individual rows in a CSV or individual .txt file.
-- `TextUnit` - A chunk of text to analyze. The size of these chunks, their overlap, and whether they adhere to any data boundaries may be configured below. A common use case is to set `CHUNK_BY_COLUMNS` to `id` so that there is a 1-to-many relationship between documents and TextUnits instead of a many-to-many.
+- `Document` - An input document into the system. These either represent individual rows in a CSV or individual .txt files.
+- `TextUnit` - A chunk of text to analyze. The size of these chunks, their overlap, and whether they adhere to any data boundaries may be configured below.
 - `Entity` - An entity extracted from a TextUnit. These represent people, places, events, or some other entity-model that you provide.
 - `Relationship` - A relationship between two entities.
 - `Covariate` - Extracted claim information, which contains statements about entities which may be time-bound.
@@ -25,30 +25,26 @@ flowchart TB
     documents[Documents] --> chunk[Chunk]
     chunk --> textUnits[Text Units]
     end
-    subgraph phase2[Phase 2: Graph Extraction]
+    subgraph phase2[Phase 2: Document Processing]
+    documents --> link_to_text_units[Link to TextUnits]
+    textUnits --> link_to_text_units
+    link_to_text_units --> document_outputs[Documents Table]
+    end
+    subgraph phase3[Phase 3 Graph Extraction]
     textUnits --> graph_extract[Entity & Relationship Extraction]
     graph_extract --> graph_summarize[Entity & Relationship Summarization]
     graph_summarize --> claim_extraction[Claim Extraction]
     claim_extraction --> graph_outputs[Graph Tables]
     end
-    subgraph phase3[Phase 3: Graph Augmentation]
+    subgraph phase4[Phase 4: Graph Augmentation]
     graph_outputs --> community_detect[Community Detection]
     community_detect --> community_outputs[Communities Table]
     end
-    subgraph phase4[Phase 4: Community Summarization]
+    subgraph phase5[Phase 5: Community Summarization]
     community_outputs --> summarized_communities[Community Summarization]
     summarized_communities --> community_report_outputs[Community Reports Table]
     end
-    subgraph phase5[Phase 5: Document Processing]
-    documents --> link_to_text_units[Link to TextUnits]
-    textUnits --> link_to_text_units
-    link_to_text_units --> document_outputs[Documents Table]
-    end
-    subgraph phase6[Phase 6: Network Visualization]
-    graph_outputs --> graph_embed[Graph Embedding]
-    graph_embed --> combine_nodes[Final Entities]
-    end
-    subgraph phase7[Phase 7: Text Embeddings]
+    subgraph phase6[Phase 6: Text Embeddings]
     textUnits --> text_embed[Text Embedding]
     graph_outputs --> description_embed[Description Embedding]
     community_report_outputs --> content_embed[Content Embedding]
@@ -73,10 +69,30 @@ flowchart LR
 
 ```
 
-## Phase 2: Graph Extraction
+## Phase 2: Document Processing
+
+In this phase of the workflow, we create the _Documents_ table for the knowledge model. Final documents are not used directly in GraphRAG, but this step links them to their constituent text units for provenance in your own applications.
+
+```mermaid
+---
+title: Document Processing
+---
+flowchart LR
+    aug[Augment] --> dp[Link to TextUnits] --> dg[Documents Table]
+```
+
+### Link to TextUnits
+
+In this step, we link each document to the text-units that were created in the first phase. This allows us to understand which documents are related to which text-units and vice-versa.
+
+### Documents Table
+
+At this point, we can export the **Documents** table into the knowledge Model.
+
+## Phase 3: Graph Extraction
 
 In this phase, we analyze each text unit and extract our graph primitives: _Entities_, _Relationships_, and _Claims_.
-Entities and Relationships are extracted at once in our _entity_extract_ verb, and claims are extracted in our _claim_extract_ verb. Results are then combined and passed into following phases of the pipeline.
+Entities and Relationships are extracted at once in our _extract_graph_ workflow, and claims are extracted in our _extract_claims_ workflow. Results are then combined and passed into following phases of the pipeline.
 
 ```mermaid
 ---
@@ -87,9 +103,11 @@ flowchart LR
     tu --> ce[Claim Extraction]
 ```
 
+> Note: if you are using the [FastGraphRAG](https://microsoft.github.io/graphrag/index/methods/#fastgraphrag) option, entity and relationship extraction will be performed using NLP to conserve LLM resources, and claim extraction will always be skipped.
+
 ### Entity & Relationship Extraction
 
-In this first step of graph extraction, we process each text-unit in order to extract entities and relationships out of the raw text using the LLM. The output of this step is a subgraph-per-TextUnit containing a list of **entities** with a _title_, _type_, and _description_, and a list of **relationships** with a _source_, _target_, and _description_.
+In this first step of graph extraction, we process each text-unit to extract entities and relationships out of the raw text using the LLM. The output of this step is a subgraph-per-TextUnit containing a list of **entities** with a _title_, _type_, and _description_, and a list of **relationships** with a _source_, _target_, and _description_.
 
 These subgraphs are merged together - any entities with the same _title_ and _type_ are merged by creating an array of their descriptions. Similarly, any relationships with the same _source_ and _target_ are merged by creating an array of their descriptions.
 
@@ -103,9 +121,9 @@ Finally, as an independent workflow, we extract claims from the source TextUnits
 
 Note: claim extraction is _optional_ and turned off by default. This is because claim extraction generally requires prompt tuning to be useful.
 
-## Phase 3: Graph Augmentation
+## Phase 4: Graph Augmentation
 
-Now that we have a usable graph of entities and relationships, we want to understand their community structure. These give us explicit ways of understanding the topological structure of our graph.
+Now that we have a usable graph of entities and relationships, we want to understand their community structure. These give us explicit ways of understanding the organization of our graph.
 
 ```mermaid
 ---
@@ -123,7 +141,7 @@ In this step, we generate a hierarchy of entity communities using the Hierarchic
 
 Once our graph augmentation steps are complete, the final **Entities**, **Relationships**, and **Communities** tables are exported.
 
-## Phase 4: Community Summarization
+## Phase 5: Community Summarization
 
 ```mermaid
 ---
@@ -149,31 +167,6 @@ In this step, each _community report_ is then summarized via the LLM for shortha
 
 At this point, some bookkeeping work is performed and we export the **Community Reports** tables.
 
-## Phase 5: Document Processing
-
-In this phase of the workflow, we create the _Documents_ table for the knowledge model.
-
-```mermaid
----
-title: Document Processing
----
-flowchart LR
-    aug[Augment] --> dp[Link to TextUnits] --> dg[Documents Table]
-```
-
-### Augment with Columns (CSV Only)
-
-If the workflow is operating on CSV data, you may configure your workflow to add additional fields to Documents output. These fields should exist on the incoming CSV tables. Details about configuring this can be found in the [configuration documentation](../config/overview.md).
-
-### Link to TextUnits
-
-In this step, we link each document to the text-units that were created in the first phase. This allows us to understand which documents are related to which text-units and vice-versa.
-
-### Documents Table
-
-At this point, we can export the **Documents** table into the knowledge Model.
-
-
 ## Phase 6: Text Embedding
 
 For all artifacts that require downstream vector search, we generate text embeddings as a final step. These embeddings are written directly to a configured vector store. By default we embed entity descriptions, text unit text, and community report text.
diff --git a/docs/index/inputs.md b/docs/index/inputs.md
index 4bc5ad620a..b62bceacfa 100644
--- a/docs/index/inputs.md
+++ b/docs/index/inputs.md
@@ -18,15 +18,15 @@ Also see the [outputs](outputs.md) documentation for the final documents table s
 
 ## Bring-your-own DataFrame
 
-As of version 2.6.0, GraphRAG's [indexing API method](https://github.com/microsoft/graphrag/blob/main/graphrag/api/index.py) allows you to pass in your own pandas DataFrame and bypass all of the input loading/parsing described in the next section. This is convenient if you have content in a format or storage location we don't support out-of-the-box. __You must ensure that your input DataFrame conforms to the schema described above.__ All of the chunking behavior described later will proceed exactly the same.
+GraphRAG's [indexing API method](https://github.com/microsoft/graphrag/blob/main/graphrag/api/index.py) allows you to pass in your own pandas DataFrame and bypass all of the input loading/parsing described in the next section. This is convenient if you have content in a format or storage location we don't support out-of-the-box. _You must ensure that your input DataFrame conforms to the schema described above._ All of the chunking behavior described later will proceed exactly the same.
 
 ## Custom File Handling
 
-As of version 3.0.0, we have migrated to using an injectable InputReader provider class. This means you can implement any input file handling you want in a class that extends InputReader and register it with the InputReaderFactory. See the [architecture page](https://microsoft.github.io/graphrag/index/architecture/) for more info on our standard provider pattern.
+We use an injectable InputReader provider class. This means you can implement any input file handling you want in a class that extends InputReader and register it with the InputReaderFactory. See the [architecture page](https://microsoft.github.io/graphrag/index/architecture/) for more info on our standard provider pattern.
 
 ## Formats
 
-We support three file formats out-of-the-box. This covers the overwhelming majority of use cases we have encountered. If you have a different format, we recommend writing a script to convert to one of these, which are widely used and supported by many tools and libraries.
+We support three file formats out-of-the-box. This covers the overwhelming majority of use cases we have encountered. If you have a different format, we recommend either implementing your own InputReader or writing a script to convert to one of these, which are widely used and supported by many tools and libraries.
 
 ### Plain Text
 
@@ -44,7 +44,7 @@ JSON files (typically ending in a .json extension) contain [structured objects](
 
 ## Metadata
 
-With the structured file formats (CSV and JSON) you can configure any number of columns to be added to a persisted `metadata` field in the DataFrame. This is configured by supplying a list of columns name to collect. If this is configured, the output `metadata` column will have a dict containing a key for each column, and the value of the column for that document. This metadata can optionally be used later in the GraphRAG pipeline.
+With the structured file formats (CSV and JSON) you can configure any number of columns to be added to a persisted `metadata` field in the DataFrame. This is configured by supplying a list of column names to collect. If this is configured, the output `metadata` column will have a dict containing a key for each column, and the value of the column for that document. This metadata can optionally be used later in the GraphRAG pipeline.
 
 ### Example
 
@@ -72,7 +72,7 @@ Documents DataFrame
 
 ## Chunking and Metadata
 
-As described on the [default dataflow](default_dataflow.md#phase-1-compose-textunits) page, documents are *chunked* into smaller "text units" for processing. This is done because document content size often exceeds the available context window for a given language model. There are a handful of settings you can adjust for this chunking, the most relevant being the `chunk_size` and `overlap`. We now also support a metadata processing scheme that can improve indexing results for some use cases. We will describe this feature in detail here.
+As described on the [default dataflow](default_dataflow.md#phase-1-compose-textunits) page, documents are *chunked* into smaller "text units" for processing. This is done because document content size often exceeds the available context window for a given language model. There are a handful of settings you can adjust for this chunking, the most relevant being the `chunk_size` and `overlap`. We also support a metadata processing scheme that can improve indexing results for some use cases. We will describe this feature in detail here.
 
 Imagine the following scenario: you are indexing a collection of news articles. Each article text starts with a headline and author, and then proceeds with the content. When documents are chunked, they are split evenly according to your configured chunk size. In other words, the first *n* tokens are read into a text unit, and then the next *n*, until the end of the content. This means that front matter at the beginning of the document (such as the headline and author in this example) *is not copied to each chunk*. It only exists in the first chunk. When we later retrieve those chunks for summarization, they may therefore be missing shared information about the source document that should always be provided to the model. We have configuration options to copy repeated content into each text unit to address this issue.
 
@@ -89,7 +89,7 @@ Next, the `chunks` block needs to instruct the chunker how to handle this metada
 
 ### Examples
 
-The following are several examples to help illustrate how chunking config and metadate prepending works for each file format. Note that we are using word count here as "tokens" for the illustration, but language model tokens are [not equivalent to words](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them).
+The following are several examples to help illustrate how chunking config and metadata prepending works for each file format. Note that we are using word count here as "tokens" for the illustration, but language model tokens are [not equivalent to words](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them).
 
 #### Text files
 
diff --git a/docs/index/methods.md b/docs/index/methods.md
index 9f785a0ae7..3c10cfb21d 100644
--- a/docs/index/methods.md
+++ b/docs/index/methods.md
@@ -13,7 +13,7 @@ This is the method described in the original [blog post](https://www.microsoft.c
 - claim extraction (optional): LLM is prompted to extract and describe claims from each text unit.
 - community report generation: entity and relationship descriptions (and optionally claims) for each community are collected and used to prompt the LLM to generate a summary report.
 
-`graphrag index --method standard`. This is the default method, so the method param can actual be omitted.
+`graphrag index --method standard`. This is the default method, so the method param can be omitted on the command line.
 
 ## FastGraphRAG
 
@@ -23,7 +23,7 @@ FastGraphRAG is a method that substitutes some of the language model reasoning f
 - relationship extraction: relationships are defined as text unit co-occurrence between entity pairs. There is no description.
 - entity summarization: not necessary.
 - relationship summarization: not necessary.
-- claim extraction (optional): unused.
+- claim extraction: unused.
 - community report generation: The direct text unit content containing each entity noun phrase is collected and used to prompt the LLM to generate a summary report.
 
 `graphrag index --method fast`
@@ -41,4 +41,4 @@ You can install it manually by running `python -m spacy download <model_name>`,
 
 ## Choosing a Method
 
-Standard GraphRAG provides a rich description of real-world entities and relationships, but is more expensive than FastGraphRAG. We estimate graph extraction to constitute roughly 75% of indexing cost. FastGraphRAG is therefore much cheaper, but the tradeoff is that the extracted graph is less directly relevant for use outside of GraphRAG, and the graph tends to be quite a bit noisier. If high fidelity entities and graph exploration are important to your use case, we recommend staying with traditional GraphRAG. If your use case is primarily aimed at summary questions using global search, FastGraphRAG provides high quality summarization at much less LLM cost.
+Standard GraphRAG provides a rich description of real-world entities and relationships, but is more expensive than FastGraphRAG. We estimate graph extraction to constitute roughly 75% of indexing cost. FastGraphRAG is therefore much cheaper, but the tradeoff is that the extracted graph is less directly relevant for use outside of GraphRAG, and the graph tends to be quite a bit noisier. If high fidelity entities and graph exploration are important to your use case, we recommend staying with traditional GraphRAG. If your use case is primarily aimed at summary questions using global search, FastGraphRAG provides high quality summarization with much lower language model cost.
diff --git a/docs/index/overview.md b/docs/index/overview.md
index 550909f6a2..d3974cc645 100644
--- a/docs/index/overview.md
+++ b/docs/index/overview.md
@@ -7,8 +7,7 @@ Indexing Pipelines are configurable. They are composed of workflows, standard an
 - extract entities, relationships and claims from raw text
 - perform community detection in entities
 - generate community summaries and reports at multiple levels of granularity
-- embed entities into a graph vector space
-- embed text chunks into a textual vector space
+- embed text into a vector space
 
 The outputs of the pipeline are stored as Parquet tables by default, and embeddings are written to your configured vector store.
 
diff --git a/docs/prompt_tuning/auto_prompt_tuning.md b/docs/prompt_tuning/auto_prompt_tuning.md
index 31375db3da..440741f571 100644
--- a/docs/prompt_tuning/auto_prompt_tuning.md
+++ b/docs/prompt_tuning/auto_prompt_tuning.md
@@ -1,8 +1,8 @@
 # Auto Prompt Tuning ⚙�
 
-GraphRAG provides the ability to create domain adapted prompts for the generation of the knowledge graph. This step is optional, though it is highly encouraged to run it as it will yield better results when executing an Index Run.
+GraphRAG provides the ability to create domain-adapted prompts for the generation of the knowledge graph. This step is optional, though it is highly encouraged to run it as it will yield better results when executing an Index Run.
 
-These are generated by loading the inputs, splitting them into chunks (text units) and then running a series of LLM invocations and template substitutions to generate the final prompts. We suggest using the default values provided by the script, but in this page you'll find the detail of each in case you want to further explore and tweak the prompt tuning algorithm.
+These are generated by loading the inputs, splitting them into chunks (text units) and then running a series of LLM invocations and template substitutions to generate the final prompts. We suggest using the default values provided by the script, but in this page you'll find the details of each in case you want to further explore and tweak the prompt tuning algorithm.
 
 <p align="center">
 <img src="../../img/auto-tune-diagram.png" alt="Figure 1: Auto Tuning Conceptual Diagram." width="850" align="center" />
@@ -73,17 +73,17 @@ The auto tuning feature ingests the input data and then divides it into text uni
 After that, it uses one of the following selection methods to pick a sample to work with for prompt generation:
 
 - `random`: Select text units randomly. This is the default and recommended option.
-- `top`: Select the head n text units.
+- `top`: Select the head _n_ text units.
 - `all`: Use all text units for the generation. Use only with small datasets; this option is not usually recommended.
-- `auto`: Embed text units in a lower-dimensional space and select the k nearest neighbors to the centroid. This is useful when you have a large dataset and want to select a representative sample.
+- `auto`: Embed text units in a lower-dimensional space and select the _k_ nearest neighbors to the centroid. This is useful when you have a large dataset and want to select a representative sample.
 
-## Modify Env Vars
+## Modify Config
 
 After running auto tuning, you should modify the following config variables to pick up the new prompts on your index run. Note: Please make sure to update the correct path to the generated prompts, in this example we are using the default "prompts" path.
 
 ```yaml
-entity_extraction:
-  prompt: "prompts/entity_extraction.txt"
+extract_graph:
+  prompt: "prompts/extract_graph.txt"
 
 summarize_descriptions:
   prompt: "prompts/summarize_descriptions.txt"
diff --git a/docs/prompt_tuning/overview.md b/docs/prompt_tuning/overview.md
index a12cebb257..e8518f6b21 100644
--- a/docs/prompt_tuning/overview.md
+++ b/docs/prompt_tuning/overview.md
@@ -8,8 +8,8 @@ The default prompts are the simplest way to get started with the GraphRAG system
 
 ## Auto Tuning
 
-Auto Tuning leverages your input data and LLM interactions to create domain adapted prompts for the generation of the knowledge graph. It is highly encouraged to run it as it will yield better results when executing an Index Run. For more details about how to use it, please refer to the [Auto Tuning](auto_prompt_tuning.md) documentation.
+Auto Tuning leverages your input data and LLM interactions to create domain-adapted prompts for the generation of the knowledge graph. It is highly encouraged to run it as it will yield better results when executing an Index Run. For more details about how to use it, please refer to the [Auto Tuning](auto_prompt_tuning.md) page.
 
 ## Manual Tuning
 
-Manual tuning is an advanced use-case. Most users will want to use the Auto Tuning feature instead. Details about how to use manual configuration are available in the [manual tuning](manual_prompt_tuning.md) documentation.
+Manual tuning is an advanced use-case. Most users will want to use the Auto Tuning feature instead. Details about how to use manual configuration are available in the [manual tuning](manual_prompt_tuning.md) page.
diff --git a/docs/query/drift_search.md b/docs/query/drift_search.md
index e3d9ddc021..80c0607330 100644
--- a/docs/query/drift_search.md
+++ b/docs/query/drift_search.md
@@ -21,7 +21,7 @@ DRIFT Search introduces a new approach to local search queries by including comm
 
 Below are the key parameters of the [DRIFTSearch class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/drift_search/search.py):
 
-- `llm`: OpenAI model object to be used for response generation
+* `model`: Language model chat completion object to be used for response generation
 - `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main/graphrag/query/structured_search/drift_search/drift_context.py) object to be used for preparing context data from community reports and query information
 - `config`: model to define the DRIFT Search hyperparameters. [DRIFT Config model](https://github.com/microsoft/graphrag/blob/main/graphrag/config/models/drift_search_config.py)
 - `tokenizer`: token encoder for tracking the budget for the algorithm.
diff --git a/docs/query/global_search.md b/docs/query/global_search.md
index a9685d70be..da453dbe66 100644
--- a/docs/query/global_search.md
+++ b/docs/query/global_search.md
@@ -54,7 +54,7 @@ The quality of the global search’s response can be heavily influenced by the l
 
 Below are the key parameters of the [GlobalSearch class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/global_search/search.py):
 
-* `llm`: OpenAI model object to be used for response generation
+* `model`: Language model chat completion object to be used for response generation
 * `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/global_search/community_context.py) object to be used for preparing context data from community reports
 * `map_system_prompt`: prompt template used in the `map` stage. Default template can be found at [map_system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/global_search_map_system_prompt.py)
 * `reduce_system_prompt`: prompt template used in the `reduce` stage, default template can be found at [reduce_system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/global_search_reduce_system_prompt.py)
diff --git a/docs/query/local_search.md b/docs/query/local_search.md
index bf0f43e3ce..2cd77e640e 100644
--- a/docs/query/local_search.md
+++ b/docs/query/local_search.md
@@ -48,7 +48,7 @@ Given a user query and, optionally, the conversation history, the local search m
 
 Below are the key parameters of the [LocalSearch class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/search.py):
 
-* `llm`: OpenAI model object to be used for response generation
+* `model`: Language model chat completion object to be used for response generation
 * `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects
 * `system_prompt`: prompt template used to generate the search response. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/local_search_system_prompt.py)
 * `response_type`: free-form text describing the desired response type and format (e.g., `Multiple Paragraphs`, `Multi-Page Report`)
diff --git a/docs/query/overview.md b/docs/query/overview.md
index dd375b8b41..bf2b612642 100644
--- a/docs/query/overview.md
+++ b/docs/query/overview.md
@@ -1,34 +1,35 @@
 # Query Engine 🔎
 
-The Query Engine is the retrieval module of the Graph RAG Library. It is one of the two main components of the Graph RAG library, the other being the Indexing Pipeline (see [Indexing Pipeline](../index/overview.md)).
+The Query Engine is the retrieval module of the GraphRAG library, and operates over completed [indexes](../index/overview.md).
 It is responsible for the following tasks:
 
 - [Local Search](#local-search)
 - [Global Search](#global-search)
 - [DRIFT Search](#drift-search)
+- Basic Search
 - [Question Generation](#question-generation)
 
 ## Local Search
 
-Local search method generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents (e.g. What are the healing properties of chamomile?).
+Local search generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents (e.g. What are the healing properties of chamomile?).
 
-For more details about how Local Search works please refer to the [Local Search](local_search.md) documentation.
+For more details about how Local Search works please refer to the [Local Search](local_search.md) page.
 
 ## Global Search
 
-Global search method generates answers by searching over all AI-generated community reports in a map-reduce fashion. This is a resource-intensive method, but often gives good responses for questions that require an understanding of the dataset as a whole (e.g. What are the most significant values of the herbs mentioned in this notebook?).
+Global search generates answers by searching over all AI-generated community reports in a map-reduce fashion. This is a resource-intensive method, but often gives good responses for questions that require an understanding of the dataset as a whole (e.g. What are the most significant values of the herbs mentioned in this notebook?).
 
-More about this can be checked at the [Global Search](global_search.md) documentation.
+More about this is provided on the [Global Search](global_search.md) page.
 
 ## DRIFT Search
 
-DRIFT Search introduces a new approach to local search queries by including community information in the search process. This greatly expands the breadth of the query’s starting point and leads to retrieval and usage of a far higher variety of facts in the final answer. This addition expands the GraphRAG query engine by providing a more comprehensive option for local search, which uses community insights to refine a query into detailed follow-up questions.
+DRIFT Search introduces a new approach to local search queries by including community information in the search process. This greatly expands the breadth of the query’s starting point and leads to retrieval and usage of a far higher variety of facts in the final answer. This expands the GraphRAG query engine by providing a more comprehensive option for local search, which uses community insights to refine a query into detailed follow-up questions.
 
-To learn more about DRIFT Search, please refer to the [DRIFT Search](drift_search.md) documentation.
+To learn more about DRIFT Search, please refer to the [DRIFT Search](drift_search.md) page.
 
 ## Basic Search
 
-GraphRAG includes a rudimentary implementation of basic vector RAG to make it easy to compare different search results based on the type of question you are asking. You can specify the top `k` txt unit chunks to include in the summarization context.
+GraphRAG includes a rudimentary implementation of basic vector RAG to make it easy to compare different search results based on the type of question you are asking. You can specify the top `k` text unit chunks to include in the summarization context.
 
 ## Question Generation
 
diff --git a/docs/query/question_generation.md b/docs/query/question_generation.md
index 525a465499..6f5da81e5d 100644
--- a/docs/query/question_generation.md
+++ b/docs/query/question_generation.md
@@ -11,7 +11,7 @@ Given a list of prior user questions, the question generation method uses the sa
 
 Below are the key parameters of the [Question Generation class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/local_gen.py):
 
-* `llm`: OpenAI model object to be used for response generation
+* `model`: Language model chat completion object to be used for response generation
 * `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects, using the same context builder class as in local search
 * `system_prompt`: prompt template used to generate candidate questions. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/prompts/query/question_gen_system_prompt.py)
 * `llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call
diff --git a/unified-search-app/README.md b/unified-search-app/README.md
index 31f9be28c4..6e7ccc82f0 100644
--- a/unified-search-app/README.md
+++ b/unified-search-app/README.md
@@ -39,8 +39,8 @@ Unified Search supports multiple GraphRAG indexes by using a directory listing f
 For example, if you have a folder of GraphRAG indexes called "projects" and inside that you ran the Getting Started instructions, your listing.json in the projects folder could look like:
 ```json
 [{
-    "key": "ragtest-demo",
-    "path": "ragtest",
+    "key": "christmas-demo",
+    "path": "christmas",
     "name": "A Christmas Carol",
     "description": "Getting Started index of the novel A Christmas Carol",
     "community_level": 2
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index a573b9daa5..dc64e0e77c 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,6 +7,7 @@
 import logging
 from typing import TYPE_CHECKING
 
+import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -17,8 +18,6 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
-import graphrag.api as api
-
 if TYPE_CHECKING:
     import pandas as pd
 

From e0cce31f5417bf9b8627420c224e6d2539246c7a Mon Sep 17 00:00:00 2001
From: Derek Worthen <worthend.derek@gmail.com>
Date: Mon, 10 Nov 2025 07:57:03 -0800
Subject: [PATCH 23/40] Graphrag config (#2119)

* Add load_config to graphrag-common package.
---
 docs/examples_notebooks/api_overview.ipynb    |  17 +-
 docs/examples_notebooks/input_documents.ipynb |  14 +-
 docs/get_started.md                           |  72 +++---
 docs/index/byog.md                            |   2 +-
 docs/prompt_tuning/auto_prompt_tuning.md      |  10 +-
 packages/graphrag-common/README.md            |  68 ++++++
 .../graphrag_common/config/__init__.py        |   8 +
 .../graphrag_common/config/load_config.py     | 205 ++++++++++++++++++
 packages/graphrag-common/pyproject.toml       |   5 +-
 packages/graphrag/graphrag/api/query.py       |  20 +-
 packages/graphrag/graphrag/cache/factory.py   |   6 +-
 packages/graphrag/graphrag/cli/index.py       |  21 +-
 packages/graphrag/graphrag/cli/initialize.py  |  23 +-
 packages/graphrag/graphrag/cli/main.py        | 119 ++--------
 packages/graphrag/graphrag/cli/prompt_tune.py |   7 +-
 packages/graphrag/graphrag/cli/query.py       |  44 ++--
 .../graphrag/config/create_graphrag_config.py |  43 ----
 packages/graphrag/graphrag/config/defaults.py |   1 -
 .../graphrag/config/environment_reader.py     | 155 -------------
 .../graphrag/graphrag/config/load_config.py   | 170 ++-------------
 .../config/models/community_reports_config.py |   8 +-
 .../config/models/extract_claims_config.py    |   4 +-
 .../config/models/extract_graph_config.py     |   4 +-
 .../config/models/graph_rag_config.py         |  31 +--
 .../models/summarize_descriptions_config.py   |   4 +-
 .../graphrag/graphrag/config/read_dotenv.py   |  25 ---
 .../graphrag/index/run/run_pipeline.py        |   4 +-
 .../workflows/create_community_reports.py     |   2 +-
 .../create_community_reports_text.py          |   2 +-
 .../index/workflows/extract_covariates.py     |   2 +-
 .../graphrag/index/workflows/extract_graph.py |   6 +-
 .../update_entities_relationships.py          |   2 +-
 packages/graphrag/graphrag/logger/factory.py  |   3 +-
 .../graphrag/logger/standard_logging.py       |   2 +-
 packages/graphrag/graphrag/utils/api.py       |   9 +-
 packages/graphrag/pyproject.toml              |   2 -
 pyproject.toml                                |   6 +-
 tests/integration/cache/test_factory.py       |   2 +-
 .../logging/test_standard_logging.py          |  22 +-
 tests/smoke/test_fixtures.py                  |   3 +-
 tests/unit/config/test_config.py              |  65 +++---
 tests/unit/config/utils.py                    |   5 +-
 tests/unit/indexing/test_init_content.py      |   5 +-
 tests/unit/load_config/__init__.py            |   2 +
 tests/unit/load_config/config.py              |  27 +++
 .../load_config/fixtures/config_with_env.yaml |  10 +
 .../load_config/fixtures/invalid_config.yaml  |   1 +
 .../fixtures/invalid_config_format.yaml       |   8 +
 tests/unit/load_config/fixtures/settings.yaml |  10 +
 tests/unit/load_config/fixtures/test.env      |   1 +
 tests/unit/load_config/test_load_config.py    | 157 ++++++++++++++
 tests/verbs/test_create_base_text_units.py    |   8 +-
 tests/verbs/test_create_communities.py        |   4 +-
 tests/verbs/test_create_community_reports.py  |   4 +-
 tests/verbs/test_create_final_documents.py    |   6 +-
 tests/verbs/test_create_final_text_units.py   |   4 +-
 tests/verbs/test_extract_covariates.py        |   4 +-
 tests/verbs/test_extract_graph.py             |  10 +-
 tests/verbs/test_extract_graph_nlp.py         |   4 +-
 tests/verbs/test_finalize_graph.py            |   4 +-
 tests/verbs/test_generate_text_embeddings.py  |   4 +-
 tests/verbs/test_pipeline_state.py            |   5 +-
 tests/verbs/test_prune_graph.py               |   4 +-
 .../data_sources/blob_source.py               |   3 +-
 uv.lock                                       |  14 +-
 65 files changed, 786 insertions(+), 736 deletions(-)
 create mode 100644 packages/graphrag-common/graphrag_common/config/__init__.py
 create mode 100644 packages/graphrag-common/graphrag_common/config/load_config.py
 delete mode 100644 packages/graphrag/graphrag/config/create_graphrag_config.py
 delete mode 100644 packages/graphrag/graphrag/config/environment_reader.py
 delete mode 100644 packages/graphrag/graphrag/config/read_dotenv.py
 create mode 100644 tests/unit/load_config/__init__.py
 create mode 100644 tests/unit/load_config/config.py
 create mode 100644 tests/unit/load_config/fixtures/config_with_env.yaml
 create mode 100644 tests/unit/load_config/fixtures/invalid_config.yaml
 create mode 100644 tests/unit/load_config/fixtures/invalid_config_format.yaml
 create mode 100644 tests/unit/load_config/fixtures/settings.yaml
 create mode 100644 tests/unit/load_config/fixtures/test.env
 create mode 100644 tests/unit/load_config/test_load_config.py

diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index 86b0184b29..06187a5771 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -16,7 +16,7 @@
    "source": [
     "## API Overview\n",
     "\n",
-    "This notebook provides a demonstration of how to interact with graphrag as a library using the API as opposed to the CLI. Note that graphrag's CLI actually connects to the library through this API for all operations. "
+    "This notebook provides a demonstration of how to interact with graphrag as a library using the API as opposed to the CLI. Note that graphrag's CLI actually connects to the library through this API for all operations.\n"
    ]
   },
   {
@@ -48,16 +48,17 @@
    "metadata": {},
    "source": [
     "## Prerequisite\n",
+    "\n",
     "As a prerequisite to all API operations, a `GraphRagConfig` object is required. It is the primary means to control the behavior of graphrag and can be instantiated from a `settings.yaml` configuration file.\n",
     "\n",
-    "Please refer to the [CLI docs](https://microsoft.github.io/graphrag/cli/#init) for more detailed information on how to generate the `settings.yaml` file."
+    "Please refer to the [CLI docs](https://microsoft.github.io/graphrag/cli/#init) for more detailed information on how to generate the `settings.yaml` file.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Generate a `GraphRagConfig` object"
+    "### Generate a `GraphRagConfig` object\n"
    ]
   },
   {
@@ -77,14 +78,14 @@
    "source": [
     "## Indexing API\n",
     "\n",
-    "*Indexing* is the process of ingesting raw text data and constructing a knowledge graph. GraphRAG currently supports plaintext (`.txt`) and `.csv` file formats."
+    "_Indexing_ is the process of ingesting raw text data and constructing a knowledge graph. GraphRAG currently supports plaintext (`.txt`) and `.csv` file formats.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Build an index"
+    "## Build an index\n"
    ]
   },
   {
@@ -107,7 +108,7 @@
    "source": [
     "## Query an index\n",
     "\n",
-    "To query an index, several index files must first be read into memory and passed to the query API. "
+    "To query an index, several index files must first be read into memory and passed to the query API.\n"
    ]
   },
   {
@@ -138,7 +139,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The response object is the official reponse from graphrag while the context object holds various metadata regarding the querying process used to obtain the final response."
+    "The response object is the official reponse from graphrag while the context object holds various metadata regarding the querying process used to obtain the final response.\n"
    ]
   },
   {
@@ -154,7 +155,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Digging into the context a bit more provides users with extremely granular information such as what sources of data (down to the level of text chunks) were ultimately retrieved and used as part of the context sent to the LLM model)."
+    "Digging into the context a bit more provides users with extremely granular information such as what sources of data (down to the level of text chunks) were ultimately retrieved and used as part of the context sent to the LLM model).\n"
    ]
   },
   {
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 73e51780d8..b9af6075ab 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -18,7 +18,7 @@
     "\n",
     "Newer versions of GraphRAG let you submit a dataframe directly instead of running through the input processing step. This notebook demonstrates with regular or update runs.\n",
     "\n",
-    "If performing an update, the assumption is that your dataframe contains only the new documents to add to the index."
+    "If performing an update, the assumption is that your dataframe contains only the new documents to add to the index.\n"
    ]
   },
   {
@@ -54,7 +54,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Generate a `GraphRagConfig` object"
+    "### Generate a `GraphRagConfig` object\n"
    ]
   },
   {
@@ -72,14 +72,14 @@
    "source": [
     "## Indexing API\n",
     "\n",
-    "*Indexing* is the process of ingesting raw text data and constructing a knowledge graph. GraphRAG currently supports plaintext (`.txt`) and `.csv` file formats."
+    "_Indexing_ is the process of ingesting raw text data and constructing a knowledge graph. GraphRAG currently supports plaintext (`.txt`) and `.csv` file formats.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Build an index"
+    "## Build an index\n"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "source": [
     "## Query an index\n",
     "\n",
-    "To query an index, several index files must first be read into memory and passed to the query API. "
+    "To query an index, several index files must first be read into memory and passed to the query API.\n"
    ]
   },
   {
@@ -140,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The response object is the official reponse from graphrag while the context object holds various metadata regarding the querying process used to obtain the final response."
+    "The response object is the official reponse from graphrag while the context object holds various metadata regarding the querying process used to obtain the final response.\n"
    ]
   },
   {
@@ -156,7 +156,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Digging into the context a bit more provides users with extremely granular information such as what sources of data (down to the level of text chunks) were ultimately retrieved and used as part of the context sent to the LLM model)."
+    "Digging into the context a bit more provides users with extremely granular information such as what sources of data (down to the level of text chunks) were ultimately retrieved and used as part of the context sent to the LLM model).\n"
    ]
   },
   {
diff --git a/docs/get_started.md b/docs/get_started.md
index 5de773924d..9a6f496383 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -10,40 +10,59 @@ The following is a simple end-to-end example for using GraphRAG on the command l
 
 It shows how to use the system to index some text, and then use the indexed data to answer questions about the documents.
 
-# Install GraphRAG
+## Install GraphRAG
+
+To get started, create a project space and python virtual environment to install `graphrag`.
+
+### Create Project Space
 
 ```bash
-pip install graphrag
+mkdir graphrag_quickstart
+cd graphrag_quickstart
+python -m venv .venv
 ```
+### Activate Python Virtual Environment - Unix/MacOS
 
-# Running the Indexer
-We need to set up a data project and some initial configuration. First let's get a sample dataset ready:
+```bash
+source .venv/bin/activate
+```
 
-```sh
-mkdir -p ./christmas/input
+### Activate Python Virtual Environment - Windows
+
+```bash
+.venv\Scripts\activate
 ```
 
-Get a copy of A Christmas Carol by Charles Dickens from a trusted source:
+### Install GraphRAG
 
-```sh
-curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./christmas/input/book.txt
+```bash
+python -m pip install graphrag
 ```
 
-## Set Up Your Workspace Variables
+### Initialize GraphRAG
 
 To initialize your workspace, first run the `graphrag init` command.
-Since we have already configured a directory named `./christmas` in the previous step, run the following command:
 
 ```sh
-graphrag init --root ./christmas
+graphrag init
 ```
 
-This will create two files: `.env` and `settings.yaml` in the `./christmas` directory.
+This will create two files, `.env` and `settings.yaml`, and a directory `input`, in the current directory.
 
+- `input` Location of text files to process with `graphrag`.
 - `.env` contains the environment variables required to run the GraphRAG pipeline. If you inspect the file, you'll see a single environment variable defined,
   `GRAPHRAG_API_KEY=<API_KEY>`. Replace `<API_KEY>` with your own OpenAI or Azure API key.
 - `settings.yaml` contains the settings for the pipeline. You can modify this file to change the settings for the pipeline.
-  <br/>
+
+### Download Sample Text
+
+Get a copy of A Christmas Carol by Charles Dickens from a trusted source:
+
+```sh
+curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./input/book.txt
+```
+
+## Set Up Workspace Variables
 
 ### Using OpenAI
 
@@ -56,13 +75,14 @@ In addition to setting your API key, Azure OpenAI users should set the variables
 ```yaml
 type: chat
 model_provider: azure
+model: gpt-4.1
+deployment_name: <AZURE_DEPLOYMENT_NAME>
 api_base: https://<instance>.openai.azure.com
 api_version: 2024-02-15-preview # You can customize this for other versions
 ```
 
-Most people tend to name their deployments the same as their model - if yours are different, add the `deployment_name` as well.
-
 #### Using Managed Auth on Azure
+
 To use managed auth, edit the auth_type in your model config and *remove* the api_key line:
 
 ```yaml
@@ -71,38 +91,34 @@ auth_type: azure_managed_identity # Default auth_type is is api_key
 
 You will also need to login with [az login](https://learn.microsoft.com/en-us/cli/azure/authenticate-azure-cli) and select the subscription with your endpoint.
 
-## Running the Indexing pipeline
+## Index
 
-Now we're ready to run the pipeline!
+Now we're ready to index!
 
 ```sh
-graphrag index --root ./christmas
+graphrag index
 ```
 
 ![pipeline executing from the CLI](img/pipeline-running.png)
 
-This process will usually take a few minutes to run. Once the pipeline is complete, you should see a new folder called `./christmas/output` with a series of parquet files.
+This process will usually take a few minutes to run. Once the pipeline is complete, you should see a new folder called `./output` with a series of parquet files.
 
-# Using the Query Engine
+# Query
 
 Now let's ask some questions using this dataset.
 
 Here is an example using Global search to ask a high-level question:
 
 ```sh
-graphrag query \
---root ./christmas \
---method global \
---query "What are the top themes in this story?"
+graphrag query "What are the top themes in this story?"
 ```
 
 Here is an example using Local search to ask a more specific question about a particular character:
 
 ```sh
 graphrag query \
---root ./christmas \
---method local \
---query "Who is Scrooge and what are his main relationships?"
+"Who is Scrooge and what are his main relationships?" \
+--method local
 ```
 
 Please refer to [Query Engine](query/overview.md) docs for detailed information about how to leverage our Local and Global search mechanisms for extracting meaningful insights from data after the Indexer has wrapped up execution.
diff --git a/docs/index/byog.md b/docs/index/byog.md
index 7866e4b8f6..acd7679348 100644
--- a/docs/index/byog.md
+++ b/docs/index/byog.md
@@ -65,4 +65,4 @@ Putting it all together:
 
 - `output`: Create an output folder and put your entities and relationships (and optionally text_units) parquet files in it.
 - Update your config as noted above to only run the workflows subset you need.
-- Run `graphrag index --root <your project root>`
\ No newline at end of file
+- Run `graphrag index --root <your_project_root>`
\ No newline at end of file
diff --git a/docs/prompt_tuning/auto_prompt_tuning.md b/docs/prompt_tuning/auto_prompt_tuning.md
index 440741f571..eb77044cd5 100644
--- a/docs/prompt_tuning/auto_prompt_tuning.md
+++ b/docs/prompt_tuning/auto_prompt_tuning.md
@@ -20,16 +20,14 @@ Before running auto tuning, ensure you have already initialized your workspace w
 You can run the main script from the command line with various options:
 
 ```bash
-graphrag prompt-tune [--root ROOT] [--config CONFIG] [--domain DOMAIN]  [--selection-method METHOD] [--limit LIMIT] [--language LANGUAGE] \
+graphrag prompt-tune [--root ROOT] [--domain DOMAIN]  [--selection-method METHOD] [--limit LIMIT] [--language LANGUAGE] \
 [--max-tokens MAX_TOKENS] [--chunk-size CHUNK_SIZE] [--n-subset-max N_SUBSET_MAX] [--k K] \
 [--min-examples-required MIN_EXAMPLES_REQUIRED] [--discover-entity-types] [--output OUTPUT]
 ```
 
 ## Command-Line Options
 
-- `--config` (required): The path to the configuration file. This is required to load the data and model settings.
-
-- `--root` (optional): The data project root directory, including the config files (YML, JSON, or .env). Defaults to the current directory.
+- `--root` (optional): Path to the project directory that contains the config file (settings.yaml). Defaults to the current directory.
 
 - `--domain` (optional): The domain related to your input data, such as 'space science', 'microbiology', or 'environmental news'. If left empty, the domain will be inferred from the input data.
 
@@ -56,7 +54,7 @@ graphrag prompt-tune [--root ROOT] [--config CONFIG] [--domain DOMAIN]  [--selec
 ## Example Usage
 
 ```bash
-python -m graphrag prompt-tune --root /path/to/project --config /path/to/settings.yaml --domain "environmental news" \
+python -m graphrag prompt-tune --root /path/to/project --domain "environmental news" \
 --selection-method random --limit 10 --language English --max-tokens 2048 --chunk-size 256 --min-examples-required 3 \
 --no-discover-entity-types --output /path/to/output
 ```
@@ -64,7 +62,7 @@ python -m graphrag prompt-tune --root /path/to/project --config /path/to/setting
 or, with minimal configuration (suggested):
 
 ```bash
-python -m graphrag prompt-tune --root /path/to/project --config /path/to/settings.yaml --no-discover-entity-types
+python -m graphrag prompt-tune --root /path/to/project --no-discover-entity-types
 ```
 
 ## Document Selection Methods
diff --git a/packages/graphrag-common/README.md b/packages/graphrag-common/README.md
index 4447dc7219..45565a33a7 100644
--- a/packages/graphrag-common/README.md
+++ b/packages/graphrag-common/README.md
@@ -48,4 +48,72 @@ single2 = factory.create("some_other_strategy", {"value": "ignored"})
 assert single1 is single2
 assert single1.get_value() == "singleton"
 assert single2.get_value() == "singleton"
+```
+
+## Config module
+
+```python
+from pydantic import BaseModel, Field
+from graphrag_common.config import load_config
+
+from pathlib import Path
+
+class Logging(BaseModel):
+    """Test nested model."""
+
+    directory: str = Field(default="output/logs")
+    filename: str = Field(default="logs.txt")
+
+class Config(BaseModel):
+    """Test configuration model."""
+
+    name: str = Field(description="Name field.")
+    logging: Logging = Field(description="Nested model field.")
+
+# Basic - by default:
+# - searches for Path.cwd() / settings.[yaml|yml|json] 
+# - sets the CWD to the directory containing the config file.
+#   so if no custom config path is provided than CWD remains unchanged.
+# - loads config_directory/.env file
+# - parses ${env} in the config file
+config = load_config(Config)
+
+# Custom file location
+config = load_config(Config, "path_to_config_filename_or_directory_containing_settings.[yaml|yml|json]")
+
+# Using a custom file extension with 
+# custom config parser (str) -> dict[str, Any]
+config = load_config(
+    config_initializer=Config,
+    config_path="config.toml",
+    config_parser=lambda contents: toml.loads(contents) # Needs toml pypi package
+)
+
+# With overrides - provided values override whats in the config file
+# Only overrides what is specified - recursively merges settings.
+config = load_config(
+    config_initializer=Config,
+    overrides={
+        "name": "some name",
+        "logging": {
+            "filename": "my_logs.txt"
+        }
+    },
+)
+
+# By default, sets CWD to directory containing config file
+# So custom config paths will change the CWD.
+config = load_config(
+    config_initializer=Config,
+    config_path="some/path/to/config.yaml",
+    set_cwd=True # default
+)
+
+# now cwd == some/path/to
+assert Path.cwd() == "some/path/to"
+
+# And now throughout the codebase resolving relative paths in config
+# will resolve relative to the config directory
+Path(config.logging.directory) == "some/path/to/output/logs"
+
 ```
\ No newline at end of file
diff --git a/packages/graphrag-common/graphrag_common/config/__init__.py b/packages/graphrag-common/graphrag_common/config/__init__.py
new file mode 100644
index 0000000000..71f24b79b7
--- /dev/null
+++ b/packages/graphrag-common/graphrag_common/config/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The GraphRAG config module."""
+
+from graphrag_common.config.load_config import ConfigParsingError, load_config
+
+__all__ = ["ConfigParsingError", "load_config"]
diff --git a/packages/graphrag-common/graphrag_common/config/load_config.py b/packages/graphrag-common/graphrag_common/config/load_config.py
new file mode 100644
index 0000000000..c8929149f1
--- /dev/null
+++ b/packages/graphrag-common/graphrag_common/config/load_config.py
@@ -0,0 +1,205 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Load configuration."""
+
+import json
+import os
+from collections.abc import Callable
+from pathlib import Path
+from string import Template
+from typing import Any, TypeVar
+
+import yaml
+from dotenv import load_dotenv
+
+T = TypeVar("T", covariant=True)
+
+_default_config_files = ["settings.yaml", "settings.yml", "settings.json"]
+
+
+class ConfigParsingError(ValueError):
+    """Configuration Parsing Error."""
+
+    def __init__(self, msg: str) -> None:
+        """Initialize the ConfigParsingError."""
+        super().__init__(msg)
+
+
+def _get_config_file_path(config_dir_or_file: Path) -> Path:
+    """Resolve the config path from the given directory or file."""
+    config_dir_or_file = Path(config_dir_or_file)
+
+    if config_dir_or_file.is_file():
+        return config_dir_or_file
+
+    if not config_dir_or_file.is_dir():
+        msg = f"Invalid config path: {config_dir_or_file} is not a directory"
+        raise FileNotFoundError(msg)
+
+    for file in _default_config_files:
+        if (config_dir_or_file / file).is_file():
+            return config_dir_or_file / file
+
+    msg = f"No 'settings.[yaml|yml|json]' config file found in directory: {config_dir_or_file}"
+    raise FileNotFoundError(msg)
+
+
+def _load_dotenv(env_file_path: Path, required: bool) -> None:
+    """Load the .env file if it exists."""
+    if not env_file_path.is_file():
+        if not required:
+            return
+        msg = f"dot_env_path not found: {env_file_path}"
+        raise FileNotFoundError(msg)
+    load_dotenv(env_file_path)
+
+
+def _parse_json(data: str) -> dict[str, Any]:
+    """Parse JSON data."""
+    return json.loads(data)
+
+
+def _parse_yaml(data: str) -> dict[str, Any]:
+    """Parse YAML data."""
+    return yaml.safe_load(data)
+
+
+def _get_parser_for_file(file_path: str | Path) -> Callable[[str], dict[str, Any]]:
+    """Get the parser for the given file path."""
+    file_path = Path(file_path).resolve()
+    match file_path.suffix.lower():
+        case ".json":
+            return _parse_json
+        case ".yaml" | ".yml":
+            return _parse_yaml
+        case _:
+            msg = (
+                f"Failed to parse, {file_path}. Unsupported file extension, "
+                + f"{file_path.suffix}. Pass in a custom config_parser argument or "
+                + "use one of the supported file extensions, .json, .yaml, .yml, .toml."
+            )
+            raise ConfigParsingError(msg)
+
+
+def _parse_env_variables(text: str) -> str:
+    """Parse environment variables in the configuration text."""
+    try:
+        return Template(text).substitute(os.environ)
+    except KeyError as error:
+        msg = f"Environment variable not found: {error}"
+        raise ConfigParsingError(msg) from error
+
+
+def _recursive_merge_dicts(dest: dict[str, Any], src: dict[str, Any]) -> None:
+    """Recursively merge two dictionaries in place."""
+    for key, value in src.items():
+        if isinstance(value, dict):
+            if isinstance(dest.get(key), dict):
+                _recursive_merge_dicts(dest[key], value)
+            else:
+                dest[key] = value
+        else:
+            dest[key] = value
+
+
+def load_config(
+    config_initializer: Callable[..., T],
+    config_path: str | Path | None = None,
+    overrides: dict[str, Any] | None = None,
+    set_cwd: bool = True,
+    parse_env_vars: bool = True,
+    load_dot_env_file: bool = True,
+    dot_env_path: str | Path | None = None,
+    config_parser: Callable[[str], dict[str, Any]] | None = None,
+    file_encoding: str = "utf-8",
+) -> T:
+    """Load configuration from a file.
+
+    Parameters
+    ----------
+    config_initializer : Callable[..., T]
+        Configuration constructor/initializer.
+        Should accept **kwargs to initialize the configuration,
+        e.g., Config(**kwargs).
+    config_path : str | Path | None, optional (default=None)
+        Path to the configuration directory containing settings.[yaml|yml|json].
+        Or path to a configuration file itself.
+        If None, search the current working directory for
+        settings.[yaml|yml|json].
+    overrides : dict[str, Any] | None, optional (default=None)
+        Configuration overrides.
+        Useful for overriding configuration settings programmatically,
+        perhaps from CLI flags.
+    set_cwd : bool, optional (default=True)
+        Whether to set the current working directory to the directory
+        containing the configuration file. Helpful for resolving relative paths
+        in the configuration file.
+    parse_env_vars : bool, optional (default=True)
+        Whether to parse environment variables in the configuration text.
+    load_dot_env_file : bool, optional (default=True)
+        Whether to load the .env file prior to parsing environment variables.
+    dot_env_path : str | Path | None, optional (default=None)
+        Optional .env file to load prior to parsing env variables.
+        If None and load_dot_env_file is True, looks for a .env file in the
+        same directory as the config file.
+    config_parser : Callable[[str], dict[str, Any]] | None, optional (default=None)
+        function to parse the configuration text, (str) -> dict[str, Any].
+        If None, the parser is inferred from the file extension.
+        Supported extensions: .json, .yaml, .yml.
+    file_encoding : str, optional (default="utf-8")
+        File encoding to use when reading the configuration file.
+
+    Returns
+    -------
+    T
+        The initialized configuration object.
+
+    Raises
+    ------
+    FileNotFoundError
+        - If the config file is not found.
+        - If the .env file is not found when parse_env_vars is True and dot_env_path is provided.
+
+    ConfigParsingError
+        - If an environment variable is not found when parsing env variables.
+        - If there was a problem merging the overrides with the configuration.
+        - If parser=None and load_config was unable to determine how to parse
+        the file based on the file extension.
+        - If the parser fails to parse the configuration text.
+    """
+    config_path = Path(config_path).resolve() if config_path else Path.cwd()
+    config_path = _get_config_file_path(config_path)
+
+    file_contents = config_path.read_text(encoding=file_encoding)
+
+    if parse_env_vars:
+        if load_dot_env_file:
+            required = dot_env_path is not None
+            dot_env_path = (
+                Path(dot_env_path) if dot_env_path else config_path.parent / ".env"
+            )
+            _load_dotenv(dot_env_path, required=required)
+        file_contents = _parse_env_variables(file_contents)
+
+    if config_parser is None:
+        config_parser = _get_parser_for_file(config_path)
+
+    config_data: dict[str, Any] = {}
+    try:
+        config_data = config_parser(file_contents)
+    except Exception as error:
+        msg = f"Failed to parse config_path: {config_path}. Error: {error}"
+        raise ConfigParsingError(msg) from error
+
+    if overrides is not None:
+        try:
+            _recursive_merge_dicts(config_data, overrides)
+        except Exception as error:
+            msg = f"Failed to merge overrides with config_path: {config_path}. Error: {error}"
+            raise ConfigParsingError(msg) from error
+
+    if set_cwd:
+        os.chdir(config_path.parent)
+
+    return config_initializer(**config_data)
diff --git a/packages/graphrag-common/pyproject.toml b/packages/graphrag-common/pyproject.toml
index e8396e2cdb..b2bcb952e3 100644
--- a/packages/graphrag-common/pyproject.toml
+++ b/packages/graphrag-common/pyproject.toml
@@ -30,7 +30,10 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
 ]
-dependencies = []
+dependencies = [
+    "python-dotenv>=1.0.1",
+    "pyyaml>=6.0.2",
+]
 
 [project.urls]
 Source = "https://github.com/microsoft/graphrag"
diff --git a/packages/graphrag/graphrag/api/query.py b/packages/graphrag/graphrag/api/query.py
index e49a0976df..f573559bcb 100644
--- a/packages/graphrag/graphrag/api/query.py
+++ b/packages/graphrag/graphrag/api/query.py
@@ -167,13 +167,9 @@ def global_search_streaming(
     entities_ = read_indexer_entities(
         entities, communities, community_level=community_level
     )
-    map_prompt = load_search_prompt(config.root_dir, config.global_search.map_prompt)
-    reduce_prompt = load_search_prompt(
-        config.root_dir, config.global_search.reduce_prompt
-    )
-    knowledge_prompt = load_search_prompt(
-        config.root_dir, config.global_search.knowledge_prompt
-    )
+    map_prompt = load_search_prompt(config.global_search.map_prompt)
+    reduce_prompt = load_search_prompt(config.global_search.reduce_prompt)
+    knowledge_prompt = load_search_prompt(config.global_search.knowledge_prompt)
 
     logger.debug("Executing streaming global search query: %s", query)
     search_engine = get_global_search_engine(
@@ -304,7 +300,7 @@ def local_search_streaming(
 
     entities_ = read_indexer_entities(entities, communities, community_level)
     covariates_ = read_indexer_covariates(covariates) if covariates is not None else []
-    prompt = load_search_prompt(config.root_dir, config.local_search.prompt)
+    prompt = load_search_prompt(config.local_search.prompt)
 
     logger.debug("Executing streaming local search query: %s", query)
     search_engine = get_local_search_engine(
@@ -435,10 +431,8 @@ def drift_search_streaming(
     entities_ = read_indexer_entities(entities, communities, community_level)
     reports = read_indexer_reports(community_reports, communities, community_level)
     read_indexer_report_embeddings(reports, full_content_embedding_store)
-    prompt = load_search_prompt(config.root_dir, config.drift_search.prompt)
-    reduce_prompt = load_search_prompt(
-        config.root_dir, config.drift_search.reduce_prompt
-    )
+    prompt = load_search_prompt(config.drift_search.prompt)
+    reduce_prompt = load_search_prompt(config.drift_search.reduce_prompt)
 
     logger.debug("Executing streaming drift search query: %s", query)
     search_engine = get_drift_search_engine(
@@ -538,7 +532,7 @@ def basic_search_streaming(
         embedding_name=text_unit_text_embedding,
     )
 
-    prompt = load_search_prompt(config.root_dir, config.basic_search.prompt)
+    prompt = load_search_prompt(config.basic_search.prompt)
 
     logger.debug("Executing streaming basic search query: %s", query)
     search_engine = get_basic_search_engine(
diff --git a/packages/graphrag/graphrag/cache/factory.py b/packages/graphrag/graphrag/cache/factory.py
index 1f2c700a4e..971c22c6d5 100644
--- a/packages/graphrag/graphrag/cache/factory.py
+++ b/packages/graphrag/graphrag/cache/factory.py
@@ -28,11 +28,9 @@ class CacheFactory(Factory[PipelineCache]):
 
 
 # --- register built-in cache implementations ---
-def create_file_cache(root_dir: str, base_dir: str, **kwargs) -> PipelineCache:
+def create_file_cache(**kwargs) -> PipelineCache:
     """Create a file-based cache implementation."""
-    # Create storage with base_dir in kwargs since FilePipelineStorage expects it there
-    storage_kwargs = {"base_dir": root_dir, **kwargs}
-    storage = FilePipelineStorage(**storage_kwargs).child(base_dir)
+    storage = FilePipelineStorage(**kwargs)
     return JsonPipelineCache(storage)
 
 
diff --git a/packages/graphrag/graphrag/cli/index.py b/packages/graphrag/graphrag/cli/index.py
index b5464d2544..0a638f63e9 100644
--- a/packages/graphrag/graphrag/cli/index.py
+++ b/packages/graphrag/graphrag/cli/index.py
@@ -45,18 +45,11 @@ def index_cli(
     verbose: bool,
     memprofile: bool,
     cache: bool,
-    config_filepath: Path | None,
     dry_run: bool,
     skip_validation: bool,
-    output_dir: Path | None,
 ):
     """Run the pipeline with the given config."""
-    cli_overrides = {}
-    if output_dir:
-        cli_overrides["output.base_dir"] = str(output_dir)
-        cli_overrides["reporting.base_dir"] = str(output_dir)
-        cli_overrides["update_index_output.base_dir"] = str(output_dir)
-    config = load_config(root_dir, config_filepath, cli_overrides)
+    config = load_config(root_dir=root_dir)
     _run_index(
         config=config,
         method=method,
@@ -75,18 +68,12 @@ def update_cli(
     verbose: bool,
     memprofile: bool,
     cache: bool,
-    config_filepath: Path | None,
     skip_validation: bool,
-    output_dir: Path | None,
 ):
     """Run the pipeline with the given config."""
-    cli_overrides = {}
-    if output_dir:
-        cli_overrides["output.base_dir"] = str(output_dir)
-        cli_overrides["reporting.base_dir"] = str(output_dir)
-        cli_overrides["update_index_output.base_dir"] = str(output_dir)
-
-    config = load_config(root_dir, config_filepath, cli_overrides)
+    config = load_config(
+        root_dir=root_dir,
+    )
 
     _run_index(
         config=config,
diff --git a/packages/graphrag/graphrag/cli/initialize.py b/packages/graphrag/graphrag/cli/initialize.py
index 09215f8c5d..8dbaf30f93 100644
--- a/packages/graphrag/graphrag/cli/initialize.py
+++ b/packages/graphrag/graphrag/cli/initialize.py
@@ -6,6 +6,7 @@
 import logging
 from pathlib import Path
 
+from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.init_content import INIT_DOTENV, INIT_YAML
 from graphrag.prompts.index.community_report import (
     COMMUNITY_REPORT_PROMPT,
@@ -51,26 +52,27 @@ def initialize_project_at(path: Path, force: bool) -> None:
         If the project already exists and force is False.
     """
     logger.info("Initializing project at %s", path)
-    root = Path(path)
-    if not root.exists():
-        root.mkdir(parents=True, exist_ok=True)
+    root = Path(path).resolve()
+    root.mkdir(parents=True, exist_ok=True)
 
     settings_yaml = root / "settings.yaml"
     if settings_yaml.exists() and not force:
         msg = f"Project already initialized at {root}"
         raise ValueError(msg)
 
-    with settings_yaml.open("wb") as file:
-        file.write(INIT_YAML.encode(encoding="utf-8", errors="strict"))
+    input_path = (
+        root / (graphrag_config_defaults.input.storage.base_dir or "input")
+    ).resolve()
+    input_path.mkdir(parents=True, exist_ok=True)
+
+    settings_yaml.write_text(INIT_YAML, encoding="utf-8", errors="strict")
 
     dotenv = root / ".env"
     if not dotenv.exists() or force:
-        with dotenv.open("wb") as file:
-            file.write(INIT_DOTENV.encode(encoding="utf-8", errors="strict"))
+        dotenv.write_text(INIT_DOTENV, encoding="utf-8", errors="strict")
 
     prompts_dir = root / "prompts"
-    if not prompts_dir.exists():
-        prompts_dir.mkdir(parents=True, exist_ok=True)
+    prompts_dir.mkdir(parents=True, exist_ok=True)
 
     prompts = {
         "extract_graph": GRAPH_EXTRACTION_PROMPT,
@@ -91,5 +93,4 @@ def initialize_project_at(path: Path, force: bool) -> None:
     for name, content in prompts.items():
         prompt_file = prompts_dir / f"{name}.txt"
         if not prompt_file.exists() or force:
-            with prompt_file.open("wb") as file:
-                file.write(content.encode(encoding="utf-8", errors="strict"))
+            prompt_file.write_text(content, encoding="utf-8", errors="strict")
diff --git a/packages/graphrag/graphrag/cli/main.py b/packages/graphrag/graphrag/cli/main.py
index 7b4d3bb0cd..89768e736d 100644
--- a/packages/graphrag/graphrag/cli/main.py
+++ b/packages/graphrag/graphrag/cli/main.py
@@ -94,12 +94,13 @@ def completer(incomplete: str) -> list[str]:
 @app.command("init")
 def _initialize_cli(
     root: Path = typer.Option(
-        Path(),
+        Path.cwd(),
         "--root",
         "-r",
         help="The project root directory.",
         dir_okay=True,
         writable=True,
+        file_okay=False,
         resolve_path=True,
         autocompletion=ROOT_AUTOCOMPLETE,
     ),
@@ -118,23 +119,14 @@ def _initialize_cli(
 
 @app.command("index")
 def _index_cli(
-    config: Path | None = typer.Option(
-        None,
-        "--config",
-        "-c",
-        help="The configuration to use.",
-        exists=True,
-        file_okay=True,
-        readable=True,
-        autocompletion=CONFIG_AUTOCOMPLETE,
-    ),
     root: Path = typer.Option(
-        Path(),
+        Path.cwd(),
         "--root",
         "-r",
         help="The project root directory.",
         exists=True,
         dir_okay=True,
+        file_okay=False,
         writable=True,
         resolve_path=True,
         autocompletion=ROOT_AUTOCOMPLETE,
@@ -174,18 +166,6 @@ def _index_cli(
         "--skip-validation",
         help="Skip any preflight validation. Useful when running no LLM steps.",
     ),
-    output: Path | None = typer.Option(
-        None,
-        "--output",
-        "-o",
-        help=(
-            "Indexing pipeline output directory. "
-            "Overrides output.base_dir in the configuration file."
-        ),
-        dir_okay=True,
-        writable=True,
-        resolve_path=True,
-    ),
 ) -> None:
     """Build a knowledge graph index."""
     from graphrag.cli.index import index_cli
@@ -195,33 +175,22 @@ def _index_cli(
         verbose=verbose,
         memprofile=memprofile,
         cache=cache,
-        config_filepath=config,
         dry_run=dry_run,
         skip_validation=skip_validation,
-        output_dir=output,
         method=method,
     )
 
 
 @app.command("update")
 def _update_cli(
-    config: Path | None = typer.Option(
-        None,
-        "--config",
-        "-c",
-        help="The configuration to use.",
-        exists=True,
-        file_okay=True,
-        readable=True,
-        autocompletion=CONFIG_AUTOCOMPLETE,
-    ),
     root: Path = typer.Option(
-        Path(),
+        Path.cwd(),
         "--root",
         "-r",
         help="The project root directory.",
         exists=True,
         dir_okay=True,
+        file_okay=False,
         writable=True,
         resolve_path=True,
         autocompletion=ROOT_AUTOCOMPLETE,
@@ -253,18 +222,6 @@ def _update_cli(
         "--skip-validation",
         help="Skip any preflight validation. Useful when running no LLM steps.",
     ),
-    output: Path | None = typer.Option(
-        None,
-        "--output",
-        "-o",
-        help=(
-            "Indexing pipeline output directory. "
-            "Overrides output.base_dir in the configuration file."
-        ),
-        dir_okay=True,
-        writable=True,
-        resolve_path=True,
-    ),
 ) -> None:
     """
     Update an existing knowledge graph index.
@@ -278,9 +235,7 @@ def _update_cli(
         verbose=verbose,
         memprofile=memprofile,
         cache=cache,
-        config_filepath=config,
         skip_validation=skip_validation,
-        output_dir=output,
         method=method,
     )
 
@@ -288,26 +243,17 @@ def _update_cli(
 @app.command("prompt-tune")
 def _prompt_tune_cli(
     root: Path = typer.Option(
-        Path(),
+        Path.cwd(),
         "--root",
         "-r",
         help="The project root directory.",
         exists=True,
         dir_okay=True,
+        file_okay=False,
         writable=True,
         resolve_path=True,
         autocompletion=ROOT_AUTOCOMPLETE,
     ),
-    config: Path | None = typer.Option(
-        None,
-        "--config",
-        "-c",
-        help="The configuration to use.",
-        exists=True,
-        file_okay=True,
-        readable=True,
-        autocompletion=CONFIG_AUTOCOMPLETE,
-    ),
     verbose: bool = typer.Option(
         False,
         "--verbose",
@@ -392,7 +338,6 @@ def _prompt_tune_cli(
     loop.run_until_complete(
         prompt_tune(
             root=root,
-            config=config,
             domain=domain,
             verbose=verbose,
             selection_method=selection_method,
@@ -412,28 +357,27 @@ def _prompt_tune_cli(
 
 @app.command("query")
 def _query_cli(
+    query: str = typer.Argument(
+        help="The query to execute.",
+    ),
+    root: Path = typer.Option(
+        Path.cwd(),
+        "--root",
+        "-r",
+        help="The project root directory.",
+        exists=True,
+        dir_okay=True,
+        file_okay=False,
+        writable=True,
+        resolve_path=True,
+        autocompletion=ROOT_AUTOCOMPLETE,
+    ),
     method: SearchMethod = typer.Option(
-        ...,
+        SearchMethod.GLOBAL,
         "--method",
         "-m",
         help="The query algorithm to use.",
     ),
-    query: str = typer.Option(
-        ...,
-        "--query",
-        "-q",
-        help="The query to execute.",
-    ),
-    config: Path | None = typer.Option(
-        None,
-        "--config",
-        "-c",
-        help="The configuration to use.",
-        exists=True,
-        file_okay=True,
-        readable=True,
-        autocompletion=CONFIG_AUTOCOMPLETE,
-    ),
     verbose: bool = typer.Option(
         False,
         "--verbose",
@@ -451,17 +395,6 @@ def _query_cli(
         resolve_path=True,
         autocompletion=ROOT_AUTOCOMPLETE,
     ),
-    root: Path = typer.Option(
-        Path(),
-        "--root",
-        "-r",
-        help="The project root directory.",
-        exists=True,
-        dir_okay=True,
-        writable=True,
-        resolve_path=True,
-        autocompletion=ROOT_AUTOCOMPLETE,
-    ),
     community_level: int = typer.Option(
         2,
         "--community-level",
@@ -500,7 +433,6 @@ def _query_cli(
     match method:
         case SearchMethod.LOCAL:
             run_local_search(
-                config_filepath=config,
                 data_dir=data,
                 root_dir=root,
                 community_level=community_level,
@@ -511,7 +443,6 @@ def _query_cli(
             )
         case SearchMethod.GLOBAL:
             run_global_search(
-                config_filepath=config,
                 data_dir=data,
                 root_dir=root,
                 community_level=community_level,
@@ -523,7 +454,6 @@ def _query_cli(
             )
         case SearchMethod.DRIFT:
             run_drift_search(
-                config_filepath=config,
                 data_dir=data,
                 root_dir=root,
                 community_level=community_level,
@@ -534,7 +464,6 @@ def _query_cli(
             )
         case SearchMethod.BASIC:
             run_basic_search(
-                config_filepath=config,
                 data_dir=data,
                 root_dir=root,
                 response_type=response_type,
diff --git a/packages/graphrag/graphrag/cli/prompt_tune.py b/packages/graphrag/graphrag/cli/prompt_tune.py
index 2646776f6f..2ba02e0ead 100644
--- a/packages/graphrag/graphrag/cli/prompt_tune.py
+++ b/packages/graphrag/graphrag/cli/prompt_tune.py
@@ -24,7 +24,6 @@
 
 async def prompt_tune(
     root: Path,
-    config: Path | None,
     domain: str | None,
     verbose: bool,
     selection_method: api.DocSelectionType,
@@ -43,7 +42,6 @@ async def prompt_tune(
 
     Parameters
     ----------
-    - config: The configuration file.
     - root: The root directory.
     - domain: The domain to map the input documents to.
     - verbose: Enable verbose logging.
@@ -58,8 +56,9 @@ async def prompt_tune(
     - k: The number of documents to select when using auto selection method.
     - min_examples_required: The minimum number of examples required for entity extraction prompts.
     """
-    root_path = Path(root).resolve()
-    graph_config = load_config(root_path, config)
+    graph_config = load_config(
+        root_dir=root,
+    )
 
     # override chunking config in the configuration
     if chunk_size != graph_config.chunks.size:
diff --git a/packages/graphrag/graphrag/cli/query.py b/packages/graphrag/graphrag/cli/query.py
index 914196b865..93163db19d 100644
--- a/packages/graphrag/graphrag/cli/query.py
+++ b/packages/graphrag/graphrag/cli/query.py
@@ -22,7 +22,6 @@
 
 
 def run_global_search(
-    config_filepath: Path | None,
     data_dir: Path | None,
     root_dir: Path,
     community_level: int | None,
@@ -36,11 +35,13 @@ def run_global_search(
 
     Loads index files required for global search and calls the Query API.
     """
-    root = root_dir.resolve()
-    cli_overrides = {}
+    cli_overrides: dict[str, Any] = {}
     if data_dir:
-        cli_overrides["output.base_dir"] = str(data_dir)
-    config = load_config(root, config_filepath, cli_overrides)
+        cli_overrides["output"] = {"base_dir": str(data_dir)}
+    config = load_config(
+        root_dir=root_dir,
+        cli_overrides=cli_overrides,
+    )
 
     dataframe_dict = _resolve_output_files(
         config=config,
@@ -108,7 +109,6 @@ def on_context(context: Any) -> None:
 
 
 def run_local_search(
-    config_filepath: Path | None,
     data_dir: Path | None,
     root_dir: Path,
     community_level: int,
@@ -121,11 +121,13 @@ def run_local_search(
 
     Loads index files required for local search and calls the Query API.
     """
-    root = root_dir.resolve()
-    cli_overrides = {}
+    cli_overrides: dict[str, Any] = {}
     if data_dir:
-        cli_overrides["output.base_dir"] = str(data_dir)
-    config = load_config(root, config_filepath, cli_overrides)
+        cli_overrides["output"] = {"base_dir": str(data_dir)}
+    config = load_config(
+        root_dir=root_dir,
+        cli_overrides=cli_overrides,
+    )
 
     dataframe_dict = _resolve_output_files(
         config=config,
@@ -204,7 +206,6 @@ def on_context(context: Any) -> None:
 
 
 def run_drift_search(
-    config_filepath: Path | None,
     data_dir: Path | None,
     root_dir: Path,
     community_level: int,
@@ -217,11 +218,13 @@ def run_drift_search(
 
     Loads index files required for local search and calls the Query API.
     """
-    root = root_dir.resolve()
-    cli_overrides = {}
+    cli_overrides: dict[str, Any] = {}
     if data_dir:
-        cli_overrides["output.base_dir"] = str(data_dir)
-    config = load_config(root, config_filepath, cli_overrides)
+        cli_overrides["output"] = {"base_dir": str(data_dir)}
+    config = load_config(
+        root_dir=root_dir,
+        cli_overrides=cli_overrides,
+    )
 
     dataframe_dict = _resolve_output_files(
         config=config,
@@ -295,7 +298,6 @@ def on_context(context: Any) -> None:
 
 
 def run_basic_search(
-    config_filepath: Path | None,
     data_dir: Path | None,
     root_dir: Path,
     response_type: str,
@@ -307,11 +309,13 @@ def run_basic_search(
 
     Loads index files required for basic search and calls the Query API.
     """
-    root = root_dir.resolve()
-    cli_overrides = {}
+    cli_overrides: dict[str, Any] = {}
     if data_dir:
-        cli_overrides["output.base_dir"] = str(data_dir)
-    config = load_config(root, config_filepath, cli_overrides)
+        cli_overrides["output"] = {"base_dir": str(data_dir)}
+    config = load_config(
+        root_dir=root_dir,
+        cli_overrides=cli_overrides,
+    )
 
     dataframe_dict = _resolve_output_files(
         config=config,
diff --git a/packages/graphrag/graphrag/config/create_graphrag_config.py b/packages/graphrag/graphrag/config/create_graphrag_config.py
deleted file mode 100644
index 59d7699e71..0000000000
--- a/packages/graphrag/graphrag/config/create_graphrag_config.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration, loaded from environment variables."""
-
-from pathlib import Path
-from typing import Any
-
-from graphrag.config.models.graph_rag_config import GraphRagConfig
-
-
-def create_graphrag_config(
-    values: dict[str, Any] | None = None,
-    root_dir: str | None = None,
-) -> GraphRagConfig:
-    """Load Configuration Parameters from a dictionary.
-
-    Parameters
-    ----------
-    values : dict[str, Any] | None
-        Dictionary of configuration values to pass into pydantic model.
-    root_dir : str | None
-        Root directory for the project.
-    skip_validation : bool
-        Skip pydantic model validation of the configuration.
-        This is useful for testing and mocking purposes but
-        should not be used in the core code or API.
-
-    Returns
-    -------
-    GraphRagConfig
-        The configuration object.
-
-    Raises
-    ------
-    ValidationError
-        If the configuration values do not satisfy pydantic validation.
-    """
-    values = values or {}
-    if root_dir:
-        root_path = Path(root_dir).resolve()
-        values["root_dir"] = str(root_path)
-    return GraphRagConfig(**values)
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index f57bf610f9..88449a6050 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -386,7 +386,6 @@ class VectorStoreDefaults:
 class GraphRagConfigDefaults:
     """Default values for GraphRAG."""
 
-    root_dir: str = ""
     models: dict = field(default_factory=dict)
     reporting: ReportingDefaults = field(default_factory=ReportingDefaults)
     storage: StorageDefaults = field(default_factory=StorageDefaults)
diff --git a/packages/graphrag/graphrag/config/environment_reader.py b/packages/graphrag/graphrag/config/environment_reader.py
deleted file mode 100644
index 258422666c..0000000000
--- a/packages/graphrag/graphrag/config/environment_reader.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A configuration reader utility class."""
-
-from collections.abc import Callable
-from contextlib import contextmanager
-from enum import Enum
-from typing import Any, TypeVar
-
-from environs import Env
-
-T = TypeVar("T")
-
-KeyValue = str | Enum
-EnvKeySet = str | list[str]
-
-
-def read_key(value: KeyValue) -> str:
-    """Read a key value."""
-    if not isinstance(value, str):
-        return value.value.lower()
-    return value.lower()
-
-
-class EnvironmentReader:
-    """A configuration reader utility class."""
-
-    _env: Env
-    _config_stack: list[dict]
-
-    def __init__(self, env: Env):
-        self._env = env
-        self._config_stack = []
-
-    @property
-    def env(self):
-        """Get the environment object."""
-        return self._env
-
-    def _read_env(
-        self, env_key: str | list[str], default_value: T, read: Callable[[str, T], T]
-    ) -> T | None:
-        if isinstance(env_key, str):
-            env_key = [env_key]
-
-        for k in env_key:
-            result = read(k.upper(), default_value)
-            if result is not default_value:
-                return result
-
-        return default_value
-
-    def envvar_prefix(self, prefix: KeyValue):
-        """Set the environment variable prefix."""
-        prefix = read_key(prefix)
-        prefix = f"{prefix}_".upper()
-        return self._env.prefixed(prefix)
-
-    def use(self, value: Any | None):
-        """Create a context manager to push the value into the config_stack."""
-
-        @contextmanager
-        def config_context():
-            self._config_stack.append(value or {})
-            try:
-                yield
-            finally:
-                self._config_stack.pop()
-
-        return config_context()
-
-    @property
-    def section(self) -> dict:
-        """Get the current section."""
-        return self._config_stack[-1] if self._config_stack else {}
-
-    def str(
-        self,
-        key: KeyValue,
-        env_key: EnvKeySet | None = None,
-        default_value: str | None = None,
-    ) -> str | None:
-        """Read a configuration value."""
-        key = read_key(key)
-        if self.section and key in self.section:
-            return self.section[key]
-
-        return self._read_env(
-            env_key or key, default_value, (lambda k, dv: self._env(k, dv))
-        )
-
-    def int(
-        self,
-        key: KeyValue,
-        env_key: EnvKeySet | None = None,
-        default_value: int | None = None,
-    ) -> int | None:
-        """Read an integer configuration value."""
-        key = read_key(key)
-        if self.section and key in self.section:
-            return int(self.section[key])
-        return self._read_env(
-            env_key or key, default_value, lambda k, dv: self._env.int(k, dv)
-        )
-
-    def bool(
-        self,
-        key: KeyValue,
-        env_key: EnvKeySet | None = None,
-        default_value: bool | None = None,
-    ) -> bool | None:
-        """Read an integer configuration value."""
-        key = read_key(key)
-        if self.section and key in self.section:
-            return bool(self.section[key])
-
-        return self._read_env(
-            env_key or key, default_value, lambda k, dv: self._env.bool(k, dv)
-        )
-
-    def float(
-        self,
-        key: KeyValue,
-        env_key: EnvKeySet | None = None,
-        default_value: float | None = None,
-    ) -> float | None:
-        """Read a float configuration value."""
-        key = read_key(key)
-        if self.section and key in self.section:
-            return float(self.section[key])
-        return self._read_env(
-            env_key or key, default_value, lambda k, dv: self._env.float(k, dv)
-        )
-
-    def list(
-        self,
-        key: KeyValue,
-        env_key: EnvKeySet | None = None,
-        default_value: list | None = None,
-    ) -> list | None:
-        """Parse an list configuration value."""
-        key = read_key(key)
-        result = None
-        if self.section and key in self.section:
-            result = self.section[key]
-            if isinstance(result, list):
-                return result
-
-        if result is None:
-            result = self.str(key, env_key)
-        if result is not None:
-            result = [s.strip() for s in result.split(",")]
-            return [s for s in result if s]
-        return default_value
diff --git a/packages/graphrag/graphrag/config/load_config.py b/packages/graphrag/graphrag/config/load_config.py
index de9026037d..104bd59d43 100644
--- a/packages/graphrag/graphrag/config/load_config.py
+++ b/packages/graphrag/graphrag/config/load_config.py
@@ -3,149 +3,16 @@
 
 """Default method for loading config."""
 
-import json
-import os
 from pathlib import Path
-from string import Template
 from typing import Any
 
-import yaml
-from dotenv import load_dotenv
+from graphrag_common.config import load_config as lc
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 
-_default_config_files = ["settings.yaml", "settings.yml", "settings.json"]
-
-
-def _search_for_config_in_root_dir(root: str | Path) -> Path | None:
-    """Resolve the config path from the given root directory.
-
-    Parameters
-    ----------
-    root : str | Path
-        The path to the root directory containing the config file.
-        Searches for a default config file (settings.{yaml,yml,json}).
-
-    Returns
-    -------
-    Path | None
-        returns a Path if there is a config in the root directory
-        Otherwise returns None.
-    """
-    root = Path(root)
-
-    if not root.is_dir():
-        msg = f"Invalid config path: {root} is not a directory"
-        raise FileNotFoundError(msg)
-
-    for file in _default_config_files:
-        if (root / file).is_file():
-            return root / file
-
-    return None
-
-
-def _parse_env_variables(text: str) -> str:
-    """Parse environment variables in the configuration text.
-
-    Parameters
-    ----------
-    text : str
-        The configuration text.
-
-    Returns
-    -------
-    str
-        The configuration text with environment variables parsed.
-
-    Raises
-    ------
-    KeyError
-        If an environment variable is not found.
-    """
-    return Template(text).substitute(os.environ)
-
-
-def _load_dotenv(config_path: Path | str) -> None:
-    """Load the .env file if it exists in the same directory as the config file.
-
-    Parameters
-    ----------
-    config_path : Path | str
-        The path to the config file.
-    """
-    config_path = Path(config_path)
-    dotenv_path = config_path.parent / ".env"
-    if dotenv_path.exists():
-        load_dotenv(dotenv_path)
-
-
-def _get_config_path(root_dir: Path, config_filepath: Path | None) -> Path:
-    """Get the configuration file path.
-
-    Parameters
-    ----------
-    root_dir : str | Path
-        The root directory of the project. Will search for the config file in this directory.
-    config_filepath : str | None
-        The path to the config file.
-        If None, searches for config file in root.
-
-    Returns
-    -------
-    Path
-        The configuration file path.
-    """
-    if config_filepath:
-        config_path = config_filepath.resolve()
-        if not config_path.exists():
-            msg = f"Specified Config file not found: {config_path}"
-            raise FileNotFoundError(msg)
-    else:
-        config_path = _search_for_config_in_root_dir(root_dir)
-
-    if not config_path:
-        msg = f"Config file not found in root directory: {root_dir}"
-        raise FileNotFoundError(msg)
-
-    return config_path
-
-
-def _apply_overrides(data: dict[str, Any], overrides: dict[str, Any]) -> None:
-    """Apply the overrides to the raw configuration."""
-    for key, value in overrides.items():
-        keys = key.split(".")
-        target = data
-        current_path = keys[0]
-        for k in keys[:-1]:
-            current_path += f".{k}"
-            target_obj = target.get(k, {})
-            if not isinstance(target_obj, dict):
-                msg = f"Cannot override non-dict value: data[{current_path}] is not a dict."
-                raise TypeError(msg)
-            target[k] = target_obj
-            target = target[k]
-        target[keys[-1]] = value
-
-
-def _parse(file_extension: str, contents: str) -> dict[str, Any]:
-    """Parse configuration."""
-    match file_extension:
-        case ".yaml" | ".yml":
-            return yaml.safe_load(contents)
-        case ".json":
-            return json.loads(contents)
-        case _:
-            msg = (
-                f"Unable to parse config. Unsupported file extension: {file_extension}"
-            )
-            raise ValueError(msg)
-
 
 def load_config(
-    root_dir: Path,
-    config_filepath: Path | None = None,
+    root_dir: str | Path,
     cli_overrides: dict[str, Any] | None = None,
 ) -> GraphRagConfig:
     """Load configuration from a file.
@@ -153,13 +20,11 @@ def load_config(
     Parameters
     ----------
     root_dir : str | Path
-        The root directory of the project. Will search for the config file in this directory.
-    config_filepath : str | None
-        The path to the config file.
-        If None, searches for config file in root.
+        The root directory of the project.
+        Searches for settings.[yaml|yml|json] config files.
     cli_overrides : dict[str, Any] | None
-        A flat dictionary of cli overrides.
-        Example: {'output.base_dir': 'override_value'}
+        A nested dictionary of cli overrides.
+        Example: {'output': {'base_dir': 'override_value'}}
 
     Returns
     -------
@@ -170,22 +35,13 @@ def load_config(
     ------
     FileNotFoundError
         If the config file is not found.
-    ValueError
-        If the config file extension is not supported.
-    TypeError
-        If applying cli overrides to the config fails.
-    KeyError
-        If config file references a non-existent environment variable.
+    ConfigParsingError
+        If there was an error parsing the config file or its environment variables.
     ValidationError
         If there are pydantic validation errors when instantiating the config.
     """
-    root = root_dir.resolve()
-    config_path = _get_config_path(root, config_filepath)
-    _load_dotenv(config_path)
-    config_extension = config_path.suffix
-    config_text = config_path.read_text(encoding="utf-8")
-    config_text = _parse_env_variables(config_text)
-    config_data = _parse(config_extension, config_text)
-    if cli_overrides:
-        _apply_overrides(config_data, cli_overrides)
-    return create_graphrag_config(config_data, root_dir=str(root))
+    return lc(
+        config_initializer=GraphRagConfig,
+        config_path=root_dir,
+        overrides=cli_overrides,
+    )
diff --git a/packages/graphrag/graphrag/config/models/community_reports_config.py b/packages/graphrag/graphrag/config/models/community_reports_config.py
index 1257124bde..c4f920cefe 100644
--- a/packages/graphrag/graphrag/config/models/community_reports_config.py
+++ b/packages/graphrag/graphrag/config/models/community_reports_config.py
@@ -51,15 +51,13 @@ class CommunityReportsConfig(BaseModel):
         default=graphrag_config_defaults.community_reports.max_input_length,
     )
 
-    def resolved_prompts(self, root_dir: str) -> CommunityReportPrompts:
+    def resolved_prompts(self) -> CommunityReportPrompts:
         """Get the resolved community report extraction prompts."""
         return CommunityReportPrompts(
-            graph_prompt=(Path(root_dir) / self.graph_prompt).read_text(
-                encoding="utf-8"
-            )
+            graph_prompt=Path(self.graph_prompt).read_text(encoding="utf-8")
             if self.graph_prompt
             else COMMUNITY_REPORT_PROMPT,
-            text_prompt=(Path(root_dir) / self.text_prompt).read_text(encoding="utf-8")
+            text_prompt=Path(self.text_prompt).read_text(encoding="utf-8")
             if self.text_prompt
             else COMMUNITY_REPORT_TEXT_PROMPT,
         )
diff --git a/packages/graphrag/graphrag/config/models/extract_claims_config.py b/packages/graphrag/graphrag/config/models/extract_claims_config.py
index 77a633b05f..63fec7ac5a 100644
--- a/packages/graphrag/graphrag/config/models/extract_claims_config.py
+++ b/packages/graphrag/graphrag/config/models/extract_claims_config.py
@@ -47,10 +47,10 @@ class ExtractClaimsConfig(BaseModel):
         default=graphrag_config_defaults.extract_claims.max_gleanings,
     )
 
-    def resolved_prompts(self, root_dir: str) -> ClaimExtractionPrompts:
+    def resolved_prompts(self) -> ClaimExtractionPrompts:
         """Get the resolved claim extraction prompts."""
         return ClaimExtractionPrompts(
-            extraction_prompt=(Path(root_dir) / self.prompt).read_text(encoding="utf-8")
+            extraction_prompt=Path(self.prompt).read_text(encoding="utf-8")
             if self.prompt
             else EXTRACT_CLAIMS_PROMPT,
         )
diff --git a/packages/graphrag/graphrag/config/models/extract_graph_config.py b/packages/graphrag/graphrag/config/models/extract_graph_config.py
index 8a61585e5c..81c8df4235 100644
--- a/packages/graphrag/graphrag/config/models/extract_graph_config.py
+++ b/packages/graphrag/graphrag/config/models/extract_graph_config.py
@@ -43,10 +43,10 @@ class ExtractGraphConfig(BaseModel):
         default=graphrag_config_defaults.extract_graph.max_gleanings,
     )
 
-    def resolved_prompts(self, root_dir: str) -> ExtractGraphPrompts:
+    def resolved_prompts(self) -> ExtractGraphPrompts:
         """Get the resolved graph extraction prompts."""
         return ExtractGraphPrompts(
-            extraction_prompt=(Path(root_dir) / self.prompt).read_text(encoding="utf-8")
+            extraction_prompt=Path(self.prompt).read_text(encoding="utf-8")
             if self.prompt
             else GRAPH_EXTRACTION_PROMPT,
         )
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 71509f0176..15d02eaf3a 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -53,22 +53,6 @@ def __str__(self):
         """Get a string representation."""
         return self.model_dump_json(indent=4)
 
-    root_dir: str = Field(
-        description="The root directory for the configuration.",
-        default=graphrag_config_defaults.root_dir,
-    )
-
-    def _validate_root_dir(self) -> None:
-        """Validate the root directory."""
-        if self.root_dir.strip() == "":
-            self.root_dir = str(Path.cwd())
-
-        root_dir = Path(self.root_dir).resolve()
-        if not root_dir.is_dir():
-            msg = f"Invalid root directory: {self.root_dir} is not a directory."
-            raise FileNotFoundError(msg)
-        self.root_dir = str(root_dir)
-
     models: dict[str, LanguageModelConfig] = Field(
         description="Available language model configurations.",
         default=graphrag_config_defaults.models,
@@ -156,7 +140,7 @@ def _validate_input_base_dir(self) -> None:
                 msg = "input storage base directory is required for file input storage. Please rerun `graphrag init` and set the input storage configuration."
                 raise ValueError(msg)
             self.input.storage.base_dir = str(
-                (Path(self.root_dir) / self.input.storage.base_dir).resolve()
+                Path(self.input.storage.base_dir).resolve()
             )
 
     chunks: ChunkingConfig = Field(
@@ -179,9 +163,7 @@ def _validate_output_base_dir(self) -> None:
             if not self.output.base_dir:
                 msg = "output base directory is required for file output. Please rerun `graphrag init` and set the output configuration."
                 raise ValueError(msg)
-            self.output.base_dir = str(
-                (Path(self.root_dir) / self.output.base_dir).resolve()
-            )
+            self.output.base_dir = str(Path(self.output.base_dir).resolve())
 
     update_index_output: StorageConfig = Field(
         description="The output configuration for the updated index.",
@@ -198,7 +180,7 @@ def _validate_update_index_output_base_dir(self) -> None:
                 msg = "update_index_output base directory is required for file output. Please rerun `graphrag init` and set the update_index_output configuration."
                 raise ValueError(msg)
             self.update_index_output.base_dir = str(
-                (Path(self.root_dir) / self.update_index_output.base_dir).resolve()
+                Path(self.update_index_output.base_dir).resolve()
             )
 
     cache: CacheConfig = Field(
@@ -217,9 +199,7 @@ def _validate_reporting_base_dir(self) -> None:
             if self.reporting.base_dir.strip() == "":
                 msg = "Reporting base directory is required for file reporting. Please rerun `graphrag init` and set the reporting configuration."
                 raise ValueError(msg)
-            self.reporting.base_dir = str(
-                (Path(self.root_dir) / self.reporting.base_dir).resolve()
-            )
+            self.reporting.base_dir = str(Path(self.reporting.base_dir).resolve())
 
     vector_store: VectorStoreConfig = Field(
         description="The vector store configuration.", default=VectorStoreConfig()
@@ -315,7 +295,7 @@ def _validate_vector_store_db_uri(self) -> None:
             if not store.db_uri or store.db_uri.strip == "":
                 msg = "Vector store URI is required for LanceDB. Please rerun `graphrag init` and set the vector store configuration."
                 raise ValueError(msg)
-            store.db_uri = str((Path(self.root_dir) / store.db_uri).resolve())
+            store.db_uri = str(Path(store.db_uri).resolve())
 
     def _validate_factories(self) -> None:
         """Validate the factories used in the configuration."""
@@ -349,7 +329,6 @@ def get_language_model_config(self, model_id: str) -> LanguageModelConfig:
     @model_validator(mode="after")
     def _validate_model(self):
         """Validate the model configuration."""
-        self._validate_root_dir()
         self._validate_models()
         self._validate_input_pattern()
         self._validate_input_base_dir()
diff --git a/packages/graphrag/graphrag/config/models/summarize_descriptions_config.py b/packages/graphrag/graphrag/config/models/summarize_descriptions_config.py
index 3414db71ec..024d2d964b 100644
--- a/packages/graphrag/graphrag/config/models/summarize_descriptions_config.py
+++ b/packages/graphrag/graphrag/config/models/summarize_descriptions_config.py
@@ -43,10 +43,10 @@ class SummarizeDescriptionsConfig(BaseModel):
         default=graphrag_config_defaults.summarize_descriptions.max_input_tokens,
     )
 
-    def resolved_prompts(self, root_dir: str) -> SummarizeDescriptionsPrompts:
+    def resolved_prompts(self) -> SummarizeDescriptionsPrompts:
         """Get the resolved description summarization prompts."""
         return SummarizeDescriptionsPrompts(
-            summarize_prompt=(Path(root_dir) / self.prompt).read_text(encoding="utf-8")
+            summarize_prompt=Path(self.prompt).read_text(encoding="utf-8")
             if self.prompt
             else SUMMARIZE_PROMPT,
         )
diff --git a/packages/graphrag/graphrag/config/read_dotenv.py b/packages/graphrag/graphrag/config/read_dotenv.py
deleted file mode 100644
index a2da5d43fe..0000000000
--- a/packages/graphrag/graphrag/config/read_dotenv.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing the read_dotenv utility."""
-
-import logging
-import os
-from pathlib import Path
-
-from dotenv import dotenv_values
-
-logger = logging.getLogger(__name__)
-
-
-def read_dotenv(root: str) -> None:
-    """Read a .env file in the given root path."""
-    env_path = Path(root) / ".env"
-    if env_path.exists():
-        logger.info("Loading pipeline .env file")
-        env_config = dotenv_values(f"{env_path}")
-        for key, value in env_config.items():
-            if key not in os.environ:
-                os.environ[key] = value or ""
-    else:
-        logger.info("No .env file found at %s", root)
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index f652db7acd..a0b2011eab 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -35,11 +35,9 @@ async def run_pipeline(
     input_documents: pd.DataFrame | None = None,
 ) -> AsyncIterable[PipelineRunResult]:
     """Run all workflows using a simplified pipeline."""
-    root_dir = config.root_dir
-
     input_storage = create_storage_from_config(config.input.storage)
     output_storage = create_storage_from_config(config.output)
-    cache = create_cache_from_config(config.cache, root_dir)
+    cache = create_cache_from_config(config.cache)
 
     # load existing state in case any workflows are stateful
     state_json = await output_storage.get("context.json")
diff --git a/packages/graphrag/graphrag/index/workflows/create_community_reports.py b/packages/graphrag/graphrag/index/workflows/create_community_reports.py
index 0415cb3b0b..981e47227e 100644
--- a/packages/graphrag/graphrag/index/workflows/create_community_reports.py
+++ b/packages/graphrag/graphrag/index/workflows/create_community_reports.py
@@ -55,7 +55,7 @@ async def run_workflow(
         claims = await load_table_from_storage("covariates", context.output_storage)
 
     model_config = config.get_language_model_config(config.community_reports.model_id)
-    prompts = config.community_reports.resolved_prompts(config.root_dir)
+    prompts = config.community_reports.resolved_prompts()
 
     model = ModelManager().get_or_create_chat_model(
         name=config.community_reports.model_instance_name,
diff --git a/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py
index 94d79ca572..ac269df8bc 100644
--- a/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py
+++ b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py
@@ -56,7 +56,7 @@ async def run_workflow(
 
     tokenizer = get_tokenizer(model_config)
 
-    prompts = config.community_reports.resolved_prompts(config.root_dir)
+    prompts = config.community_reports.resolved_prompts()
 
     output = await create_community_reports_text(
         entities,
diff --git a/packages/graphrag/graphrag/index/workflows/extract_covariates.py b/packages/graphrag/graphrag/index/workflows/extract_covariates.py
index 52da07451e..99a8f279ed 100644
--- a/packages/graphrag/graphrag/index/workflows/extract_covariates.py
+++ b/packages/graphrag/graphrag/index/workflows/extract_covariates.py
@@ -45,7 +45,7 @@ async def run_workflow(
             cache=context.cache,
         )
 
-        prompts = config.extract_claims.resolved_prompts(config.root_dir)
+        prompts = config.extract_claims.resolved_prompts()
 
         output = await extract_covariates(
             text_units=text_units,
diff --git a/packages/graphrag/graphrag/index/workflows/extract_graph.py b/packages/graphrag/graphrag/index/workflows/extract_graph.py
index 1a28a3978a..de9c454261 100644
--- a/packages/graphrag/graphrag/index/workflows/extract_graph.py
+++ b/packages/graphrag/graphrag/index/workflows/extract_graph.py
@@ -36,7 +36,7 @@ async def run_workflow(
     extraction_model_config = config.get_language_model_config(
         config.extract_graph.model_id
     )
-    extraction_prompts = config.extract_graph.resolved_prompts(config.root_dir)
+    extraction_prompts = config.extract_graph.resolved_prompts()
     extraction_model = ModelManager().get_or_create_chat_model(
         name=config.extract_graph.model_instance_name,
         model_type=extraction_model_config.type,
@@ -47,9 +47,7 @@ async def run_workflow(
     summarization_model_config = config.get_language_model_config(
         config.summarize_descriptions.model_id
     )
-    summarization_prompts = config.summarize_descriptions.resolved_prompts(
-        config.root_dir
-    )
+    summarization_prompts = config.summarize_descriptions.resolved_prompts()
     summarization_model = ModelManager().get_or_create_chat_model(
         name=config.summarize_descriptions.model_instance_name,
         model_type=summarization_model_config.type,
diff --git a/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
index 69bdefa3f2..1245303559 100644
--- a/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
+++ b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
@@ -81,7 +81,7 @@ async def _update_entities_and_relationships(
     summarization_model_config = config.get_language_model_config(
         config.summarize_descriptions.model_id
     )
-    prompts = config.summarize_descriptions.resolved_prompts(config.root_dir)
+    prompts = config.summarize_descriptions.resolved_prompts()
     model = ModelManager().get_or_create_chat_model(
         name="summarize_descriptions",
         model_type=summarization_model_config.type,
diff --git a/packages/graphrag/graphrag/logger/factory.py b/packages/graphrag/graphrag/logger/factory.py
index 744a563272..1094d080f3 100644
--- a/packages/graphrag/graphrag/logger/factory.py
+++ b/packages/graphrag/graphrag/logger/factory.py
@@ -32,10 +32,9 @@ class LoggerFactory(Factory[logging.Handler]):
 # --- register built-in logger implementations ---
 def create_file_logger(**kwargs) -> logging.Handler:
     """Create a file-based logger."""
-    root_dir = kwargs["root_dir"]
     base_dir = kwargs["base_dir"]
     filename = kwargs["filename"]
-    log_dir = Path(root_dir) / base_dir
+    log_dir = Path(base_dir)
     log_dir.mkdir(parents=True, exist_ok=True)
     log_file_path = log_dir / filename
 
diff --git a/packages/graphrag/graphrag/logger/standard_logging.py b/packages/graphrag/graphrag/logger/standard_logging.py
index 31296e12d9..d9e4d0f26f 100644
--- a/packages/graphrag/graphrag/logger/standard_logging.py
+++ b/packages/graphrag/graphrag/logger/standard_logging.py
@@ -77,7 +77,7 @@ def init_loggers(
 
     reporting_config = config.reporting
     config_dict = reporting_config.model_dump()
-    args = {**config_dict, "root_dir": config.root_dir, "filename": filename}
+    args = {**config_dict, "filename": filename}
 
     handler = LoggerFactory().create(reporting_config.type, args)
     logger.addHandler(handler)
diff --git a/packages/graphrag/graphrag/utils/api.py b/packages/graphrag/graphrag/utils/api.py
index a972d21db9..f264c1a9ed 100644
--- a/packages/graphrag/graphrag/utils/api.py
+++ b/packages/graphrag/graphrag/utils/api.py
@@ -87,7 +87,7 @@ def reformat_context_data(context_data: dict) -> dict:
     return final_format
 
 
-def load_search_prompt(root_dir: str, prompt_config: str | None) -> str | None:
+def load_search_prompt(prompt_config: str | None) -> str | None:
     """
     Load the search prompt from disk if configured.
 
@@ -95,7 +95,7 @@ def load_search_prompt(root_dir: str, prompt_config: str | None) -> str | None:
 
     """
     if prompt_config:
-        prompt_file = Path(root_dir) / prompt_config
+        prompt_file = Path(prompt_config).resolve()
         if prompt_file.exists():
             return prompt_file.read_bytes().decode(encoding="utf-8")
     return None
@@ -110,13 +110,12 @@ def create_storage_from_config(output: StorageConfig) -> PipelineStorage:
     )
 
 
-def create_cache_from_config(cache: CacheConfig, root_dir: str) -> PipelineCache:
+def create_cache_from_config(cache: CacheConfig) -> PipelineCache:
     """Create a cache object from the config."""
     cache_config = cache.model_dump()
-    args = {**cache_config, "root_dir": root_dir}
     return CacheFactory().create(
         strategy=cache_config["type"],
-        init_args=args,
+        init_args=cache_config,
     )
 
 
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index a3167a8045..a7b97f1f0f 100644
--- a/packages/graphrag/pyproject.toml
+++ b/packages/graphrag/pyproject.toml
@@ -52,8 +52,6 @@ dependencies = [
     "pandas>=2.2.3",
     "pyarrow>=17.0.0",
     "pydantic>=2.10.3",
-    "python-dotenv>=1.0.1",
-    "pyyaml>=6.0.2",
     "spacy>=3.8.4",
     "textblob>=0.18.0.post0",
     "tiktoken>=0.11.0",
diff --git a/pyproject.toml b/pyproject.toml
index 199fd6a514..3cb1ae1d67 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -217,7 +217,11 @@ convention = "numpy"
 
 # https://github.com/microsoft/pyright/blob/9f81564a4685ff5c55edd3959f9b39030f590b2f/docs/configuration.md#sample-pyprojecttoml-file
 [tool.pyright]
-include = ["packages/graphrag/graphrag", "packages/graphrag-common/graphrag_common", "tests"]
+include = [
+    "packages/graphrag/graphrag", 
+    "packages/graphrag-common/graphrag_common", 
+    "tests"
+]
 exclude = ["**/node_modules", "**/__pycache__"]
 
 [tool.pytest.ini_options]
diff --git a/tests/integration/cache/test_factory.py b/tests/integration/cache/test_factory.py
index 53ec3cba56..766adc1f8f 100644
--- a/tests/integration/cache/test_factory.py
+++ b/tests/integration/cache/test_factory.py
@@ -34,7 +34,7 @@ def test_create_memory_cache():
 def test_create_file_cache():
     cache = CacheFactory().create(
         strategy=CacheType.file.value,
-        init_args={"root_dir": "/tmp", "base_dir": "testcache"},
+        init_args={"base_dir": "testcache"},
     )
     assert isinstance(cache, JsonPipelineCache)
 
diff --git a/tests/integration/logging/test_standard_logging.py b/tests/integration/logging/test_standard_logging.py
index 106e57f9fa..6bb28a7343 100644
--- a/tests/integration/logging/test_standard_logging.py
+++ b/tests/integration/logging/test_standard_logging.py
@@ -4,6 +4,7 @@
 """Tests for standard logging functionality."""
 
 import logging
+import os
 import tempfile
 from pathlib import Path
 
@@ -38,7 +39,11 @@ def test_logger_hierarchy():
 def test_init_loggers_file_config():
     """Test that init_loggers works with file configuration."""
     with tempfile.TemporaryDirectory() as temp_dir:
-        config = get_default_graphrag_config(root_dir=temp_dir)
+        # Need to manually change cwd since we are not using load_config
+        # to create graphrag config.
+        cwd = Path.cwd()
+        os.chdir(temp_dir)
+        config = get_default_graphrag_config()
 
         # call init_loggers with file config
         init_loggers(config=config)
@@ -68,12 +73,17 @@ def test_init_loggers_file_config():
             if isinstance(handler, logging.FileHandler):
                 handler.close()
         logger.handlers.clear()
+        os.chdir(cwd)
 
 
 def test_init_loggers_file_verbose():
     """Test that init_loggers works with verbose flag."""
     with tempfile.TemporaryDirectory() as temp_dir:
-        config = get_default_graphrag_config(root_dir=temp_dir)
+        # Need to manually change cwd since we are not using load_config
+        # to create graphrag config.
+        cwd = Path.cwd()
+        os.chdir(temp_dir)
+        config = get_default_graphrag_config()
 
         # call init_loggers with file config
         init_loggers(config=config, verbose=True)
@@ -96,12 +106,17 @@ def test_init_loggers_file_verbose():
             if isinstance(handler, logging.FileHandler):
                 handler.close()
         logger.handlers.clear()
+        os.chdir(cwd)
 
 
 def test_init_loggers_custom_filename():
     """Test that init_loggers works with custom filename."""
     with tempfile.TemporaryDirectory() as temp_dir:
-        config = get_default_graphrag_config(root_dir=temp_dir)
+        # Need to manually change cwd since we are not using load_config
+        # to create graphrag config.
+        cwd = Path.cwd()
+        os.chdir(temp_dir)
+        config = get_default_graphrag_config()
 
         # call init_loggers with file config
         init_loggers(config=config, filename="custom-log.log")
@@ -117,3 +132,4 @@ def test_init_loggers_custom_filename():
             if isinstance(handler, logging.FileHandler):
                 handler.close()
         logger.handlers.clear()
+        os.chdir(cwd)
diff --git a/tests/smoke/test_fixtures.py b/tests/smoke/test_fixtures.py
index b6966930e7..9821bed551 100644
--- a/tests/smoke/test_fixtures.py
+++ b/tests/smoke/test_fixtures.py
@@ -204,14 +204,13 @@ def __run_query(self, root: Path, query_config: dict[str, str]):
             "run",
             "poe",
             "query",
+            query_config["query"],
             "--root",
             root.resolve().as_posix(),
             "--method",
             query_config["method"],
             "--community-level",
             str(query_config.get("community_level", 2)),
-            "--query",
-            query_config["query"],
         ]
 
         logger.info("running command ", " ".join(command))
diff --git a/tests/unit/config/test_config.py b/tests/unit/config/test_config.py
index ddf1d17a5f..545e66b7c5 100644
--- a/tests/unit/config/test_config.py
+++ b/tests/unit/config/test_config.py
@@ -7,9 +7,9 @@
 
 import graphrag.config.defaults as defs
 import pytest
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.enums import AuthType, ModelType
 from graphrag.config.load_config import load_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from pydantic import ValidationError
 
 from tests.unit.config.utils import (
@@ -33,14 +33,14 @@ def test_missing_openai_required_api_key() -> None:
 
     # API Key required for OpenAIChat
     with pytest.raises(ValidationError):
-        create_graphrag_config({"models": model_config_missing_api_key})
+        GraphRagConfig(models=model_config_missing_api_key)
 
     # API Key required for OpenAIEmbedding
     model_config_missing_api_key[defs.DEFAULT_CHAT_MODEL_ID]["type"] = (
         ModelType.Embedding
     )
     with pytest.raises(ValidationError):
-        create_graphrag_config({"models": model_config_missing_api_key})
+        GraphRagConfig(models=model_config_missing_api_key)
 
 
 def test_missing_azure_api_key() -> None:
@@ -58,13 +58,13 @@ def test_missing_azure_api_key() -> None:
     }
 
     with pytest.raises(ValidationError):
-        create_graphrag_config({"models": model_config_missing_api_key})
+        GraphRagConfig(models=model_config_missing_api_key)
 
     # API Key not required for managed identity
     model_config_missing_api_key[defs.DEFAULT_CHAT_MODEL_ID]["auth_type"] = (
         AuthType.AzureManagedIdentity
     )
-    create_graphrag_config({"models": model_config_missing_api_key})
+    GraphRagConfig(models=model_config_missing_api_key)
 
 
 def test_conflicting_auth_type() -> None:
@@ -79,7 +79,7 @@ def test_conflicting_auth_type() -> None:
     }
 
     with pytest.raises(ValidationError):
-        create_graphrag_config({"models": model_config_invalid_auth_type})
+        GraphRagConfig(models=model_config_invalid_auth_type)
 
 
 def test_conflicting_azure_api_key() -> None:
@@ -98,7 +98,7 @@ def test_conflicting_azure_api_key() -> None:
     }
 
     with pytest.raises(ValidationError):
-        create_graphrag_config({"models": model_config_conflicting_api_key})
+        GraphRagConfig(models=model_config_conflicting_api_key)
 
 
 base_azure_model_config = {
@@ -117,12 +117,12 @@ def test_missing_azure_api_base() -> None:
     del missing_api_base_config["api_base"]
 
     with pytest.raises(ValidationError):
-        create_graphrag_config({
-            "models": {
+        GraphRagConfig(
+            models={
                 defs.DEFAULT_CHAT_MODEL_ID: missing_api_base_config,
                 defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
-            }
-        })
+            }  # type: ignore
+        )
 
 
 def test_missing_azure_api_version() -> None:
@@ -130,46 +130,49 @@ def test_missing_azure_api_version() -> None:
     del missing_api_version_config["api_version"]
 
     with pytest.raises(ValidationError):
-        create_graphrag_config({
-            "models": {
+        GraphRagConfig(
+            models={
                 defs.DEFAULT_CHAT_MODEL_ID: missing_api_version_config,
                 defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
-            }
-        })
+            }  # type: ignore
+        )
 
 
 def test_default_config() -> None:
     expected = get_default_graphrag_config()
-    actual = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    actual = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     assert_graphrag_configs(actual, expected)
 
 
 @mock.patch.dict(os.environ, {"CUSTOM_API_KEY": FAKE_API_KEY}, clear=True)
 def test_load_minimal_config() -> None:
-    cwd = Path(__file__).parent
-    root_dir = (cwd / "fixtures" / "minimal_config").resolve()
-    expected = get_default_graphrag_config(str(root_dir))
-    actual = load_config(root_dir=root_dir)
+    cwd = Path.cwd()
+    root_dir = (Path(__file__).parent / "fixtures" / "minimal_config").resolve()
+    os.chdir(root_dir)
+    expected = get_default_graphrag_config()
+
+    actual = load_config(
+        root_dir=root_dir,
+    )
     assert_graphrag_configs(actual, expected)
+    # Need to reset cwd after test
+    os.chdir(cwd)
 
 
 @mock.patch.dict(os.environ, {"CUSTOM_API_KEY": FAKE_API_KEY}, clear=True)
 def test_load_config_with_cli_overrides() -> None:
-    cwd = Path(__file__).parent
-    root_dir = (cwd / "fixtures" / "minimal_config").resolve()
+    cwd = Path.cwd()
+    root_dir = (Path(__file__).parent / "fixtures" / "minimal_config").resolve()
+    os.chdir(root_dir)
     output_dir = "some_output_dir"
     expected_output_base_dir = root_dir / output_dir
-    expected = get_default_graphrag_config(str(root_dir))
+    expected = get_default_graphrag_config()
     expected.output.base_dir = str(expected_output_base_dir)
+
     actual = load_config(
         root_dir=root_dir,
-        cli_overrides={"output.base_dir": output_dir},
+        cli_overrides={"output": {"base_dir": output_dir}},
     )
     assert_graphrag_configs(actual, expected)
-
-
-def test_load_config_missing_env_vars() -> None:
-    cwd = Path(__file__).parent
-    root_dir = (cwd / "fixtures" / "minimal_config_missing_env_var").resolve()
-    with pytest.raises(KeyError):
-        load_config(root_dir=root_dir)
+    # Need to reset cwd after test
+    os.chdir(cwd)
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index c7dbb4cd5c..001518f62a 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -54,11 +54,10 @@
 }
 
 
-def get_default_graphrag_config(root_dir: str | None = None) -> GraphRagConfig:
+def get_default_graphrag_config() -> GraphRagConfig:
     return GraphRagConfig(**{
         **asdict(defs.graphrag_config_defaults),
         "models": DEFAULT_MODEL_CONFIG,
-        **({"root_dir": root_dir} if root_dir else {}),
     })
 
 
@@ -350,8 +349,6 @@ def assert_basic_search_configs(
 
 
 def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) -> None:
-    assert actual.root_dir == expected.root_dir
-
     a_keys = sorted(actual.models.keys())
     e_keys = sorted(expected.models.keys())
     assert len(a_keys) == len(e_keys)
diff --git a/tests/unit/indexing/test_init_content.py b/tests/unit/indexing/test_init_content.py
index cfd59cd9ad..61f2d11fa6 100644
--- a/tests/unit/indexing/test_init_content.py
+++ b/tests/unit/indexing/test_init_content.py
@@ -5,14 +5,13 @@
 from typing import Any, cast
 
 import yaml
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.init_content import INIT_YAML
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 
 
 def test_init_yaml():
     data = yaml.load(INIT_YAML, Loader=yaml.FullLoader)
-    config = create_graphrag_config(data)
+    config = GraphRagConfig(**data)
     GraphRagConfig.model_validate(config, strict=True)
 
 
@@ -26,5 +25,5 @@ def uncomment_line(line: str) -> str:
 
     content = "\n".join([uncomment_line(line) for line in lines])
     data = yaml.load(content, Loader=yaml.FullLoader)
-    config = create_graphrag_config(data)
+    config = GraphRagConfig(**data)
     GraphRagConfig.model_validate(config, strict=True)
diff --git a/tests/unit/load_config/__init__.py b/tests/unit/load_config/__init__.py
new file mode 100644
index 0000000000..0a3e38adfb
--- /dev/null
+++ b/tests/unit/load_config/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
diff --git a/tests/unit/load_config/config.py b/tests/unit/load_config/config.py
new file mode 100644
index 0000000000..f3b77feb65
--- /dev/null
+++ b/tests/unit/load_config/config.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Config models for load_config unit tests."""
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class TestNestedModel(BaseModel):
+    """Test nested model."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    nested_str: str = Field(description="A nested field.")
+    nested_int: int = Field(description="Another nested field.")
+
+
+class TestConfigModel(BaseModel):
+    """Test configuration model."""
+
+    model_config = ConfigDict(extra="forbid")
+    __test__ = False  # type: ignore
+
+    name: str = Field(description="Name field.")
+    value: int = Field(description="Value field.")
+    nested: TestNestedModel = Field(description="Nested model field.")
+    nested_list: list[TestNestedModel] = Field(description="List of nested models.")
diff --git a/tests/unit/load_config/fixtures/config_with_env.yaml b/tests/unit/load_config/fixtures/config_with_env.yaml
new file mode 100644
index 0000000000..ecefbbc457
--- /dev/null
+++ b/tests/unit/load_config/fixtures/config_with_env.yaml
@@ -0,0 +1,10 @@
+name: ${LOAD_CONFIG_NAME}
+value: 100
+nested:
+  nested_str: nested_value
+  nested_int: 42
+nested_list:
+  - nested_str: list_value_1
+    nested_int: 7
+  - nested_str: list_value_2
+    nested_int: 8
\ No newline at end of file
diff --git a/tests/unit/load_config/fixtures/invalid_config.yaml b/tests/unit/load_config/fixtures/invalid_config.yaml
new file mode 100644
index 0000000000..d2da11d0eb
--- /dev/null
+++ b/tests/unit/load_config/fixtures/invalid_config.yaml
@@ -0,0 +1 @@
+name: test_name
\ No newline at end of file
diff --git a/tests/unit/load_config/fixtures/invalid_config_format.yaml b/tests/unit/load_config/fixtures/invalid_config_format.yaml
new file mode 100644
index 0000000000..b851bf08c7
--- /dev/null
+++ b/tests/unit/load_config/fixtures/invalid_config_format.yaml
@@ -0,0 +1,8 @@
+{
+  "key": "value",
+  "invalid_yaml": true
+}
+{
+  "key": "value",
+  "invalid_yaml": true
+}
\ No newline at end of file
diff --git a/tests/unit/load_config/fixtures/settings.yaml b/tests/unit/load_config/fixtures/settings.yaml
new file mode 100644
index 0000000000..a54919d1eb
--- /dev/null
+++ b/tests/unit/load_config/fixtures/settings.yaml
@@ -0,0 +1,10 @@
+name: test_name
+value: 100
+nested:
+  nested_str: nested_value
+  nested_int: 42
+nested_list:
+  - nested_str: list_value_1
+    nested_int: 7
+  - nested_str: list_value_2
+    nested_int: 8
\ No newline at end of file
diff --git a/tests/unit/load_config/fixtures/test.env b/tests/unit/load_config/fixtures/test.env
new file mode 100644
index 0000000000..0ca30592c0
--- /dev/null
+++ b/tests/unit/load_config/fixtures/test.env
@@ -0,0 +1 @@
+LOAD_CONFIG_NAME=env_name
\ No newline at end of file
diff --git a/tests/unit/load_config/test_load_config.py b/tests/unit/load_config/test_load_config.py
new file mode 100644
index 0000000000..0945cf214f
--- /dev/null
+++ b/tests/unit/load_config/test_load_config.py
@@ -0,0 +1,157 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Unit tests for graphrag-config.load_config."""
+
+import os
+from pathlib import Path
+
+import pytest
+from graphrag_common.config import ConfigParsingError, load_config
+from pydantic import ValidationError
+
+from .config import TestConfigModel
+
+
+def test_load_config_validation():
+    """Test loading config validation."""
+
+    with pytest.raises(
+        FileNotFoundError,
+    ):
+        _ = load_config(TestConfigModel, "non_existent_config.yaml")
+
+    config_directory = Path(__file__).parent / "fixtures"
+    invalid_config_formatting_path = config_directory / "invalid_config_format.yaml"
+
+    with pytest.raises(
+        FileNotFoundError,
+    ):
+        _ = load_config(
+            config_initializer=TestConfigModel,
+            config_path=invalid_config_formatting_path,
+            dot_env_path="non_existent.env",
+        )
+
+    # Using yaml to parse invalid json formatting
+    with pytest.raises(
+        ConfigParsingError,
+    ):
+        _ = load_config(TestConfigModel, invalid_config_formatting_path)
+
+    invalid_config_path = config_directory / "invalid_config.yaml"
+
+    # Test validation error from config model
+    with pytest.raises(
+        ValidationError,
+    ):
+        _ = load_config(
+            config_initializer=TestConfigModel,
+            config_path=invalid_config_path,
+            set_cwd=False,
+        )
+
+
+def test_load_config():
+    """Test loading configuration."""
+
+    config_directory = Path(__file__).parent / "fixtures"
+    config_path = config_directory / "settings.yaml"
+
+    # Load from dir
+    config = load_config(
+        config_initializer=TestConfigModel, config_path=config_directory, set_cwd=False
+    )
+
+    assert config.name == "test_name"
+    assert config.value == 100
+    assert config.nested.nested_str == "nested_value"
+    assert config.nested.nested_int == 42
+    assert len(config.nested_list) == 2
+    assert config.nested_list[0].nested_str == "list_value_1"
+    assert config.nested_list[0].nested_int == 7
+    assert config.nested_list[1].nested_str == "list_value_2"
+    assert config.nested_list[1].nested_int == 8
+
+    # Should not have changed directories
+    root_repo_dir = Path(__file__).parent.parent.parent.parent.resolve()
+    assert Path.cwd().resolve() == root_repo_dir
+
+    config = load_config(
+        config_initializer=TestConfigModel,
+        config_path=config_path,
+        set_cwd=False,
+    )
+
+    assert config.name == "test_name"
+    assert config.value == 100
+    assert config.nested.nested_str == "nested_value"
+    assert config.nested.nested_int == 42
+    assert len(config.nested_list) == 2
+    assert config.nested_list[0].nested_str == "list_value_1"
+    assert config.nested_list[0].nested_int == 7
+    assert config.nested_list[1].nested_str == "list_value_2"
+    assert config.nested_list[1].nested_int == 8
+
+    overrides = {
+        "value": 65537,
+        "nested": {"nested_int": 84},
+        "nested_list": [
+            {"nested_str": "overridden_list_value_1", "nested_int": 23},
+        ],
+    }
+
+    cwd = Path.cwd()
+    config_with_overrides = load_config(
+        config_initializer=TestConfigModel,
+        config_path=config_path,
+        overrides=overrides,
+    )
+
+    # Should have changed directories to the config file location
+    assert Path.cwd() == config_directory
+    assert (
+        Path("some/new/path").resolve()
+        == (config_directory / "some/new/path").resolve()
+    )
+    # Reset cwd
+    os.chdir(cwd)
+
+    assert config_with_overrides.name == "test_name"
+    assert config_with_overrides.value == 65537
+    assert config_with_overrides.nested.nested_str == "nested_value"
+    assert config_with_overrides.nested.nested_int == 84
+    assert len(config_with_overrides.nested_list) == 1
+    assert config_with_overrides.nested_list[0].nested_str == "overridden_list_value_1"
+    assert config_with_overrides.nested_list[0].nested_int == 23
+
+    config_with_env_vars_path = config_directory / "config_with_env.yaml"
+
+    # Config contains env vars that do not exist
+    # and no .env file is provided
+    with pytest.raises(
+        ConfigParsingError,
+    ):
+        _ = load_config(
+            config_initializer=TestConfigModel,
+            config_path=config_with_env_vars_path,
+            load_dot_env_file=False,
+            set_cwd=False,
+        )
+
+    env_path = config_directory / "test.env"
+    config_with_env_vars = load_config(
+        config_initializer=TestConfigModel,
+        config_path=config_with_env_vars_path,
+        dot_env_path=env_path,
+    )
+
+    assert config_with_env_vars.name == "env_name"
+    assert config_with_env_vars.value == 100
+    assert config_with_env_vars.nested.nested_str == "nested_value"
+    assert config_with_env_vars.nested.nested_int == 42
+    assert len(config_with_env_vars.nested_list) == 2
+    assert config_with_env_vars.nested_list[0].nested_str == "list_value_1"
+    assert config_with_env_vars.nested_list[0].nested_int == 7
+    assert config_with_env_vars.nested_list[1].nested_str == "list_value_2"
+    assert config_with_env_vars.nested_list[1].nested_int == 8
diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py
index 87148a981d..73b72fc2cb 100644
--- a/tests/verbs/test_create_base_text_units.py
+++ b/tests/verbs/test_create_base_text_units.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.workflows.create_base_text_units import run_workflow
 from graphrag.utils.storage import load_table_from_storage
 
@@ -19,7 +19,7 @@ async def test_create_base_text_units():
 
     context = await create_test_context()
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
 
     await run_workflow(config, context)
 
@@ -33,7 +33,7 @@ async def test_create_base_text_units_metadata():
 
     context = await create_test_context()
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.input.metadata = ["title"]
     config.chunks.prepend_metadata = True
 
@@ -50,7 +50,7 @@ async def test_create_base_text_units_metadata_included_in_chunk():
 
     context = await create_test_context()
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.input.metadata = ["title"]
     config.chunks.prepend_metadata = True
     config.chunks.chunk_size_includes_metadata = True
diff --git a/tests/verbs/test_create_communities.py b/tests/verbs/test_create_communities.py
index 1f51667cf3..5754c814a4 100644
--- a/tests/verbs/test_create_communities.py
+++ b/tests/verbs/test_create_communities.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COMMUNITIES_FINAL_COLUMNS
 from graphrag.index.workflows.create_communities import (
     run_workflow,
@@ -26,7 +26,7 @@ async def test_create_communities():
         ],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
 
     await run_workflow(
         config,
diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py
index 3947d4dc58..d479120ce2 100644
--- a/tests/verbs/test_create_community_reports.py
+++ b/tests/verbs/test_create_community_reports.py
@@ -2,7 +2,7 @@
 # Licensed under the MIT License
 
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
 from graphrag.index.operations.summarize_communities.community_reports_extractor import (
     CommunityReportResponse,
@@ -50,7 +50,7 @@ async def test_create_community_reports():
         ]
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.models["default_chat_model"].type = "mock_chat"
     config.models["default_chat_model"].responses = MOCK_RESPONSES  # type: ignore
 
diff --git a/tests/verbs/test_create_final_documents.py b/tests/verbs/test_create_final_documents.py
index 89ff2d7c5c..05f4b60691 100644
--- a/tests/verbs/test_create_final_documents.py
+++ b/tests/verbs/test_create_final_documents.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import DOCUMENTS_FINAL_COLUMNS
 from graphrag.index.workflows.create_final_documents import (
     run_workflow,
@@ -24,7 +24,7 @@ async def test_create_final_documents():
         storage=["text_units"],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
 
     await run_workflow(config, context)
 
@@ -41,7 +41,7 @@ async def test_create_final_documents_with_metadata_column():
         storage=["text_units"],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.input.metadata = ["title"]
 
     # simulate the metadata construction during initial input loading
diff --git a/tests/verbs/test_create_final_text_units.py b/tests/verbs/test_create_final_text_units.py
index 979f48d5b4..d8d7686d41 100644
--- a/tests/verbs/test_create_final_text_units.py
+++ b/tests/verbs/test_create_final_text_units.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import TEXT_UNITS_FINAL_COLUMNS
 from graphrag.index.workflows.create_final_text_units import (
     run_workflow,
@@ -28,7 +28,7 @@ async def test_create_final_text_units():
         ],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.extract_claims.enabled = True
 
     await run_workflow(config, context)
diff --git a/tests/verbs/test_extract_covariates.py b/tests/verbs/test_extract_covariates.py
index d4645a34f9..3351f04273 100644
--- a/tests/verbs/test_extract_covariates.py
+++ b/tests/verbs/test_extract_covariates.py
@@ -1,8 +1,8 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.enums import ModelType
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COVARIATES_FINAL_COLUMNS
 from graphrag.index.workflows.extract_covariates import (
     run_workflow,
@@ -30,7 +30,7 @@ async def test_extract_covariates():
         storage=["text_units"],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.extract_claims.enabled = True
     config.extract_claims.description = "description"
     llm_settings = config.get_language_model_config(config.extract_claims.model_id)
diff --git a/tests/verbs/test_extract_graph.py b/tests/verbs/test_extract_graph.py
index 28dbfcf3c0..7bdc38e4a2 100644
--- a/tests/verbs/test_extract_graph.py
+++ b/tests/verbs/test_extract_graph.py
@@ -1,8 +1,8 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.enums import ModelType
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.workflows.extract_graph import (
     run_workflow,
 )
@@ -43,12 +43,12 @@ async def test_extract_graph():
     extraction_model = DEFAULT_CHAT_MODEL_CONFIG.copy()
     extraction_model["type"] = ModelType.MockChat
     extraction_model["responses"] = MOCK_LLM_ENTITY_RESPONSES  # type: ignore
-    config = create_graphrag_config({
-        "models": {
+    config = GraphRagConfig(
+        models={
             "default_chat_model": extraction_model,
             "default_embedding_model": DEFAULT_EMBEDDING_MODEL_CONFIG,
-        }
-    })
+        }  # type: ignore
+    )
 
     summarize_llm_settings = config.get_language_model_config(
         config.summarize_descriptions.model_id
diff --git a/tests/verbs/test_extract_graph_nlp.py b/tests/verbs/test_extract_graph_nlp.py
index 92da89288c..0c1f54c66e 100644
--- a/tests/verbs/test_extract_graph_nlp.py
+++ b/tests/verbs/test_extract_graph_nlp.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.workflows.extract_graph_nlp import (
     run_workflow,
 )
@@ -18,7 +18,7 @@ async def test_extract_graph_nlp():
         storage=["text_units"],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
 
     await run_workflow(config, context)
 
diff --git a/tests/verbs/test_finalize_graph.py b/tests/verbs/test_finalize_graph.py
index 0950738ae7..cce5adad94 100644
--- a/tests/verbs/test_finalize_graph.py
+++ b/tests/verbs/test_finalize_graph.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import (
     ENTITIES_FINAL_COLUMNS,
     RELATIONSHIPS_FINAL_COLUMNS,
@@ -21,7 +21,7 @@
 async def test_finalize_graph():
     context = await _prep_tables()
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
 
     await run_workflow(config, context)
 
diff --git a/tests/verbs/test_generate_text_embeddings.py b/tests/verbs/test_generate_text_embeddings.py
index 33254874e7..e9e6c00afa 100644
--- a/tests/verbs/test_generate_text_embeddings.py
+++ b/tests/verbs/test_generate_text_embeddings.py
@@ -1,11 +1,11 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.embeddings import (
     all_embeddings,
 )
 from graphrag.config.enums import ModelType
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.workflows.generate_text_embeddings import (
     run_workflow,
 )
@@ -28,7 +28,7 @@ async def test_generate_text_embeddings():
         ]
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     llm_settings = config.get_language_model_config(config.embed_text.model_id)
     llm_settings.type = ModelType.MockEmbedding
 
diff --git a/tests/verbs/test_pipeline_state.py b/tests/verbs/test_pipeline_state.py
index 306cd84256..2e41ecc4af 100644
--- a/tests/verbs/test_pipeline_state.py
+++ b/tests/verbs/test_pipeline_state.py
@@ -3,7 +3,6 @@
 
 """Tests for pipeline state passthrough."""
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import create_run_context
 from graphrag.index.typing.context import PipelineRunContext
@@ -32,7 +31,7 @@ async def test_pipeline_state():
     PipelineFactory.register("workflow_1", run_workflow_1)
     PipelineFactory.register("workflow_2", run_workflow_2)
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.workflows = ["workflow_1", "workflow_2"]
     context = create_run_context()
 
@@ -45,7 +44,7 @@ async def test_pipeline_state():
 async def test_pipeline_existing_state():
     PipelineFactory.register("workflow_2", run_workflow_2)
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.workflows = ["workflow_2"]
     context = create_run_context(state={"count": 4})
 
diff --git a/tests/verbs/test_prune_graph.py b/tests/verbs/test_prune_graph.py
index 6ed0001973..426230161a 100644
--- a/tests/verbs/test_prune_graph.py
+++ b/tests/verbs/test_prune_graph.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.config.models.prune_graph_config import PruneGraphConfig
 from graphrag.index.workflows.prune_graph import (
     run_workflow,
@@ -19,7 +19,7 @@ async def test_prune_graph():
         storage=["entities", "relationships"],
     )
 
-    config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
+    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.prune_graph = PruneGraphConfig(
         min_node_freq=4, min_node_degree=0, min_edge_weight_pct=0
     )
diff --git a/unified-search-app/app/knowledge_loader/data_sources/blob_source.py b/unified-search-app/app/knowledge_loader/data_sources/blob_source.py
index f54ed71854..63774d79f7 100644
--- a/unified-search-app/app/knowledge_loader/data_sources/blob_source.py
+++ b/unified-search-app/app/knowledge_loader/data_sources/blob_source.py
@@ -13,7 +13,6 @@
 import yaml
 from azure.identity import DefaultAzureCredential
 from azure.storage.blob import BlobServiceClient, ContainerClient
-from graphrag.config.create_graphrag_config import create_graphrag_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from knowledge_loader.data_sources.typing import Datasource
 
@@ -114,7 +113,7 @@ def read_settings(
             str_settings = settings.read().decode("utf-8")
             config = os.path.expandvars(str_settings)
             settings_yaml = yaml.safe_load(config)
-            graphrag_config = create_graphrag_config(values=settings_yaml)
+            graphrag_config = GraphRagConfig(**settings_yaml)
         except Exception as err:
             if throw_on_missing:
                 error_msg = f"File {file} does not exist"
diff --git a/uv.lock b/uv.lock
index 761cd308f0..6ef295df9d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1055,8 +1055,6 @@ dependencies = [
     { name = "pandas" },
     { name = "pyarrow" },
     { name = "pydantic" },
-    { name = "python-dotenv" },
-    { name = "pyyaml" },
     { name = "spacy" },
     { name = "textblob" },
     { name = "tiktoken" },
@@ -1086,8 +1084,6 @@ requires-dist = [
     { name = "pandas", specifier = ">=2.2.3" },
     { name = "pyarrow", specifier = ">=17.0.0" },
     { name = "pydantic", specifier = ">=2.10.3" },
-    { name = "python-dotenv", specifier = ">=1.0.1" },
-    { name = "pyyaml", specifier = ">=6.0.2" },
     { name = "spacy", specifier = ">=3.8.4" },
     { name = "textblob", specifier = ">=0.18.0.post0" },
     { name = "tiktoken", specifier = ">=0.11.0" },
@@ -1100,6 +1096,16 @@ requires-dist = [
 name = "graphrag-common"
 version = "2.7.0"
 source = { editable = "packages/graphrag-common" }
+dependencies = [
+    { name = "python-dotenv" },
+    { name = "pyyaml" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "python-dotenv", specifier = ">=1.0.1" },
+    { name = "pyyaml", specifier = ">=6.0.2" },
+]
 
 [[package]]
 name = "graphrag-monorepo"

From 4512ce027f2a1e94b3f68e1ba5af4dd9b49971f1 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 11 Nov 2025 10:11:08 -0800
Subject: [PATCH 24/40] Empty graph guards (#2126)

* Remove networkx from graph_extractor and clean out redundancy

* Bubble pipeline error to console
---
 packages/graphrag/graphrag/api/index.py       |   3 +-
 .../callbacks/console_workflow_callbacks.py   |   4 +
 .../callbacks/noop_workflow_callbacks.py      |   3 +
 .../graphrag/callbacks/workflow_callbacks.py  |   4 +
 .../callbacks/workflow_callbacks_manager.py   |   6 +
 packages/graphrag/graphrag/cli/index.py       |  11 +-
 .../operations/extract_graph/extract_graph.py |  61 ++---
 .../extract_graph/graph_extractor.py          | 240 ++++++------------
 .../index/operations/extract_graph/typing.py  |  45 ----
 .../summarize_communities/typing.py           |   2 -
 .../graphrag/index/run/run_pipeline.py        |   4 +-
 .../index/typing/pipeline_run_result.py       |   2 +-
 .../test_gi_entity_extraction.py              | 208 +++------------
 13 files changed, 160 insertions(+), 433 deletions(-)
 delete mode 100644 packages/graphrag/graphrag/index/operations/extract_graph/typing.py

diff --git a/packages/graphrag/graphrag/api/index.py b/packages/graphrag/graphrag/api/index.py
index 7265e46187..c15ea2e786 100644
--- a/packages/graphrag/graphrag/api/index.py
+++ b/packages/graphrag/graphrag/api/index.py
@@ -86,8 +86,9 @@ async def build_index(
         input_documents=input_documents,
     ):
         outputs.append(output)
-        if output.errors and len(output.errors) > 0:
+        if output.error is not None:
             logger.error("Workflow %s completed with errors", output.workflow)
+            workflow_callbacks.pipeline_error(output.error)
         else:
             logger.info("Workflow %s completed successfully", output.workflow)
         logger.debug(str(output.result))
diff --git a/packages/graphrag/graphrag/callbacks/console_workflow_callbacks.py b/packages/graphrag/graphrag/callbacks/console_workflow_callbacks.py
index dbe7e0d552..547c5ed258 100644
--- a/packages/graphrag/graphrag/callbacks/console_workflow_callbacks.py
+++ b/packages/graphrag/graphrag/callbacks/console_workflow_callbacks.py
@@ -37,6 +37,10 @@ def workflow_end(self, name: str, instance: object) -> None:
         if self._verbose:
             print(instance)
 
+    def pipeline_error(self, error: BaseException) -> None:
+        """Execute this callback when an error occurs in the pipeline."""
+        print(f"Pipeline error: {error}")
+
     def progress(self, progress: Progress) -> None:
         """Handle when progress occurs."""
         complete = progress.completed_items or 0
diff --git a/packages/graphrag/graphrag/callbacks/noop_workflow_callbacks.py b/packages/graphrag/graphrag/callbacks/noop_workflow_callbacks.py
index 9f9ac2aee0..19aba39a0c 100644
--- a/packages/graphrag/graphrag/callbacks/noop_workflow_callbacks.py
+++ b/packages/graphrag/graphrag/callbacks/noop_workflow_callbacks.py
@@ -25,3 +25,6 @@ def workflow_end(self, name: str, instance: object) -> None:
 
     def progress(self, progress: Progress) -> None:
         """Handle when progress occurs."""
+
+    def pipeline_error(self, error: BaseException) -> None:
+        """Execute this callback when an error occurs in the pipeline."""
diff --git a/packages/graphrag/graphrag/callbacks/workflow_callbacks.py b/packages/graphrag/graphrag/callbacks/workflow_callbacks.py
index 0429cff809..3fb09710f9 100644
--- a/packages/graphrag/graphrag/callbacks/workflow_callbacks.py
+++ b/packages/graphrag/graphrag/callbacks/workflow_callbacks.py
@@ -35,3 +35,7 @@ def workflow_end(self, name: str, instance: object) -> None:
     def progress(self, progress: Progress) -> None:
         """Handle when progress occurs."""
         ...
+
+    def pipeline_error(self, error: BaseException) -> None:
+        """Execute this callback when an error occurs in the pipeline."""
+        ...
diff --git a/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py b/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
index 1ca0c097e5..6a030ec66a 100644
--- a/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
+++ b/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
@@ -50,3 +50,9 @@ def progress(self, progress: Progress) -> None:
         for callback in self._callbacks:
             if hasattr(callback, "progress"):
                 callback.progress(progress)
+
+    def pipeline_error(self, error: BaseException) -> None:
+        """Execute this callback when an error occurs in the pipeline."""
+        for callback in self._callbacks:
+            if hasattr(callback, "pipeline_error"):
+                callback.pipeline_error(error)
diff --git a/packages/graphrag/graphrag/cli/index.py b/packages/graphrag/graphrag/cli/index.py
index 0a638f63e9..d686a38f13 100644
--- a/packages/graphrag/graphrag/cli/index.py
+++ b/packages/graphrag/graphrag/cli/index.py
@@ -134,15 +134,6 @@ def _run_index(
             verbose=verbose,
         )
     )
-    encountered_errors = any(
-        output.errors and len(output.errors) > 0 for output in outputs
-    )
-
-    if encountered_errors:
-        logger.error(
-            "Errors occurred during the pipeline run, see logs for more details."
-        )
-    else:
-        logger.info("All workflows completed successfully.")
+    encountered_errors = any(output.error is not None for output in outputs)
 
     sys.exit(1 if encountered_errors else 0)
diff --git a/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py b/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
index 3aa87404ec..0d2ab1146e 100644
--- a/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
+++ b/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
@@ -5,17 +5,11 @@
 
 import logging
 
-import networkx as nx
 import pandas as pd
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.enums import AsyncType
 from graphrag.index.operations.extract_graph.graph_extractor import GraphExtractor
-from graphrag.index.operations.extract_graph.typing import (
-    Document,
-    EntityExtractionResult,
-    EntityTypes,
-)
 from graphrag.index.utils.derive_from_rows import derive_from_rows
 from graphrag.language_model.protocol.base import ChatModel
 
@@ -42,14 +36,15 @@ async def run_strategy(row):
         text = row[text_column]
         id = row[id_column]
         result = await run_extract_graph(
-            [Document(text=text, id=id)],
-            entity_types,
-            model,
-            prompt,
-            max_gleanings,
+            text=text,
+            source_id=id,
+            entity_types=entity_types,
+            model=model,
+            prompt=prompt,
+            max_gleanings=max_gleanings,
         )
         num_started += 1
-        return [result.entities, result.relationships, result.graph]
+        return result
 
     results = await derive_from_rows(
         text_units,
@@ -64,8 +59,8 @@ async def run_strategy(row):
     relationship_dfs = []
     for result in results:
         if result:
-            entity_dfs.append(pd.DataFrame(result[0]))
-            relationship_dfs.append(pd.DataFrame(result[1]))
+            entity_dfs.append(result[0])
+            relationship_dfs.append(result[1])
 
     entities = _merge_entities(entity_dfs)
     relationships = _merge_relationships(relationship_dfs)
@@ -74,12 +69,13 @@ async def run_strategy(row):
 
 
 async def run_extract_graph(
-    docs: list[Document],
-    entity_types: EntityTypes,
+    text: str,
+    source_id: str,
+    entity_types: list[str],
     model: ChatModel,
     prompt: str,
     max_gleanings: int,
-) -> EntityExtractionResult:
+) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Run the graph intelligence entity extraction strategy."""
     extractor = GraphExtractor(
         model=model,
@@ -89,36 +85,15 @@ async def run_extract_graph(
             "Entity Extraction Error", exc_info=e, extra={"stack": s, "details": d}
         ),
     )
-    text_list = [doc.text.strip() for doc in docs]
+    text = text.strip()
 
-    results = await extractor(
-        list(text_list),
+    entities_df, relationships_df = await extractor(
+        text,
         entity_types=entity_types,
+        source_id=source_id,
     )
 
-    graph = results.output
-    # Map the "source_id" back to the "id" field
-    for _, node in graph.nodes(data=True):  # type: ignore
-        if node is not None:
-            node["source_id"] = ",".join(
-                docs[int(id)].id for id in node["source_id"].split(",")
-            )
-
-    for _, _, edge in graph.edges(data=True):  # type: ignore
-        if edge is not None:
-            edge["source_id"] = ",".join(
-                docs[int(id)].id for id in edge["source_id"].split(",")
-            )
-
-    entities = [
-        ({"title": item[0], **(item[1] or {})})
-        for item in graph.nodes(data=True)
-        if item is not None
-    ]
-
-    relationships = nx.to_pandas_edgelist(graph)
-
-    return EntityExtractionResult(entities, relationships, graph)
+    return (entities_df, relationships_df)
 
 
 def _merge_entities(entity_dfs) -> pd.DataFrame:
diff --git a/packages/graphrag/graphrag/index/operations/extract_graph/graph_extractor.py b/packages/graphrag/graphrag/index/operations/extract_graph/graph_extractor.py
index 8e98eb7ec4..6d37bf4688 100644
--- a/packages/graphrag/graphrag/index/operations/extract_graph/graph_extractor.py
+++ b/packages/graphrag/graphrag/index/operations/extract_graph/graph_extractor.py
@@ -1,16 +1,14 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'GraphExtractionResult' and 'GraphExtractor' models."""
+"""Graph extraction helpers that return tabular data."""
 
 import logging
 import re
 import traceback
-from collections.abc import Mapping
-from dataclasses import dataclass
 from typing import Any
 
-import networkx as nx
+import pandas as pd
 
 from graphrag.index.typing.error_handler import ErrorHandlerFn
 from graphrag.index.utils.string import clean_str
@@ -27,24 +25,14 @@
 TUPLE_DELIMITER = "<|>"
 RECORD_DELIMITER = "##"
 COMPLETION_DELIMITER = "<|COMPLETE|>"
-DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event"]
 
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class GraphExtractionResult:
-    """Unipartite graph extraction result class definition."""
-
-    output: nx.Graph
-    source_docs: dict[Any, Any]
-
-
 class GraphExtractor:
     """Unipartite graph extractor class definition."""
 
     _model: ChatModel
-    _join_descriptions: bool
     _extraction_prompt: str
     _max_gleanings: int
     _on_error: ErrorHandlerFn
@@ -54,51 +42,40 @@ def __init__(
         model: ChatModel,
         prompt: str,
         max_gleanings: int,
-        join_descriptions=True,
         on_error: ErrorHandlerFn | None = None,
     ):
         """Init method definition."""
         self._model = model
-        self._join_descriptions = join_descriptions
         self._extraction_prompt = prompt
         self._max_gleanings = max_gleanings
         self._on_error = on_error or (lambda _e, _s, _d: None)
 
     async def __call__(
-        self, texts: list[str], entity_types: list[str]
-    ) -> GraphExtractionResult:
-        """Call method definition."""
-        all_records: dict[int, str] = {}
-        source_doc_map: dict[int, str] = {}
-
-        for doc_index, text in enumerate(texts):
-            try:
-                # Invoke the entity extraction
-                result = await self._process_document(text, entity_types)
-                source_doc_map[doc_index] = text
-                all_records[doc_index] = result
-            except Exception as e:
-                logger.exception("error extracting graph")
-                self._on_error(
-                    e,
-                    traceback.format_exc(),
-                    {
-                        "doc_index": doc_index,
-                        "text": text,
-                    },
-                )
-
-        output = await self._process_results(
-            all_records,
+        self, text: str, entity_types: list[str], source_id: str
+    ) -> tuple[pd.DataFrame, pd.DataFrame]:
+        """Extract entities and relationships from the supplied text."""
+        try:
+            # Invoke the entity extraction
+            result = await self._process_document(text, entity_types)
+        except Exception as e:  # pragma: no cover - defensive logging
+            logger.exception("error extracting graph")
+            self._on_error(
+                e,
+                traceback.format_exc(),
+                {
+                    "source_id": source_id,
+                    "text": text,
+                },
+            )
+            return _empty_entities_df(), _empty_relationships_df()
+
+        return self._process_result(
+            result,
+            source_id,
             TUPLE_DELIMITER,
             RECORD_DELIMITER,
         )
 
-        return GraphExtractionResult(
-            output=output,
-            source_docs=source_doc_map,
-        )
-
     async def _process_document(self, text: str, entity_types: list[str]) -> str:
         response = await self._model.achat(
             self._extraction_prompt.format(**{
@@ -133,125 +110,68 @@ async def _process_document(self, text: str, entity_types: list[str]) -> str:
 
         return results
 
-    async def _process_results(
+    def _process_result(
         self,
-        results: dict[int, str],
+        result: str,
+        source_id: str,
         tuple_delimiter: str,
         record_delimiter: str,
-    ) -> nx.Graph:
-        """Parse the result string to create an undirected unipartite graph.
-
-        Args:
-            - results - dict of results from the extraction chain
-            - tuple_delimiter - delimiter between tuples in an output record, default is '<|>'
-            - record_delimiter - delimiter between records, default is '##'
-        Returns:
-            - output - unipartite graph in graphML format
-        """
-        graph = nx.Graph()
-        for source_doc_id, extracted_data in results.items():
-            records = [r.strip() for r in extracted_data.split(record_delimiter)]
-
-            for record in records:
-                record = re.sub(r"^\(|\)$", "", record.strip())
-                record_attributes = record.split(tuple_delimiter)
-
-                if record_attributes[0] == '"entity"' and len(record_attributes) >= 4:
-                    # add this record as a node in the G
-                    entity_name = clean_str(record_attributes[1].upper())
-                    entity_type = clean_str(record_attributes[2].upper())
-                    entity_description = clean_str(record_attributes[3])
-
-                    if entity_name in graph.nodes():
-                        node = graph.nodes[entity_name]
-                        if self._join_descriptions:
-                            node["description"] = "\n".join(
-                                list({
-                                    *_unpack_descriptions(node),
-                                    entity_description,
-                                })
-                            )
-                        else:
-                            if len(entity_description) > len(node["description"]):
-                                node["description"] = entity_description
-                        node["source_id"] = ", ".join(
-                            list({
-                                *_unpack_source_ids(node),
-                                str(source_doc_id),
-                            })
-                        )
-                        node["type"] = (
-                            entity_type if entity_type != "" else node["type"]
-                        )
-                    else:
-                        graph.add_node(
-                            entity_name,
-                            type=entity_type,
-                            description=entity_description,
-                            source_id=str(source_doc_id),
-                        )
-
-                if (
-                    record_attributes[0] == '"relationship"'
-                    and len(record_attributes) >= 5
-                ):
-                    # add this record as edge
-                    source = clean_str(record_attributes[1].upper())
-                    target = clean_str(record_attributes[2].upper())
-                    edge_description = clean_str(record_attributes[3])
-                    edge_source_id = clean_str(str(source_doc_id))
-                    try:
-                        weight = float(record_attributes[-1])
-                    except ValueError:
-                        weight = 1.0
-
-                    if source not in graph.nodes():
-                        graph.add_node(
-                            source,
-                            type="",
-                            description="",
-                            source_id=edge_source_id,
-                        )
-                    if target not in graph.nodes():
-                        graph.add_node(
-                            target,
-                            type="",
-                            description="",
-                            source_id=edge_source_id,
-                        )
-                    if graph.has_edge(source, target):
-                        edge_data = graph.get_edge_data(source, target)
-                        if edge_data is not None:
-                            weight += edge_data["weight"]
-                            if self._join_descriptions:
-                                edge_description = "\n".join(
-                                    list({
-                                        *_unpack_descriptions(edge_data),
-                                        edge_description,
-                                    })
-                                )
-                            edge_source_id = ", ".join(
-                                list({
-                                    *_unpack_source_ids(edge_data),
-                                    str(source_doc_id),
-                                })
-                            )
-                    graph.add_edge(
-                        source,
-                        target,
-                        weight=weight,
-                        description=edge_description,
-                        source_id=edge_source_id,
-                    )
+    ) -> tuple[pd.DataFrame, pd.DataFrame]:
+        """Parse the result string into entity and relationship data frames."""
+        entities: list[dict[str, Any]] = []
+        relationships: list[dict[str, Any]] = []
+
+        records = [r.strip() for r in result.split(record_delimiter)]
+
+        for raw_record in records:
+            record = re.sub(r"^\(|\)$", "", raw_record.strip())
+            if not record or record == COMPLETION_DELIMITER:
+                continue
+
+            record_attributes = record.split(tuple_delimiter)
+            record_type = record_attributes[0]
+
+            if record_type == '"entity"' and len(record_attributes) >= 4:
+                entity_name = clean_str(record_attributes[1].upper())
+                entity_type = clean_str(record_attributes[2].upper())
+                entity_description = clean_str(record_attributes[3])
+                entities.append({
+                    "title": entity_name,
+                    "type": entity_type,
+                    "description": entity_description,
+                    "source_id": source_id,
+                })
+
+            if record_type == '"relationship"' and len(record_attributes) >= 5:
+                source = clean_str(record_attributes[1].upper())
+                target = clean_str(record_attributes[2].upper())
+                edge_description = clean_str(record_attributes[3])
+                try:
+                    weight = float(record_attributes[-1])
+                except ValueError:
+                    weight = 1.0
+
+                relationships.append({
+                    "source": source,
+                    "target": target,
+                    "description": edge_description,
+                    "source_id": source_id,
+                    "weight": weight,
+                })
+
+        entities_df = pd.DataFrame(entities) if entities else _empty_entities_df()
+        relationships_df = (
+            pd.DataFrame(relationships) if relationships else _empty_relationships_df()
+        )
 
-        return graph
+        return entities_df, relationships_df
 
 
-def _unpack_descriptions(data: Mapping) -> list[str]:
-    value = data.get("description", None)
-    return [] if value is None else value.split("\n")
+def _empty_entities_df() -> pd.DataFrame:
+    return pd.DataFrame(columns=["title", "type", "description", "source_id"])
 
 
-def _unpack_source_ids(data: Mapping) -> list[str]:
-    value = data.get("source_id", None)
-    return [] if value is None else value.split(", ")
+def _empty_relationships_df() -> pd.DataFrame:
+    return pd.DataFrame(
+        columns=["source", "target", "weight", "description", "source_id"]
+    )
diff --git a/packages/graphrag/graphrag/index/operations/extract_graph/typing.py b/packages/graphrag/graphrag/index/operations/extract_graph/typing.py
deleted file mode 100644
index d74eb9a476..0000000000
--- a/packages/graphrag/graphrag/index/operations/extract_graph/typing.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'Document' and 'EntityExtractionResult' models."""
-
-from collections.abc import Awaitable, Callable
-from dataclasses import dataclass
-from typing import Any
-
-import networkx as nx
-
-from graphrag.cache.pipeline_cache import PipelineCache
-
-ExtractedEntity = dict[str, Any]
-ExtractedRelationship = dict[str, Any]
-StrategyConfig = dict[str, Any]
-EntityTypes = list[str]
-
-
-@dataclass
-class Document:
-    """Document class definition."""
-
-    text: str
-    id: str
-
-
-@dataclass
-class EntityExtractionResult:
-    """Entity extraction result class definition."""
-
-    entities: list[ExtractedEntity]
-    relationships: list[ExtractedRelationship]
-    graph: nx.Graph | None
-
-
-EntityExtractStrategy = Callable[
-    [
-        list[Document],
-        EntityTypes,
-        PipelineCache,
-        StrategyConfig,
-    ],
-    Awaitable[EntityExtractionResult],
-]
diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/typing.py b/packages/graphrag/graphrag/index/operations/summarize_communities/typing.py
index 709c5ccc6a..e59c4f33b1 100644
--- a/packages/graphrag/graphrag/index/operations/summarize_communities/typing.py
+++ b/packages/graphrag/graphrag/index/operations/summarize_communities/typing.py
@@ -10,9 +10,7 @@
 
 from graphrag.language_model.protocol.base import ChatModel
 
-ExtractedEntity = dict[str, Any]
 RowContext = dict[str, Any]
-EntityTypes = list[str]
 Claim = dict[str, Any]
 
 
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index a0b2011eab..a373e43b76 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -119,7 +119,7 @@ async def _run_pipeline(
             result = await workflow_function(config, context)
             context.callbacks.workflow_end(name, result)
             yield PipelineRunResult(
-                workflow=name, result=result.result, state=context.state, errors=None
+                workflow=name, result=result.result, state=context.state, error=None
             )
             context.stats.workflows[name] = {"overall": time.time() - work_time}
             if result.stop:
@@ -133,7 +133,7 @@ async def _run_pipeline(
     except Exception as e:
         logger.exception("error running workflow %s", last_workflow)
         yield PipelineRunResult(
-            workflow=last_workflow, result=None, state=context.state, errors=[e]
+            workflow=last_workflow, result=None, state=context.state, error=e
         )
 
 
diff --git a/packages/graphrag/graphrag/index/typing/pipeline_run_result.py b/packages/graphrag/graphrag/index/typing/pipeline_run_result.py
index f6a68d82a0..b39e030268 100644
--- a/packages/graphrag/graphrag/index/typing/pipeline_run_result.py
+++ b/packages/graphrag/graphrag/index/typing/pipeline_run_result.py
@@ -19,4 +19,4 @@ class PipelineRunResult:
     """The result of the workflow function. This can be anything - we use it only for logging downstream, and expect each workflow function to write official outputs to the provided storage."""
     state: PipelineState
     """Ongoing pipeline context state object."""
-    errors: list[BaseException] | None
+    error: BaseException | None
diff --git a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
index c27d905c70..0751adb588 100644
--- a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
+++ b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
@@ -3,206 +3,76 @@
 import unittest
 
 from graphrag.index.operations.extract_graph.extract_graph import run_extract_graph
-from graphrag.index.operations.extract_graph.typing import (
-    Document,
-)
 from graphrag.prompts.index.extract_graph import GRAPH_EXTRACTION_PROMPT
 
 from tests.unit.indexing.verbs.helpers.mock_llm import create_mock_llm
 
+SIMPLE_EXTRACTION_RESPONSE = """
+("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
+##
+("entity"<|>TEST_ENTITY_2<|>COMPANY<|>TEST_ENTITY_2 owns TEST_ENTITY_1 and also shares an address with TEST_ENTITY_1)
+##
+("entity"<|>TEST_ENTITY_3<|>PERSON<|>TEST_ENTITY_3 is director of TEST_ENTITY_1)
+##
+("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_2<|>TEST_ENTITY_1 and TEST_ENTITY_2 are related because TEST_ENTITY_1 is 100% owned by TEST_ENTITY_2 and the two companies also share the same address)<|>2)
+##
+("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_3<|>TEST_ENTITY_1 and TEST_ENTITY_3 are related because TEST_ENTITY_3 is director of TEST_ENTITY_1<|>1))
+""".strip()
+
 
 class TestRunChain(unittest.IsolatedAsyncioTestCase):
     async def test_run_extract_graph_single_document_correct_entities_returned(self):
-        results = await run_extract_graph(
-            docs=[Document("test_text", "1")],
+        entities_df, _ = await run_extract_graph(
+            text="test_text",
+            source_id="1",
             entity_types=["person"],
             max_gleanings=0,
             model=create_mock_llm(
-                responses=[
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_2<|>COMPANY<|>TEST_ENTITY_2 owns TEST_ENTITY_1 and also shares an address with TEST_ENTITY_1)
-                    ##
-                    ("entity"<|>TEST_ENTITY_3<|>PERSON<|>TEST_ENTITY_3 is director of TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_2<|>TEST_ENTITY_1 and TEST_ENTITY_2 are related because TEST_ENTITY_1 is 100% owned by TEST_ENTITY_2 and the two companies also share the same address)<|>2)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_3<|>TEST_ENTITY_1 and TEST_ENTITY_3 are related because TEST_ENTITY_3 is director of TEST_ENTITY_1<|>1))
-                    """.strip()
-                ],
+                responses=[SIMPLE_EXTRACTION_RESPONSE],
                 name="test_run_extract_graph_single_document_correct_entities_returned",
             ),
             prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
-        # self.assertItemsEqual isn't available yet, or I am just silly
-        # so we sort the lists and compare them
-        assert sorted(["TEST_ENTITY_1", "TEST_ENTITY_2", "TEST_ENTITY_3"]) == sorted([
-            entity["title"] for entity in results.entities
-        ])
-
-    async def test_run_extract_graph_multiple_documents_correct_entities_returned(
-        self,
-    ):
-        results = await run_extract_graph(
-            docs=[Document("text_1", "1"), Document("text_2", "2")],
-            entity_types=["person"],
-            max_gleanings=0,
-            model=create_mock_llm(
-                responses=[
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_2<|>COMPANY<|>TEST_ENTITY_2 owns TEST_ENTITY_1 and also shares an address with TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_2<|>TEST_ENTITY_1 and TEST_ENTITY_2 are related because TEST_ENTITY_1 is 100% owned by TEST_ENTITY_2 and the two companies also share the same address)<|>2)
-                    ##
-                    """.strip(),
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_3<|>PERSON<|>TEST_ENTITY_3 is director of TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_3<|>TEST_ENTITY_1 and TEST_ENTITY_3 are related because TEST_ENTITY_3 is director of TEST_ENTITY_1<|>1))
-                    """.strip(),
-                ],
-                name="test_run_extract_graph_multiple_documents_correct_entities_returned",
-            ),
-            prompt=GRAPH_EXTRACTION_PROMPT,
-        )
-
-        # self.assertItemsEqual isn't available yet, or I am just silly
-        # so we sort the lists and compare them
-        assert sorted(["TEST_ENTITY_1", "TEST_ENTITY_2", "TEST_ENTITY_3"]) == sorted([
-            entity["title"] for entity in results.entities
-        ])
-
-    async def test_run_extract_graph_multiple_documents_correct_edges_returned(self):
-        results = await run_extract_graph(
-            docs=[Document("text_1", "1"), Document("text_2", "2")],
-            entity_types=["person"],
-            max_gleanings=0,
-            model=create_mock_llm(
-                responses=[
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_2<|>COMPANY<|>TEST_ENTITY_2 owns TEST_ENTITY_1 and also shares an address with TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_2<|>TEST_ENTITY_1 and TEST_ENTITY_2 are related because TEST_ENTITY_1 is 100% owned by TEST_ENTITY_2 and the two companies also share the same address)<|>2)
-                    ##
-                    """.strip(),
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_3<|>PERSON<|>TEST_ENTITY_3 is director of TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_3<|>TEST_ENTITY_1 and TEST_ENTITY_3 are related because TEST_ENTITY_3 is director of TEST_ENTITY_1<|>1))
-                    """.strip(),
-                ],
-                name="test_run_extract_graph_multiple_documents_correct_edges_returned",
-            ),
-            prompt=GRAPH_EXTRACTION_PROMPT,
+        assert sorted(["TEST_ENTITY_1", "TEST_ENTITY_2", "TEST_ENTITY_3"]) == sorted(
+            entities_df["title"].tolist()
         )
 
-        # self.assertItemsEqual isn't available yet, or I am just silly
-        # so we sort the lists and compare them
-        graph = results.graph
-        assert graph is not None, "No graph returned!"
-
-        # convert to strings for more visual comparison
-        edges_str = sorted([f"{edge[0]} -> {edge[1]}" for edge in graph.edges])
-        assert edges_str == sorted([
-            "TEST_ENTITY_1 -> TEST_ENTITY_2",
-            "TEST_ENTITY_1 -> TEST_ENTITY_3",
-        ])
-
-    async def test_run_extract_graph_multiple_documents_correct_entity_source_ids_mapped(
-        self,
-    ):
-        results = await run_extract_graph(
-            docs=[Document("text_1", "1"), Document("text_2", "2")],
+    async def test_run_extract_graph_single_document_correct_edges_returned(self):
+        _, relationships_df = await run_extract_graph(
+            text="test_text",
+            source_id="1",
             entity_types=["person"],
             max_gleanings=0,
             model=create_mock_llm(
-                responses=[
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_2<|>COMPANY<|>TEST_ENTITY_2 owns TEST_ENTITY_1 and also shares an address with TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_2<|>TEST_ENTITY_1 and TEST_ENTITY_2 are related because TEST_ENTITY_1 is 100% owned by TEST_ENTITY_2 and the two companies also share the same address)<|>2)
-                    ##
-                    """.strip(),
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_3<|>PERSON<|>TEST_ENTITY_3 is director of TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_3<|>TEST_ENTITY_1 and TEST_ENTITY_3 are related because TEST_ENTITY_3 is director of TEST_ENTITY_1<|>1))
-                    """.strip(),
-                ],
-                name="test_run_extract_graph_multiple_documents_correct_entity_source_ids_mapped",
+                responses=[SIMPLE_EXTRACTION_RESPONSE],
+                name="test_run_extract_graph_single_document_correct_edges_returned",
             ),
             prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
-        graph = results.graph
-        assert graph is not None, "No graph returned!"
+        edges = relationships_df.to_dict("records")
+        assert len(edges) == 2
 
-        # TODO: The edges might come back in any order, but we're assuming they're coming
-        # back in the order that we passed in the docs, that might not be true
-        assert (
-            graph.nodes["TEST_ENTITY_3"].get("source_id") == "2"
-        )  # TEST_ENTITY_3 should be in just 2
-        assert (
-            graph.nodes["TEST_ENTITY_2"].get("source_id") == "1"
-        )  # TEST_ENTITY_2 should be in just 1
-        ids_str = graph.nodes["TEST_ENTITY_1"].get("source_id") or ""
-        assert sorted(ids_str.split(",")) == sorted([
-            "1",
-            "2",
-        ])  # TEST_ENTITY_1 should be 1 and 2
+        relationship_pairs = {(edge["source"], edge["target"]) for edge in edges}
+        assert relationship_pairs == {
+            ("TEST_ENTITY_1", "TEST_ENTITY_2"),
+            ("TEST_ENTITY_1", "TEST_ENTITY_3"),
+        }
 
-    async def test_run_extract_graph_multiple_documents_correct_edge_source_ids_mapped(
-        self,
-    ):
-        results = await run_extract_graph(
-            docs=[Document("text_1", "1"), Document("text_2", "2")],
+    async def test_run_extract_graph_single_document_source_ids_mapped(self):
+        entities_df, relationships_df = await run_extract_graph(
+            text="test_text",
+            source_id="1",
             entity_types=["person"],
             max_gleanings=0,
             model=create_mock_llm(
-                responses=[
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_2<|>COMPANY<|>TEST_ENTITY_2 owns TEST_ENTITY_1 and also shares an address with TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_2<|>TEST_ENTITY_1 and TEST_ENTITY_2 are related because TEST_ENTITY_1 is 100% owned by TEST_ENTITY_2 and the two companies also share the same address)<|>2)
-                    ##
-                    """.strip(),
-                    """
-                    ("entity"<|>TEST_ENTITY_1<|>COMPANY<|>TEST_ENTITY_1 is a test company)
-                    ##
-                    ("entity"<|>TEST_ENTITY_3<|>PERSON<|>TEST_ENTITY_3 is director of TEST_ENTITY_1)
-                    ##
-                    ("relationship"<|>TEST_ENTITY_1<|>TEST_ENTITY_3<|>TEST_ENTITY_1 and TEST_ENTITY_3 are related because TEST_ENTITY_3 is director of TEST_ENTITY_1<|>1))
-                    """.strip(),
-                ],
-                name="test_run_extract_graph_multiple_documents_correct_edge_source_ids_mapped",
+                responses=[SIMPLE_EXTRACTION_RESPONSE],
+                name="test_run_extract_graph_single_document_source_ids_mapped",
             ),
             prompt=GRAPH_EXTRACTION_PROMPT,
         )
 
-        graph = results.graph
-        assert graph is not None, "No graph returned!"
-        edges = list(graph.edges(data=True))
-
-        # should only have 2 edges
-        assert len(edges) == 2
+        assert all(source_id == "1" for source_id in entities_df["source_id"])
 
-        # Sort by source_id for consistent ordering
-        edge_source_ids = sorted([edge[2].get("source_id", "") for edge in edges])
-        assert edge_source_ids[0].split(",") == ["1"]
-        assert edge_source_ids[1].split(",") == ["2"]
+        assert all(source_id == "1" for source_id in relationships_df["source_id"])

From a4ffc3d34cfb568b8a0417fe619583877f17c590 Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Mon, 17 Nov 2025 13:10:54 -0600
Subject: [PATCH 25/40] Remove embeddings optional new (#2128)

* remove optional embeddings

* fix test

* fix tests

* fix pipeline

* fix test

* fix test

* fix test

* fix tests

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 docs/config/yaml.md                           | 26 +++------
 docs/examples_notebooks/api_overview.ipynb    |  9 ++--
 .../index_migration_to_v1.ipynb               |  2 -
 docs/examples_notebooks/input_documents.ipynb |  9 ++--
 .../graphrag/graphrag/config/embeddings.py    | 20 ++-----
 .../workflows/generate_text_embeddings.py     | 54 +------------------
 .../index/workflows/update_text_embeddings.py |  5 --
 tests/fixtures/min-csv/config.json            |  6 +--
 tests/fixtures/text/config.json               |  6 +--
 tests/unit/utils/test_embeddings.py           |  8 +--
 tests/verbs/test_create_community_reports.py  |  9 ++--
 tests/verbs/test_generate_text_embeddings.py  | 11 +---
 unified-search-app/app/app_logic.py           |  3 +-
 13 files changed, 43 insertions(+), 125 deletions(-)

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index ace57e3b1c..76c177b60f 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -182,14 +182,9 @@ Where to put all vectors for the system. Configured for lancedb by default. This
 
 The supported embeddings are:
 
-- `text_unit.text`
-- `document.text`
-- `entity.title`
-- `entity.description`
-- `relationship.description`
-- `community.title`
-- `community.summary`
-- `community.full_content`
+- `text_unit_text`
+- `entity_description`
+- `community_full_content`
 
 For example:
 
@@ -199,12 +194,12 @@ vector_store:
   db_uri: output/lancedb
   index_prefix: "christmas-carol"
   embeddings_schema:
-    text_unit.text:
+    text_unit_text:
       index_name: "text-unit-embeddings"
       id_field: "id_custom"
       vector_field: "vector_custom"
       vector_size: 3072
-    entity.description:
+    entity_description:
       id_field: "id_custom"
 
 ```
@@ -224,14 +219,9 @@ By default, the GraphRAG indexer will only export embeddings required for our qu
 
 Supported embeddings names are:
 
-- `text_unit.text`
-- `document.text`
-- `entity.title`
-- `entity.description`
-- `relationship.description`
-- `community.title`
-- `community.summary`
-- `community.full_content`
+- `text_unit_text`
+- `entity_description`
+- `community_full_content`
 
 #### Fields
 
diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index 06187a5771..abcd7832fc 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,10 +28,11 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
+    "\n",
+    "import graphrag.api as api"
    ]
   },
   {
@@ -170,7 +171,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "graphrag-monorepo",
    "language": "python",
    "name": "python3"
   },
@@ -184,7 +185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples_notebooks/index_migration_to_v1.ipynb b/docs/examples_notebooks/index_migration_to_v1.ipynb
index 4a89d95305..68a06358e6 100644
--- a/docs/examples_notebooks/index_migration_to_v1.ipynb
+++ b/docs/examples_notebooks/index_migration_to_v1.ipynb
@@ -229,8 +229,6 @@
     "tokenizer = get_tokenizer(model_config)\n",
     "\n",
     "await generate_text_embeddings(\n",
-    "    documents=None,\n",
-    "    relationships=None,\n",
     "    text_units=final_text_units,\n",
     "    entities=final_entities,\n",
     "    community_reports=final_community_reports,\n",
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index b9af6075ab..505c0fe1f3 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,10 +30,11 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
+    "\n",
+    "import graphrag.api as api"
    ]
   },
   {
@@ -171,7 +172,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "graphrag",
+   "display_name": "graphrag-monorepo",
    "language": "python",
    "name": "python3"
   },
@@ -185,7 +186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/packages/graphrag/graphrag/config/embeddings.py b/packages/graphrag/graphrag/config/embeddings.py
index 60711b8aa6..c06567acfa 100644
--- a/packages/graphrag/graphrag/config/embeddings.py
+++ b/packages/graphrag/graphrag/config/embeddings.py
@@ -3,22 +3,12 @@
 
 """A module containing embeddings values."""
 
-entity_title_embedding = "entity.title"
-entity_description_embedding = "entity.description"
-relationship_description_embedding = "relationship.description"
-document_text_embedding = "document.text"
-community_title_embedding = "community.title"
-community_summary_embedding = "community.summary"
-community_full_content_embedding = "community.full_content"
-text_unit_text_embedding = "text_unit.text"
+entity_description_embedding = "entity_description"
+community_full_content_embedding = "community_full_content"
+text_unit_text_embedding = "text_unit_text"
 
 all_embeddings: set[str] = {
-    entity_title_embedding,
     entity_description_embedding,
-    relationship_description_embedding,
-    document_text_embedding,
-    community_title_embedding,
-    community_summary_embedding,
     community_full_content_embedding,
     text_unit_text_embedding,
 }
@@ -47,5 +37,5 @@ def create_index_name(
         raise KeyError(msg)
 
     if index_prefix:
-        return f"{index_prefix}-{embedding_name}".replace(".", "-")
-    return embedding_name.replace(".", "-")
+        return f"{index_prefix}-{embedding_name}"
+    return embedding_name
diff --git a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
index 6e474437f5..ef24d3e348 100644
--- a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
+++ b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
@@ -10,13 +10,8 @@
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.embeddings import (
     community_full_content_embedding,
-    community_summary_embedding,
-    community_title_embedding,
     create_index_name,
-    document_text_embedding,
     entity_description_embedding,
-    entity_title_embedding,
-    relationship_description_embedding,
     text_unit_text_embedding,
 )
 from graphrag.config.models.graph_rag_config import GraphRagConfig
@@ -47,29 +42,14 @@ async def run_workflow(
     logger.info("Workflow started: generate_text_embeddings")
     embedded_fields = config.embed_text.names
     logger.info("Embedding the following fields: %s", embedded_fields)
-    documents = None
-    relationships = None
     text_units = None
     entities = None
     community_reports = None
-    if document_text_embedding in embedded_fields:
-        documents = await load_table_from_storage("documents", context.output_storage)
-    if relationship_description_embedding in embedded_fields:
-        relationships = await load_table_from_storage(
-            "relationships", context.output_storage
-        )
     if text_unit_text_embedding in embedded_fields:
         text_units = await load_table_from_storage("text_units", context.output_storage)
-    if (
-        entity_title_embedding in embedded_fields
-        or entity_description_embedding in embedded_fields
-    ):
+    if entity_description_embedding in embedded_fields:
         entities = await load_table_from_storage("entities", context.output_storage)
-    if (
-        community_title_embedding in embedded_fields
-        or community_summary_embedding in embedded_fields
-        or community_full_content_embedding in embedded_fields
-    ):
+    if community_full_content_embedding in embedded_fields:
         community_reports = await load_table_from_storage(
             "community_reports", context.output_storage
         )
@@ -87,8 +67,6 @@ async def run_workflow(
     tokenizer = get_tokenizer(model_config)
 
     output = await generate_text_embeddings(
-        documents=documents,
-        relationships=relationships,
         text_units=text_units,
         entities=entities,
         community_reports=community_reports,
@@ -115,8 +93,6 @@ async def run_workflow(
 
 
 async def generate_text_embeddings(
-    documents: pd.DataFrame | None,
-    relationships: pd.DataFrame | None,
     text_units: pd.DataFrame | None,
     entities: pd.DataFrame | None,
     community_reports: pd.DataFrame | None,
@@ -131,26 +107,12 @@ async def generate_text_embeddings(
 ) -> dict[str, pd.DataFrame]:
     """All the steps to generate all embeddings."""
     embedding_param_map = {
-        document_text_embedding: {
-            "data": documents.loc[:, ["id", "text"]] if documents is not None else None,
-            "embed_column": "text",
-        },
-        relationship_description_embedding: {
-            "data": relationships.loc[:, ["id", "description"]]
-            if relationships is not None
-            else None,
-            "embed_column": "description",
-        },
         text_unit_text_embedding: {
             "data": text_units.loc[:, ["id", "text"]]
             if text_units is not None
             else None,
             "embed_column": "text",
         },
-        entity_title_embedding: {
-            "data": entities.loc[:, ["id", "title"]] if entities is not None else None,
-            "embed_column": "title",
-        },
         entity_description_embedding: {
             "data": entities.loc[:, ["id", "title", "description"]].assign(
                 title_description=lambda df: df["title"] + ":" + df["description"]
@@ -159,18 +121,6 @@ async def generate_text_embeddings(
             else None,
             "embed_column": "title_description",
         },
-        community_title_embedding: {
-            "data": community_reports.loc[:, ["id", "title"]]
-            if community_reports is not None
-            else None,
-            "embed_column": "title",
-        },
-        community_summary_embedding: {
-            "data": community_reports.loc[:, ["id", "summary"]]
-            if community_reports is not None
-            else None,
-            "embed_column": "summary",
-        },
         community_full_content_embedding: {
             "data": community_reports.loc[:, ["id", "full_content"]]
             if community_reports is not None
diff --git a/packages/graphrag/graphrag/index/workflows/update_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/update_text_embeddings.py
index 0fc5f1fdeb..4c6c280650 100644
--- a/packages/graphrag/graphrag/index/workflows/update_text_embeddings.py
+++ b/packages/graphrag/graphrag/index/workflows/update_text_embeddings.py
@@ -26,9 +26,6 @@ async def run_workflow(
     output_storage, _, _ = get_update_storages(
         config, context.state["update_timestamp"]
     )
-
-    final_documents_df = context.state["incremental_update_final_documents"]
-    merged_relationships_df = context.state["incremental_update_merged_relationships"]
     merged_text_units = context.state["incremental_update_merged_text_units"]
     merged_entities_df = context.state["incremental_update_merged_entities"]
     merged_community_reports = context.state[
@@ -50,8 +47,6 @@ async def run_workflow(
     tokenizer = get_tokenizer(model_config)
 
     result = await generate_text_embeddings(
-        documents=final_documents_df,
-        relationships=merged_relationships_df,
         text_units=merged_text_units,
         entities=merged_entities_df,
         community_reports=merged_community_reports,
diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json
index ca2bd7d823..c28508be8b 100644
--- a/tests/fixtures/min-csv/config.json
+++ b/tests/fixtures/min-csv/config.json
@@ -85,9 +85,9 @@
             ],
             "max_runtime": 150,
             "expected_artifacts": [
-                "embeddings.text_unit.text.parquet",
-                "embeddings.entity.description.parquet",
-                "embeddings.community.full_content.parquet"
+                "embeddings.text_unit_text.parquet",
+                "embeddings.entity_description.parquet",
+                "embeddings.community_full_content.parquet"
             ]
         }
     },
diff --git a/tests/fixtures/text/config.json b/tests/fixtures/text/config.json
index eef957da2f..792b91c48c 100644
--- a/tests/fixtures/text/config.json
+++ b/tests/fixtures/text/config.json
@@ -84,9 +84,9 @@
             ],
             "max_runtime": 150,
             "expected_artifacts": [
-                "embeddings.text_unit.text.parquet",
-                "embeddings.entity.description.parquet",
-                "embeddings.community.full_content.parquet"
+                "embeddings.text_unit_text.parquet",
+                "embeddings.entity_description.parquet",
+                "embeddings.community_full_content.parquet"
             ]
         }
     },
diff --git a/tests/unit/utils/test_embeddings.py b/tests/unit/utils/test_embeddings.py
index 343ee812b5..63d0619c1d 100644
--- a/tests/unit/utils/test_embeddings.py
+++ b/tests/unit/utils/test_embeddings.py
@@ -6,8 +6,8 @@
 
 
 def test_create_index_name():
-    collection = create_index_name("default", "entity.title")
-    assert collection == "default-entity-title"
+    collection = create_index_name("default", "entity_description")
+    assert collection == "default-entity_description"
 
 
 def test_create_index_name_invalid_embedding_throws():
@@ -16,5 +16,5 @@ def test_create_index_name_invalid_embedding_throws():
 
 
 def test_create_index_name_invalid_embedding_does_not_throw():
-    collection = create_index_name("default", "invalid.name", validate=False)
-    assert collection == "default-invalid-name"
+    collection = create_index_name("default", "invalid_name", validate=False)
+    assert collection == "default-invalid_name"
diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py
index d479120ce2..561f54108b 100644
--- a/tests/verbs/test_create_community_reports.py
+++ b/tests/verbs/test_create_community_reports.py
@@ -4,15 +4,16 @@
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
-from graphrag.index.operations.summarize_communities.community_reports_extractor import (
-    CommunityReportResponse,
-    FindingModel,
-)
 from graphrag.index.workflows.create_community_reports import (
     run_workflow,
 )
 from graphrag.utils.storage import load_table_from_storage
 
+from graphrag.index.operations.summarize_communities.community_reports_extractor import (
+    CommunityReportResponse,
+    FindingModel,
+)
+
 from .util import (
     DEFAULT_MODEL_CONFIG,
     compare_outputs,
diff --git a/tests/verbs/test_generate_text_embeddings.py b/tests/verbs/test_generate_text_embeddings.py
index e9e6c00afa..f65e0c642c 100644
--- a/tests/verbs/test_generate_text_embeddings.py
+++ b/tests/verbs/test_generate_text_embeddings.py
@@ -44,18 +44,9 @@ async def test_generate_text_embeddings():
 
     # entity description should always be here, let's assert its format
     entity_description_embeddings = await load_table_from_storage(
-        "embeddings.entity.description", context.output_storage
+        "embeddings.entity_description", context.output_storage
     )
 
     assert len(entity_description_embeddings.columns) == 2
     assert "id" in entity_description_embeddings.columns
     assert "embedding" in entity_description_embeddings.columns
-
-    # every other embedding is optional but we've turned them all on, so check a random one
-    document_text_embeddings = await load_table_from_storage(
-        "embeddings.document.text", context.output_storage
-    )
-
-    assert len(document_text_embeddings.columns) == 2
-    assert "id" in document_text_embeddings.columns
-    assert "embedding" in document_text_embeddings.columns
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index dc64e0e77c..a573b9daa5 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,7 +7,6 @@
 import logging
 from typing import TYPE_CHECKING
 
-import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -18,6 +17,8 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
+import graphrag.api as api
+
 if TYPE_CHECKING:
     import pandas as pd
 

From d6e6191d8450a886adb19953b20e39af7e8ecdf7 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Mon, 17 Nov 2025 13:59:58 -0800
Subject: [PATCH 26/40] Format

---
 docs/examples_notebooks/api_overview.ipynb    | 5 ++---
 docs/examples_notebooks/input_documents.ipynb | 5 ++---
 tests/verbs/test_create_community_reports.py  | 9 ++++-----
 unified-search-app/app/app_logic.py           | 3 +--
 4 files changed, 9 insertions(+), 13 deletions(-)

diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index abcd7832fc..2a0c0f15de 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,11 +28,10 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
+    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
-    "\n",
-    "import graphrag.api as api"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
   },
   {
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 505c0fe1f3..5657770eaf 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,11 +30,10 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
+    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
-    "\n",
-    "import graphrag.api as api"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
   },
   {
diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py
index 561f54108b..d479120ce2 100644
--- a/tests/verbs/test_create_community_reports.py
+++ b/tests/verbs/test_create_community_reports.py
@@ -4,15 +4,14 @@
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
-from graphrag.index.workflows.create_community_reports import (
-    run_workflow,
-)
-from graphrag.utils.storage import load_table_from_storage
-
 from graphrag.index.operations.summarize_communities.community_reports_extractor import (
     CommunityReportResponse,
     FindingModel,
 )
+from graphrag.index.workflows.create_community_reports import (
+    run_workflow,
+)
+from graphrag.utils.storage import load_table_from_storage
 
 from .util import (
     DEFAULT_MODEL_CONFIG,
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index a573b9daa5..dc64e0e77c 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,6 +7,7 @@
 import logging
 from typing import TYPE_CHECKING
 
+import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -17,8 +18,6 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
-import graphrag.api as api
-
 if TYPE_CHECKING:
     import pandas as pd
 

From 7bf82b78ab88f2badbc7b3cf5e1d449614486329 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Mon, 17 Nov 2025 15:45:07 -0800
Subject: [PATCH 27/40] Add empty checks for NLP graphs (#2133)

---
 .../operations/build_noun_graph/build_noun_graph.py |  4 +++-
 .../graphrag/index/operations/prune_graph.py        |  3 +++
 .../graphrag/index/workflows/extract_graph.py       | 13 ++++---------
 .../graphrag/index/workflows/extract_graph_nlp.py   | 13 +++++++++++++
 .../graphrag/index/workflows/prune_graph.py         | 10 ++++++++++
 5 files changed, 33 insertions(+), 10 deletions(-)

diff --git a/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
index 8d3310e766..5e34aa654c 100644
--- a/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
+++ b/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
@@ -98,9 +98,11 @@ def _extract_edges(
     Input: nodes_df with schema [id, title, frequency, text_unit_ids]
     Returns: edges_df with schema [source, target, weight, text_unit_ids]
     """
+    if nodes_df.empty:
+        return pd.DataFrame(columns=["source", "target", "weight", "text_unit_ids"])
+
     text_units_df = nodes_df.explode("text_unit_ids")
     text_units_df = text_units_df.rename(columns={"text_unit_ids": "text_unit_id"})
-
     text_units_df = (
         text_units_df.groupby("text_unit_id")
         .agg({"title": lambda x: list(x) if len(x) > 1 else np.nan})
diff --git a/packages/graphrag/graphrag/index/operations/prune_graph.py b/packages/graphrag/graphrag/index/operations/prune_graph.py
index b03dfb7b17..6eb6b60065 100644
--- a/packages/graphrag/graphrag/index/operations/prune_graph.py
+++ b/packages/graphrag/graphrag/index/operations/prune_graph.py
@@ -64,6 +64,9 @@ def prune_graph(
         ])
 
     # remove edges by min weight
+    if len(graph.edges) == 0:
+        return graph
+
     if min_edge_weight_pct > 0:
         min_edge_weight = np.percentile(
             [data[schemas.EDGE_WEIGHT] for _, _, data in graph.edges(data=True)],
diff --git a/packages/graphrag/graphrag/index/workflows/extract_graph.py b/packages/graphrag/graphrag/index/workflows/extract_graph.py
index de9c454261..8ce8669109 100644
--- a/packages/graphrag/graphrag/index/workflows/extract_graph.py
+++ b/packages/graphrag/graphrag/index/workflows/extract_graph.py
@@ -121,14 +121,14 @@ async def extract_graph(
         async_type=extraction_async_type,
     )
 
-    if not _validate_data(extracted_entities):
-        error_msg = "Entity Extraction failed. No entities detected during extraction."
+    if len(extracted_entities) == 0:
+        error_msg = "Graph Extraction failed. No entities detected during extraction."
         logger.error(error_msg)
         raise ValueError(error_msg)
 
-    if not _validate_data(extracted_relationships):
+    if len(extracted_relationships) == 0:
         error_msg = (
-            "Entity Extraction failed. No relationships detected during extraction."
+            "Graph Extraction failed. No relationships detected during extraction."
         )
         logger.error(error_msg)
         raise ValueError(error_msg)
@@ -180,8 +180,3 @@ async def get_summarized_entities_relationships(
     extracted_entities.drop(columns=["description"], inplace=True)
     entities = extracted_entities.merge(entity_summaries, on="title", how="left")
     return entities, relationships
-
-
-def _validate_data(df: pd.DataFrame) -> bool:
-    """Validate that the dataframe has data."""
-    return len(df) > 0
diff --git a/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py b/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
index b49f3e7a1f..fd00cbfa79 100644
--- a/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
+++ b/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
@@ -75,6 +75,19 @@ async def extract_graph_nlp(
         cache=cache,
     )
 
+    if len(extracted_nodes) == 0:
+        error_msg = (
+            "NLP Graph Extraction failed. No entities detected during extraction."
+        )
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
+    if len(extracted_edges) == 0:
+        error_msg = (
+            "NLP Graph Extraction failed. No relationships detected during extraction."
+        )
+        logger.error(error_msg)
+
     # add in any other columns required by downstream workflows
     extracted_nodes["type"] = "NOUN PHRASE"
     extracted_nodes["description"] = ""
diff --git a/packages/graphrag/graphrag/index/workflows/prune_graph.py b/packages/graphrag/graphrag/index/workflows/prune_graph.py
index 8bb48df7ee..da4e2bc835 100644
--- a/packages/graphrag/graphrag/index/workflows/prune_graph.py
+++ b/packages/graphrag/graphrag/index/workflows/prune_graph.py
@@ -69,6 +69,16 @@ def prune_graph(
         lcc_only=pruning_config.lcc_only,
     )
 
+    if len(pruned.nodes) == 0:
+        error_msg = "Graph Pruning failed. No entities remain."
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
+    if len(pruned.edges) == 0:
+        error_msg = "Graph Pruning failed. No relationships remain."
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
     pruned_nodes, pruned_edges = graph_to_dataframes(
         pruned, node_columns=["title"], edge_columns=["source", "target"]
     )

From 20a96cb35e21a84e69b0e7150a95071ba75b3c41 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Mon, 24 Nov 2025 10:05:47 -0800
Subject: [PATCH 28/40] Init command asks for models (#2137)

* Add init prompting for models

* Remove hard-coded model config validation

* Switch to typer option prompt for full CLI use with models

* Update getting started for init model input

* Bump request timeout and overall smoke test timeout
---
 docs/get_started.md                           |  2 ++
 packages/graphrag/graphrag/api/index.py       |  4 ---
 packages/graphrag/graphrag/cli/index.py       |  6 ----
 packages/graphrag/graphrag/cli/initialize.py  | 11 ++++--
 packages/graphrag/graphrag/cli/main.py        | 34 +++++++++++--------
 packages/graphrag/graphrag/config/defaults.py |  2 +-
 packages/graphrag/graphrag/config/errors.py   |  9 -----
 .../graphrag/graphrag/config/init_content.py  |  4 +--
 .../config/models/graph_rag_config.py         | 20 -----------
 tests/fixtures/min-csv/config.json            |  2 +-
 tests/fixtures/text/config.json               |  2 +-
 11 files changed, 35 insertions(+), 61 deletions(-)

diff --git a/docs/get_started.md b/docs/get_started.md
index 9a6f496383..a173611744 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -47,6 +47,8 @@ To initialize your workspace, first run the `graphrag init` command.
 graphrag init
 ```
 
+When prompted, specify the default chat and embedding models you would like to use in your config.
+
 This will create two files, `.env` and `settings.yaml`, and a directory `input`, in the current directory.
 
 - `input` Location of text files to process with `graphrag`.
diff --git a/packages/graphrag/graphrag/api/index.py b/packages/graphrag/graphrag/api/index.py
index c15ea2e786..cc9d0956d5 100644
--- a/packages/graphrag/graphrag/api/index.py
+++ b/packages/graphrag/graphrag/api/index.py
@@ -30,7 +30,6 @@ async def build_index(
     config: GraphRagConfig,
     method: IndexingMethod | str = IndexingMethod.Standard,
     is_update_run: bool = False,
-    memory_profile: bool = False,
     callbacks: list[WorkflowCallbacks] | None = None,
     additional_context: dict[str, Any] | None = None,
     verbose: bool = False,
@@ -67,9 +66,6 @@ async def build_index(
 
     outputs: list[PipelineRunResult] = []
 
-    if memory_profile:
-        logger.warning("New pipeline does not yet support memory profiling.")
-
     logger.info("Initializing indexing pipeline...")
     # todo: this could propagate out to the cli for better clarity, but will be a breaking api change
     method = _get_method(method, is_update_run)
diff --git a/packages/graphrag/graphrag/cli/index.py b/packages/graphrag/graphrag/cli/index.py
index d686a38f13..60b724ea98 100644
--- a/packages/graphrag/graphrag/cli/index.py
+++ b/packages/graphrag/graphrag/cli/index.py
@@ -43,7 +43,6 @@ def index_cli(
     root_dir: Path,
     method: IndexingMethod,
     verbose: bool,
-    memprofile: bool,
     cache: bool,
     dry_run: bool,
     skip_validation: bool,
@@ -55,7 +54,6 @@ def index_cli(
         method=method,
         is_update_run=False,
         verbose=verbose,
-        memprofile=memprofile,
         cache=cache,
         dry_run=dry_run,
         skip_validation=skip_validation,
@@ -66,7 +64,6 @@ def update_cli(
     root_dir: Path,
     method: IndexingMethod,
     verbose: bool,
-    memprofile: bool,
     cache: bool,
     skip_validation: bool,
 ):
@@ -80,7 +77,6 @@ def update_cli(
         method=method,
         is_update_run=True,
         verbose=verbose,
-        memprofile=memprofile,
         cache=cache,
         dry_run=False,
         skip_validation=skip_validation,
@@ -92,7 +88,6 @@ def _run_index(
     method,
     is_update_run,
     verbose,
-    memprofile,
     cache,
     dry_run,
     skip_validation,
@@ -129,7 +124,6 @@ def _run_index(
             config=config,
             method=method,
             is_update_run=is_update_run,
-            memory_profile=memprofile,
             callbacks=[ConsoleWorkflowCallbacks(verbose=verbose)],
             verbose=verbose,
         )
diff --git a/packages/graphrag/graphrag/cli/initialize.py b/packages/graphrag/graphrag/cli/initialize.py
index 8dbaf30f93..d29866e5c4 100644
--- a/packages/graphrag/graphrag/cli/initialize.py
+++ b/packages/graphrag/graphrag/cli/initialize.py
@@ -35,7 +35,9 @@
 logger = logging.getLogger(__name__)
 
 
-def initialize_project_at(path: Path, force: bool) -> None:
+def initialize_project_at(
+    path: Path, force: bool, model: str, embedding_model: str
+) -> None:
     """
     Initialize the project at the given path.
 
@@ -64,8 +66,11 @@ def initialize_project_at(path: Path, force: bool) -> None:
         root / (graphrag_config_defaults.input.storage.base_dir or "input")
     ).resolve()
     input_path.mkdir(parents=True, exist_ok=True)
-
-    settings_yaml.write_text(INIT_YAML, encoding="utf-8", errors="strict")
+    # using replace with custom tokens instead of format here because we have a placeholder for GRAPHRAG_API_KEY that is used later for .env overlay
+    formatted = INIT_YAML.replace("<DEFAULT_CHAT_MODEL>", model).replace(
+        "<DEFAULT_EMBEDDING_MODEL>", embedding_model
+    )
+    settings_yaml.write_text(formatted, encoding="utf-8", errors="strict")
 
     dotenv = root / ".env"
     if not dotenv.exists() or force:
diff --git a/packages/graphrag/graphrag/cli/main.py b/packages/graphrag/graphrag/cli/main.py
index 89768e736d..e62de47554 100644
--- a/packages/graphrag/graphrag/cli/main.py
+++ b/packages/graphrag/graphrag/cli/main.py
@@ -10,7 +10,11 @@
 
 import typer
 
-from graphrag.config.defaults import graphrag_config_defaults
+from graphrag.config.defaults import (
+    DEFAULT_CHAT_MODEL,
+    DEFAULT_EMBEDDING_MODEL,
+    graphrag_config_defaults,
+)
 from graphrag.config.enums import IndexingMethod, SearchMethod
 from graphrag.prompt_tune.defaults import LIMIT, MAX_TOKEN_COUNT, N_SUBSET_MAX, K
 from graphrag.prompt_tune.types import DocSelectionType
@@ -104,6 +108,18 @@ def _initialize_cli(
         resolve_path=True,
         autocompletion=ROOT_AUTOCOMPLETE,
     ),
+    model: str = typer.Option(
+        DEFAULT_CHAT_MODEL,
+        "--model",
+        "-m",
+        prompt="Specify the default chat model to use",
+    ),
+    embedding_model: str = typer.Option(
+        DEFAULT_EMBEDDING_MODEL,
+        "--embedding",
+        "-e",
+        prompt="Specify the default embedding model to use",
+    ),
     force: bool = typer.Option(
         False,
         "--force",
@@ -114,7 +130,9 @@ def _initialize_cli(
     """Generate a default configuration file."""
     from graphrag.cli.initialize import initialize_project_at
 
-    initialize_project_at(path=root, force=force)
+    initialize_project_at(
+        path=root, force=force, model=model, embedding_model=embedding_model
+    )
 
 
 @app.command("index")
@@ -143,11 +161,6 @@ def _index_cli(
         "-v",
         help="Run the indexing pipeline with verbose logging",
     ),
-    memprofile: bool = typer.Option(
-        False,
-        "--memprofile",
-        help="Run the indexing pipeline with memory profiling",
-    ),
     dry_run: bool = typer.Option(
         False,
         "--dry-run",
@@ -173,7 +186,6 @@ def _index_cli(
     index_cli(
         root_dir=root,
         verbose=verbose,
-        memprofile=memprofile,
         cache=cache,
         dry_run=dry_run,
         skip_validation=skip_validation,
@@ -207,11 +219,6 @@ def _update_cli(
         "-v",
         help="Run the indexing pipeline with verbose logging.",
     ),
-    memprofile: bool = typer.Option(
-        False,
-        "--memprofile",
-        help="Run the indexing pipeline with memory profiling.",
-    ),
     cache: bool = typer.Option(
         True,
         "--cache/--no-cache",
@@ -233,7 +240,6 @@ def _update_cli(
     update_cli(
         root_dir=root,
         verbose=verbose,
-        memprofile=memprofile,
         cache=cache,
         skip_validation=skip_validation,
         method=method,
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index 88449a6050..29ed5a3bea 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -274,7 +274,7 @@ class LanguageModelDefaults:
     n: int = 1
     frequency_penalty: float = 0.0
     presence_penalty: float = 0.0
-    request_timeout: float = 180.0
+    request_timeout: float = 600.0
     api_base: None = None
     api_version: None = None
     deployment_name: None = None
diff --git a/packages/graphrag/graphrag/config/errors.py b/packages/graphrag/graphrag/config/errors.py
index 32a20b838c..6dbe25ad40 100644
--- a/packages/graphrag/graphrag/config/errors.py
+++ b/packages/graphrag/graphrag/config/errors.py
@@ -33,15 +33,6 @@ def __init__(self, llm_type: str) -> None:
         super().__init__(msg)
 
 
-class LanguageModelConfigMissingError(ValueError):
-    """Missing model configuration error."""
-
-    def __init__(self, key: str = "") -> None:
-        """Init method definition."""
-        msg = f'A {key} model configuration is required. Please rerun `graphrag init` and set models["{key}"] in settings.yaml.'
-        super().__init__(msg)
-
-
 class ConflictingSettingsError(ValueError):
     """Missing model configuration error."""
 
diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py
index 1cbccf74df..4d58c62e97 100644
--- a/packages/graphrag/graphrag/config/init_content.py
+++ b/packages/graphrag/graphrag/config/init_content.py
@@ -23,7 +23,7 @@
     model_provider: {defs.DEFAULT_MODEL_PROVIDER}
     auth_type: {defs.DEFAULT_CHAT_MODEL_AUTH_TYPE.value} # or azure_managed_identity
     api_key: ${{GRAPHRAG_API_KEY}} # set this in the generated .env file, or remove if managed identity
-    model: {defs.DEFAULT_CHAT_MODEL}
+    model: <DEFAULT_CHAT_MODEL>
     # api_base: https://<instance>.openai.azure.com
     # api_version: 2024-05-01-preview
     model_supports_json: true # recommended if this is available for your model.
@@ -37,7 +37,7 @@
     model_provider: {defs.DEFAULT_MODEL_PROVIDER}
     auth_type: {defs.DEFAULT_EMBEDDING_MODEL_AUTH_TYPE.value}
     api_key: ${{GRAPHRAG_API_KEY}}
-    model: {defs.DEFAULT_EMBEDDING_MODEL}
+    model: <DEFAULT_EMBEDDING_MODEL>
     # api_base: https://<instance>.openai.azure.com
     # api_version: 2024-05-01-preview
     concurrent_requests: {language_model_defaults.concurrent_requests}
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 15d02eaf3a..61e21f3a85 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -11,7 +11,6 @@
 import graphrag.config.defaults as defs
 from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import VectorStoreType
-from graphrag.config.errors import LanguageModelConfigMissingError
 from graphrag.config.models.basic_search_config import BasicSearchConfig
 from graphrag.config.models.cache_config import CacheConfig
 from graphrag.config.models.chunking_config import ChunkingConfig
@@ -58,24 +57,6 @@ def __str__(self):
         default=graphrag_config_defaults.models,
     )
 
-    def _validate_models(self) -> None:
-        """Validate the models configuration.
-
-        Ensure both a default chat model and default embedding model
-        have been defined. Other models may also be defined but
-        defaults are required for the time being as places of the
-        code fallback to default model configs instead
-        of specifying a specific model.
-
-        TODO: Don't fallback to default models elsewhere in the code.
-        Forcing code to specify a model to use and allowing for any
-        names for model configurations.
-        """
-        if defs.DEFAULT_CHAT_MODEL_ID not in self.models:
-            raise LanguageModelConfigMissingError(defs.DEFAULT_CHAT_MODEL_ID)
-        if defs.DEFAULT_EMBEDDING_MODEL_ID not in self.models:
-            raise LanguageModelConfigMissingError(defs.DEFAULT_EMBEDDING_MODEL_ID)
-
     def _validate_retry_services(self) -> None:
         """Validate the retry services configuration."""
         retry_factory = RetryFactory()
@@ -329,7 +310,6 @@ def get_language_model_config(self, model_id: str) -> LanguageModelConfig:
     @model_validator(mode="after")
     def _validate_model(self):
         """Validate the model configuration."""
-        self._validate_models()
         self._validate_input_pattern()
         self._validate_input_base_dir()
         self._validate_reporting_base_dir()
diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json
index c28508be8b..88d38f1e61 100644
--- a/tests/fixtures/min-csv/config.json
+++ b/tests/fixtures/min-csv/config.json
@@ -51,7 +51,7 @@
                 "period",
                 "size"
             ],
-            "max_runtime": 1200,
+            "max_runtime": 2000,
             "expected_artifacts": ["community_reports.parquet"]
         },
         "create_final_text_units": {
diff --git a/tests/fixtures/text/config.json b/tests/fixtures/text/config.json
index 792b91c48c..6248a08044 100644
--- a/tests/fixtures/text/config.json
+++ b/tests/fixtures/text/config.json
@@ -50,7 +50,7 @@
                 "period",
                 "size"
             ],
-            "max_runtime": 1200,
+            "max_runtime": 2000,
             "expected_artifacts": ["community_reports.parquet"]
         },
         "create_final_text_units": {

From 4404668aa84c9c6f710f1bd85425657d6cd7ce7b Mon Sep 17 00:00:00 2001
From: Derek Worthen <worthend.derek@gmail.com>
Date: Mon, 15 Dec 2025 09:32:19 -0800
Subject: [PATCH 29/40] Add graphrag-storage. (#2127)

* Add graphrag-storage.
---
 docs/config/yaml.md                           |   2 +-
 .../graphrag_common/factory/__init__.py       |   4 +-
 .../graphrag_common/factory/factory.py        |   5 +-
 packages/graphrag-storage/README.md           |  86 +++++++++++
 .../graphrag_storage/__init__.py              |  20 +++
 .../graphrag_storage/azure_blob_storage.py}   | 102 +++++--------
 .../graphrag_storage/azure_cosmos_storage.py} |  58 +++----
 .../graphrag_storage/file_storage.py}         |  46 +++---
 .../graphrag_storage/memory_storage.py}       |  21 ++-
 .../graphrag_storage/py.typed                 |   0
 .../graphrag_storage/storage.py               | 141 ++++++++++++++++++
 .../graphrag_storage/storage_config.py        |  48 ++++++
 .../graphrag_storage/storage_factory.py       |  78 ++++++++++
 .../graphrag_storage/storage_type.py          |  16 ++
 packages/graphrag-storage/pyproject.toml      |  49 ++++++
 packages/graphrag/graphrag/cache/factory.py   |  15 +-
 .../graphrag/cache/json_pipeline_cache.py     |   7 +-
 packages/graphrag/graphrag/cli/query.py       |   5 +-
 packages/graphrag/graphrag/config/defaults.py |  14 +-
 .../graphrag/graphrag/config/init_content.py  |   4 +-
 .../config/models/graph_rag_config.py         |   8 +-
 .../graphrag/config/models/input_config.py    |   2 +-
 .../graphrag/config/models/storage_config.py  |  52 -------
 .../graphrag/index/input/input_reader.py      |   5 +-
 .../index/operations/snapshot_graphml.py      |   5 +-
 .../graphrag/index/run/run_pipeline.py        |  14 +-
 packages/graphrag/graphrag/index/run/utils.py |  24 +--
 .../graphrag/graphrag/index/typing/context.py |   8 +-
 .../index/update/incremental_index.py         |  14 +-
 .../index/workflows/load_update_documents.py  |   4 +-
 .../index/workflows/update_communities.py     |   9 +-
 .../workflows/update_community_reports.py     |   8 +-
 .../index/workflows/update_covariates.py      |   8 +-
 .../update_entities_relationships.py          |   8 +-
 .../index/workflows/update_text_units.py      |   8 +-
 .../graphrag/prompt_tune/loader/input.py      |   4 +-
 .../graphrag/graphrag/storage/__init__.py     |   4 -
 packages/graphrag/graphrag/storage/factory.py |  33 ----
 .../graphrag/storage/pipeline_storage.py      |  96 ------------
 packages/graphrag/graphrag/utils/api.py       |  12 --
 packages/graphrag/graphrag/utils/storage.py   |  11 +-
 packages/graphrag/pyproject.toml              |   1 +
 pyproject.toml                                |   4 +
 tests/integration/cache/test_factory.py       |   2 +-
 ...peline_storage.py => test_blob_storage.py} |  23 +--
 .../storage/test_cosmosdb_storage.py          |  20 +--
 tests/integration/storage/test_factory.py     | 109 +++++++-------
 ...peline_storage.py => test_file_storage.py} |  12 +-
 tests/smoke/test_fixtures.py                  |   4 +-
 tests/unit/config/utils.py                    |  35 +----
 .../cache/test_file_pipeline_cache.py         |   6 +-
 tests/unit/indexing/input/test_csv_loader.py  |  11 +-
 tests/unit/indexing/input/test_json_loader.py |  13 +-
 tests/unit/indexing/input/test_txt_loader.py  |   9 +-
 tests/unit/load_config/fixtures/config.yaml   |  10 ++
 uv.lock                                       |  28 ++++
 56 files changed, 793 insertions(+), 552 deletions(-)
 create mode 100644 packages/graphrag-storage/README.md
 create mode 100644 packages/graphrag-storage/graphrag_storage/__init__.py
 rename packages/{graphrag/graphrag/storage/blob_pipeline_storage.py => graphrag-storage/graphrag_storage/azure_blob_storage.py} (75%)
 rename packages/{graphrag/graphrag/storage/cosmosdb_pipeline_storage.py => graphrag-storage/graphrag_storage/azure_cosmos_storage.py} (91%)
 rename packages/{graphrag/graphrag/storage/file_pipeline_storage.py => graphrag-storage/graphrag_storage/file_storage.py} (73%)
 rename packages/{graphrag/graphrag/storage/memory_pipeline_storage.py => graphrag-storage/graphrag_storage/memory_storage.py} (79%)
 create mode 100644 packages/graphrag-storage/graphrag_storage/py.typed
 create mode 100644 packages/graphrag-storage/graphrag_storage/storage.py
 create mode 100644 packages/graphrag-storage/graphrag_storage/storage_config.py
 create mode 100644 packages/graphrag-storage/graphrag_storage/storage_factory.py
 create mode 100644 packages/graphrag-storage/graphrag_storage/storage_type.py
 create mode 100644 packages/graphrag-storage/pyproject.toml
 delete mode 100644 packages/graphrag/graphrag/config/models/storage_config.py
 delete mode 100644 packages/graphrag/graphrag/storage/__init__.py
 delete mode 100644 packages/graphrag/graphrag/storage/factory.py
 delete mode 100644 packages/graphrag/graphrag/storage/pipeline_storage.py
 rename tests/integration/storage/{test_blob_pipeline_storage.py => test_blob_storage.py} (82%)
 rename tests/integration/storage/{test_file_pipeline_storage.py => test_file_storage.py} (86%)
 create mode 100644 tests/unit/load_config/fixtures/config.yaml

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 76c177b60f..c82ff5bb12 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -81,7 +81,7 @@ Our pipeline can ingest .csv, .txt, or .json data from an input location. See th
 #### Fields
 
 - `storage` **StorageConfig**
-  - `type` **file|blob|cosmosdb** - The storage type to use. Default=`file`
+  - `type` **file|memory|blob|cosmosdb** - The storage type to use. Default=`file`
   - `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
   - `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
   - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
diff --git a/packages/graphrag-common/graphrag_common/factory/__init__.py b/packages/graphrag-common/graphrag_common/factory/__init__.py
index ff9d0dce40..86b102f265 100644
--- a/packages/graphrag-common/graphrag_common/factory/__init__.py
+++ b/packages/graphrag-common/graphrag_common/factory/__init__.py
@@ -3,6 +3,6 @@
 
 """The GraphRAG factory module."""
 
-from graphrag_common.factory.factory import Factory
+from graphrag_common.factory.factory import Factory, ServiceScope
 
-__all__ = ["Factory"]
+__all__ = ["Factory", "ServiceScope"]
diff --git a/packages/graphrag-common/graphrag_common/factory/factory.py b/packages/graphrag-common/graphrag_common/factory/factory.py
index d9aa236cc4..8f57606aa3 100644
--- a/packages/graphrag-common/graphrag_common/factory/factory.py
+++ b/packages/graphrag-common/graphrag_common/factory/factory.py
@@ -84,11 +84,14 @@ def create(self, strategy: str, init_args: dict[str, Any] | None = None) -> T:
             msg = f"Strategy '{strategy}' is not registered. Registered strategies are: {', '.join(list(self._service_initializers.keys()))}"
             raise ValueError(msg)
 
+        # Delete entries with value None
+        init_args = {k: v for k, v in (init_args or {}).items() if v is not None}
+
         service_descriptor = self._service_initializers[strategy]
         if service_descriptor.scope == "singleton":
             if strategy not in self._initialized_services:
                 self._initialized_services[strategy] = service_descriptor.initializer(
-                    **(init_args or {})
+                    **init_args
                 )
             return self._initialized_services[strategy]
 
diff --git a/packages/graphrag-storage/README.md b/packages/graphrag-storage/README.md
new file mode 100644
index 0000000000..6eaf729242
--- /dev/null
+++ b/packages/graphrag-storage/README.md
@@ -0,0 +1,86 @@
+# GraphRAG Storage
+
+## Basic
+
+```python
+import asyncio
+from graphrag_storage import StorageConfig, create_storage, StorageType
+
+async def run():
+    storage = create_storage(
+        StorageConfig(
+            type=StorageType.File
+            base_dir="output"
+        )
+    )
+
+    await storage.set("my_key", "value")
+    print(await storage.get("my_key"))
+
+if __name__ == "__main__":
+    asyncio.run(run())
+```
+
+## Custom Storage
+
+```python
+import asyncio
+from typing import Any
+from graphrag_storage import Storage, StorageConfig, create_storage, register_storage
+
+class MyStorage(Storage):
+    def __init__(self, some_setting: str, optional_setting: str = "default setting", **kwargs: Any):
+        # Validate settings and initialize
+        ...
+
+    #Implement rest of interface
+    ...
+
+register_storage("MyStorage", MyStorage)
+
+async def run():
+    storage = create_storage(
+        StorageConfig(
+            type="MyStorage"
+            some_setting="My Setting"
+        )
+    )
+    # Or use the factory directly to instantiate with a dict instead of using
+    # StorageConfig + create_factory
+    # from graphrag_storage.storage_factory import storage_factory
+    # storage = storage_factory.create(strategy="MyStorage", init_args={"some_setting": "My Setting"})
+
+    await storage.set("my_key", "value")
+    print(await storage.get("my_key"))
+
+if __name__ == "__main__":
+    asyncio.run(run())
+```
+
+### Details
+
+By default, the `create_storage` comes with the following storage providers registered that correspond to the entries in the `StorageType` enum. 
+
+- `FileStorage`
+- `AzureBlobStorage`
+- `AzureCosmosStorage`
+- `MemoryStorage`
+
+The preregistration happens dynamically, e.g., `FileStorage` is only imported and registered if you request a `FileStorage` with `create_storage(StorageType.File, ...)`. There is no need to manually import and register builtin storage providers when using `create_storage`.
+
+If you want a clean factory with no preregistered storage providers then directly import `storage_factory` and bypass using `create_storage`. The downside is that `storage_factory.create` uses a dict for init args instead of the strongly typed `StorageConfig` used with `create_storage`.
+
+```python
+from graphrag_storage.storage_factory import storage_factory
+from graphrag_storage.file_storage import FileStorage
+
+# storage_factory has no preregistered providers so you must register any
+# providers you plan on using.
+# May also register a custom implementation, see above for example.
+storage_factory.register("my_storage_key", FileStorage)
+
+storage = storage_factory.create(strategy="my_storage_key", init_args={"base_dir": "...", "other_settings": "..."})
+
+...
+
+```
\ No newline at end of file
diff --git a/packages/graphrag-storage/graphrag_storage/__init__.py b/packages/graphrag-storage/graphrag_storage/__init__.py
new file mode 100644
index 0000000000..2ae67be741
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/__init__.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The GraphRAG Storage package."""
+
+from graphrag_storage.storage import Storage
+from graphrag_storage.storage_config import StorageConfig
+from graphrag_storage.storage_factory import (
+    create_storage,
+    register_storage,
+)
+from graphrag_storage.storage_type import StorageType
+
+__all__ = [
+    "Storage",
+    "StorageConfig",
+    "StorageType",
+    "create_storage",
+    "register_storage",
+]
diff --git a/packages/graphrag/graphrag/storage/blob_pipeline_storage.py b/packages/graphrag-storage/graphrag_storage/azure_blob_storage.py
similarity index 75%
rename from packages/graphrag/graphrag/storage/blob_pipeline_storage.py
rename to packages/graphrag-storage/graphrag_storage/azure_blob_storage.py
index 1435cb387d..bec1bdb465 100644
--- a/packages/graphrag/graphrag/storage/blob_pipeline_storage.py
+++ b/packages/graphrag-storage/graphrag_storage/azure_blob_storage.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""Azure Blob Storage implementation of PipelineStorage."""
+"""Azure Blob Storage implementation of Storage."""
 
 import logging
 import re
@@ -12,15 +12,15 @@
 from azure.identity import DefaultAzureCredential
 from azure.storage.blob import BlobServiceClient
 
-from graphrag.storage.pipeline_storage import (
-    PipelineStorage,
+from graphrag_storage.storage import (
+    Storage,
     get_timestamp_formatted_with_local_tz,
 )
 
 logger = logging.getLogger(__name__)
 
 
-class BlobPipelineStorage(PipelineStorage):
+class AzureBlobStorage(Storage):
     """The Blob-Storage implementation."""
 
     _connection_string: str | None
@@ -28,45 +28,52 @@ class BlobPipelineStorage(PipelineStorage):
     _base_dir: str | None
     _encoding: str
     _storage_account_blob_url: str | None
+    _blob_service_client: BlobServiceClient
+    _storage_account_name: str | None
 
-    def __init__(self, **kwargs: Any) -> None:
+    def __init__(
+        self,
+        container_name: str,
+        account_url: str | None = None,
+        connection_string: str | None = None,
+        base_dir: str | None = None,
+        encoding: str = "utf-8",
+        **kwargs: Any,
+    ) -> None:
         """Create a new BlobStorage instance."""
-        connection_string = kwargs.get("connection_string")
-        storage_account_blob_url = kwargs.get("storage_account_blob_url")
-        base_dir = kwargs.get("base_dir")
-        container_name = kwargs["container_name"]
-        if container_name is None:
-            msg = "No container name provided for blob storage."
-            raise ValueError(msg)
-        if connection_string is None and storage_account_blob_url is None:
-            msg = "No storage account blob url provided for blob storage."
+        if connection_string is not None and account_url is not None:
+            msg = "AzureBlobStorage requires only one of connection_string or storage_account_blob_url to be specified, not both."
+            logger.error(msg)
             raise ValueError(msg)
 
+        _validate_blob_container_name(container_name)
+
         logger.info(
-            "Creating blob storage at [%s] and base_dir [%s]", container_name, base_dir
+            "Creating blob storage at [%s] and base_dir [%s]",
+            container_name,
+            base_dir,
         )
         if connection_string:
             self._blob_service_client = BlobServiceClient.from_connection_string(
                 connection_string
             )
-        else:
-            if storage_account_blob_url is None:
-                msg = "Either connection_string or storage_account_blob_url must be provided."
-                raise ValueError(msg)
-
+        elif account_url:
             self._blob_service_client = BlobServiceClient(
-                account_url=storage_account_blob_url,
+                account_url=account_url,
                 credential=DefaultAzureCredential(),
             )
-        self._encoding = kwargs.get("encoding", "utf-8")
+        else:
+            msg = "AzureBlobStorage requires either a connection_string or storage_account_blob_url to be specified."
+            logger.error(msg)
+            raise ValueError(msg)
+
+        self._encoding = encoding
         self._container_name = container_name
         self._connection_string = connection_string
         self._base_dir = base_dir
-        self._storage_account_blob_url = storage_account_blob_url
+        self._storage_account_blob_url = account_url
         self._storage_account_name = (
-            storage_account_blob_url.split("//")[1].split(".")[0]
-            if storage_account_blob_url
-            else None
+            account_url.split("//")[1].split(".")[0] if account_url else None
         )
         self._create_container()
 
@@ -208,17 +215,17 @@ async def delete(self, key: str) -> None:
     async def clear(self) -> None:
         """Clear the cache."""
 
-    def child(self, name: str | None) -> "PipelineStorage":
+    def child(self, name: str | None) -> "Storage":
         """Create a child storage instance."""
         if name is None:
             return self
         path = str(Path(self._base_dir) / name) if self._base_dir else name
-        return BlobPipelineStorage(
+        return AzureBlobStorage(
             connection_string=self._connection_string,
             container_name=self._container_name,
             encoding=self._encoding,
             base_dir=path,
-            storage_account_blob_url=self._storage_account_blob_url,
+            account_url=self._storage_account_blob_url,
         )
 
     def keys(self) -> list[str]:
@@ -245,7 +252,7 @@ async def get_creation_date(self, key: str) -> str:
             return ""
 
 
-def validate_blob_container_name(container_name: str):
+def _validate_blob_container_name(container_name: str) -> None:
     """
     Check if the provided blob container name is valid based on Azure rules.
 
@@ -265,34 +272,7 @@ def validate_blob_container_name(container_name: str):
     -------
         bool: True if valid, False otherwise.
     """
-    # Check the length of the name
-    if len(container_name) < 3 or len(container_name) > 63:
-        return ValueError(
-            f"Container name must be between 3 and 63 characters in length. Name provided was {len(container_name)} characters long."
-        )
-
-    # Check if the name starts with a letter or number
-    if not container_name[0].isalnum():
-        return ValueError(
-            f"Container name must start with a letter or number. Starting character was {container_name[0]}."
-        )
-
-    # Check for valid characters (letters, numbers, hyphen) and lowercase letters
-    if not re.match(r"^[a-z0-9-]+$", container_name):
-        return ValueError(
-            f"Container name must only contain:\n- lowercase letters\n- numbers\n- or hyphens\nName provided was {container_name}."
-        )
-
-    # Check for consecutive hyphens
-    if "--" in container_name:
-        return ValueError(
-            f"Container name cannot contain consecutive hyphens. Name provided was {container_name}."
-        )
-
-    # Check for hyphens at the end of the name
-    if container_name[-1] == "-":
-        return ValueError(
-            f"Container name cannot end with a hyphen. Name provided was {container_name}."
-        )
-
-    return True
+    # Match alphanumeric or single hyphen not at the start or end, repeated 3-63 times.
+    if not re.match(r"^(?:[0-9a-z]|(?<!^)-(?!$)){3,63}$", container_name):
+        msg = f"Container name must be between 3 and 63 characters long and contain only lowercase letters, numbers, or hyphens. Name provided was {container_name}."
+        raise ValueError(msg)
diff --git a/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py b/packages/graphrag-storage/graphrag_storage/azure_cosmos_storage.py
similarity index 91%
rename from packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
rename to packages/graphrag-storage/graphrag_storage/azure_cosmos_storage.py
index a12da0ee5f..ff3ec6decb 100644
--- a/packages/graphrag/graphrag/storage/cosmosdb_pipeline_storage.py
+++ b/packages/graphrag-storage/graphrag_storage/azure_cosmos_storage.py
@@ -16,17 +16,17 @@
 from azure.cosmos.exceptions import CosmosResourceNotFoundError
 from azure.cosmos.partition_key import PartitionKey
 from azure.identity import DefaultAzureCredential
-
 from graphrag.logger.progress import Progress
-from graphrag.storage.pipeline_storage import (
-    PipelineStorage,
+
+from graphrag_storage.storage import (
+    Storage,
     get_timestamp_formatted_with_local_tz,
 )
 
 logger = logging.getLogger(__name__)
 
 
-class CosmosDBPipelineStorage(PipelineStorage):
+class AzureCosmosStorage(Storage):
     """The CosmosDB-Storage Implementation."""
 
     _cosmos_client: CosmosClient
@@ -39,41 +39,47 @@ class CosmosDBPipelineStorage(PipelineStorage):
     _encoding: str
     _no_id_prefixes: list[str]
 
-    def __init__(self, **kwargs: Any) -> None:
+    def __init__(
+        self,
+        database_name: str,
+        container_name: str,
+        connection_string: str | None = None,
+        account_url: str | None = None,
+        encoding: str = "utf-8",
+        **kwargs: Any,
+    ) -> None:
         """Create a CosmosDB storage instance."""
         logger.info("Creating cosmosdb storage")
-        cosmosdb_account_url = kwargs.get("cosmosdb_account_url")
-        connection_string = kwargs.get("connection_string")
-        database_name = kwargs["base_dir"]
-        container_name = kwargs["container_name"]
-        if not database_name:
-            msg = "No base_dir provided for database name"
+        database_name = database_name
+        if database_name is None:
+            msg = "CosmosDB Storage requires a base_dir to be specified. This is used as the database name."
+            logger.error(msg)
             raise ValueError(msg)
-        if connection_string is None and cosmosdb_account_url is None:
-            msg = "connection_string or cosmosdb_account_url is required."
+
+        if connection_string is not None and account_url is not None:
+            msg = "CosmosDB Storage requires either a connection_string or cosmosdb_account_url to be specified, not both."
+            logger.error(msg)
             raise ValueError(msg)
 
         if connection_string:
             self._cosmos_client = CosmosClient.from_connection_string(connection_string)
-        else:
-            if cosmosdb_account_url is None:
-                msg = (
-                    "Either connection_string or cosmosdb_account_url must be provided."
-                )
-                raise ValueError(msg)
+        elif account_url:
             self._cosmos_client = CosmosClient(
-                url=cosmosdb_account_url,
+                url=account_url,
                 credential=DefaultAzureCredential(),
             )
-        self._encoding = kwargs.get("encoding", "utf-8")
+        else:
+            msg = "CosmosDB Storage requires either a connection_string or cosmosdb_account_url to be specified."
+            logger.error(msg)
+            raise ValueError(msg)
+
+        self._encoding = encoding
         self._database_name = database_name
         self._connection_string = connection_string
-        self._cosmosdb_account_url = cosmosdb_account_url
+        self._cosmosdb_account_url = account_url
         self._container_name = container_name
         self._cosmosdb_account_name = (
-            cosmosdb_account_url.split("//")[1].split(".")[0]
-            if cosmosdb_account_url
-            else None
+            account_url.split("//")[1].split(".")[0] if account_url else None
         )
         self._no_id_prefixes = []
         logger.debug(
@@ -307,7 +313,7 @@ def keys(self) -> list[str]:
         msg = "CosmosDB storage does yet not support listing keys."
         raise NotImplementedError(msg)
 
-    def child(self, name: str | None) -> PipelineStorage:
+    def child(self, name: str | None) -> "Storage":
         """Create a child storage instance."""
         return self
 
diff --git a/packages/graphrag/graphrag/storage/file_pipeline_storage.py b/packages/graphrag-storage/graphrag_storage/file_storage.py
similarity index 73%
rename from packages/graphrag/graphrag/storage/file_pipeline_storage.py
rename to packages/graphrag-storage/graphrag_storage/file_storage.py
index 52402c8bd6..547659abcd 100644
--- a/packages/graphrag/graphrag/storage/file_pipeline_storage.py
+++ b/packages/graphrag-storage/graphrag_storage/file_storage.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""File-based Storage implementation of PipelineStorage."""
+"""File-based Storage implementation of Storage."""
 
 import logging
 import os
@@ -16,26 +16,26 @@
 from aiofiles.os import remove
 from aiofiles.ospath import exists
 
-from graphrag.storage.pipeline_storage import (
-    PipelineStorage,
+from graphrag_storage.storage import (
+    Storage,
     get_timestamp_formatted_with_local_tz,
 )
 
 logger = logging.getLogger(__name__)
 
 
-class FilePipelineStorage(PipelineStorage):
+class FileStorage(Storage):
     """File storage class definition."""
 
-    _base_dir: str
+    _base_dir: Path
     _encoding: str
 
-    def __init__(self, **kwargs: Any) -> None:
+    def __init__(self, base_dir: str, encoding: str = "utf-8", **kwargs: Any) -> None:
         """Create a file based storage."""
-        self._base_dir = kwargs.get("base_dir", "")
-        self._encoding = kwargs.get("encoding", "utf-8")
+        self._base_dir = Path(base_dir).resolve()
+        self._encoding = encoding
         logger.info("Creating file storage at [%s]", self._base_dir)
-        Path(self._base_dir).mkdir(parents=True, exist_ok=True)
+        self._base_dir.mkdir(parents=True, exist_ok=True)
 
     def find(
         self,
@@ -45,7 +45,7 @@ def find(
         logger.info(
             "Search [%s] for files matching [%s]", self._base_dir, file_pattern.pattern
         )
-        all_files = list(Path(self._base_dir).rglob("**/*"))
+        all_files = list(self._base_dir.rglob("**/*"))
         logger.debug("All files and folders: %s", [file.name for file in all_files])
         num_loaded = 0
         num_total = len(all_files)
@@ -53,7 +53,7 @@ def find(
         for file in all_files:
             match = file_pattern.search(f"{file}")
             if match:
-                filename = f"{file}".replace(str(Path(self._base_dir)), "", 1)
+                filename = f"{file}".replace(str(self._base_dir), "", 1)
                 if filename.startswith(os.sep):
                     filename = filename[1:]
                 yield filename
@@ -71,7 +71,7 @@ async def get(
         self, key: str, as_bytes: bool | None = False, encoding: str | None = None
     ) -> Any:
         """Get method definition."""
-        file_path = join_path(self._base_dir, key)
+        file_path = _join_path(self._base_dir, key)
 
         if await self.has(key):
             return await self._read_file(file_path, as_bytes, encoding)
@@ -101,7 +101,7 @@ async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
         write_type = "wb" if is_bytes else "w"
         encoding = None if is_bytes else encoding or self._encoding
         async with aiofiles.open(
-            join_path(self._base_dir, key),
+            _join_path(self._base_dir, key),
             cast("Any", write_type),
             encoding=encoding,
         ) as f:
@@ -109,35 +109,35 @@ async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
 
     async def has(self, key: str) -> bool:
         """Has method definition."""
-        return await exists(join_path(self._base_dir, key))
+        return await exists(_join_path(self._base_dir, key))
 
     async def delete(self, key: str) -> None:
         """Delete method definition."""
         if await self.has(key):
-            await remove(join_path(self._base_dir, key))
+            await remove(_join_path(self._base_dir, key))
 
     async def clear(self) -> None:
         """Clear method definition."""
-        for file in Path(self._base_dir).glob("*"):
+        for file in self._base_dir.glob("*"):
             if file.is_dir():
                 shutil.rmtree(file)
             else:
                 file.unlink()
 
-    def child(self, name: str | None) -> "PipelineStorage":
+    def child(self, name: str | None) -> "Storage":
         """Create a child storage instance."""
         if name is None:
             return self
-        child_path = str(Path(self._base_dir) / Path(name))
-        return FilePipelineStorage(base_dir=child_path, encoding=self._encoding)
+        child_path = str(self._base_dir / name)
+        return FileStorage(base_dir=child_path, encoding=self._encoding)
 
     def keys(self) -> list[str]:
         """Return the keys in the storage."""
-        return [item.name for item in Path(self._base_dir).iterdir() if item.is_file()]
+        return [item.name for item in self._base_dir.iterdir() if item.is_file()]
 
     async def get_creation_date(self, key: str) -> str:
         """Get the creation date of a file."""
-        file_path = Path(join_path(self._base_dir, key))
+        file_path = _join_path(self._base_dir, key)
 
         creation_timestamp = file_path.stat().st_ctime
         creation_time_utc = datetime.fromtimestamp(creation_timestamp, tz=timezone.utc)
@@ -145,6 +145,6 @@ async def get_creation_date(self, key: str) -> str:
         return get_timestamp_formatted_with_local_tz(creation_time_utc)
 
 
-def join_path(file_path: str, file_name: str) -> Path:
+def _join_path(file_path: Path, file_name: str) -> Path:
     """Join a path and a file. Independent of the OS."""
-    return Path(file_path) / Path(file_name).parent / Path(file_name).name
+    return (file_path / Path(file_name).parent / Path(file_name).name).resolve()
diff --git a/packages/graphrag/graphrag/storage/memory_pipeline_storage.py b/packages/graphrag-storage/graphrag_storage/memory_storage.py
similarity index 79%
rename from packages/graphrag/graphrag/storage/memory_pipeline_storage.py
rename to packages/graphrag-storage/graphrag_storage/memory_storage.py
index 3567e3d1e3..f92a52a204 100644
--- a/packages/graphrag/graphrag/storage/memory_pipeline_storage.py
+++ b/packages/graphrag-storage/graphrag_storage/memory_storage.py
@@ -1,24 +1,29 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'InMemoryStorage' model."""
+"""In-memory storage implementation."""
 
 from typing import TYPE_CHECKING, Any
 
-from graphrag.storage.file_pipeline_storage import FilePipelineStorage
+from graphrag_storage.file_storage import FileStorage
 
 if TYPE_CHECKING:
-    from graphrag.storage.pipeline_storage import PipelineStorage
+    from graphrag_storage.storage import Storage
 
 
-class MemoryPipelineStorage(FilePipelineStorage):
+class MemoryStorage(FileStorage):
     """In memory storage class definition."""
 
     _storage: dict[str, Any]
 
-    def __init__(self):
+    def __init__(self, **kwargs: Any) -> None:
         """Init method definition."""
-        super().__init__()
+        kwargs = {
+            "base_dir": "",
+            **kwargs,
+        }
+        kwargs.pop("type", None)
+        super().__init__(**kwargs)
         self._storage = {}
 
     async def get(
@@ -69,9 +74,9 @@ async def clear(self) -> None:
         """Clear the storage."""
         self._storage.clear()
 
-    def child(self, name: str | None) -> "PipelineStorage":
+    def child(self, name: str | None) -> "Storage":
         """Create a child storage instance."""
-        return MemoryPipelineStorage()
+        return MemoryStorage()
 
     def keys(self) -> list[str]:
         """Return the keys in the storage."""
diff --git a/packages/graphrag-storage/graphrag_storage/py.typed b/packages/graphrag-storage/graphrag_storage/py.typed
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/packages/graphrag-storage/graphrag_storage/storage.py b/packages/graphrag-storage/graphrag_storage/storage.py
new file mode 100644
index 0000000000..d8016d4ae7
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/storage.py
@@ -0,0 +1,141 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Abstract base class for storage."""
+
+import re
+from abc import ABC, abstractmethod
+from collections.abc import Iterator
+from datetime import datetime
+from typing import Any
+
+
+class Storage(ABC):
+    """Provide a storage interface."""
+
+    @abstractmethod
+    def __init__(self, **kwargs: Any) -> None:
+        """Create a storage instance."""
+
+    @abstractmethod
+    def find(
+        self,
+        file_pattern: re.Pattern[str],
+    ) -> Iterator[str]:
+        """Find files in the storage using a file pattern.
+
+        Args
+        ----
+            - file_pattern: re.Pattern[str]
+                The file pattern to use for finding files.
+
+        Returns
+        -------
+            Iterator[str]:
+                An iterator over the found file keys.
+
+        """
+
+    @abstractmethod
+    async def get(
+        self, key: str, as_bytes: bool | None = None, encoding: str | None = None
+    ) -> Any:
+        """Get the value for the given key.
+
+        Args
+        ----
+            - key: str
+                The key to get the value for.
+            - as_bytes: bool | None, optional (default=None)
+                Whether or not to return the value as bytes.
+            - encoding: str | None, optional (default=None)
+                The encoding to use when decoding the value.
+
+        Returns
+        -------
+            Any:
+                The value for the given key.
+        """
+
+    @abstractmethod
+    async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
+        """Set the value for the given key.
+
+        Args
+        ----
+            - key: str
+                The key to set the value for.
+            - value: Any
+                The value to set.
+        """
+
+    @abstractmethod
+    async def has(self, key: str) -> bool:
+        """Return True if the given key exists in the storage.
+
+        Args
+        ----
+            - key: str
+                The key to check for.
+
+        Returns
+        -------
+            bool:
+                True if the key exists in the storage, False otherwise.
+        """
+
+    @abstractmethod
+    async def delete(self, key: str) -> None:
+        """Delete the given key from the storage.
+
+        Args
+        ----
+            - key: str
+                The key to delete.
+        """
+
+    @abstractmethod
+    async def clear(self) -> None:
+        """Clear the storage."""
+
+    @abstractmethod
+    def child(self, name: str | None) -> "Storage":
+        """Create a child storage instance.
+
+        Args
+        ----
+            - name: str | None
+                The name of the child storage.
+
+        Returns
+        -------
+            Storage
+                The child storage instance.
+
+        """
+
+    @abstractmethod
+    def keys(self) -> list[str]:
+        """List all keys in the storage."""
+
+    @abstractmethod
+    async def get_creation_date(self, key: str) -> str:
+        """Get the creation date for the given key.
+
+        Args
+        ----
+            - key: str
+                The key to get the creation date for.
+
+        Returns
+        -------
+            str:
+                The creation date for the given key.
+        """
+
+
+def get_timestamp_formatted_with_local_tz(timestamp: datetime) -> str:
+    """Get the formatted timestamp with the local time zone."""
+    creation_time_local = timestamp.astimezone()
+
+    return creation_time_local.strftime("%Y-%m-%d %H:%M:%S %z")
diff --git a/packages/graphrag-storage/graphrag_storage/storage_config.py b/packages/graphrag-storage/graphrag_storage/storage_config.py
new file mode 100644
index 0000000000..45cd63a734
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/storage_config.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Storage configuration model."""
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from graphrag_storage.storage_type import StorageType
+
+
+class StorageConfig(BaseModel):
+    """The default configuration section for storage."""
+
+    model_config = ConfigDict(extra="allow")
+    """Allow extra fields to support custom storage implementations."""
+
+    type: str = Field(
+        description="The storage type to use. Builtin types include 'File', 'AzureBlob', and 'AzureCosmos'.",
+        default=StorageType.File,
+    )
+
+    encoding: str | None = Field(
+        description="The encoding to use for file storage.",
+        default=None,
+    )
+
+    base_dir: str | None = Field(
+        description="The base directory for the output when using file or AzureBlob storage.",
+        default=None,
+    )
+
+    connection_string: str | None = Field(
+        description="The connection string for remote services.",
+        default=None,
+    )
+
+    container_name: str | None = Field(
+        description="The Azure Blob Storage container name or CosmosDB container name to use.",
+        default=None,
+    )
+    account_url: str | None = Field(
+        description="The account url for Azure services.",
+        default=None,
+    )
+    database_name: str | None = Field(
+        description="The database name to use.",
+        default=None,
+    )
diff --git a/packages/graphrag-storage/graphrag_storage/storage_factory.py b/packages/graphrag-storage/graphrag_storage/storage_factory.py
new file mode 100644
index 0000000000..0b525fec5f
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/storage_factory.py
@@ -0,0 +1,78 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+
+"""Storage factory implementation."""
+
+from collections.abc import Callable
+
+from graphrag_common.factory import Factory, ServiceScope
+
+from graphrag_storage.storage import Storage
+from graphrag_storage.storage_config import StorageConfig
+from graphrag_storage.storage_type import StorageType
+
+
+class StorageFactory(Factory[Storage]):
+    """A factory class for storage implementations."""
+
+
+storage_factory = StorageFactory()
+
+
+def register_storage(
+    storage_type: str,
+    storage_initializer: Callable[..., Storage],
+    scope: ServiceScope = "transient",
+) -> None:
+    """Register a custom storage implementation.
+
+    Args
+    ----
+        - storage_type: str
+            The storage id to register.
+        - storage_initializer: Callable[..., Storage]
+            The storage initializer to register.
+    """
+    storage_factory.register(storage_type, storage_initializer, scope)
+
+
+def create_storage(config: StorageConfig) -> Storage:
+    """Create a storage implementation based on the given configuration.
+
+    Args
+    ----
+        - config: StorageConfig
+            The storage configuration to use.
+
+    Returns
+    -------
+        Storage
+            The created storage implementation.
+    """
+    config_model = config.model_dump()
+    storage_strategy = config.type
+
+    if storage_strategy not in storage_factory:
+        match storage_strategy:
+            case StorageType.File:
+                from graphrag_storage.file_storage import FileStorage
+
+                register_storage(StorageType.File, FileStorage)
+            case StorageType.Memory:
+                from graphrag_storage.memory_storage import MemoryStorage
+
+                register_storage(StorageType.Memory, MemoryStorage)
+            case StorageType.AzureBlob:
+                from graphrag_storage.azure_blob_storage import AzureBlobStorage
+
+                register_storage(StorageType.AzureBlob, AzureBlobStorage)
+            case StorageType.AzureCosmos:
+                from graphrag_storage.azure_cosmos_storage import AzureCosmosStorage
+
+                register_storage(StorageType.AzureCosmos, AzureCosmosStorage)
+            case _:
+                msg = f"StorageConfig.type '{storage_strategy}' is not registered in the StorageFactory. Registered types: {', '.join(storage_factory.keys())}."
+                raise ValueError(msg)
+
+    return storage_factory.create(storage_strategy, config_model)
diff --git a/packages/graphrag-storage/graphrag_storage/storage_type.py b/packages/graphrag-storage/graphrag_storage/storage_type.py
new file mode 100644
index 0000000000..dd2b1376fd
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/storage_type.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+
+"""Builtin storage implementation types."""
+
+from enum import StrEnum
+
+
+class StorageType(StrEnum):
+    """Enum for storage types."""
+
+    File = "file"
+    Memory = "memory"
+    AzureBlob = "blob"
+    AzureCosmos = "cosmosdb"
diff --git a/packages/graphrag-storage/pyproject.toml b/packages/graphrag-storage/pyproject.toml
new file mode 100644
index 0000000000..464189f950
--- /dev/null
+++ b/packages/graphrag-storage/pyproject.toml
@@ -0,0 +1,49 @@
+[project]
+name = "graphrag-storage"
+version = "2.7.0"
+description = "GraphRAG storage package."
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = "MIT"
+readme = "README.md"
+license-files = ["LICENSE"]
+requires-python = ">=3.10,<3.13"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+dependencies = [
+    "aiofiles>=24.1.0",
+    "azure-cosmos>=4.9.0",
+    "azure-identity>=1.19.0",
+    "azure-storage-blob>=12.24.0",
+    "graphrag-common==2.7.0",
+    "pandas>=2.2.3",
+    "pydantic>=2.10.3",
+]
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
+
diff --git a/packages/graphrag/graphrag/cache/factory.py b/packages/graphrag/graphrag/cache/factory.py
index 971c22c6d5..d62d8c420b 100644
--- a/packages/graphrag/graphrag/cache/factory.py
+++ b/packages/graphrag/graphrag/cache/factory.py
@@ -12,9 +12,6 @@
 from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.enums import CacheType
-from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
-from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
-from graphrag.storage.file_pipeline_storage import FilePipelineStorage
 
 
 class CacheFactory(Factory[PipelineCache]):
@@ -30,19 +27,25 @@ class CacheFactory(Factory[PipelineCache]):
 # --- register built-in cache implementations ---
 def create_file_cache(**kwargs) -> PipelineCache:
     """Create a file-based cache implementation."""
-    storage = FilePipelineStorage(**kwargs)
+    from graphrag_storage.file_storage import FileStorage
+
+    storage = FileStorage(**kwargs)
     return JsonPipelineCache(storage)
 
 
 def create_blob_cache(**kwargs) -> PipelineCache:
     """Create a blob storage-based cache implementation."""
-    storage = BlobPipelineStorage(**kwargs)
+    from graphrag_storage.azure_blob_storage import AzureBlobStorage
+
+    storage = AzureBlobStorage(**kwargs)
     return JsonPipelineCache(storage)
 
 
 def create_cosmosdb_cache(**kwargs) -> PipelineCache:
     """Create a CosmosDB-based cache implementation."""
-    storage = CosmosDBPipelineStorage(**kwargs)
+    from graphrag_storage.azure_cosmos_storage import AzureCosmosStorage
+
+    storage = AzureCosmosStorage(**kwargs)
     return JsonPipelineCache(storage)
 
 
diff --git a/packages/graphrag/graphrag/cache/json_pipeline_cache.py b/packages/graphrag/graphrag/cache/json_pipeline_cache.py
index 84cd180c52..22b438936e 100644
--- a/packages/graphrag/graphrag/cache/json_pipeline_cache.py
+++ b/packages/graphrag/graphrag/cache/json_pipeline_cache.py
@@ -6,17 +6,18 @@
 import json
 from typing import Any
 
+from graphrag_storage import Storage
+
 from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.storage.pipeline_storage import PipelineStorage
 
 
 class JsonPipelineCache(PipelineCache):
     """File pipeline cache class definition."""
 
-    _storage: PipelineStorage
+    _storage: Storage
     _encoding: str
 
-    def __init__(self, storage: PipelineStorage, encoding="utf-8"):
+    def __init__(self, storage: Storage, encoding="utf-8"):
         """Init method definition."""
         self._storage = storage
         self._encoding = encoding
diff --git a/packages/graphrag/graphrag/cli/query.py b/packages/graphrag/graphrag/cli/query.py
index 93163db19d..6ce049ddcc 100644
--- a/packages/graphrag/graphrag/cli/query.py
+++ b/packages/graphrag/graphrag/cli/query.py
@@ -8,11 +8,12 @@
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
+from graphrag_storage import create_storage
+
 import graphrag.api as api
 from graphrag.callbacks.noop_query_callbacks import NoopQueryCallbacks
 from graphrag.config.load_config import load_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.utils.api import create_storage_from_config
 from graphrag.utils.storage import load_table_from_storage, storage_has_table
 
 if TYPE_CHECKING:
@@ -376,7 +377,7 @@ def _resolve_output_files(
 ) -> dict[str, Any]:
     """Read indexing output files to a dataframe dict."""
     dataframe_dict = {}
-    storage_obj = create_storage_from_config(config.output)
+    storage_obj = create_storage(config.output)
     for name in output_list:
         df_value = asyncio.run(load_table_from_storage(name=name, storage=storage_obj))
         dataframe_dict[name] = df_value
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index 29ed5a3bea..cefcc676f3 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -7,6 +7,8 @@
 from pathlib import Path
 from typing import ClassVar
 
+from graphrag_storage import StorageType
+
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
     AsyncType,
@@ -17,7 +19,6 @@
     ModelType,
     NounPhraseExtractorType,
     ReportingType,
-    StorageType,
     VectorStoreType,
 )
 from graphrag.index.operations.build_noun_graph.np_extractors.stop_words import (
@@ -230,12 +231,13 @@ class GlobalSearchDefaults:
 class StorageDefaults:
     """Default values for storage."""
 
-    type: ClassVar[StorageType] = StorageType.file
+    type: str = StorageType.File
+    encoding: str | None = None
     base_dir: str | None = None
-    connection_string: None = None
-    container_name: None = None
-    storage_account_blob_url: None = None
-    cosmosdb_account_url: None = None
+    azure_connection_string: None = None
+    azure_container_name: None = None
+    azure_storage_account_blob_url: None = None
+    azure_cosmosdb_account_url: None = None
 
 
 @dataclass
diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py
index 4d58c62e97..01554d9ce5 100644
--- a/packages/graphrag/graphrag/config/init_content.py
+++ b/packages/graphrag/graphrag/config/init_content.py
@@ -50,7 +50,7 @@
 
 input:
   storage:
-    type: {graphrag_config_defaults.input.storage.type.value} # or blob
+    type: {graphrag_config_defaults.input.storage.type} # or blob, cosmosdb
     base_dir: "{graphrag_config_defaults.input.storage.base_dir}"
   file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json]
 
@@ -63,7 +63,7 @@
 ## connection_string and container_name must be provided
 
 output:
-  type: {graphrag_config_defaults.output.type.value} # [file, blob, cosmosdb]
+  type: {graphrag_config_defaults.output.type} # or blob, cosmosdb
   base_dir: "{graphrag_config_defaults.output.base_dir}"
     
 cache:
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 61e21f3a85..101a37b379 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -6,6 +6,7 @@
 from pathlib import Path
 
 from devtools import pformat
+from graphrag_storage import StorageConfig, StorageType
 from pydantic import BaseModel, Field, model_validator
 
 import graphrag.config.defaults as defs
@@ -28,7 +29,6 @@
 from graphrag.config.models.prune_graph_config import PruneGraphConfig
 from graphrag.config.models.reporting_config import ReportingConfig
 from graphrag.config.models.snapshots_config import SnapshotsConfig
-from graphrag.config.models.storage_config import StorageConfig
 from graphrag.config.models.summarize_descriptions_config import (
     SummarizeDescriptionsConfig,
 )
@@ -116,7 +116,7 @@ def _validate_input_pattern(self) -> None:
 
     def _validate_input_base_dir(self) -> None:
         """Validate the input base directory."""
-        if self.input.storage.type == defs.StorageType.file:
+        if self.input.storage.type == StorageType.File:
             if not self.input.storage.base_dir:
                 msg = "input storage base directory is required for file input storage. Please rerun `graphrag init` and set the input storage configuration."
                 raise ValueError(msg)
@@ -140,7 +140,7 @@ def _validate_input_base_dir(self) -> None:
 
     def _validate_output_base_dir(self) -> None:
         """Validate the output base directory."""
-        if self.output.type == defs.StorageType.file:
+        if self.output.type == StorageType.File:
             if not self.output.base_dir:
                 msg = "output base directory is required for file output. Please rerun `graphrag init` and set the output configuration."
                 raise ValueError(msg)
@@ -156,7 +156,7 @@ def _validate_output_base_dir(self) -> None:
 
     def _validate_update_index_output_base_dir(self) -> None:
         """Validate the update index output base directory."""
-        if self.update_index_output.type == defs.StorageType.file:
+        if self.update_index_output.type == StorageType.File:
             if not self.update_index_output.base_dir:
                 msg = "update_index_output base directory is required for file output. Please rerun `graphrag init` and set the update_index_output configuration."
                 raise ValueError(msg)
diff --git a/packages/graphrag/graphrag/config/models/input_config.py b/packages/graphrag/graphrag/config/models/input_config.py
index bc34d9402d..c3c30f6302 100644
--- a/packages/graphrag/graphrag/config/models/input_config.py
+++ b/packages/graphrag/graphrag/config/models/input_config.py
@@ -3,12 +3,12 @@
 
 """Parameterization settings for the default configuration."""
 
+from graphrag_storage import StorageConfig
 from pydantic import BaseModel, Field
 
 import graphrag.config.defaults as defs
 from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import InputFileType
-from graphrag.config.models.storage_config import StorageConfig
 
 
 class InputConfig(BaseModel):
diff --git a/packages/graphrag/graphrag/config/models/storage_config.py b/packages/graphrag/graphrag/config/models/storage_config.py
deleted file mode 100644
index 7491454c0a..0000000000
--- a/packages/graphrag/graphrag/config/models/storage_config.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from pathlib import Path
-
-from pydantic import BaseModel, Field, field_validator
-
-from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.enums import StorageType
-
-
-class StorageConfig(BaseModel):
-    """The default configuration section for storage."""
-
-    type: StorageType | str = Field(
-        description="The storage type to use.",
-        default=graphrag_config_defaults.storage.type,
-    )
-    base_dir: str | None = Field(
-        description="The base directory for the output.",
-        default=graphrag_config_defaults.storage.base_dir,
-    )
-
-    # Validate the base dir for multiple OS (use Path)
-    # if not using a cloud storage type.
-    @field_validator("base_dir", mode="before")
-    @classmethod
-    def validate_base_dir(cls, value, info):
-        """Ensure that base_dir is a valid filesystem path when using local storage."""
-        # info.data contains other field values, including 'type'
-        if info.data.get("type") != StorageType.file:
-            return value
-        return str(Path(value))
-
-    connection_string: str | None = Field(
-        description="The storage connection string to use.",
-        default=graphrag_config_defaults.storage.connection_string,
-    )
-    container_name: str | None = Field(
-        description="The storage container name to use.",
-        default=graphrag_config_defaults.storage.container_name,
-    )
-    storage_account_blob_url: str | None = Field(
-        description="The storage account blob url to use.",
-        default=graphrag_config_defaults.storage.storage_account_blob_url,
-    )
-    cosmosdb_account_url: str | None = Field(
-        description="The cosmosdb account url to use.",
-        default=graphrag_config_defaults.storage.cosmosdb_account_url,
-    )
diff --git a/packages/graphrag/graphrag/index/input/input_reader.py b/packages/graphrag/graphrag/index/input/input_reader.py
index ed0add9f97..98a713e509 100644
--- a/packages/graphrag/graphrag/index/input/input_reader.py
+++ b/packages/graphrag/graphrag/index/input/input_reader.py
@@ -13,8 +13,9 @@
 import pandas as pd
 
 if TYPE_CHECKING:
+    from graphrag_storage import Storage
+
     from graphrag.config.models.input_config import InputConfig
-    from graphrag.storage.pipeline_storage import PipelineStorage
 
 logger = logging.getLogger(__name__)
 
@@ -22,7 +23,7 @@
 class InputReader(metaclass=ABCMeta):
     """Provide a cache interface for the pipeline."""
 
-    def __init__(self, storage: PipelineStorage, config: InputConfig, **kwargs):
+    def __init__(self, storage: Storage, config: InputConfig, **kwargs):
         self._storage = storage
         self._config = config
 
diff --git a/packages/graphrag/graphrag/index/operations/snapshot_graphml.py b/packages/graphrag/graphrag/index/operations/snapshot_graphml.py
index c1eb9b0688..9124038401 100644
--- a/packages/graphrag/graphrag/index/operations/snapshot_graphml.py
+++ b/packages/graphrag/graphrag/index/operations/snapshot_graphml.py
@@ -4,14 +4,13 @@
 """A module containing snapshot_graphml method definition."""
 
 import networkx as nx
-
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag_storage import Storage
 
 
 async def snapshot_graphml(
     input: str | nx.Graph,
     name: str,
-    storage: PipelineStorage,
+    storage: Storage,
 ) -> None:
     """Take a entire snapshot of a graph to standard graphml format."""
     graphml = input if isinstance(input, str) else "\n".join(nx.generate_graphml(input))
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index a373e43b76..4eef24f14c 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -12,6 +12,7 @@
 from typing import Any
 
 import pandas as pd
+from graphrag_storage import Storage, create_storage
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
@@ -19,8 +20,7 @@
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.pipeline import Pipeline
 from graphrag.index.typing.pipeline_run_result import PipelineRunResult
-from graphrag.storage.pipeline_storage import PipelineStorage
-from graphrag.utils.api import create_cache_from_config, create_storage_from_config
+from graphrag.utils.api import create_cache_from_config
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -35,8 +35,8 @@ async def run_pipeline(
     input_documents: pd.DataFrame | None = None,
 ) -> AsyncIterable[PipelineRunResult]:
     """Run all workflows using a simplified pipeline."""
-    input_storage = create_storage_from_config(config.input.storage)
-    output_storage = create_storage_from_config(config.output)
+    input_storage = create_storage(config.input.storage)
+    output_storage = create_storage(config.output)
     cache = create_cache_from_config(config.cache)
 
     # load existing state in case any workflows are stateful
@@ -49,7 +49,7 @@ async def run_pipeline(
     if is_update_run:
         logger.info("Running incremental indexing.")
 
-        update_storage = create_storage_from_config(config.update_index_output)
+        update_storage = create_storage(config.update_index_output)
         # we use this to store the new subset index, and will merge its content with the previous index
         update_timestamp = time.strftime("%Y%m%d-%H%M%S")
         timestamped_storage = update_storage.child(update_timestamp)
@@ -156,8 +156,8 @@ async def _dump_json(context: PipelineRunContext) -> None:
 
 
 async def _copy_previous_output(
-    storage: PipelineStorage,
-    copy_storage: PipelineStorage,
+    storage: Storage,
+    copy_storage: Storage,
 ):
     for file in storage.find(re.compile(r"\.parquet$")):
         base_name = file[0].replace(".parquet", "")
diff --git a/packages/graphrag/graphrag/index/run/utils.py b/packages/graphrag/graphrag/index/run/utils.py
index 52b1f0bd31..03e789746a 100644
--- a/packages/graphrag/graphrag/index/run/utils.py
+++ b/packages/graphrag/graphrag/index/run/utils.py
@@ -3,6 +3,9 @@
 
 """Utility functions for the GraphRAG run module."""
 
+from graphrag_storage import Storage, create_storage
+from graphrag_storage.memory_storage import MemoryStorage
+
 from graphrag.cache.memory_pipeline_cache import InMemoryCache
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
@@ -12,15 +15,12 @@
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.state import PipelineState
 from graphrag.index.typing.stats import PipelineRunStats
-from graphrag.storage.memory_pipeline_storage import MemoryPipelineStorage
-from graphrag.storage.pipeline_storage import PipelineStorage
-from graphrag.utils.api import create_storage_from_config
 
 
 def create_run_context(
-    input_storage: PipelineStorage | None = None,
-    output_storage: PipelineStorage | None = None,
-    previous_storage: PipelineStorage | None = None,
+    input_storage: Storage | None = None,
+    output_storage: Storage | None = None,
+    previous_storage: Storage | None = None,
     cache: PipelineCache | None = None,
     callbacks: WorkflowCallbacks | None = None,
     stats: PipelineRunStats | None = None,
@@ -28,9 +28,9 @@ def create_run_context(
 ) -> PipelineRunContext:
     """Create the run context for the pipeline."""
     return PipelineRunContext(
-        input_storage=input_storage or MemoryPipelineStorage(),
-        output_storage=output_storage or MemoryPipelineStorage(),
-        previous_storage=previous_storage or MemoryPipelineStorage(),
+        input_storage=input_storage or MemoryStorage(),
+        output_storage=output_storage or MemoryStorage(),
+        previous_storage=previous_storage or MemoryStorage(),
         cache=cache or InMemoryCache(),
         callbacks=callbacks or NoopWorkflowCallbacks(),
         stats=stats or PipelineRunStats(),
@@ -50,10 +50,10 @@ def create_callback_chain(
 
 def get_update_storages(
     config: GraphRagConfig, timestamp: str
-) -> tuple[PipelineStorage, PipelineStorage, PipelineStorage]:
+) -> tuple[Storage, Storage, Storage]:
     """Get storage objects for the update index run."""
-    output_storage = create_storage_from_config(config.output)
-    update_storage = create_storage_from_config(config.update_index_output)
+    output_storage = create_storage(config.output)
+    update_storage = create_storage(config.update_index_output)
     timestamped_storage = update_storage.child(timestamp)
     delta_storage = timestamped_storage.child("delta")
     previous_storage = timestamped_storage.child("previous")
diff --git a/packages/graphrag/graphrag/index/typing/context.py b/packages/graphrag/graphrag/index/typing/context.py
index ef2e1f7ea5..465ec7214c 100644
--- a/packages/graphrag/graphrag/index/typing/context.py
+++ b/packages/graphrag/graphrag/index/typing/context.py
@@ -10,7 +10,7 @@
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.index.typing.state import PipelineState
 from graphrag.index.typing.stats import PipelineRunStats
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag_storage import Storage
 
 
 @dataclass
@@ -18,11 +18,11 @@ class PipelineRunContext:
     """Provides the context for the current pipeline run."""
 
     stats: PipelineRunStats
-    input_storage: PipelineStorage
+    input_storage: Storage
     "Storage for input documents."
-    output_storage: PipelineStorage
+    output_storage: Storage
     "Long-term storage for pipeline verbs to use. Items written here will be written to the storage provider."
-    previous_storage: PipelineStorage
+    previous_storage: Storage
     "Storage for previous pipeline run when running in update mode."
     cache: PipelineCache
     "Cache instance for reading previous LLM responses."
diff --git a/packages/graphrag/graphrag/index/update/incremental_index.py b/packages/graphrag/graphrag/index/update/incremental_index.py
index ac56e30df4..81f917e187 100644
--- a/packages/graphrag/graphrag/index/update/incremental_index.py
+++ b/packages/graphrag/graphrag/index/update/incremental_index.py
@@ -7,8 +7,8 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_storage import Storage
 
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import (
     load_table_from_storage,
     write_table_to_storage,
@@ -31,16 +31,14 @@ class InputDelta:
     deleted_inputs: pd.DataFrame
 
 
-async def get_delta_docs(
-    input_dataset: pd.DataFrame, storage: PipelineStorage
-) -> InputDelta:
+async def get_delta_docs(input_dataset: pd.DataFrame, storage: Storage) -> InputDelta:
     """Get the delta between the input dataset and the final documents.
 
     Parameters
     ----------
     input_dataset : pd.DataFrame
         The input dataset.
-    storage : PipelineStorage
+    storage : Storage
         The Pipeline storage.
 
     Returns
@@ -65,9 +63,9 @@ async def get_delta_docs(
 
 async def concat_dataframes(
     name: str,
-    previous_storage: PipelineStorage,
-    delta_storage: PipelineStorage,
-    output_storage: PipelineStorage,
+    previous_storage: Storage,
+    delta_storage: Storage,
+    output_storage: Storage,
 ) -> pd.DataFrame:
     """Concatenate dataframes."""
     old_df = await load_table_from_storage(name, previous_storage)
diff --git a/packages/graphrag/graphrag/index/workflows/load_update_documents.py b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
index 7755091017..e68471eae7 100644
--- a/packages/graphrag/graphrag/index/workflows/load_update_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
@@ -6,6 +6,7 @@
 import logging
 
 import pandas as pd
+from graphrag_storage import Storage
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.input.factory import InputReaderFactory
@@ -13,7 +14,6 @@
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.update.incremental_index import get_delta_docs
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -47,7 +47,7 @@ async def run_workflow(
 
 async def load_update_documents(
     input_reader: InputReader,
-    previous_storage: PipelineStorage,
+    previous_storage: Storage,
 ) -> pd.DataFrame:
     """Load and parse update-only input documents into a standard format."""
     input_documents = await input_reader.read_files()
diff --git a/packages/graphrag/graphrag/index/workflows/update_communities.py b/packages/graphrag/graphrag/index/workflows/update_communities.py
index b7e3e6a343..da4fdef147 100644
--- a/packages/graphrag/graphrag/index/workflows/update_communities.py
+++ b/packages/graphrag/graphrag/index/workflows/update_communities.py
@@ -5,12 +5,13 @@
 
 import logging
 
+from graphrag_storage import Storage
+
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.update.communities import _update_and_merge_communities
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -37,9 +38,9 @@ async def run_workflow(
 
 
 async def _update_communities(
-    previous_storage: PipelineStorage,
-    delta_storage: PipelineStorage,
-    output_storage: PipelineStorage,
+    previous_storage: Storage,
+    delta_storage: Storage,
+    output_storage: Storage,
 ) -> dict:
     """Update the communities output."""
     old_communities = await load_table_from_storage("communities", previous_storage)
diff --git a/packages/graphrag/graphrag/index/workflows/update_community_reports.py b/packages/graphrag/graphrag/index/workflows/update_community_reports.py
index 42576aca27..790f9fc296 100644
--- a/packages/graphrag/graphrag/index/workflows/update_community_reports.py
+++ b/packages/graphrag/graphrag/index/workflows/update_community_reports.py
@@ -6,13 +6,13 @@
 import logging
 
 import pandas as pd
+from graphrag_storage import Storage
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.update.communities import _update_and_merge_community_reports
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -43,9 +43,9 @@ async def run_workflow(
 
 
 async def _update_community_reports(
-    previous_storage: PipelineStorage,
-    delta_storage: PipelineStorage,
-    output_storage: PipelineStorage,
+    previous_storage: Storage,
+    delta_storage: Storage,
+    output_storage: Storage,
     community_id_mapping: dict,
 ) -> pd.DataFrame:
     """Update the community reports output."""
diff --git a/packages/graphrag/graphrag/index/workflows/update_covariates.py b/packages/graphrag/graphrag/index/workflows/update_covariates.py
index f0bf29a6ae..09f8b4053d 100644
--- a/packages/graphrag/graphrag/index/workflows/update_covariates.py
+++ b/packages/graphrag/graphrag/index/workflows/update_covariates.py
@@ -7,12 +7,12 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_storage import Storage
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import (
     load_table_from_storage,
     storage_has_table,
@@ -43,9 +43,9 @@ async def run_workflow(
 
 
 async def _update_covariates(
-    previous_storage: PipelineStorage,
-    delta_storage: PipelineStorage,
-    output_storage: PipelineStorage,
+    previous_storage: Storage,
+    delta_storage: Storage,
+    output_storage: Storage,
 ) -> None:
     """Update the covariates output."""
     old_covariates = await load_table_from_storage("covariates", previous_storage)
diff --git a/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
index 1245303559..2ddd171457 100644
--- a/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
+++ b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
@@ -6,6 +6,7 @@
 import logging
 
 import pandas as pd
+from graphrag_storage import Storage
 
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
@@ -17,7 +18,6 @@
 from graphrag.index.update.relationships import _update_and_merge_relationships
 from graphrag.index.workflows.extract_graph import get_summarized_entities_relationships
 from graphrag.language_model.manager import ModelManager
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -55,9 +55,9 @@ async def run_workflow(
 
 
 async def _update_entities_and_relationships(
-    previous_storage: PipelineStorage,
-    delta_storage: PipelineStorage,
-    output_storage: PipelineStorage,
+    previous_storage: Storage,
+    delta_storage: Storage,
+    output_storage: Storage,
     config: GraphRagConfig,
     cache: PipelineCache,
     callbacks: WorkflowCallbacks,
diff --git a/packages/graphrag/graphrag/index/workflows/update_text_units.py b/packages/graphrag/graphrag/index/workflows/update_text_units.py
index 392533f16b..c97f89ce7a 100644
--- a/packages/graphrag/graphrag/index/workflows/update_text_units.py
+++ b/packages/graphrag/graphrag/index/workflows/update_text_units.py
@@ -7,12 +7,12 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_storage import Storage
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -40,9 +40,9 @@ async def run_workflow(
 
 
 async def _update_text_units(
-    previous_storage: PipelineStorage,
-    delta_storage: PipelineStorage,
-    output_storage: PipelineStorage,
+    previous_storage: Storage,
+    delta_storage: Storage,
+    output_storage: Storage,
     entity_id_mapping: dict,
 ) -> pd.DataFrame:
     """Update the text units output."""
diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py
index 5e9fccb440..c810b0ce41 100644
--- a/packages/graphrag/graphrag/prompt_tune/loader/input.py
+++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_storage import create_storage
 
 from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
@@ -25,7 +26,6 @@
 )
 from graphrag.prompt_tune.types import DocSelectionType
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
-from graphrag.utils.api import create_storage_from_config
 
 
 def _sample_chunks_from_embeddings(
@@ -63,7 +63,7 @@ async def load_docs_in_chunks(
         cache=NoopPipelineCache(),
     )
     tokenizer = get_tokenizer(embeddings_llm_settings)
-    input_storage = create_storage_from_config(config.input.storage)
+    input_storage = create_storage(config.input.storage)
     input_reader = InputReaderFactory().create(
         config.input.file_type,
         {"storage": input_storage, "config": config.input},
diff --git a/packages/graphrag/graphrag/storage/__init__.py b/packages/graphrag/graphrag/storage/__init__.py
deleted file mode 100644
index b21f077cb1..0000000000
--- a/packages/graphrag/graphrag/storage/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The storage package root."""
diff --git a/packages/graphrag/graphrag/storage/factory.py b/packages/graphrag/graphrag/storage/factory.py
deleted file mode 100644
index 738a46420b..0000000000
--- a/packages/graphrag/graphrag/storage/factory.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Factory functions for creating storage."""
-
-from __future__ import annotations
-
-from graphrag_common.factory import Factory
-
-from graphrag.config.enums import StorageType
-from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
-from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
-from graphrag.storage.file_pipeline_storage import FilePipelineStorage
-from graphrag.storage.memory_pipeline_storage import MemoryPipelineStorage
-from graphrag.storage.pipeline_storage import PipelineStorage
-
-
-class StorageFactory(Factory[PipelineStorage]):
-    """A factory class for storage implementations.
-
-    Includes a method for users to register a custom storage implementation.
-
-    Configuration arguments are passed to each storage implementation as kwargs
-    for individual enforcement of required/optional arguments.
-    """
-
-
-# --- register built-in storage implementations ---
-storage_factory = StorageFactory()
-storage_factory.register(StorageType.blob.value, BlobPipelineStorage)
-storage_factory.register(StorageType.cosmosdb.value, CosmosDBPipelineStorage)
-storage_factory.register(StorageType.file.value, FilePipelineStorage)
-storage_factory.register(StorageType.memory.value, MemoryPipelineStorage)
diff --git a/packages/graphrag/graphrag/storage/pipeline_storage.py b/packages/graphrag/graphrag/storage/pipeline_storage.py
deleted file mode 100644
index 5c79921736..0000000000
--- a/packages/graphrag/graphrag/storage/pipeline_storage.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'PipelineStorage' model."""
-
-import re
-from abc import ABCMeta, abstractmethod
-from collections.abc import Iterator
-from datetime import datetime
-from typing import Any
-
-
-class PipelineStorage(metaclass=ABCMeta):
-    """Provide a storage interface for the pipeline. This is where the pipeline will store its output data."""
-
-    @abstractmethod
-    def find(
-        self,
-        file_pattern: re.Pattern[str],
-    ) -> Iterator[str]:
-        """Find files in the storage using a file pattern."""
-
-    @abstractmethod
-    async def get(
-        self, key: str, as_bytes: bool | None = None, encoding: str | None = None
-    ) -> Any:
-        """Get the value for the given key.
-
-        Args:
-            - key - The key to get the value for.
-            - as_bytes - Whether or not to return the value as bytes.
-
-        Returns
-        -------
-            - output - The value for the given key.
-        """
-
-    @abstractmethod
-    async def set(self, key: str, value: Any, encoding: str | None = None) -> None:
-        """Set the value for the given key.
-
-        Args:
-            - key - The key to set the value for.
-            - value - The value to set.
-        """
-
-    @abstractmethod
-    async def has(self, key: str) -> bool:
-        """Return True if the given key exists in the storage.
-
-        Args:
-            - key - The key to check for.
-
-        Returns
-        -------
-            - output - True if the key exists in the storage, False otherwise.
-        """
-
-    @abstractmethod
-    async def delete(self, key: str) -> None:
-        """Delete the given key from the storage.
-
-        Args:
-            - key - The key to delete.
-        """
-
-    @abstractmethod
-    async def clear(self) -> None:
-        """Clear the storage."""
-
-    @abstractmethod
-    def child(self, name: str | None) -> "PipelineStorage":
-        """Create a child storage instance."""
-
-    @abstractmethod
-    def keys(self) -> list[str]:
-        """List all keys in the storage."""
-
-    @abstractmethod
-    async def get_creation_date(self, key: str) -> str:
-        """Get the creation date for the given key.
-
-        Args:
-            - key - The key to get the creation date for.
-
-        Returns
-        -------
-            - output - The creation date for the given key.
-        """
-
-
-def get_timestamp_formatted_with_local_tz(timestamp: datetime) -> str:
-    """Get the formatted timestamp with the local time zone."""
-    creation_time_local = timestamp.astimezone()
-
-    return creation_time_local.strftime("%Y-%m-%d %H:%M:%S %z")
diff --git a/packages/graphrag/graphrag/utils/api.py b/packages/graphrag/graphrag/utils/api.py
index f264c1a9ed..2d83d692ff 100644
--- a/packages/graphrag/graphrag/utils/api.py
+++ b/packages/graphrag/graphrag/utils/api.py
@@ -10,10 +10,7 @@
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.embeddings import create_index_name
 from graphrag.config.models.cache_config import CacheConfig
-from graphrag.config.models.storage_config import StorageConfig
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.storage.factory import StorageFactory
-from graphrag.storage.pipeline_storage import PipelineStorage
 from graphrag.vector_stores.base import (
     BaseVectorStore,
 )
@@ -101,15 +98,6 @@ def load_search_prompt(prompt_config: str | None) -> str | None:
     return None
 
 
-def create_storage_from_config(output: StorageConfig) -> PipelineStorage:
-    """Create a storage object from the config."""
-    storage_config = output.model_dump()
-    return StorageFactory().create(
-        storage_config["type"],
-        storage_config,
-    )
-
-
 def create_cache_from_config(cache: CacheConfig) -> PipelineCache:
     """Create a cache object from the config."""
     cache_config = cache.model_dump()
diff --git a/packages/graphrag/graphrag/utils/storage.py b/packages/graphrag/graphrag/utils/storage.py
index 8534330a15..852d066091 100644
--- a/packages/graphrag/graphrag/utils/storage.py
+++ b/packages/graphrag/graphrag/utils/storage.py
@@ -7,13 +7,12 @@
 from io import BytesIO
 
 import pandas as pd
-
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag_storage import Storage
 
 logger = logging.getLogger(__name__)
 
 
-async def load_table_from_storage(name: str, storage: PipelineStorage) -> pd.DataFrame:
+async def load_table_from_storage(name: str, storage: Storage) -> pd.DataFrame:
     """Load a parquet from the storage instance."""
     filename = f"{name}.parquet"
     if not await storage.has(filename):
@@ -28,17 +27,17 @@ async def load_table_from_storage(name: str, storage: PipelineStorage) -> pd.Dat
 
 
 async def write_table_to_storage(
-    table: pd.DataFrame, name: str, storage: PipelineStorage
+    table: pd.DataFrame, name: str, storage: Storage
 ) -> None:
     """Write a table to storage."""
     await storage.set(f"{name}.parquet", table.to_parquet())
 
 
-async def delete_table_from_storage(name: str, storage: PipelineStorage) -> None:
+async def delete_table_from_storage(name: str, storage: Storage) -> None:
     """Delete a table to storage."""
     await storage.delete(f"{name}.parquet")
 
 
-async def storage_has_table(name: str, storage: PipelineStorage) -> bool:
+async def storage_has_table(name: str, storage: Storage) -> bool:
     """Check if a table exists in storage."""
     return await storage.has(f"{name}.parquet")
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index a7b97f1f0f..7b7eec259d 100644
--- a/packages/graphrag/pyproject.toml
+++ b/packages/graphrag/pyproject.toml
@@ -41,6 +41,7 @@ dependencies = [
     "devtools>=0.12.2",
     "environs>=11.0.0",
     "graphrag-common==2.7.0",
+    "graphrag-storage==2.7.0",
     "graspologic-native>=1.2.5",
     "json-repair>=0.30.3",
     "lancedb>=0.17.0",
diff --git a/pyproject.toml b/pyproject.toml
index 3cb1ae1d67..1979df5cb7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,6 +54,7 @@ members = ["packages/*"]
 
 [tool.uv.sources]
 graphrag-common = { workspace = true }
+graphrag-storage = { workspace = true }
 
 # Keep poethepoet for task management to minimize changes
 [tool.poe.tasks]
@@ -69,6 +70,7 @@ _semversioner_changelog = "semversioner changelog > CHANGELOG.md"
 # Add more update toml tasks as packages are added
 _semversioner_update_graphrag_toml_version = "update-toml update --file packages/graphrag/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_common_toml_version = "update-toml update --file packages/graphrag-common/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_graphrag_storage_toml_version = "update-toml update --file packages/graphrag-storage/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_workspace_dependency_versions = "python -m scripts.update_workspace_dependency_versions"
 semversioner_add = "semversioner add-change"
 coverage_report = 'coverage report --omit "**/tests/**" --show-missing'
@@ -103,6 +105,7 @@ sequence = [
     # Add more update toml tasks as packages are added
     '_semversioner_update_graphrag_toml_version',
     '_semversioner_update_graphrag_common_toml_version',
+    '_semversioner_update_graphrag_storage_toml_version',
     '_semversioner_update_workspace_dependency_versions',
     '_sync',
 ]
@@ -220,6 +223,7 @@ convention = "numpy"
 include = [
     "packages/graphrag/graphrag", 
     "packages/graphrag-common/graphrag_common", 
+    "packages/graphrag-storage/graphrag_storage", 
     "tests"
 ]
 exclude = ["**/node_modules", "**/__pycache__"]
diff --git a/tests/integration/cache/test_factory.py b/tests/integration/cache/test_factory.py
index 766adc1f8f..361d46a4ea 100644
--- a/tests/integration/cache/test_factory.py
+++ b/tests/integration/cache/test_factory.py
@@ -56,7 +56,7 @@ def test_create_blob_cache():
 def test_create_cosmosdb_cache():
     init_args = {
         "connection_string": WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        "base_dir": "testdatabase",
+        "database_name": "testdatabase",
         "container_name": "testcontainer",
     }
     cache = CacheFactory().create(
diff --git a/tests/integration/storage/test_blob_pipeline_storage.py b/tests/integration/storage/test_blob_storage.py
similarity index 82%
rename from tests/integration/storage/test_blob_pipeline_storage.py
rename to tests/integration/storage/test_blob_storage.py
index 818b588bd6..ec996e91a2 100644
--- a/tests/integration/storage/test_blob_pipeline_storage.py
+++ b/tests/integration/storage/test_blob_storage.py
@@ -5,14 +5,14 @@
 import re
 from datetime import datetime
 
-from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
+from graphrag_storage.azure_blob_storage import AzureBlobStorage
 
 # cspell:disable-next-line well-known-key
 WELL_KNOWN_BLOB_STORAGE_KEY = "DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1;"
 
 
 async def test_find():
-    storage = BlobPipelineStorage(
+    storage = AzureBlobStorage(
         connection_string=WELL_KNOWN_BLOB_STORAGE_KEY,
         container_name="testfind",
     )
@@ -41,25 +41,10 @@ async def test_find():
         storage._delete_container()  # noqa: SLF001
 
 
-async def test_dotprefix():
-    storage = BlobPipelineStorage(
-        connection_string=WELL_KNOWN_BLOB_STORAGE_KEY,
-        container_name="testfind",
-        path_prefix=".",
-    )
-    try:
-        await storage.set("input/christmas.txt", "Merry Christmas!", encoding="utf-8")
-        items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
-        assert items == ["input/christmas.txt"]
-    finally:
-        storage._delete_container()  # noqa: SLF001
-
-
 async def test_get_creation_date():
-    storage = BlobPipelineStorage(
+    storage = AzureBlobStorage(
         connection_string=WELL_KNOWN_BLOB_STORAGE_KEY,
         container_name="testfind",
-        path_prefix=".",
     )
     try:
         await storage.set("input/christmas.txt", "Merry Christmas!", encoding="utf-8")
@@ -74,7 +59,7 @@ async def test_get_creation_date():
 
 
 async def test_child():
-    parent = BlobPipelineStorage(
+    parent = AzureBlobStorage(
         connection_string=WELL_KNOWN_BLOB_STORAGE_KEY,
         container_name="testchild",
     )
diff --git a/tests/integration/storage/test_cosmosdb_storage.py b/tests/integration/storage/test_cosmosdb_storage.py
index 3d6128872f..a044cc754f 100644
--- a/tests/integration/storage/test_cosmosdb_storage.py
+++ b/tests/integration/storage/test_cosmosdb_storage.py
@@ -8,7 +8,7 @@
 from datetime import datetime
 
 import pytest
-from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
+from graphrag_storage.azure_cosmos_storage import AzureCosmosStorage
 
 # cspell:disable-next-line well-known-key
 WELL_KNOWN_COSMOS_CONNECTION_STRING = "AccountEndpoint=https://127.0.0.1:8081/;AccountKey=C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
@@ -21,9 +21,9 @@
 
 
 async def test_find():
-    storage = CosmosDBPipelineStorage(
+    storage = AzureCosmosStorage(
         connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        base_dir="testfind",
+        database_name="testfind",
         container_name="testfindcontainer",
     )
     try:
@@ -64,22 +64,22 @@ async def test_find():
 
 
 async def test_child():
-    storage = CosmosDBPipelineStorage(
+    storage = AzureCosmosStorage(
         connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        base_dir="testchild",
+        database_name="testchild",
         container_name="testchildcontainer",
     )
     try:
         child_storage = storage.child("child")
-        assert type(child_storage) is CosmosDBPipelineStorage
+        assert type(child_storage) is AzureCosmosStorage
     finally:
         await storage.clear()
 
 
 async def test_clear():
-    storage = CosmosDBPipelineStorage(
+    storage = AzureCosmosStorage(
         connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        base_dir="testclear",
+        database_name="testclear",
         container_name="testclearcontainer",
     )
     try:
@@ -107,9 +107,9 @@ async def test_clear():
 
 
 async def test_get_creation_date():
-    storage = CosmosDBPipelineStorage(
+    storage = AzureCosmosStorage(
         connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        base_dir="testclear",
+        database_name="testclear",
         container_name="testclearcontainer",
     )
     try:
diff --git a/tests/integration/storage/test_factory.py b/tests/integration/storage/test_factory.py
index 87a2960dbc..f4d3092e78 100644
--- a/tests/integration/storage/test_factory.py
+++ b/tests/integration/storage/test_factory.py
@@ -8,13 +8,17 @@
 import sys
 
 import pytest
-from graphrag.config.enums import StorageType
-from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
-from graphrag.storage.cosmosdb_pipeline_storage import CosmosDBPipelineStorage
-from graphrag.storage.factory import StorageFactory
-from graphrag.storage.file_pipeline_storage import FilePipelineStorage
-from graphrag.storage.memory_pipeline_storage import MemoryPipelineStorage
-from graphrag.storage.pipeline_storage import PipelineStorage
+from graphrag_storage import (
+    Storage,
+    StorageConfig,
+    StorageType,
+    create_storage,
+    register_storage,
+)
+from graphrag_storage.azure_blob_storage import AzureBlobStorage
+from graphrag_storage.azure_cosmos_storage import AzureCosmosStorage
+from graphrag_storage.file_storage import FileStorage
+from graphrag_storage.memory_storage import MemoryStorage
 
 # cspell:disable-next-line well-known-key
 WELL_KNOWN_BLOB_STORAGE_KEY = "DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1;"
@@ -24,14 +28,14 @@
 
 @pytest.mark.skip(reason="Blob storage emulator is not available in this environment")
 def test_create_blob_storage():
-    kwargs = {
-        "type": "blob",
-        "connection_string": WELL_KNOWN_BLOB_STORAGE_KEY,
-        "base_dir": "testbasedir",
-        "container_name": "testcontainer",
-    }
-    storage = StorageFactory().create(StorageType.blob.value, kwargs)
-    assert isinstance(storage, BlobPipelineStorage)
+    config = StorageConfig(
+        type=StorageType.AzureBlob,
+        connection_string=WELL_KNOWN_BLOB_STORAGE_KEY,
+        base_dir="testbasedir",
+        container_name="testcontainer",
+    )
+    storage = create_storage(config)
+    assert isinstance(storage, AzureBlobStorage)
 
 
 @pytest.mark.skipif(
@@ -39,63 +43,61 @@ def test_create_blob_storage():
     reason="cosmosdb emulator is only available on windows runners at this time",
 )
 def test_create_cosmosdb_storage():
-    kwargs = {
-        "type": "cosmosdb",
-        "connection_string": WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        "base_dir": "testdatabase",
-        "container_name": "testcontainer",
-    }
-    storage = StorageFactory().create(StorageType.cosmosdb.value, kwargs)
-    assert isinstance(storage, CosmosDBPipelineStorage)
+    config = StorageConfig(
+        type=StorageType.AzureCosmos,
+        connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
+        database_name="testdatabase",
+        container_name="testcontainer",
+    )
+    storage = create_storage(config)
+    assert isinstance(storage, AzureCosmosStorage)
 
 
 def test_create_file():
-    kwargs = {"type": "file", "base_dir": "/tmp/teststorage"}
-    storage = StorageFactory().create(StorageType.file.value, kwargs)
-    assert isinstance(storage, FilePipelineStorage)
+    config = StorageConfig(
+        type=StorageType.File,
+        base_dir="/tmp/teststorage",
+    )
+    storage = create_storage(config)
+    assert isinstance(storage, FileStorage)
 
 
 def test_create_memory_storage():
-    kwargs = {}  # MemoryPipelineStorage doesn't accept any constructor parameters
-    storage = StorageFactory().create(StorageType.memory.value, kwargs)
-    assert isinstance(storage, MemoryPipelineStorage)
+    config = StorageConfig(
+        base_dir="",
+        type=StorageType.Memory,
+    )
+    storage = create_storage(config)
+    assert isinstance(storage, MemoryStorage)
 
 
 def test_register_and_create_custom_storage():
     """Test registering and creating a custom storage type."""
     from unittest.mock import MagicMock
 
-    # Create a mock that satisfies the PipelineStorage interface
-    custom_storage_class = MagicMock(spec=PipelineStorage)
+    # Create a mock that satisfies the Storage interface
+    custom_storage_class = MagicMock(spec=Storage)
     # Make the mock return a mock instance when instantiated
     instance = MagicMock()
-    # We can set attributes on the mock instance, even if they don't exist on PipelineStorage
+    # We can set attributes on the mock instance, even if they don't exist on Storage
     instance.initialized = True
     custom_storage_class.return_value = instance
 
-    StorageFactory().register("custom", lambda **kwargs: custom_storage_class(**kwargs))
-    storage = StorageFactory().create("custom", {})
+    register_storage("custom", lambda **kwargs: custom_storage_class(**kwargs))
+    storage = create_storage(StorageConfig(type="custom"))
 
     assert custom_storage_class.called
     assert storage is instance
     # Access the attribute we set on our mock
     assert storage.initialized is True  # type: ignore # Attribute only exists on our mock
 
-    # Check if it's in the list of registered storage types
-    assert "custom" in StorageFactory()
-
-
-def test_get_storage_types():
-    # Check that built-in types are registered
-    assert StorageType.file.value in StorageFactory()
-    assert StorageType.memory.value in StorageFactory()
-    assert StorageType.blob.value in StorageFactory()
-    assert StorageType.cosmosdb.value in StorageFactory()
-
 
 def test_create_unknown_storage():
-    with pytest.raises(ValueError, match="Strategy 'unknown' is not registered\\."):
-        StorageFactory().create("unknown")
+    with pytest.raises(
+        ValueError,
+        match="StorageConfig\\.type 'unknown' is not registered in the StorageFactory\\.",
+    ):
+        create_storage(StorageConfig(type="unknown"))
 
 
 def test_register_class_directly_works():
@@ -104,9 +106,7 @@ def test_register_class_directly_works():
     from collections.abc import Iterator
     from typing import Any
 
-    from graphrag.storage.pipeline_storage import PipelineStorage
-
-    class CustomStorage(PipelineStorage):
+    class CustomStorage(Storage):
         def __init__(self, **kwargs):
             pass
 
@@ -133,7 +133,7 @@ async def has(self, key: str) -> bool:
         async def clear(self) -> None:
             pass
 
-        def child(self, name: str | None) -> "PipelineStorage":
+        def child(self, name: str | None) -> "Storage":
             return self
 
         def keys(self) -> list[str]:
@@ -143,11 +143,8 @@ async def get_creation_date(self, key: str) -> str:
             return "2024-01-01 00:00:00 +0000"
 
     # StorageFactory allows registering classes directly (no TypeError)
-    StorageFactory().register("custom_class", CustomStorage)
-
-    # Verify it was registered
-    assert "custom_class" in StorageFactory()
+    register_storage("custom_class", CustomStorage)
 
     # Test creating an instance
-    storage = StorageFactory().create("custom_class")
+    storage = create_storage(StorageConfig(type="custom_class"))
     assert isinstance(storage, CustomStorage)
diff --git a/tests/integration/storage/test_file_pipeline_storage.py b/tests/integration/storage/test_file_storage.py
similarity index 86%
rename from tests/integration/storage/test_file_pipeline_storage.py
rename to tests/integration/storage/test_file_storage.py
index 95e329b6bf..0852cd99fb 100644
--- a/tests/integration/storage/test_file_pipeline_storage.py
+++ b/tests/integration/storage/test_file_storage.py
@@ -7,17 +7,15 @@
 from datetime import datetime
 from pathlib import Path
 
-from graphrag.storage.file_pipeline_storage import (
-    FilePipelineStorage,
+from graphrag_storage.file_storage import (
+    FileStorage,
 )
 
 __dirname__ = os.path.dirname(__file__)
 
 
 async def test_find():
-    storage = FilePipelineStorage(
-        base_dir="tests/fixtures/text/input",
-    )
+    storage = FileStorage(base_dir="tests/fixtures/text/input")
     items = list(storage.find(file_pattern=re.compile(r".*\.txt$")))
     assert items == [str(Path("dulce.txt"))]
     output = await storage.get("dulce.txt")
@@ -32,7 +30,7 @@ async def test_find():
 
 
 async def test_get_creation_date():
-    storage = FilePipelineStorage(
+    storage = FileStorage(
         base_dir="tests/fixtures/text/input",
     )
 
@@ -45,7 +43,7 @@ async def test_get_creation_date():
 
 
 async def test_child():
-    storage = FilePipelineStorage()
+    storage = FileStorage(base_dir="")
     storage = storage.child("tests/fixtures/text/input")
     items = list(storage.find(re.compile(r".*\.txt$")))
     assert items == [str(Path("dulce.txt"))]
diff --git a/tests/smoke/test_fixtures.py b/tests/smoke/test_fixtures.py
index 9821bed551..53205c7c09 100644
--- a/tests/smoke/test_fixtures.py
+++ b/tests/smoke/test_fixtures.py
@@ -17,7 +17,7 @@
 from graphrag.query.context_builder.community_context import (
     NO_COMMUNITY_RECORDS_WARNING,
 )
-from graphrag.storage.blob_pipeline_storage import BlobPipelineStorage
+from graphrag_storage.azure_blob_storage import AzureBlobStorage
 
 logger = logging.getLogger(__name__)
 
@@ -94,7 +94,7 @@ async def prepare_azurite_data(input_path: str, azure: dict) -> Callable[[], Non
     input_base_dir = azure.get("input_base_dir")
 
     root = Path(input_path)
-    input_storage = BlobPipelineStorage(
+    input_storage = AzureBlobStorage(
         connection_string=WELL_KNOWN_AZURITE_CONNECTION_STRING,
         container_name=input_container,
     )
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 001518f62a..83e18ad546 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -25,11 +25,11 @@
 from graphrag.config.models.prune_graph_config import PruneGraphConfig
 from graphrag.config.models.reporting_config import ReportingConfig
 from graphrag.config.models.snapshots_config import SnapshotsConfig
-from graphrag.config.models.storage_config import StorageConfig
 from graphrag.config.models.summarize_descriptions_config import (
     SummarizeDescriptionsConfig,
 )
 from graphrag.config.models.vector_store_config import VectorStoreConfig
+from graphrag_storage import StorageConfig
 from pydantic import BaseModel
 
 FAKE_API_KEY = "NOT_AN_API_KEY"
@@ -126,24 +126,14 @@ def assert_reporting_configs(
     assert actual.storage_account_blob_url == expected.storage_account_blob_url
 
 
-def assert_output_configs(actual: StorageConfig, expected: StorageConfig) -> None:
+def assert_storage_config(actual: StorageConfig, expected: StorageConfig) -> None:
     assert expected.type == actual.type
     assert expected.base_dir == actual.base_dir
     assert expected.connection_string == actual.connection_string
     assert expected.container_name == actual.container_name
-    assert expected.storage_account_blob_url == actual.storage_account_blob_url
-    assert expected.cosmosdb_account_url == actual.cosmosdb_account_url
-
-
-def assert_update_output_configs(
-    actual: StorageConfig, expected: StorageConfig
-) -> None:
-    assert expected.type == actual.type
-    assert expected.base_dir == actual.base_dir
-    assert expected.connection_string == actual.connection_string
-    assert expected.container_name == actual.container_name
-    assert expected.storage_account_blob_url == actual.storage_account_blob_url
-    assert expected.cosmosdb_account_url == actual.cosmosdb_account_url
+    assert expected.account_url == actual.account_url
+    assert expected.encoding == actual.encoding
+    assert expected.database_name == actual.database_name
 
 
 def assert_cache_configs(actual: CacheConfig, expected: CacheConfig) -> None:
@@ -156,15 +146,8 @@ def assert_cache_configs(actual: CacheConfig, expected: CacheConfig) -> None:
 
 
 def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None:
-    assert actual.storage.type == expected.storage.type
+    assert_storage_config(actual.storage, expected.storage)
     assert actual.file_type == expected.file_type
-    assert actual.storage.base_dir == expected.storage.base_dir
-    assert actual.storage.connection_string == expected.storage.connection_string
-    assert (
-        actual.storage.storage_account_blob_url
-        == expected.storage.storage_account_blob_url
-    )
-    assert actual.storage.container_name == expected.storage.container_name
     assert actual.encoding == expected.encoding
     assert actual.file_pattern == expected.file_pattern
     assert actual.text_column == expected.text_column
@@ -358,11 +341,9 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) ->
 
     assert_vector_store_configs(actual.vector_store, expected.vector_store)
     assert_reporting_configs(actual.reporting, expected.reporting)
-    assert_output_configs(actual.output, expected.output)
+    assert_storage_config(actual.output, expected.output)
 
-    assert_update_output_configs(
-        actual.update_index_output, expected.update_index_output
-    )
+    assert_storage_config(actual.update_index_output, expected.update_index_output)
 
     assert_cache_configs(actual.cache, expected.cache)
     assert_input_configs(actual.input, expected.input)
diff --git a/tests/unit/indexing/cache/test_file_pipeline_cache.py b/tests/unit/indexing/cache/test_file_pipeline_cache.py
index c392b4e08e..c672d3718f 100644
--- a/tests/unit/indexing/cache/test_file_pipeline_cache.py
+++ b/tests/unit/indexing/cache/test_file_pipeline_cache.py
@@ -5,15 +5,15 @@
 import unittest
 
 from graphrag.cache.json_pipeline_cache import JsonPipelineCache
-from graphrag.storage.file_pipeline_storage import (
-    FilePipelineStorage,
+from graphrag_storage.file_storage import (
+    FileStorage,
 )
 
 TEMP_DIR = "./.tmp"
 
 
 def create_cache():
-    storage = FilePipelineStorage(base_dir=os.path.join(os.getcwd(), ".tmp"))
+    storage = FileStorage(base_dir=os.path.join(os.getcwd(), ".tmp"))
     return JsonPipelineCache(storage)
 
 
diff --git a/tests/unit/indexing/input/test_csv_loader.py b/tests/unit/indexing/input/test_csv_loader.py
index 8a6b0e351d..b0dc645e1b 100644
--- a/tests/unit/indexing/input/test_csv_loader.py
+++ b/tests/unit/indexing/input/test_csv_loader.py
@@ -3,9 +3,8 @@
 
 from graphrag.config.enums import InputFileType
 from graphrag.config.models.input_config import InputConfig
-from graphrag.config.models.storage_config import StorageConfig
 from graphrag.index.input.factory import InputReaderFactory
-from graphrag.utils.api import create_storage_from_config
+from graphrag_storage import StorageConfig, create_storage
 
 
 async def test_csv_loader_one_file():
@@ -16,7 +15,7 @@ async def test_csv_loader_one_file():
         file_type=InputFileType.csv,
         file_pattern=".*\\.csv$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -35,7 +34,7 @@ async def test_csv_loader_one_file_with_title():
         file_pattern=".*\\.csv$",
         title_column="title",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -55,7 +54,7 @@ async def test_csv_loader_one_file_with_metadata():
         title_column="title",
         metadata=["title"],
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -74,7 +73,7 @@ async def test_csv_loader_multiple_files():
         file_type=InputFileType.csv,
         file_pattern=".*\\.csv$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
diff --git a/tests/unit/indexing/input/test_json_loader.py b/tests/unit/indexing/input/test_json_loader.py
index 1ce7001aab..3959096b1c 100644
--- a/tests/unit/indexing/input/test_json_loader.py
+++ b/tests/unit/indexing/input/test_json_loader.py
@@ -3,9 +3,8 @@
 
 from graphrag.config.enums import InputFileType
 from graphrag.config.models.input_config import InputConfig
-from graphrag.config.models.storage_config import StorageConfig
 from graphrag.index.input.factory import InputReaderFactory
-from graphrag.utils.api import create_storage_from_config
+from graphrag_storage import StorageConfig, create_storage
 
 
 async def test_json_loader_one_file_one_object():
@@ -16,7 +15,7 @@ async def test_json_loader_one_file_one_object():
         file_type=InputFileType.json,
         file_pattern=".*\\.json$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -34,7 +33,7 @@ async def test_json_loader_one_file_multiple_objects():
         file_type=InputFileType.json,
         file_pattern=".*\\.json$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -54,7 +53,7 @@ async def test_json_loader_one_file_with_title():
         file_pattern=".*\\.json$",
         title_column="title",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -74,7 +73,7 @@ async def test_json_loader_one_file_with_metadata():
         title_column="title",
         metadata=["title"],
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -92,7 +91,7 @@ async def test_json_loader_multiple_files():
         file_type=InputFileType.json,
         file_pattern=".*\\.json$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
diff --git a/tests/unit/indexing/input/test_txt_loader.py b/tests/unit/indexing/input/test_txt_loader.py
index 239f622d72..57ded82507 100644
--- a/tests/unit/indexing/input/test_txt_loader.py
+++ b/tests/unit/indexing/input/test_txt_loader.py
@@ -3,9 +3,8 @@
 
 from graphrag.config.enums import InputFileType
 from graphrag.config.models.input_config import InputConfig
-from graphrag.config.models.storage_config import StorageConfig
 from graphrag.index.input.factory import InputReaderFactory
-from graphrag.utils.api import create_storage_from_config
+from graphrag_storage import StorageConfig, create_storage
 
 
 async def test_txt_loader_one_file():
@@ -16,7 +15,7 @@ async def test_txt_loader_one_file():
         file_type=InputFileType.text,
         file_pattern=".*\\.txt$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -35,7 +34,7 @@ async def test_txt_loader_one_file_with_metadata():
         file_pattern=".*\\.txt$",
         metadata=["title"],
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
@@ -54,7 +53,7 @@ async def test_txt_loader_multiple_files():
         file_type=InputFileType.text,
         file_pattern=".*\\.txt$",
     )
-    storage = create_storage_from_config(config.storage)
+    storage = create_storage(config.storage)
     documents = (
         await InputReaderFactory()
         .create(config.file_type, {"storage": storage, "config": config})
diff --git a/tests/unit/load_config/fixtures/config.yaml b/tests/unit/load_config/fixtures/config.yaml
new file mode 100644
index 0000000000..a54919d1eb
--- /dev/null
+++ b/tests/unit/load_config/fixtures/config.yaml
@@ -0,0 +1,10 @@
+name: test_name
+value: 100
+nested:
+  nested_str: nested_value
+  nested_int: 42
+nested_list:
+  - nested_str: list_value_1
+    nested_int: 7
+  - nested_str: list_value_2
+    nested_int: 8
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 6ef295df9d..8ec2e3830a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -12,6 +12,7 @@ members = [
     "graphrag",
     "graphrag-common",
     "graphrag-monorepo",
+    "graphrag-storage",
 ]
 
 [[package]]
@@ -1042,6 +1043,7 @@ dependencies = [
     { name = "devtools" },
     { name = "environs" },
     { name = "graphrag-common" },
+    { name = "graphrag-storage" },
     { name = "graspologic-native" },
     { name = "json-repair" },
     { name = "lancedb" },
@@ -1073,6 +1075,7 @@ requires-dist = [
     { name = "devtools", specifier = ">=0.12.2" },
     { name = "environs", specifier = ">=11.0.0" },
     { name = "graphrag-common", editable = "packages/graphrag-common" },
+    { name = "graphrag-storage", editable = "packages/graphrag-storage" },
     { name = "graspologic-native", specifier = ">=1.2.5" },
     { name = "json-repair", specifier = ">=0.30.3" },
     { name = "lancedb", specifier = ">=0.17.0" },
@@ -1160,6 +1163,31 @@ dev = [
     { name = "update-toml", specifier = ">=0.2.1" },
 ]
 
+[[package]]
+name = "graphrag-storage"
+version = "2.7.0"
+source = { editable = "packages/graphrag-storage" }
+dependencies = [
+    { name = "aiofiles" },
+    { name = "azure-cosmos" },
+    { name = "azure-identity" },
+    { name = "azure-storage-blob" },
+    { name = "graphrag-common" },
+    { name = "pandas" },
+    { name = "pydantic" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "aiofiles", specifier = ">=24.1.0" },
+    { name = "azure-cosmos", specifier = ">=4.9.0" },
+    { name = "azure-identity", specifier = ">=1.19.0" },
+    { name = "azure-storage-blob", specifier = ">=12.24.0" },
+    { name = "graphrag-common", editable = "packages/graphrag-common" },
+    { name = "pandas", specifier = ">=2.2.3" },
+    { name = "pydantic", specifier = ">=2.10.3" },
+]
+
 [[package]]
 name = "graspologic-native"
 version = "1.2.5"

From bffa400c890560ab05e59ffc74214cbf0c595a9b Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Mon, 15 Dec 2025 15:39:38 -0800
Subject: [PATCH 30/40] Python update (3.13) (#2149)

* Update to python 3.14 as default, with range down to 3.10

* Fix enum value in query cli

* Update pyarrow

* Update py version for storage package

* Remove 3.10

* add fastuuid

* Update Python support to 3.11-3.14 with stricter dependency constraints

- Set minimum Python version to 3.11 (removed 3.10 support)
- Added support for Python 3.14
- Updated CI workflows: single-version jobs use 3.14, matrix jobs use 3.11 and 3.14
- Fixed license format to use SPDX-compatible format for Python 3.14
- Updated pyarrow to >=22.0.0 for Python 3.14 wheel support
- Added explicit fastuuid~=0.14 and blis~=1.3 for Python 3.14 compatibility
- Replaced all loose version constraints (>=) with compatible release (~=) for better lock file control
- Applied stricter versioning to all packages: graphrag, graphrag-common, graphrag-storage, unified-search-app

* update uv lock

* Pin blis to ~=1.3.3 to ensure Python 3.14 wheel availability

* Update uv lock

* Update numpy to >=2.0.0 for Python 3.14 Windows compatibility

Numpy 1.25.x has access violation issues on Python 3.14 Windows.
Numpy 2.x has proper Python 3.14 support including Windows wheels.

* update uv lock

* Update pandas to >=2.3.0 for numpy 2.x compatibility

Pandas 2.2.x was compiled against numpy 1.x and causes ABI
incompatibility errors with numpy 2.x. Pandas 2.3.0+ supports
numpy 2.x properly.

* update uv.lock

* Add scipy>=1.15.0 for numpy 2.x compatibility

Scipy versions < 1.15.0 have C extensions built against numpy 1.x
and are incompatible with numpy 2.x, causing dtype size errors.

* update uv lock

* Update Python support to 3.11-3.13 with compatible dependencies

- Set Python version range to 3.11-3.13 (removed 3.14 support)
- Updated CI workflows: single-version jobs use 3.13, matrix jobs use 3.11 and 3.13
- Dependencies optimized for Python 3.13 compatibility:
  - pyarrow~=22.0 (has Python 3.13 wheels)
  - numpy~=1.26
  - pandas~=2.2
  - blis~=1.0
  - fastuuid~=0.13
- Applied stricter version constraints using ~= operator throughout
- Updated uv.lock with resolved dependencies

* Update numpy to 2.1+ and pandas to 2.3+ for Python 3.13 Windows compatibility

Numpy 1.26.x causes access violations on Python 3.13 Windows.
Numpy 2.1+ has proper Python 3.13 support with Windows wheels.
Pandas 2.3+ is required for numpy 2.x compatibility.

* update vsts.yml python version
---
 .github/workflows/gh-pages.yml                |    2 +-
 .github/workflows/python-checks.yml           |    2 +-
 .../workflows/python-integration-tests.yml    |    2 +-
 .github/workflows/python-notebook-tests.yml   |    2 +-
 .github/workflows/python-publish.yml          |    2 +-
 .github/workflows/python-smoke-tests.yml      |    2 +-
 .github/workflows/python-unit-tests.yml       |    2 +-
 .vsts-ci.yml                                  |    2 +-
 packages/graphrag-common/pyproject.toml       |   11 +-
 packages/graphrag-storage/pyproject.toml      |   19 +-
 packages/graphrag/graphrag/cli/main.py        |    2 +-
 packages/graphrag/pyproject.toml              |   55 +-
 pyproject.toml                                |   40 +-
 unified-search-app/pyproject.toml             |   24 +-
 uv.lock                                       | 3843 ++++++++---------
 15 files changed, 1938 insertions(+), 2072 deletions(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index 35980e3787..e6c38fa4fa 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -6,7 +6,7 @@ permissions:
   contents: write
 
 env:
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.13"
 
 jobs:
   build:
diff --git a/.github/workflows/python-checks.yml b/.github/workflows/python-checks.yml
index fdc952102f..ae11854e33 100644
--- a/.github/workflows/python-checks.yml
+++ b/.github/workflows/python-checks.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.11", "3.12"]
+        python-version: ["3.11", "3.13"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
diff --git a/.github/workflows/python-integration-tests.yml b/.github/workflows/python-integration-tests.yml
index ecfac41b7b..c1d112ee86 100644
--- a/.github/workflows/python-integration-tests.yml
+++ b/.github/workflows/python-integration-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.12"]
+        python-version: ["3.13"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # continue running all jobs even if one fails
     env:
diff --git a/.github/workflows/python-notebook-tests.yml b/.github/workflows/python-notebook-tests.yml
index 2053a8f19d..bae0f715b4 100644
--- a/.github/workflows/python-notebook-tests.yml
+++ b/.github/workflows/python-notebook-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.12"]
+        python-version: ["3.13"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
index a81a5d7747..fe38b83e59 100644
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -6,7 +6,7 @@ on:
     branches: [main]
 
 env:
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.13"
 
 jobs:
   publish:
diff --git a/.github/workflows/python-smoke-tests.yml b/.github/workflows/python-smoke-tests.yml
index 3d51439388..e055c66b67 100644
--- a/.github/workflows/python-smoke-tests.yml
+++ b/.github/workflows/python-smoke-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.12"]
+        python-version: ["3.13"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
diff --git a/.github/workflows/python-unit-tests.yml b/.github/workflows/python-unit-tests.yml
index 7cf80f9481..b2577bae0c 100644
--- a/.github/workflows/python-unit-tests.yml
+++ b/.github/workflows/python-unit-tests.yml
@@ -32,7 +32,7 @@ jobs:
     if: github.event.pull_request.draft == false
     strategy:
       matrix:
-        python-version: ["3.12"]
+        python-version: ["3.13"]
         os: [ubuntu-latest, windows-latest]
       fail-fast: false # Continue running all jobs even if one fails
     env:
diff --git a/.vsts-ci.yml b/.vsts-ci.yml
index 2a1e0964ce..5299ff91e3 100644
--- a/.vsts-ci.yml
+++ b/.vsts-ci.yml
@@ -10,7 +10,7 @@ trigger:
 
 variables:
   isMain: $[eq(variables['Build.SourceBranch'], 'refs/heads/main')]
-  pythonVersion: "3.10"
+  pythonVersion: "3.13"
   poetryVersion: "1.6.1"
   nodeVersion: "18.x"
   artifactsFullFeedName: "Resilience/resilience_python"
diff --git a/packages/graphrag-common/pyproject.toml b/packages/graphrag-common/pyproject.toml
index b2bcb952e3..14dbb34bfa 100644
--- a/packages/graphrag-common/pyproject.toml
+++ b/packages/graphrag-common/pyproject.toml
@@ -20,19 +20,18 @@ authors = [
     {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
     {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
 ]
-license = "MIT"
+license = {text = "MIT"}
 readme = "README.md"
-license-files = ["LICENSE"]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
 classifiers = [
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
-    "python-dotenv>=1.0.1",
-    "pyyaml>=6.0.2",
+    "python-dotenv~=1.0",
+    "pyyaml~=6.0",
 ]
 
 [project.urls]
diff --git a/packages/graphrag-storage/pyproject.toml b/packages/graphrag-storage/pyproject.toml
index 464189f950..c3c44b3a1e 100644
--- a/packages/graphrag-storage/pyproject.toml
+++ b/packages/graphrag-storage/pyproject.toml
@@ -20,24 +20,23 @@ authors = [
     {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
     {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
 ]
-license = "MIT"
+license = {text = "MIT"}
 readme = "README.md"
-license-files = ["LICENSE"]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
 classifiers = [
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
-    "aiofiles>=24.1.0",
-    "azure-cosmos>=4.9.0",
-    "azure-identity>=1.19.0",
-    "azure-storage-blob>=12.24.0",
+    "aiofiles~=24.1",
+    "azure-cosmos~=4.9",
+    "azure-identity~=1.19",
+    "azure-storage-blob~=12.24",
     "graphrag-common==2.7.0",
-    "pandas>=2.2.3",
-    "pydantic>=2.10.3",
+    "pandas>=2.3.0",
+    "pydantic~=2.10",
 ]
 
 [project.urls]
diff --git a/packages/graphrag/graphrag/cli/main.py b/packages/graphrag/graphrag/cli/main.py
index e62de47554..5ede2faa6a 100644
--- a/packages/graphrag/graphrag/cli/main.py
+++ b/packages/graphrag/graphrag/cli/main.py
@@ -379,7 +379,7 @@ def _query_cli(
         autocompletion=ROOT_AUTOCOMPLETE,
     ),
     method: SearchMethod = typer.Option(
-        SearchMethod.GLOBAL,
+        SearchMethod.GLOBAL.value,
         "--method",
         "-m",
         help="The query algorithm to use.",
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index 7b7eec259d..afec601e7f 100644
--- a/packages/graphrag/pyproject.toml
+++ b/packages/graphrag/pyproject.toml
@@ -21,44 +21,45 @@ authors = [
     {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
     {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
 ]
-license = "MIT"
+license = {text = "MIT"}
 readme = "README.md"
-license-files = ["LICENSE"]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
 classifiers = [
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 
 dependencies = [
-    "aiofiles>=24.1.0",
-    "azure-cosmos>=4.9.0",
-    "azure-identity>=1.19.0",
-    "azure-search-documents>=11.5.2",
-    "azure-storage-blob>=12.24.0",
-    "devtools>=0.12.2",
-    "environs>=11.0.0",
+    "aiofiles~=24.1",
+    "azure-cosmos~=4.9",
+    "azure-identity~=1.19",
+    "azure-search-documents~=11.5",
+    "azure-storage-blob~=12.24",
+    "devtools~=0.12",
+    "environs~=11.0",
     "graphrag-common==2.7.0",
     "graphrag-storage==2.7.0",
-    "graspologic-native>=1.2.5",
-    "json-repair>=0.30.3",
-    "lancedb>=0.17.0",
-    "litellm>=1.77.1",
-    "networkx>=3.4.2",
+    "graspologic-native~=1.2",
+    "json-repair~=0.30",
+    "lancedb~=0.24.1",
+    "litellm~=1.77",
+    "fastuuid~=0.13",
+    "networkx~=3.4",
     "nltk==3.9.1",
-    "numpy>=1.25.2",
-    "openai>=1.68.0",
-    "pandas>=2.2.3",
-    "pyarrow>=17.0.0",
-    "pydantic>=2.10.3",
-    "spacy>=3.8.4",
-    "textblob>=0.18.0.post0",
-    "tiktoken>=0.11.0",
-    "tqdm>=4.67.1",
-    "typing-extensions>=4.12.2",
-    "typer>=0.16.0",
+    "numpy>=2.1.0",
+    "openai~=1.68",
+    "pandas>=2.3.0",
+    "pyarrow~=22.0",
+    "pydantic~=2.10",
+    "spacy~=3.8",
+    "blis~=1.0",
+    "textblob~=0.18",
+    "tiktoken~=0.11",
+    "tqdm~=4.67",
+    "typing-extensions~=4.12",
+    "typer~=0.16",
 ]
 
 [project.scripts]
diff --git a/pyproject.toml b/pyproject.toml
index 1979df5cb7..078b29384f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,29 +21,29 @@ authors = [
     {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
     {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
 ]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
 
 [dependency-groups]
 dev = [
-    "coverage>=7.6.9",
-    "deptry>=0.21.1",
-    "ipykernel>=6.29.5",
-    "jupyter>=1.1.1",
-    "mkdocs-material>=9.5.48",
-    "mkdocs-jupyter>=0.25.1",
-    "mkdocs-exclude-search>=0.6.6",
-    "mkdocs-typer>=0.0.3",
-    "nbconvert>=7.16.4",
-    "pandas-stubs>=2.3.0.250703",
-    "poethepoet>=0.31.1",
-    "pyright>=1.1.390",
-    "pytest>=8.3.4",
-    "pytest-asyncio>=0.24.0",
-    "pytest-dotenv>=0.5.2",
-    "pytest-timeout>=2.3.1",
-    "ruff>=0.8.2",
-    "semversioner>=2.0.5",
-    "update-toml>=0.2.1",
+    "coverage~=7.6",
+    "deptry~=0.21",
+    "ipykernel~=6.29",
+    "jupyter~=1.1",
+    "mkdocs-material~=9.5",
+    "mkdocs-jupyter~=0.25",
+    "mkdocs-exclude-search~=0.6",
+    "mkdocs-typer~=0.0.3",
+    "nbconvert~=7.16",
+    "pandas-stubs~=2.3",
+    "poethepoet~=0.31",
+    "pyright~=1.1",
+    "pytest~=8.3",
+    "pytest-asyncio~=0.24",
+    "pytest-dotenv~=0.5",
+    "pytest-timeout~=2.3",
+    "ruff~=0.8",
+    "semversioner~=2.0",
+    "update-toml~=0.2",
 ]
 
 [tool.uv]
diff --git a/unified-search-app/pyproject.toml b/unified-search-app/pyproject.toml
index 798522a93d..2b55568f63 100644
--- a/unified-search-app/pyproject.toml
+++ b/unified-search-app/pyproject.toml
@@ -6,26 +6,26 @@ authors = [
     {name = "GraphRAG team"},
 ]
 readme = "README.md"
-requires-python = ">=3.10,<3.12"
+requires-python = ">=3.11,<3.14"
 
 dependencies = [
     "streamlit==1.43.0",
-    "azure-search-documents>=11.4.0",
-    "azure-storage-blob>=12.20.0",
-    "azure-identity>=1.16.0",
+    "azure-search-documents~=11.4",
+    "azure-storage-blob~=12.20",
+    "azure-identity~=1.16",
     "graphrag==2.5.0",
-    "altair>=5.3.0",
-    "streamlit-agraph>=0.0.45",
-    "st-tabs>=0.1.1",
-    "spacy>=3.8.4,<4.0.0",
+    "altair~=5.3",
+    "streamlit-agraph~=0.0.45",
+    "st-tabs~=0.1",
+    "spacy~=3.8",
 ]
 
 [project.optional-dependencies]
 dev = [
-    "poethepoet>=0.26.1",
-    "ipykernel>=6.29.4",
-    "pyright>=1.1.349",
-    "ruff>=0.4.7",
+    "poethepoet~=0.26",
+    "ipykernel~=6.29",
+    "pyright~=1.1",
+    "ruff~=0.8",
 ]
 
 [build-system]
diff --git a/uv.lock b/uv.lock
index 8ec2e3830a..cb0ecaf6e2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,10 +1,9 @@
 version = 1
-revision = 3
-requires-python = ">=3.10, <3.13"
+revision = 1
+requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11'",
+    "python_full_version < '3.12'",
 ]
 
 [manifest]
@@ -17,20 +16,20 @@ members = [
 
 [[package]]
 name = "aiofiles"
-version = "25.1.0"
+version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
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From 3201f28bea9a25a255eaeb4fb793be6b4b3f5fe7 Mon Sep 17 00:00:00 2001
From: Derek Worthen <worthend.derek@gmail.com>
Date: Tue, 16 Dec 2025 06:37:28 -0800
Subject: [PATCH 31/40] Add GraphRAG Cache package. (#2153)

* Add GraphRAG Cache package.
---
 docs/config/yaml.md                           |   14 +-
 packages/graphrag-cache/README.md             |   92 +
 .../graphrag-cache/graphrag_cache/__init__.py |   17 +
 .../graphrag_cache/cache.py}                  |   12 +-
 .../graphrag_cache/cache_config.py            |   26 +
 .../graphrag_cache/cache_factory.py           |   83 +
 .../graphrag_cache/cache_type.py              |   15 +
 .../graphrag_cache/json_cache.py}             |   34 +-
 .../graphrag_cache/memory_cache.py}           |   21 +-
 .../graphrag_cache/noop_cache.py}             |   13 +-
 .../graphrag-cache/graphrag_cache/py.typed    |    0
 packages/graphrag-cache/pyproject.toml        |   43 +
 packages/graphrag/graphrag/cache/__init__.py  |    4 -
 packages/graphrag/graphrag/cache/factory.py   |   68 -
 packages/graphrag/graphrag/config/defaults.py |   30 +-
 .../graphrag/graphrag/config/init_content.py  |   12 +-
 .../graphrag/config/models/cache_config.py    |   38 -
 .../config/models/graph_rag_config.py         |    6 +-
 .../build_noun_graph/build_noun_graph.py      |    6 +-
 .../operations/extract_covariates/typing.py   |    5 +-
 .../graphrag/index/run/run_pipeline.py        |    4 +-
 packages/graphrag/graphrag/index/run/utils.py |    8 +-
 .../graphrag/graphrag/index/typing/context.py |    4 +-
 .../index/workflows/extract_graph_nlp.py      |    4 +-
 .../update_entities_relationships.py          |    4 +-
 .../providers/litellm/chat_model.py           |    7 +-
 .../providers/litellm/embedding_model.py      |    7 +-
 .../litellm/request_wrappers/with_cache.py    |    5 +-
 .../graphrag/prompt_tune/loader/input.py      |    4 +-
 packages/graphrag/graphrag/utils/api.py       |   12 -
 packages/graphrag/pyproject.toml              |    1 +
 pyproject.toml                                |    3 +
 tests/integration/cache/test_factory.py       |  141 +-
 tests/unit/config/utils.py                    |    9 +-
 .../cache/test_file_pipeline_cache.py         |   18 +-
 uv.lock                                       | 3048 +++++++----------
 36 files changed, 1763 insertions(+), 2055 deletions(-)
 create mode 100644 packages/graphrag-cache/README.md
 create mode 100644 packages/graphrag-cache/graphrag_cache/__init__.py
 rename packages/{graphrag/graphrag/cache/pipeline_cache.py => graphrag-cache/graphrag_cache/cache.py} (86%)
 create mode 100644 packages/graphrag-cache/graphrag_cache/cache_config.py
 create mode 100644 packages/graphrag-cache/graphrag_cache/cache_factory.py
 create mode 100644 packages/graphrag-cache/graphrag_cache/cache_type.py
 rename packages/{graphrag/graphrag/cache/json_pipeline_cache.py => graphrag-cache/graphrag_cache/json_cache.py} (61%)
 rename packages/{graphrag/graphrag/cache/memory_pipeline_cache.py => graphrag-cache/graphrag_cache/memory_cache.py} (71%)
 rename packages/{graphrag/graphrag/cache/noop_pipeline_cache.py => graphrag-cache/graphrag_cache/noop_cache.py} (82%)
 create mode 100644 packages/graphrag-cache/graphrag_cache/py.typed
 create mode 100644 packages/graphrag-cache/pyproject.toml
 delete mode 100644 packages/graphrag/graphrag/cache/__init__.py
 delete mode 100644 packages/graphrag/graphrag/cache/factory.py
 delete mode 100644 packages/graphrag/graphrag/config/models/cache_config.py

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index c82ff5bb12..768abef463 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -141,12 +141,14 @@ This section controls the cache mechanism used by the pipeline. This is used to
 
 #### Fields
 
-- `type` **file|memory|blob|cosmosdb** - The storage type to use. Default=`file`
-- `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
-- `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
-- `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
-- `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
-- `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
+- `type` **json|memory|none** - The storage type to use. Default=`json`
+- `storage` **StorageConfig**
+  - `type` **file|memory|blob|cosmosdb** - The storage type to use. Default=`file`
+  - `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
+  - `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
+  - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
+  - `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
+  - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
 
 ### reporting
 
diff --git a/packages/graphrag-cache/README.md b/packages/graphrag-cache/README.md
new file mode 100644
index 0000000000..9924ce09a9
--- /dev/null
+++ b/packages/graphrag-cache/README.md
@@ -0,0 +1,92 @@
+# GraphRAG Cache
+
+## Basic
+
+```python
+import asyncio
+from graphrag_storage import StorageConfig, create_storage, StorageType
+from graphrag_cache import CacheConfig, create_cache, CacheType
+
+async def run():
+    cache = create_cache(
+        CacheConfig(
+            type=CacheType.Json
+            storage=StorageConfig(
+                type=StorageType.File,
+                base_dir="cache"
+            )
+        ),
+    )
+
+    await cache.set("my_key", {"some": "object to cache"})
+    print(await cache.get("my_key"))
+
+if __name__ == "__main__":
+    asyncio.run(run())
+```
+
+## Custom Cache
+
+```python
+import asyncio
+from typing import Any
+from graphrag_storage import Storage
+from graphrag_cache import Cache, CacheConfig, create_cache, register_cache
+
+class MyCache(Cache):
+    def __init__(self, some_setting: str, optional_setting: str = "default setting", **kwargs: Any):
+        # Validate settings and initialize
+        # View the JsonCache implementation to see how to create a cache that relies on a Storage provider.
+        ...
+
+    #Implement rest of interface
+    ...
+
+register_cache("MyCache", MyCache)
+
+async def run():
+    cache = create_cache(
+        CacheConfig(
+            type="MyCache",
+            some_setting="My Setting"
+        )
+    )
+
+    # Or use the factory directly to instantiate with a dict instead of using
+    # CacheConfig + create_factory
+    # from graphrag_cache.cache_factory import cache_factory
+    # cache = cache_factory.create(strategy="MyCache", init_args={"some_setting": "My Setting"})
+
+    await cache.set("my_key", {"some": "object to cache"})
+    print(await cache.get("my_key"))
+
+if __name__ == "__main__":
+    asyncio.run(run())
+```
+
+### Details
+
+By default, the `create_cache` comes with the following cache providers registered that correspond to the entries in the `CacheType` enum. 
+
+- `JsonCache`
+- `MemoryCache`
+- `NoopCache`
+
+The preregistration happens dynamically, e.g., `JsonCache` is only imported and registered if you request a `JsonCache` with `create_cache(CacheType.Json, ...)`. There is no need to manually import and register builtin cache providers when using `create_cache`.
+
+If you want a clean factory with no preregistered cache providers then directly import `cache_factory` and bypass using `create_cache`. The downside is that `cache_factory.create` uses a dict for init args instead of the strongly typed `CacheConfig` used with `create_cache`.
+
+```python
+from graphrag_cache.cache_factory import cache_factory
+from graphrag_cache.json_cache import JsonCache
+
+# cache_factory has no preregistered providers so you must register any
+# providers you plan on using.
+# May also register a custom implementation, see above for example.
+cache_factory.register("my_cache_impl", JsonCache)
+
+cache = cache_factory.create(strategy="my_cache_impl", init_args={"some_setting": "..."})
+
+...
+
+```
\ No newline at end of file
diff --git a/packages/graphrag-cache/graphrag_cache/__init__.py b/packages/graphrag-cache/graphrag_cache/__init__.py
new file mode 100644
index 0000000000..82ea4811d5
--- /dev/null
+++ b/packages/graphrag-cache/graphrag_cache/__init__.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The GraphRAG Cache package."""
+
+from graphrag_cache.cache import Cache
+from graphrag_cache.cache_config import CacheConfig
+from graphrag_cache.cache_factory import create_cache, register_cache
+from graphrag_cache.cache_type import CacheType
+
+__all__ = [
+    "Cache",
+    "CacheConfig",
+    "CacheType",
+    "create_cache",
+    "register_cache",
+]
diff --git a/packages/graphrag/graphrag/cache/pipeline_cache.py b/packages/graphrag-cache/graphrag_cache/cache.py
similarity index 86%
rename from packages/graphrag/graphrag/cache/pipeline_cache.py
rename to packages/graphrag-cache/graphrag_cache/cache.py
index c68c5cfb4b..26f1a96a0e 100644
--- a/packages/graphrag/graphrag/cache/pipeline_cache.py
+++ b/packages/graphrag-cache/graphrag_cache/cache.py
@@ -1,17 +1,21 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'PipelineCache' model."""
+"""Abstract base class for cache."""
 
 from __future__ import annotations
 
-from abc import ABCMeta, abstractmethod
+from abc import ABC, abstractmethod
 from typing import Any
 
 
-class PipelineCache(metaclass=ABCMeta):
+class Cache(ABC):
     """Provide a cache interface for the pipeline."""
 
+    @abstractmethod
+    def __init__(self, **kwargs: Any) -> None:
+        """Create a cache instance."""
+
     @abstractmethod
     async def get(self, key: str) -> Any:
         """Get the value for the given key.
@@ -59,7 +63,7 @@ async def clear(self) -> None:
         """Clear the cache."""
 
     @abstractmethod
-    def child(self, name: str) -> PipelineCache:
+    def child(self, name: str) -> Cache:
         """Create a child cache with the given name.
 
         Args:
diff --git a/packages/graphrag-cache/graphrag_cache/cache_config.py b/packages/graphrag-cache/graphrag_cache/cache_config.py
new file mode 100644
index 0000000000..afa2f6340c
--- /dev/null
+++ b/packages/graphrag-cache/graphrag_cache/cache_config.py
@@ -0,0 +1,26 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Cache configuration model."""
+
+from graphrag_storage import StorageConfig
+from pydantic import BaseModel, ConfigDict, Field
+
+from graphrag_cache.cache_type import CacheType
+
+
+class CacheConfig(BaseModel):
+    """The configuration section for cache."""
+
+    model_config = ConfigDict(extra="allow")
+    """Allow extra fields to support custom cache implementations."""
+
+    type: str = Field(
+        description="The cache type to use. Builtin types include 'Json', 'Memory', and 'Noop'.",
+        default=CacheType.Json,
+    )
+
+    storage: StorageConfig | None = Field(
+        description="The storage configuration to use for file-based caches such as 'Json'.",
+        default=None,
+    )
diff --git a/packages/graphrag-cache/graphrag_cache/cache_factory.py b/packages/graphrag-cache/graphrag_cache/cache_factory.py
new file mode 100644
index 0000000000..f8bbd65dcc
--- /dev/null
+++ b/packages/graphrag-cache/graphrag_cache/cache_factory.py
@@ -0,0 +1,83 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+
+"""Cache factory implementation."""
+
+from collections.abc import Callable
+
+from graphrag_common.factory import Factory, ServiceScope
+from graphrag_storage import Storage
+
+from graphrag_cache.cache import Cache
+from graphrag_cache.cache_config import CacheConfig
+from graphrag_cache.cache_type import CacheType
+
+
+class CacheFactory(Factory[Cache]):
+    """A factory class for cache implementations."""
+
+
+cache_factory = CacheFactory()
+
+
+def register_cache(
+    cache_type: str,
+    cache_initializer: Callable[..., Cache],
+    scope: ServiceScope = "transient",
+) -> None:
+    """Register a custom storage implementation.
+
+    Args
+    ----
+        - cache_type: str
+            The cache id to register.
+        - cache_initializer: Callable[..., Cache]
+            The cache initializer to register.
+    """
+    cache_factory.register(cache_type, cache_initializer, scope)
+
+
+def create_cache(config: CacheConfig, storage: Storage | None = None) -> Cache:
+    """Create a cache implementation based on the given configuration.
+
+    Args
+    ----
+        - config: CacheConfig
+            The cache configuration to use.
+        - storage: Storage | None
+            The storage implementation to use for file-based caches such as 'Json'.
+
+    Returns
+    -------
+        Cache
+            The created cache implementation.
+    """
+    config_model = config.model_dump()
+    cache_strategy = config.type
+
+    if cache_strategy not in cache_factory:
+        match cache_strategy:
+            case CacheType.Json:
+                from graphrag_cache.json_cache import JsonCache
+
+                register_cache(CacheType.Json, JsonCache)
+
+            case CacheType.Memory:
+                from graphrag_cache.memory_cache import MemoryCache
+
+                register_cache(CacheType.Memory, MemoryCache)
+
+            case CacheType.Noop:
+                from graphrag_cache.noop_cache import NoopCache
+
+                register_cache(CacheType.Noop, NoopCache)
+
+            case _:
+                msg = f"CacheConfig.type '{cache_strategy}' is not registered in the CacheFactory. Registered types: {', '.join(cache_factory.keys())}."
+                raise ValueError(msg)
+
+    if storage:
+        config_model["storage"] = storage
+
+    return cache_factory.create(strategy=cache_strategy, init_args=config_model)
diff --git a/packages/graphrag-cache/graphrag_cache/cache_type.py b/packages/graphrag-cache/graphrag_cache/cache_type.py
new file mode 100644
index 0000000000..4b1fe966e9
--- /dev/null
+++ b/packages/graphrag-cache/graphrag_cache/cache_type.py
@@ -0,0 +1,15 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+
+"""Builtin cache implementation types."""
+
+from enum import StrEnum
+
+
+class CacheType(StrEnum):
+    """Enum for cache types."""
+
+    Json = "json"
+    Memory = "memory"
+    Noop = "none"
diff --git a/packages/graphrag/graphrag/cache/json_pipeline_cache.py b/packages/graphrag-cache/graphrag_cache/json_cache.py
similarity index 61%
rename from packages/graphrag/graphrag/cache/json_pipeline_cache.py
rename to packages/graphrag-cache/graphrag_cache/json_cache.py
index 22b438936e..94d8e34a93 100644
--- a/packages/graphrag/graphrag/cache/json_pipeline_cache.py
+++ b/packages/graphrag-cache/graphrag_cache/json_cache.py
@@ -6,27 +6,35 @@
 import json
 from typing import Any
 
-from graphrag_storage import Storage
+from graphrag_storage import Storage, StorageConfig, create_storage
 
-from graphrag.cache.pipeline_cache import PipelineCache
+from graphrag_cache.cache import Cache
 
 
-class JsonPipelineCache(PipelineCache):
+class JsonCache(Cache):
     """File pipeline cache class definition."""
 
     _storage: Storage
-    _encoding: str
 
-    def __init__(self, storage: Storage, encoding="utf-8"):
+    def __init__(
+        self,
+        storage: Storage | dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> None:
         """Init method definition."""
-        self._storage = storage
-        self._encoding = encoding
+        if storage is None:
+            msg = "JsonCache requires either a Storage instance to be provided or a StorageConfig to create one."
+            raise ValueError(msg)
+        if isinstance(storage, Storage):
+            self._storage = storage
+        else:
+            self._storage = create_storage(StorageConfig(**storage))
 
-    async def get(self, key: str) -> str | None:
+    async def get(self, key: str) -> Any | None:
         """Get method definition."""
         if await self.has(key):
             try:
-                data = await self._storage.get(key, encoding=self._encoding)
+                data = await self._storage.get(key)
                 data = json.loads(data)
             except UnicodeDecodeError:
                 await self._storage.delete(key)
@@ -44,9 +52,7 @@ async def set(self, key: str, value: Any, debug_data: dict | None = None) -> Non
         if value is None:
             return
         data = {"result": value, **(debug_data or {})}
-        await self._storage.set(
-            key, json.dumps(data, ensure_ascii=False), encoding=self._encoding
-        )
+        await self._storage.set(key, json.dumps(data, ensure_ascii=False))
 
     async def has(self, key: str) -> bool:
         """Has method definition."""
@@ -61,6 +67,6 @@ async def clear(self) -> None:
         """Clear method definition."""
         await self._storage.clear()
 
-    def child(self, name: str) -> "JsonPipelineCache":
+    def child(self, name: str) -> "Cache":
         """Child method definition."""
-        return JsonPipelineCache(self._storage.child(name), encoding=self._encoding)
+        return JsonCache(storage=self._storage.child(name))
diff --git a/packages/graphrag/graphrag/cache/memory_pipeline_cache.py b/packages/graphrag-cache/graphrag_cache/memory_cache.py
similarity index 71%
rename from packages/graphrag/graphrag/cache/memory_pipeline_cache.py
rename to packages/graphrag-cache/graphrag_cache/memory_cache.py
index 62de552f96..0ab3ca4838 100644
--- a/packages/graphrag/graphrag/cache/memory_pipeline_cache.py
+++ b/packages/graphrag-cache/graphrag_cache/memory_cache.py
@@ -1,23 +1,22 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'InMemoryCache' model."""
+"""MemoryCache implementation."""
 
 from typing import Any
 
-from graphrag.cache.pipeline_cache import PipelineCache
+from graphrag_cache.cache import Cache
 
 
-class InMemoryCache(PipelineCache):
+class MemoryCache(Cache):
     """In memory cache class definition."""
 
     _cache: dict[str, Any]
     _name: str
 
-    def __init__(self, name: str | None = None):
+    def __init__(self, **kwargs: Any) -> None:
         """Init method definition."""
         self._cache = {}
-        self._name = name or ""
 
     async def get(self, key: str) -> Any:
         """Get the value for the given key.
@@ -30,7 +29,6 @@ async def get(self, key: str) -> Any:
         -------
             - output - The value for the given key.
         """
-        key = self._create_cache_key(key)
         return self._cache.get(key)
 
     async def set(self, key: str, value: Any, debug_data: dict | None = None) -> None:
@@ -40,7 +38,6 @@ async def set(self, key: str, value: Any, debug_data: dict | None = None) -> Non
             - key - The key to set the value for.
             - value - The value to set.
         """
-        key = self._create_cache_key(key)
         self._cache[key] = value
 
     async def has(self, key: str) -> bool:
@@ -53,7 +50,6 @@ async def has(self, key: str) -> bool:
         -------
             - output - True if the key exists in the storage, False otherwise.
         """
-        key = self._create_cache_key(key)
         return key in self._cache
 
     async def delete(self, key: str) -> None:
@@ -62,17 +58,12 @@ async def delete(self, key: str) -> None:
         Args:
             - key - The key to delete.
         """
-        key = self._create_cache_key(key)
         del self._cache[key]
 
     async def clear(self) -> None:
         """Clear the storage."""
         self._cache.clear()
 
-    def child(self, name: str) -> PipelineCache:
+    def child(self, name: str) -> "Cache":
         """Create a sub cache with the given name."""
-        return InMemoryCache(name)
-
-    def _create_cache_key(self, key: str) -> str:
-        """Create a cache key for the given key."""
-        return f"{self._name}{key}"
+        return MemoryCache()
diff --git a/packages/graphrag/graphrag/cache/noop_pipeline_cache.py b/packages/graphrag-cache/graphrag_cache/noop_cache.py
similarity index 82%
rename from packages/graphrag/graphrag/cache/noop_pipeline_cache.py
rename to packages/graphrag-cache/graphrag_cache/noop_cache.py
index 227ef687b8..5c3fe1368a 100644
--- a/packages/graphrag/graphrag/cache/noop_pipeline_cache.py
+++ b/packages/graphrag-cache/graphrag_cache/noop_cache.py
@@ -1,15 +1,18 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""Module containing the NoopPipelineCache implementation."""
+"""NoopCache implementation."""
 
 from typing import Any
 
-from graphrag.cache.pipeline_cache import PipelineCache
+from graphrag_cache.cache import Cache
 
 
-class NoopPipelineCache(PipelineCache):
-    """A no-op implementation of the pipeline cache, usually useful for testing."""
+class NoopCache(Cache):
+    """A no-op implementation of Cache, usually useful for testing."""
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Init method definition."""
 
     async def get(self, key: str) -> Any:
         """Get the value for the given key.
@@ -56,7 +59,7 @@ async def delete(self, key: str) -> None:
     async def clear(self) -> None:
         """Clear the cache."""
 
-    def child(self, name: str) -> PipelineCache:
+    def child(self, name: str) -> "Cache":
         """Create a child cache with the given name.
 
         Args:
diff --git a/packages/graphrag-cache/graphrag_cache/py.typed b/packages/graphrag-cache/graphrag_cache/py.typed
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/packages/graphrag-cache/pyproject.toml b/packages/graphrag-cache/pyproject.toml
new file mode 100644
index 0000000000..e87f5d81e0
--- /dev/null
+++ b/packages/graphrag-cache/pyproject.toml
@@ -0,0 +1,43 @@
+[project]
+name = "graphrag-cache"
+version = "2.7.0"
+description = "GraphRAG cache package."
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = "MIT"
+readme = "README.md"
+license-files = ["LICENSE"]
+requires-python = ">=3.10,<3.13"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+dependencies = [
+    "graphrag-common==2.7.0",
+    "graphrag-storage==2.7.0",
+]
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
diff --git a/packages/graphrag/graphrag/cache/__init__.py b/packages/graphrag/graphrag/cache/__init__.py
deleted file mode 100644
index 9c4e8be3fe..0000000000
--- a/packages/graphrag/graphrag/cache/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A package containing cache implementations."""
diff --git a/packages/graphrag/graphrag/cache/factory.py b/packages/graphrag/graphrag/cache/factory.py
deleted file mode 100644
index d62d8c420b..0000000000
--- a/packages/graphrag/graphrag/cache/factory.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Factory functions for creating a cache."""
-
-from __future__ import annotations
-
-from graphrag_common.factory import Factory
-
-from graphrag.cache.json_pipeline_cache import JsonPipelineCache
-from graphrag.cache.memory_pipeline_cache import InMemoryCache
-from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.config.enums import CacheType
-
-
-class CacheFactory(Factory[PipelineCache]):
-    """A factory class for cache implementations.
-
-    Includes a method for users to register a custom cache implementation.
-
-    Configuration arguments are passed to each cache implementation as kwargs
-    for individual enforcement of required/optional arguments.
-    """
-
-
-# --- register built-in cache implementations ---
-def create_file_cache(**kwargs) -> PipelineCache:
-    """Create a file-based cache implementation."""
-    from graphrag_storage.file_storage import FileStorage
-
-    storage = FileStorage(**kwargs)
-    return JsonPipelineCache(storage)
-
-
-def create_blob_cache(**kwargs) -> PipelineCache:
-    """Create a blob storage-based cache implementation."""
-    from graphrag_storage.azure_blob_storage import AzureBlobStorage
-
-    storage = AzureBlobStorage(**kwargs)
-    return JsonPipelineCache(storage)
-
-
-def create_cosmosdb_cache(**kwargs) -> PipelineCache:
-    """Create a CosmosDB-based cache implementation."""
-    from graphrag_storage.azure_cosmos_storage import AzureCosmosStorage
-
-    storage = AzureCosmosStorage(**kwargs)
-    return JsonPipelineCache(storage)
-
-
-def create_noop_cache(**_kwargs) -> PipelineCache:
-    """Create a no-op cache implementation."""
-    return NoopPipelineCache()
-
-
-def create_memory_cache(**kwargs) -> PipelineCache:
-    """Create a memory cache implementation."""
-    return InMemoryCache(**kwargs)
-
-
-# --- register built-in cache implementations ---
-cache_factory = CacheFactory()
-cache_factory.register(CacheType.none.value, create_noop_cache)
-cache_factory.register(CacheType.memory.value, create_memory_cache)
-cache_factory.register(CacheType.file.value, create_file_cache)
-cache_factory.register(CacheType.blob.value, create_blob_cache)
-cache_factory.register(CacheType.cosmosdb.value, create_cosmosdb_cache)
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index cefcc676f3..e330c82449 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -7,13 +7,13 @@
 from pathlib import Path
 from typing import ClassVar
 
+from graphrag_cache import CacheType
 from graphrag_storage import StorageType
 
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
     AsyncType,
     AuthType,
-    CacheType,
     ChunkStrategyType,
     InputFileType,
     ModelType,
@@ -27,6 +27,7 @@
 
 DEFAULT_INPUT_BASE_DIR = "input"
 DEFAULT_OUTPUT_BASE_DIR = "output"
+DEFAULT_CACHE_BASE_DIR = "cache"
 DEFAULT_UPDATE_OUTPUT_BASE_DIR = "update_output"
 DEFAULT_CHAT_MODEL_ID = "default_chat_model"
 DEFAULT_CHAT_MODEL_TYPE = ModelType.Chat
@@ -55,18 +56,6 @@ class BasicSearchDefaults:
     embedding_model_id: str = DEFAULT_EMBEDDING_MODEL_ID
 
 
-@dataclass
-class CacheDefaults:
-    """Default values for cache."""
-
-    type: ClassVar[CacheType] = CacheType.file
-    base_dir: str = "cache"
-    connection_string: None = None
-    container_name: None = None
-    storage_account_blob_url: None = None
-    cosmosdb_account_url: None = None
-
-
 @dataclass
 class ChunksDefaults:
     """Default values for chunks."""
@@ -260,6 +249,21 @@ class InputDefaults:
     metadata: None = None
 
 
+@dataclass
+class CacheStorageDefaults(StorageDefaults):
+    """Default values for cache storage."""
+
+    base_dir: str | None = DEFAULT_CACHE_BASE_DIR
+
+
+@dataclass
+class CacheDefaults:
+    """Default values for cache."""
+
+    type: CacheType = CacheType.Json
+    storage: CacheStorageDefaults = field(default_factory=CacheStorageDefaults)
+
+
 @dataclass
 class LanguageModelDefaults:
     """Default values for language model."""
diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py
index 01554d9ce5..4567406820 100644
--- a/packages/graphrag/graphrag/config/init_content.py
+++ b/packages/graphrag/graphrag/config/init_content.py
@@ -50,7 +50,7 @@
 
 input:
   storage:
-    type: {graphrag_config_defaults.input.storage.type} # or blob, cosmosdb
+    type: {graphrag_config_defaults.input.storage.type} # [file, blob, cosmosdb]
     base_dir: "{graphrag_config_defaults.input.storage.base_dir}"
   file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json]
 
@@ -63,12 +63,14 @@
 ## connection_string and container_name must be provided
 
 output:
-  type: {graphrag_config_defaults.output.type} # or blob, cosmosdb
+  type: {graphrag_config_defaults.output.type} # [file, blob, cosmosdb]
   base_dir: "{graphrag_config_defaults.output.base_dir}"
-    
+
 cache:
-  type: {graphrag_config_defaults.cache.type.value} # [file, blob, cosmosdb]
-  base_dir: "{graphrag_config_defaults.cache.base_dir}"
+  type: {graphrag_config_defaults.cache.type} # [json, memory, none]
+  storage:
+    type: {graphrag_config_defaults.cache.storage.type} # [file, blob, cosmosdb]
+    base_dir: "{graphrag_config_defaults.cache.storage.base_dir}"
 
 reporting:
   type: {graphrag_config_defaults.reporting.type.value} # [file, blob]
diff --git a/packages/graphrag/graphrag/config/models/cache_config.py b/packages/graphrag/graphrag/config/models/cache_config.py
deleted file mode 100644
index c301e9d3f6..0000000000
--- a/packages/graphrag/graphrag/config/models/cache_config.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from pydantic import BaseModel, Field
-
-from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.enums import CacheType
-
-
-class CacheConfig(BaseModel):
-    """The default configuration section for Cache."""
-
-    type: CacheType | str = Field(
-        description="The cache type to use.",
-        default=graphrag_config_defaults.cache.type,
-    )
-    base_dir: str = Field(
-        description="The base directory for the cache.",
-        default=graphrag_config_defaults.cache.base_dir,
-    )
-    connection_string: str | None = Field(
-        description="The cache connection string to use.",
-        default=graphrag_config_defaults.cache.connection_string,
-    )
-    container_name: str | None = Field(
-        description="The cache container name to use.",
-        default=graphrag_config_defaults.cache.container_name,
-    )
-    storage_account_blob_url: str | None = Field(
-        description="The storage account blob url to use.",
-        default=graphrag_config_defaults.cache.storage_account_blob_url,
-    )
-    cosmosdb_account_url: str | None = Field(
-        description="The cosmosdb account url to use.",
-        default=graphrag_config_defaults.cache.cosmosdb_account_url,
-    )
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 101a37b379..162fe12567 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -3,9 +3,11 @@
 
 """Parameterization settings for the default configuration."""
 
+from dataclasses import asdict
 from pathlib import Path
 
 from devtools import pformat
+from graphrag_cache import CacheConfig
 from graphrag_storage import StorageConfig, StorageType
 from pydantic import BaseModel, Field, model_validator
 
@@ -13,7 +15,6 @@
 from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import VectorStoreType
 from graphrag.config.models.basic_search_config import BasicSearchConfig
-from graphrag.config.models.cache_config import CacheConfig
 from graphrag.config.models.chunking_config import ChunkingConfig
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
@@ -165,7 +166,8 @@ def _validate_update_index_output_base_dir(self) -> None:
             )
 
     cache: CacheConfig = Field(
-        description="The cache configuration.", default=CacheConfig()
+        description="The cache configuration.",
+        default=CacheConfig(**asdict(graphrag_config_defaults.cache)),
     )
     """The cache configuration."""
 
diff --git a/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py b/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
index 5e34aa654c..7524005fb4 100644
--- a/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
+++ b/packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py
@@ -7,8 +7,8 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_cache import Cache
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.enums import AsyncType
 from graphrag.index.operations.build_noun_graph.np_extractors.base import (
     BaseNounPhraseExtractor,
@@ -24,7 +24,7 @@ async def build_noun_graph(
     normalize_edge_weights: bool,
     num_threads: int,
     async_mode: AsyncType,
-    cache: PipelineCache,
+    cache: Cache,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Build a noun graph from text units."""
     text_units = text_unit_df.loc[:, ["id", "text"]]
@@ -44,7 +44,7 @@ async def _extract_nodes(
     text_analyzer: BaseNounPhraseExtractor,
     num_threads: int,
     async_mode: AsyncType,
-    cache: PipelineCache,
+    cache: Cache,
 ) -> pd.DataFrame:
     """
     Extract initial nodes and edges from text units.
diff --git a/packages/graphrag/graphrag/index/operations/extract_covariates/typing.py b/packages/graphrag/graphrag/index/operations/extract_covariates/typing.py
index a524b2bc17..d5e82ad2fc 100644
--- a/packages/graphrag/graphrag/index/operations/extract_covariates/typing.py
+++ b/packages/graphrag/graphrag/index/operations/extract_covariates/typing.py
@@ -7,7 +7,8 @@
 from dataclasses import dataclass
 from typing import Any
 
-from graphrag.cache.pipeline_cache import PipelineCache
+from graphrag_cache import Cache
+
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 
 
@@ -42,7 +43,7 @@ class CovariateExtractionResult:
         list[str],
         dict[str, str],
         WorkflowCallbacks,
-        PipelineCache,
+        Cache,
         dict[str, Any],
     ],
     Awaitable[CovariateExtractionResult],
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index 4eef24f14c..d4f0af20b6 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -12,6 +12,7 @@
 from typing import Any
 
 import pandas as pd
+from graphrag_cache import create_cache
 from graphrag_storage import Storage, create_storage
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
@@ -20,7 +21,6 @@
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.pipeline import Pipeline
 from graphrag.index.typing.pipeline_run_result import PipelineRunResult
-from graphrag.utils.api import create_cache_from_config
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -37,7 +37,7 @@ async def run_pipeline(
     """Run all workflows using a simplified pipeline."""
     input_storage = create_storage(config.input.storage)
     output_storage = create_storage(config.output)
-    cache = create_cache_from_config(config.cache)
+    cache = create_cache(config.cache)
 
     # load existing state in case any workflows are stateful
     state_json = await output_storage.get("context.json")
diff --git a/packages/graphrag/graphrag/index/run/utils.py b/packages/graphrag/graphrag/index/run/utils.py
index 03e789746a..9c77d16de6 100644
--- a/packages/graphrag/graphrag/index/run/utils.py
+++ b/packages/graphrag/graphrag/index/run/utils.py
@@ -3,11 +3,11 @@
 
 """Utility functions for the GraphRAG run module."""
 
+from graphrag_cache import Cache
+from graphrag_cache.memory_cache import MemoryCache
 from graphrag_storage import Storage, create_storage
 from graphrag_storage.memory_storage import MemoryStorage
 
-from graphrag.cache.memory_pipeline_cache import InMemoryCache
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.callbacks.workflow_callbacks_manager import WorkflowCallbacksManager
@@ -21,7 +21,7 @@ def create_run_context(
     input_storage: Storage | None = None,
     output_storage: Storage | None = None,
     previous_storage: Storage | None = None,
-    cache: PipelineCache | None = None,
+    cache: Cache | None = None,
     callbacks: WorkflowCallbacks | None = None,
     stats: PipelineRunStats | None = None,
     state: PipelineState | None = None,
@@ -31,7 +31,7 @@ def create_run_context(
         input_storage=input_storage or MemoryStorage(),
         output_storage=output_storage or MemoryStorage(),
         previous_storage=previous_storage or MemoryStorage(),
-        cache=cache or InMemoryCache(),
+        cache=cache or MemoryCache(),
         callbacks=callbacks or NoopWorkflowCallbacks(),
         stats=stats or PipelineRunStats(),
         state=state or {},
diff --git a/packages/graphrag/graphrag/index/typing/context.py b/packages/graphrag/graphrag/index/typing/context.py
index 465ec7214c..95e7f898f9 100644
--- a/packages/graphrag/graphrag/index/typing/context.py
+++ b/packages/graphrag/graphrag/index/typing/context.py
@@ -6,7 +6,7 @@
 
 from dataclasses import dataclass
 
-from graphrag.cache.pipeline_cache import PipelineCache
+from graphrag_cache import Cache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.index.typing.state import PipelineState
 from graphrag.index.typing.stats import PipelineRunStats
@@ -24,7 +24,7 @@ class PipelineRunContext:
     "Long-term storage for pipeline verbs to use. Items written here will be written to the storage provider."
     previous_storage: Storage
     "Storage for previous pipeline run when running in update mode."
-    cache: PipelineCache
+    cache: Cache
     "Cache instance for reading previous LLM responses."
     callbacks: WorkflowCallbacks
     "Callbacks to be called during the pipeline run."
diff --git a/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py b/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
index fd00cbfa79..38810e5de4 100644
--- a/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
+++ b/packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py
@@ -6,8 +6,8 @@
 import logging
 
 import pandas as pd
+from graphrag_cache import Cache
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.enums import AsyncType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.operations.build_noun_graph.build_noun_graph import build_noun_graph
@@ -59,7 +59,7 @@ async def run_workflow(
 
 async def extract_graph_nlp(
     text_units: pd.DataFrame,
-    cache: PipelineCache,
+    cache: Cache,
     text_analyzer: BaseNounPhraseExtractor,
     normalize_edge_weights: bool,
     num_threads: int,
diff --git a/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
index 2ddd171457..468f0e5d1a 100644
--- a/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
+++ b/packages/graphrag/graphrag/index/workflows/update_entities_relationships.py
@@ -6,9 +6,9 @@
 import logging
 
 import pandas as pd
+from graphrag_cache import Cache
 from graphrag_storage import Storage
 
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.run.utils import get_update_storages
@@ -59,7 +59,7 @@ async def _update_entities_and_relationships(
     delta_storage: Storage,
     output_storage: Storage,
     config: GraphRagConfig,
-    cache: PipelineCache,
+    cache: Cache,
     callbacks: WorkflowCallbacks,
 ) -> tuple[pd.DataFrame, pd.DataFrame, dict]:
     """Update Final Entities  and Relationships output."""
diff --git a/packages/graphrag/graphrag/language_model/providers/litellm/chat_model.py b/packages/graphrag/graphrag/language_model/providers/litellm/chat_model.py
index bf4af8aff2..4e198bb423 100644
--- a/packages/graphrag/graphrag/language_model/providers/litellm/chat_model.py
+++ b/packages/graphrag/graphrag/language_model/providers/litellm/chat_model.py
@@ -38,7 +38,8 @@
 )
 
 if TYPE_CHECKING:
-    from graphrag.cache.pipeline_cache import PipelineCache
+    from graphrag_cache import Cache
+
     from graphrag.config.models.language_model_config import LanguageModelConfig
     from graphrag.language_model.response.base import ModelResponse as MR  # noqa: N817
 
@@ -113,7 +114,7 @@ async def _base_acompletion(**kwargs: Any) -> ModelResponse | CustomStreamWrappe
 
 def _create_completions(
     model_config: "LanguageModelConfig",
-    cache: "PipelineCache | None",
+    cache: "Cache | None",
     cache_key_prefix: str,
 ) -> tuple[FixedModelCompletion, AFixedModelCompletion]:
     """Wrap the base litellm completion function with the model configuration and additional features.
@@ -203,7 +204,7 @@ def __init__(
         self,
         name: str,
         config: "LanguageModelConfig",
-        cache: "PipelineCache | None" = None,
+        cache: "Cache | None" = None,
         **kwargs: Any,
     ):
         self.name = name
diff --git a/packages/graphrag/graphrag/language_model/providers/litellm/embedding_model.py b/packages/graphrag/graphrag/language_model/providers/litellm/embedding_model.py
index 17eea1e153..328eb2e16d 100644
--- a/packages/graphrag/graphrag/language_model/providers/litellm/embedding_model.py
+++ b/packages/graphrag/graphrag/language_model/providers/litellm/embedding_model.py
@@ -33,7 +33,8 @@
 )
 
 if TYPE_CHECKING:
-    from graphrag.cache.pipeline_cache import PipelineCache
+    from graphrag_cache import Cache
+
     from graphrag.config.models.language_model_config import LanguageModelConfig
 
 litellm.suppress_debug_info = True
@@ -99,7 +100,7 @@ async def _base_aembedding(**kwargs: Any) -> EmbeddingResponse:
 
 def _create_embeddings(
     model_config: "LanguageModelConfig",
-    cache: "PipelineCache | None",
+    cache: "Cache | None",
     cache_key_prefix: str,
 ) -> tuple[FixedModelEmbedding, AFixedModelEmbedding]:
     """Wrap the base litellm embedding function with the model configuration and additional features.
@@ -167,7 +168,7 @@ def __init__(
         self,
         name: str,
         config: "LanguageModelConfig",
-        cache: "PipelineCache | None" = None,
+        cache: "Cache | None" = None,
         **kwargs: Any,
     ):
         self.name = name
diff --git a/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py
index d14c97206e..c7299caaca 100644
--- a/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py
+++ b/packages/graphrag/graphrag/language_model/providers/litellm/request_wrappers/with_cache.py
@@ -15,7 +15,8 @@
 )
 
 if TYPE_CHECKING:
-    from graphrag.cache.pipeline_cache import PipelineCache
+    from graphrag_cache import Cache
+
     from graphrag.config.models.language_model_config import LanguageModelConfig
 
 
@@ -24,7 +25,7 @@ def with_cache(
     sync_fn: LitellmRequestFunc,
     async_fn: AsyncLitellmRequestFunc,
     model_config: "LanguageModelConfig",
-    cache: "PipelineCache",
+    cache: "Cache",
     request_type: Literal["chat", "embedding"],
     cache_key_prefix: str,
 ) -> tuple[LitellmRequestFunc, AsyncLitellmRequestFunc]:
diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py
index c810b0ce41..556f1dd615 100644
--- a/packages/graphrag/graphrag/prompt_tune/loader/input.py
+++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py
@@ -8,9 +8,9 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_cache.noop_cache import NoopCache
 from graphrag_storage import create_storage
 
-from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.index.input.factory import InputReaderFactory
@@ -60,7 +60,7 @@ async def load_docs_in_chunks(
         model_type=embeddings_llm_settings.type,
         config=embeddings_llm_settings,
         callbacks=NoopWorkflowCallbacks(),
-        cache=NoopPipelineCache(),
+        cache=NoopCache(),
     )
     tokenizer = get_tokenizer(embeddings_llm_settings)
     input_storage = create_storage(config.input.storage)
diff --git a/packages/graphrag/graphrag/utils/api.py b/packages/graphrag/graphrag/utils/api.py
index 2d83d692ff..2aef931170 100644
--- a/packages/graphrag/graphrag/utils/api.py
+++ b/packages/graphrag/graphrag/utils/api.py
@@ -6,10 +6,7 @@
 from pathlib import Path
 from typing import Any
 
-from graphrag.cache.factory import CacheFactory
-from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.config.embeddings import create_index_name
-from graphrag.config.models.cache_config import CacheConfig
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.vector_stores.base import (
     BaseVectorStore,
@@ -98,15 +95,6 @@ def load_search_prompt(prompt_config: str | None) -> str | None:
     return None
 
 
-def create_cache_from_config(cache: CacheConfig) -> PipelineCache:
-    """Create a cache object from the config."""
-    cache_config = cache.model_dump()
-    return CacheFactory().create(
-        strategy=cache_config["type"],
-        init_args=cache_config,
-    )
-
-
 def truncate(text: str, max_length: int) -> str:
     """Truncate a string to a maximum length."""
     if len(text) <= max_length:
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index afec601e7f..5c2cb687d7 100644
--- a/packages/graphrag/pyproject.toml
+++ b/packages/graphrag/pyproject.toml
@@ -39,6 +39,7 @@ dependencies = [
     "azure-storage-blob~=12.24",
     "devtools~=0.12",
     "environs~=11.0",
+    "graphrag-cache==2.7.0",
     "graphrag-common==2.7.0",
     "graphrag-storage==2.7.0",
     "graspologic-native~=1.2",
diff --git a/pyproject.toml b/pyproject.toml
index 078b29384f..433e45ebee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,7 @@ members = ["packages/*"]
 [tool.uv.sources]
 graphrag-common = { workspace = true }
 graphrag-storage = { workspace = true }
+graphrag-cache = { workspace = true }
 
 # Keep poethepoet for task management to minimize changes
 [tool.poe.tasks]
@@ -71,6 +72,7 @@ _semversioner_changelog = "semversioner changelog > CHANGELOG.md"
 _semversioner_update_graphrag_toml_version = "update-toml update --file packages/graphrag/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_common_toml_version = "update-toml update --file packages/graphrag-common/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_storage_toml_version = "update-toml update --file packages/graphrag-storage/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_graphrag_cache_toml_version = "update-toml update --file packages/graphrag-cache/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_workspace_dependency_versions = "python -m scripts.update_workspace_dependency_versions"
 semversioner_add = "semversioner add-change"
 coverage_report = 'coverage report --omit "**/tests/**" --show-missing'
@@ -106,6 +108,7 @@ sequence = [
     '_semversioner_update_graphrag_toml_version',
     '_semversioner_update_graphrag_common_toml_version',
     '_semversioner_update_graphrag_storage_toml_version',
+    '_semversioner_update_graphrag_cache_toml_version',
     '_semversioner_update_workspace_dependency_versions',
     '_sync',
 ]
diff --git a/tests/integration/cache/test_factory.py b/tests/integration/cache/test_factory.py
index 361d46a4ea..ef01a0a315 100644
--- a/tests/integration/cache/test_factory.py
+++ b/tests/integration/cache/test_factory.py
@@ -8,12 +8,12 @@
 import sys
 
 import pytest
-from graphrag.cache.factory import CacheFactory
-from graphrag.cache.json_pipeline_cache import JsonPipelineCache
-from graphrag.cache.memory_pipeline_cache import InMemoryCache
-from graphrag.cache.noop_pipeline_cache import NoopPipelineCache
-from graphrag.cache.pipeline_cache import PipelineCache
-from graphrag.config.enums import CacheType
+from graphrag_cache import Cache, CacheConfig, CacheType, create_cache, register_cache
+from graphrag_cache.cache_factory import cache_factory
+from graphrag_cache.json_cache import JsonCache
+from graphrag_cache.memory_cache import MemoryCache
+from graphrag_cache.noop_cache import NoopCache
+from graphrag_storage import StorageConfig, StorageType, create_storage
 
 # cspell:disable-next-line well-known-key
 WELL_KNOWN_BLOB_STORAGE_KEY = "DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1;"
@@ -22,31 +22,55 @@
 
 
 def test_create_noop_cache():
-    cache = CacheFactory().create(strategy=CacheType.none.value)
-    assert isinstance(cache, NoopPipelineCache)
+    cache = create_cache(
+        CacheConfig(
+            type=CacheType.Noop,
+        )
+    )
+    assert isinstance(cache, NoopCache)
 
 
 def test_create_memory_cache():
-    cache = CacheFactory().create(strategy=CacheType.memory.value)
-    assert isinstance(cache, InMemoryCache)
+    cache = create_cache(
+        CacheConfig(
+            type=CacheType.Memory,
+        )
+    )
+    assert isinstance(cache, MemoryCache)
 
 
 def test_create_file_cache():
-    cache = CacheFactory().create(
-        strategy=CacheType.file.value,
-        init_args={"base_dir": "testcache"},
+    storage = create_storage(
+        StorageConfig(
+            type=StorageType.Memory,
+        )
     )
-    assert isinstance(cache, JsonPipelineCache)
+    cache = create_cache(
+        CacheConfig(
+            type=CacheType.Json,
+        ),
+        storage=storage,
+    )
+    assert isinstance(cache, JsonCache)
 
 
 def test_create_blob_cache():
-    init_args = {
-        "connection_string": WELL_KNOWN_BLOB_STORAGE_KEY,
-        "container_name": "testcontainer",
-        "base_dir": "testcache",
-    }
-    cache = CacheFactory().create(strategy=CacheType.blob.value, init_args=init_args)
-    assert isinstance(cache, JsonPipelineCache)
+    storage = create_storage(
+        StorageConfig(
+            type=StorageType.AzureBlob,
+            connection_string=WELL_KNOWN_BLOB_STORAGE_KEY,
+            container_name="testcontainer",
+            base_dir="testcache",
+        )
+    )
+    cache = create_cache(
+        CacheConfig(
+            type=CacheType.Json,
+        ),
+        storage=storage,
+    )
+
+    assert isinstance(cache, JsonCache)
 
 
 @pytest.mark.skipif(
@@ -54,15 +78,21 @@ def test_create_blob_cache():
     reason="cosmosdb emulator is only available on windows runners at this time",
 )
 def test_create_cosmosdb_cache():
-    init_args = {
-        "connection_string": WELL_KNOWN_COSMOS_CONNECTION_STRING,
-        "database_name": "testdatabase",
-        "container_name": "testcontainer",
-    }
-    cache = CacheFactory().create(
-        strategy=CacheType.cosmosdb.value, init_args=init_args
+    storage = create_storage(
+        StorageConfig(
+            type=StorageType.AzureCosmos,
+            connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
+            database_name="testdatabase",
+            container_name="testcontainer",
+        )
     )
-    assert isinstance(cache, JsonPipelineCache)
+    cache = create_cache(
+        CacheConfig(
+            type=CacheType.Json,
+        ),
+        storage=storage,
+    )
+    assert isinstance(cache, JsonCache)
 
 
 def test_register_and_create_custom_cache():
@@ -70,17 +100,14 @@ def test_register_and_create_custom_cache():
     from unittest.mock import MagicMock
 
     # Create a mock that satisfies the PipelineCache interface
-    custom_cache_class = MagicMock(spec=PipelineCache)
+    custom_cache_class = MagicMock(spec=Cache)
     # Make the mock return a mock instance when instantiated
     instance = MagicMock()
     instance.initialized = True
     custom_cache_class.return_value = instance
 
-    CacheFactory().register(
-        strategy="custom",
-        initializer=lambda **kwargs: custom_cache_class(**kwargs),
-    )
-    cache = CacheFactory().create(strategy="custom")
+    register_cache("custom", lambda **kwargs: custom_cache_class(**kwargs))
+    cache = create_cache(CacheConfig(type="custom"))
 
     assert custom_cache_class.called
     assert cache is instance
@@ -88,45 +115,21 @@ def test_register_and_create_custom_cache():
     assert cache.initialized is True  # type: ignore # Attribute only exists on our mock
 
     # Check if it's in the list of registered cache types
-    assert "custom" in CacheFactory()
+    assert "custom" in cache_factory
 
 
 def test_create_unknown_cache():
-    with pytest.raises(ValueError, match="Strategy 'unknown' is not registered\\."):
-        CacheFactory().create(strategy="unknown")
-
-
-def test_is_supported_type():
-    # Test built-in types
-    assert CacheType.none.value in CacheFactory()
-    assert CacheType.memory.value in CacheFactory()
-    assert CacheType.file.value in CacheFactory()
-    assert CacheType.blob.value in CacheFactory()
-    assert CacheType.cosmosdb.value in CacheFactory()
-
-    # Test unknown type
-    assert "unknown" not in CacheFactory()
-
-
-def test_enum_and_string_compatibility():
-    """Test that both enum and string types work for cache creation."""
-    # Test with enum
-    cache_enum = CacheFactory().create(strategy=CacheType.memory)
-    assert isinstance(cache_enum, InMemoryCache)
-
-    # Test with string
-    cache_str = CacheFactory().create(strategy="memory")
-    assert isinstance(cache_str, InMemoryCache)
-
-    # Both should create the same type
-    assert type(cache_enum) is type(cache_str)
+    with pytest.raises(
+        ValueError,
+        match="CacheConfig\\.type 'unknown' is not registered in the CacheFactory\\.",
+    ):
+        create_cache(CacheConfig(type="unknown"))
 
 
 def test_register_class_directly_works():
     """Test that registering a class directly works (CacheFactory() allows this)."""
-    from graphrag.cache.pipeline_cache import PipelineCache
 
-    class CustomCache(PipelineCache):
+    class CustomCache(Cache):
         def __init__(self, **kwargs):
             pass
 
@@ -149,11 +152,11 @@ def child(self, name: str):
             return self
 
     # CacheFactory() allows registering classes directly (no TypeError)
-    CacheFactory().register("custom_class", CustomCache)
+    register_cache("custom_class", CustomCache)
 
     # Verify it was registered
-    assert "custom_class" in CacheFactory()
+    assert "custom_class" in cache_factory
 
     # Test creating an instance
-    cache = CacheFactory().create(strategy="custom_class")
+    cache = create_cache(CacheConfig(type="custom_class"))
     assert isinstance(cache, CustomCache)
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 83e18ad546..1315907e59 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -5,7 +5,6 @@
 
 import graphrag.config.defaults as defs
 from graphrag.config.models.basic_search_config import BasicSearchConfig
-from graphrag.config.models.cache_config import CacheConfig
 from graphrag.config.models.chunking_config import ChunkingConfig
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
@@ -29,6 +28,7 @@
     SummarizeDescriptionsConfig,
 )
 from graphrag.config.models.vector_store_config import VectorStoreConfig
+from graphrag_cache import CacheConfig
 from graphrag_storage import StorageConfig
 from pydantic import BaseModel
 
@@ -138,11 +138,8 @@ def assert_storage_config(actual: StorageConfig, expected: StorageConfig) -> Non
 
 def assert_cache_configs(actual: CacheConfig, expected: CacheConfig) -> None:
     assert actual.type == expected.type
-    assert actual.base_dir == expected.base_dir
-    assert actual.connection_string == expected.connection_string
-    assert actual.container_name == expected.container_name
-    assert actual.storage_account_blob_url == expected.storage_account_blob_url
-    assert actual.cosmosdb_account_url == expected.cosmosdb_account_url
+    if actual.storage and expected.storage:
+        assert_storage_config(actual.storage, expected.storage)
 
 
 def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None:
diff --git a/tests/unit/indexing/cache/test_file_pipeline_cache.py b/tests/unit/indexing/cache/test_file_pipeline_cache.py
index c672d3718f..ae38e5be4e 100644
--- a/tests/unit/indexing/cache/test_file_pipeline_cache.py
+++ b/tests/unit/indexing/cache/test_file_pipeline_cache.py
@@ -4,17 +4,23 @@
 import os
 import unittest
 
-from graphrag.cache.json_pipeline_cache import JsonPipelineCache
-from graphrag_storage.file_storage import (
-    FileStorage,
-)
+from graphrag_cache import CacheConfig, CacheType
+from graphrag_cache import create_cache as cc
+from graphrag_storage import StorageConfig, StorageType
 
 TEMP_DIR = "./.tmp"
 
 
 def create_cache():
-    storage = FileStorage(base_dir=os.path.join(os.getcwd(), ".tmp"))
-    return JsonPipelineCache(storage)
+    return cc(
+        CacheConfig(
+            type=CacheType.Json,
+            storage=StorageConfig(
+                type=StorageType.File,
+                base_dir=os.path.join(os.getcwd(), ".tmp"),
+            ),
+        ),
+    )
 
 
 class TestFilePipelineCache(unittest.IsolatedAsyncioTestCase):
diff --git a/uv.lock b/uv.lock
index cb0ecaf6e2..8072353c3a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,6 @@
 version = 1
-revision = 1
-requires-python = ">=3.11, <3.14"
+revision = 3
+requires-python = ">=3.11, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
     "python_full_version < '3.12'",
@@ -9,6 +9,7 @@ resolution-markers = [
 [manifest]
 members = [
     "graphrag",
+    "graphrag-cache",
     "graphrag-common",
     "graphrag-monorepo",
     "graphrag-storage",
@@ -18,18 +19,18 @@ members = [
 name = "aiofiles"
 version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
@@ -45,59 +46,42 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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-wheels = [
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+    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" },
 ]

From c296f1ae15a21699d3df15855696e71851d9b562 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Wed, 17 Dec 2025 10:55:26 -0800
Subject: [PATCH 32/40] Fix a bunch of module comments and function visibility
 (#2154)

---
 .../graphrag_cache/json_cache.py              |  2 +-
 .../callbacks/workflow_callbacks_manager.py   |  2 +-
 packages/graphrag/graphrag/index/input/csv.py |  2 +-
 .../graphrag/graphrag/index/input/factory.py  |  2 +-
 .../graphrag/index/input/input_reader.py      |  2 +-
 .../graphrag/graphrag/index/input/json.py     |  2 +-
 .../graphrag/graphrag/index/input/text.py     |  2 +-
 .../index/operations/chunk_text/chunk_text.py | 10 ++++-----
 .../index/operations/chunk_text/strategies.py |  2 +-
 .../index/operations/cluster_graph.py         |  2 +-
 .../index/operations/compute_degree.py        |  2 +-
 .../compute_edge_combined_degree.py           |  2 +-
 .../index/operations/embed_text/embed_text.py |  2 +-
 .../operations/embed_text/run_embed_text.py   |  2 +-
 .../operations/extract_graph/extract_graph.py |  6 ++---
 .../index/operations/graph_to_dataframes.py   |  2 +-
 .../build_mixed_context.py                    |  3 ++-
 .../summarize_communities.py                  |  2 +-
 .../description_summary_extractor.py          |  2 +-
 .../index/text_splitting/text_splitting.py    |  2 +-
 .../graphrag/index/utils/derive_from_rows.py  |  8 +------
 tests/mock_provider.py                        |  2 +-
 .../operations/chunk_text/test_chunk_text.py  | 22 +++++++++----------
 .../test_gi_entity_extraction.py              |  8 +++----
 24 files changed, 44 insertions(+), 49 deletions(-)

diff --git a/packages/graphrag-cache/graphrag_cache/json_cache.py b/packages/graphrag-cache/graphrag_cache/json_cache.py
index 94d8e34a93..dc78551cf2 100644
--- a/packages/graphrag-cache/graphrag_cache/json_cache.py
+++ b/packages/graphrag-cache/graphrag_cache/json_cache.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'JsonPipelineCache' model."""
+"""A module containing 'JsonCache' model."""
 
 import json
 from typing import Any
diff --git a/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py b/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
index 6a030ec66a..9d3803f1be 100644
--- a/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
+++ b/packages/graphrag/graphrag/callbacks/workflow_callbacks_manager.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing the WorkflowCallbacks registry."""
+"""A module containing 'WorkflowCallbacksManager' model."""
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.index.typing.pipeline_run_result import PipelineRunResult
diff --git a/packages/graphrag/graphrag/index/input/csv.py b/packages/graphrag/graphrag/index/input/csv.py
index a70863e7ae..810ffdd03c 100644
--- a/packages/graphrag/graphrag/index/input/csv.py
+++ b/packages/graphrag/graphrag/index/input/csv.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing load method definition."""
+"""A module containing 'CSVFileReader' model."""
 
 import logging
 from io import BytesIO
diff --git a/packages/graphrag/graphrag/index/input/factory.py b/packages/graphrag/graphrag/index/input/factory.py
index c938c6e4ed..bc3b8112c8 100644
--- a/packages/graphrag/graphrag/index/input/factory.py
+++ b/packages/graphrag/graphrag/index/input/factory.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing create_input method definition."""
+"""A module containing 'InputReaderFactory' model."""
 
 import logging
 
diff --git a/packages/graphrag/graphrag/index/input/input_reader.py b/packages/graphrag/graphrag/index/input/input_reader.py
index 98a713e509..18dc713dfd 100644
--- a/packages/graphrag/graphrag/index/input/input_reader.py
+++ b/packages/graphrag/graphrag/index/input/input_reader.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'PipelineCache' model."""
+"""A module containing 'InputReader' model."""
 
 from __future__ import annotations
 
diff --git a/packages/graphrag/graphrag/index/input/json.py b/packages/graphrag/graphrag/index/input/json.py
index cae7db5dbc..e9e1a7ab04 100644
--- a/packages/graphrag/graphrag/index/input/json.py
+++ b/packages/graphrag/graphrag/index/input/json.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing load method definition."""
+"""A module containing 'JSONFileReader' model."""
 
 import json
 import logging
diff --git a/packages/graphrag/graphrag/index/input/text.py b/packages/graphrag/graphrag/index/input/text.py
index dac2c4c701..f9e2d7779e 100644
--- a/packages/graphrag/graphrag/index/input/text.py
+++ b/packages/graphrag/graphrag/index/input/text.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing load method definition."""
+"""A module containing 'TextFileReader' model."""
 
 import logging
 from pathlib import Path
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py b/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
index fc50370e28..cd63fec455 100644
--- a/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
+++ b/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing _get_num_total, chunk, run_strategy and load_strategy methods definitions."""
+"""A module containing chunk_text method definitions."""
 
 from typing import Any, cast
 
@@ -54,7 +54,7 @@ def chunk_text(
     strategy: sentence
     ```
     """
-    strategy_exec = load_strategy(strategy)
+    strategy_exec = _load_strategy(strategy)
 
     num_total = _get_num_total(input, column)
     tick = progress_ticker(callbacks.progress, num_total)
@@ -67,7 +67,7 @@ def chunk_text(
         input.apply(
             cast(
                 "Any",
-                lambda x: run_strategy(
+                lambda x: _run_strategy(
                     strategy_exec,
                     x[column],
                     config,
@@ -79,7 +79,7 @@ def chunk_text(
     )
 
 
-def run_strategy(
+def _run_strategy(
     strategy_exec: ChunkStrategy,
     input: ChunkInput,
     config: ChunkingConfig,
@@ -111,7 +111,7 @@ def run_strategy(
     return results
 
 
-def load_strategy(strategy: ChunkStrategyType) -> ChunkStrategy:
+def _load_strategy(strategy: ChunkStrategyType) -> ChunkStrategy:
     """Load strategy method definition."""
     match strategy:
         case ChunkStrategyType.tokens:
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py b/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
index 9d48786e7f..85a1537e3f 100644
--- a/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
+++ b/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing chunk strategies."""
+"""A module containing run_tokens and run_sentences methods."""
 
 from collections.abc import Iterable
 
diff --git a/packages/graphrag/graphrag/index/operations/cluster_graph.py b/packages/graphrag/graphrag/index/operations/cluster_graph.py
index d7e1b0c7ca..ff4632ea17 100644
--- a/packages/graphrag/graphrag/index/operations/cluster_graph.py
+++ b/packages/graphrag/graphrag/index/operations/cluster_graph.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing cluster_graph, apply_clustering methods definition."""
+"""A module containing cluster_graph method definition."""
 
 import logging
 
diff --git a/packages/graphrag/graphrag/index/operations/compute_degree.py b/packages/graphrag/graphrag/index/operations/compute_degree.py
index b720bf6de5..8de95ad2e2 100644
--- a/packages/graphrag/graphrag/index/operations/compute_degree.py
+++ b/packages/graphrag/graphrag/index/operations/compute_degree.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing create_graph definition."""
+"""A module containing compute_degree method definition."""
 
 import networkx as nx
 import pandas as pd
diff --git a/packages/graphrag/graphrag/index/operations/compute_edge_combined_degree.py b/packages/graphrag/graphrag/index/operations/compute_edge_combined_degree.py
index f6c75b008e..440fe39222 100644
--- a/packages/graphrag/graphrag/index/operations/compute_edge_combined_degree.py
+++ b/packages/graphrag/graphrag/index/operations/compute_edge_combined_degree.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing compute_edge_combined_degree methods definition."""
+"""A module containing compute_edge_combined_degree method definition."""
 
 from typing import cast
 
diff --git a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
index d8144f8c18..08f941f908 100644
--- a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
+++ b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing embed_text, load_strategy and create_row_from_embedding_data methods definition."""
+"""A module containing embed_text method definition."""
 
 import logging
 
diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
index fc3da5ee6c..cb8b2016f5 100644
--- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
+++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing run method definition."""
+"""A module containing 'TextEmbeddingResult' model and run_embed_text method definition."""
 
 import asyncio
 import logging
diff --git a/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py b/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
index 0d2ab1146e..96661c412d 100644
--- a/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
+++ b/packages/graphrag/graphrag/index/operations/extract_graph/extract_graph.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing entity_extract methods."""
+"""A module containing extract_graph method."""
 
 import logging
 
@@ -35,7 +35,7 @@ async def run_strategy(row):
         nonlocal num_started
         text = row[text_column]
         id = row[id_column]
-        result = await run_extract_graph(
+        result = await _run_extract_graph(
             text=text,
             source_id=id,
             entity_types=entity_types,
@@ -68,7 +68,7 @@ async def run_strategy(row):
     return (entities, relationships)
 
 
-async def run_extract_graph(
+async def _run_extract_graph(
     text: str,
     source_id: str,
     entity_types: list[str],
diff --git a/packages/graphrag/graphrag/index/operations/graph_to_dataframes.py b/packages/graphrag/graphrag/index/operations/graph_to_dataframes.py
index dbc608f640..632eb12586 100644
--- a/packages/graphrag/graphrag/index/operations/graph_to_dataframes.py
+++ b/packages/graphrag/graphrag/index/operations/graph_to_dataframes.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing create_graph definition."""
+"""A module containing graph_to_dataframes method definition."""
 
 import networkx as nx
 import pandas as pd
diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py
index 0e0a4e39f0..6ae59a40c8 100644
--- a/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py
+++ b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py
@@ -1,6 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
-"""A module containing the build_mixed_context method definition."""
+
+"""A module containing build_mixed_context method definition."""
 
 import pandas as pd
 
diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py b/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py
index f6028332bc..709a4e3b5a 100644
--- a/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py
+++ b/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing create_community_reports and load_strategy methods definition."""
+"""A module containing summarize_communities method definition."""
 
 import logging
 from collections.abc import Callable
diff --git a/packages/graphrag/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py b/packages/graphrag/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
index 4a999fd685..d31a12611b 100644
--- a/packages/graphrag/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
+++ b/packages/graphrag/graphrag/index/operations/summarize_descriptions/description_summary_extractor.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'GraphExtractionResult' and 'GraphExtractor' models."""
+"""A module containing 'SummarizationResult' and 'SummarizeExtractor' models."""
 
 import json
 from dataclasses import dataclass
diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py
index caa9153ef1..cb46510afe 100644
--- a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py
+++ b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing the 'Tokenizer', 'TextSplitter', 'NoopTextSplitter' and 'TokenTextSplitter' models."""
+"""A module containing 'TokenChunkerOptions', 'TextSplitter', 'NoopTextSplitter', 'TokenTextSplitter', 'split_single_text_on_tokens', and 'split_multiple_texts_on_tokens'."""
 
 import logging
 from abc import ABC, abstractmethod
diff --git a/packages/graphrag/graphrag/index/utils/derive_from_rows.py b/packages/graphrag/graphrag/index/utils/derive_from_rows.py
index 663d78c1c8..bb4e8a3016 100644
--- a/packages/graphrag/graphrag/index/utils/derive_from_rows.py
+++ b/packages/graphrag/graphrag/index/utils/derive_from_rows.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""Apply a generic transform function to each row in a table."""
+"""A module containing derive_from_rows, derive_from_rows_asyncio_threads, and derive_from_rows_asyncio methods."""
 
 import asyncio
 import inspect
@@ -55,9 +55,6 @@ async def derive_from_rows(
             raise ValueError(msg)
 
 
-"""A module containing the derive_from_rows_async method."""
-
-
 async def derive_from_rows_asyncio_threads(
     input: pd.DataFrame,
     transform: Callable[[pd.Series], Awaitable[ItemType]],
@@ -88,9 +85,6 @@ async def execute_task(task: Coroutine) -> ItemType | None:
     )
 
 
-"""A module containing the derive_from_rows_async method."""
-
-
 async def derive_from_rows_asyncio(
     input: pd.DataFrame,
     transform: Callable[[pd.Series], Awaitable[ItemType]],
diff --git a/tests/mock_provider.py b/tests/mock_provider.py
index 524222dc8f..9ffb52fb48 100644
--- a/tests/mock_provider.py
+++ b/tests/mock_provider.py
@@ -1,7 +1,7 @@
 # Copyright (c) 2025 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing mock model provider definitions."""
+"""A module containing 'MockChatLLM' and 'MockEmbeddingLLM' models."""
 
 from collections.abc import AsyncGenerator, Generator
 from typing import Any
diff --git a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py b/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
index 33e5b7ccde..029d34fa48 100644
--- a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
+++ b/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
@@ -10,9 +10,9 @@
 from graphrag.config.enums import ChunkStrategyType
 from graphrag.index.operations.chunk_text.chunk_text import (
     _get_num_total,
+    _load_strategy,
+    _run_strategy,
     chunk_text,
-    load_strategy,
-    run_strategy,
 )
 from graphrag.index.operations.chunk_text.typing import (
     TextChunk,
@@ -36,7 +36,7 @@ def test_get_num_total_array():
 def test_load_strategy_tokens():
     strategy_type = ChunkStrategyType.tokens
 
-    strategy_loaded = load_strategy(strategy_type)
+    strategy_loaded = _load_strategy(strategy_type)
 
     assert strategy_loaded.__name__ == "run_tokens"
 
@@ -44,7 +44,7 @@ def test_load_strategy_tokens():
 def test_load_strategy_sentence():
     strategy_type = ChunkStrategyType.sentence
 
-    strategy_loaded = load_strategy(strategy_type)
+    strategy_loaded = _load_strategy(strategy_type)
 
     assert strategy_loaded.__name__ == "run_sentences"
 
@@ -55,7 +55,7 @@ def test_load_strategy_none():
     with pytest.raises(
         ValueError, match="Unknown strategy: <enum 'ChunkStrategyType'>"
     ):
-        load_strategy(strategy_type)  # type: ignore
+        _load_strategy(strategy_type)  # type: ignore
 
 
 def test_run_strategy_str():
@@ -71,7 +71,7 @@ def test_run_strategy_str():
         )
     ]
 
-    runned = run_strategy(strategy_mocked, input, config, tick)
+    runned = _run_strategy(strategy_mocked, input, config, tick)
     assert runned == ["text test for run strategy"]
 
 
@@ -93,7 +93,7 @@ def test_run_strategy_arr_str():
         "use for strategy",
     ]
 
-    runned = run_strategy(strategy_mocked, input, config, tick)
+    runned = _run_strategy(strategy_mocked, input, config, tick)
     assert runned == expected
 
 
@@ -123,7 +123,7 @@ def test_run_strategy_arr_tuple():
         ),
     ]
 
-    runned = run_strategy(strategy_mocked, input, config, tick)
+    runned = _run_strategy(strategy_mocked, input, config, tick)
     assert runned == expected
 
 
@@ -153,12 +153,12 @@ def test_run_strategy_arr_tuple_same_doc():
         ),
     ]
 
-    runned = run_strategy(strategy_mocked, input, config, tick)
+    runned = _run_strategy(strategy_mocked, input, config, tick)
     assert runned == expected
 
 
-@mock.patch("graphrag.index.operations.chunk_text.chunk_text.load_strategy")
-@mock.patch("graphrag.index.operations.chunk_text.chunk_text.run_strategy")
+@mock.patch("graphrag.index.operations.chunk_text.chunk_text._load_strategy")
+@mock.patch("graphrag.index.operations.chunk_text.chunk_text._run_strategy")
 @mock.patch("graphrag.index.operations.chunk_text.chunk_text.progress_ticker")
 def test_chunk_text(mock_progress_ticker, mock_run_strategy, mock_load_strategy):
     input_data = pd.DataFrame({"name": ["The Shining"]})
diff --git a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
index 0751adb588..7d89d54220 100644
--- a/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
+++ b/tests/unit/indexing/verbs/entities/extraction/strategies/graph_intelligence/test_gi_entity_extraction.py
@@ -2,7 +2,7 @@
 # Licensed under the MIT License
 import unittest
 
-from graphrag.index.operations.extract_graph.extract_graph import run_extract_graph
+from graphrag.index.operations.extract_graph.extract_graph import _run_extract_graph
 from graphrag.prompts.index.extract_graph import GRAPH_EXTRACTION_PROMPT
 
 from tests.unit.indexing.verbs.helpers.mock_llm import create_mock_llm
@@ -22,7 +22,7 @@
 
 class TestRunChain(unittest.IsolatedAsyncioTestCase):
     async def test_run_extract_graph_single_document_correct_entities_returned(self):
-        entities_df, _ = await run_extract_graph(
+        entities_df, _ = await _run_extract_graph(
             text="test_text",
             source_id="1",
             entity_types=["person"],
@@ -39,7 +39,7 @@ async def test_run_extract_graph_single_document_correct_entities_returned(self)
         )
 
     async def test_run_extract_graph_single_document_correct_edges_returned(self):
-        _, relationships_df = await run_extract_graph(
+        _, relationships_df = await _run_extract_graph(
             text="test_text",
             source_id="1",
             entity_types=["person"],
@@ -61,7 +61,7 @@ async def test_run_extract_graph_single_document_correct_edges_returned(self):
         }
 
     async def test_run_extract_graph_single_document_source_ids_mapped(self):
-        entities_df, relationships_df = await run_extract_graph(
+        entities_df, relationships_df = await _run_extract_graph(
             text="test_text",
             source_id="1",
             entity_types=["person"],

From c649d9f6eee6475712144a1b20bcfffe95abe312 Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Tue, 30 Dec 2025 18:08:10 -0600
Subject: [PATCH 33/40] Issue #2004 fix (#2159)

* fix issue #2004 using KeenhoChu idea in his PR

* add unit test for dynamic community selection

* add unit test for dynamic community selection implementing #2158 logic

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 docs/examples_notebooks/api_overview.ipynb    |   5 +-
 docs/examples_notebooks/input_documents.ipynb |   5 +-
 .../dynamic_community_selection.py            |   6 +-
 .../dynamic_community_selection.py            | 205 ++++++++++++++++++
 tests/verbs/test_create_community_reports.py  |   9 +-
 unified-search-app/app/app_logic.py           |   3 +-
 6 files changed, 222 insertions(+), 11 deletions(-)
 create mode 100644 tests/unit/query/context_builder/dynamic_community_selection.py

diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index 2a0c0f15de..abcd7832fc 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,10 +28,11 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
+    "\n",
+    "import graphrag.api as api"
    ]
   },
   {
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 5657770eaf..505c0fe1f3 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,10 +30,11 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
-    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
+    "\n",
+    "import graphrag.api as api"
    ]
   },
   {
diff --git a/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py
index 904478f738..3981e5c8e5 100644
--- a/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py
+++ b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py
@@ -123,8 +123,10 @@ async def select(self, query: str) -> tuple[list[CommunityReport], dict[str, Any
                     # TODO check why some sub_communities are NOT in report_df
                     if community in self.communities:
                         for child in self.communities[community].children:
-                            if child in self.reports:
-                                communities_to_rate.append(child)
+                            # Convert child to string to match self.reports key type
+                            child_str = str(child)
+                            if child_str in self.reports:
+                                communities_to_rate.append(child_str)
                             else:
                                 logger.debug(
                                     "dynamic community selection: cannot find community %s in reports",
diff --git a/tests/unit/query/context_builder/dynamic_community_selection.py b/tests/unit/query/context_builder/dynamic_community_selection.py
new file mode 100644
index 0000000000..ba63f0c774
--- /dev/null
+++ b/tests/unit/query/context_builder/dynamic_community_selection.py
@@ -0,0 +1,205 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Tests for dynamic community selection with type handling."""
+
+from unittest.mock import MagicMock
+
+from graphrag.data_model.community import Community
+from graphrag.data_model.community_report import CommunityReport
+from graphrag.query.context_builder.dynamic_community_selection import (
+    DynamicCommunitySelection,
+)
+
+
+def create_mock_tokenizer() -> MagicMock:
+    """Create a mock tokenizer."""
+    tokenizer = MagicMock()
+    tokenizer.encode.return_value = [1, 2, 3]
+    return tokenizer
+
+
+def create_mock_model() -> MagicMock:
+    """Create a mock chat model."""
+    return MagicMock()
+
+
+def test_dynamic_community_selection_handles_int_children():
+    """Test that DynamicCommunitySelection correctly handles children IDs as integers.
+
+    This tests the fix for issue #2004 where children IDs could be integers
+    while self.reports keys are strings, causing child communities to be skipped.
+    """
+    # Create communities with integer children (simulating the bug scenario)
+    # Note: Even though the type annotation says list[str], actual data may have ints
+    communities = [
+        Community(
+            id="comm-0",
+            short_id="0",
+            title="Root Community",
+            level="0",
+            parent="",
+            children=[1, 2],  # type: ignore[list-item]  # Integer children - testing bug fix
+        ),
+        Community(
+            id="comm-1",
+            short_id="1",
+            title="Child Community 1",
+            level="1",
+            parent="0",
+            children=[],
+        ),
+        Community(
+            id="comm-2",
+            short_id="2",
+            title="Child Community 2",
+            level="1",
+            parent="0",
+            children=[],
+        ),
+    ]
+
+    # Create community reports with string community_id
+    reports = [
+        CommunityReport(
+            id="report-0",
+            short_id="0",
+            title="Report 0",
+            community_id="0",
+            summary="Root community summary",
+            full_content="Root community full content",
+            rank=1.0,
+        ),
+        CommunityReport(
+            id="report-1",
+            short_id="1",
+            title="Report 1",
+            community_id="1",
+            summary="Child 1 summary",
+            full_content="Child 1 full content",
+            rank=1.0,
+        ),
+        CommunityReport(
+            id="report-2",
+            short_id="2",
+            title="Report 2",
+            community_id="2",
+            summary="Child 2 summary",
+            full_content="Child 2 full content",
+            rank=1.0,
+        ),
+    ]
+
+    model = create_mock_model()
+    tokenizer = create_mock_tokenizer()
+
+    selector = DynamicCommunitySelection(
+        community_reports=reports,
+        communities=communities,
+        model=model,
+        tokenizer=tokenizer,
+        threshold=1,
+        keep_parent=False,
+        max_level=2,
+    )
+
+    # Verify that reports are keyed by string
+    assert "0" in selector.reports
+    assert "1" in selector.reports
+    assert "2" in selector.reports
+
+    # Verify that communities are keyed by string short_id
+    assert "0" in selector.communities
+    assert "1" in selector.communities
+    assert "2" in selector.communities
+
+    # Verify that the children are properly accessible
+    # Before the fix, int children would fail the `in self.reports` check
+    root_community = selector.communities["0"]
+    for child in root_community.children:
+        child_id = str(child)
+        # This should now work with the fix
+        assert child_id in selector.reports, (
+            f"Child {child} (as '{child_id}') should be found in reports"
+        )
+
+
+def test_dynamic_community_selection_handles_str_children():
+    """Test that DynamicCommunitySelection works correctly with string children IDs."""
+    communities = [
+        Community(
+            id="comm-0",
+            short_id="0",
+            title="Root Community",
+            level="0",
+            parent="",
+            children=["1", "2"],  # String children - expected type
+        ),
+        Community(
+            id="comm-1",
+            short_id="1",
+            title="Child Community 1",
+            level="1",
+            parent="0",
+            children=[],
+        ),
+        Community(
+            id="comm-2",
+            short_id="2",
+            title="Child Community 2",
+            level="1",
+            parent="0",
+            children=[],
+        ),
+    ]
+
+    reports = [
+        CommunityReport(
+            id="report-0",
+            short_id="0",
+            title="Report 0",
+            community_id="0",
+            summary="Root community summary",
+            full_content="Root community full content",
+            rank=1.0,
+        ),
+        CommunityReport(
+            id="report-1",
+            short_id="1",
+            title="Report 1",
+            community_id="1",
+            summary="Child 1 summary",
+            full_content="Child 1 full content",
+            rank=1.0,
+        ),
+        CommunityReport(
+            id="report-2",
+            short_id="2",
+            title="Report 2",
+            community_id="2",
+            summary="Child 2 summary",
+            full_content="Child 2 full content",
+            rank=1.0,
+        ),
+    ]
+
+    model = create_mock_model()
+    tokenizer = create_mock_tokenizer()
+
+    selector = DynamicCommunitySelection(
+        community_reports=reports,
+        communities=communities,
+        model=model,
+        tokenizer=tokenizer,
+        threshold=1,
+        keep_parent=False,
+        max_level=2,
+    )
+
+    # Verify that children can be found in reports
+    root_community = selector.communities["0"]
+    for child in root_community.children:
+        child_id = str(child)
+        assert child_id in selector.reports, (
+            f"Child {child} (as '{child_id}') should be found in reports"
+        )
diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py
index d479120ce2..561f54108b 100644
--- a/tests/verbs/test_create_community_reports.py
+++ b/tests/verbs/test_create_community_reports.py
@@ -4,15 +4,16 @@
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
-from graphrag.index.operations.summarize_communities.community_reports_extractor import (
-    CommunityReportResponse,
-    FindingModel,
-)
 from graphrag.index.workflows.create_community_reports import (
     run_workflow,
 )
 from graphrag.utils.storage import load_table_from_storage
 
+from graphrag.index.operations.summarize_communities.community_reports_extractor import (
+    CommunityReportResponse,
+    FindingModel,
+)
+
 from .util import (
     DEFAULT_MODEL_CONFIG,
     compare_outputs,
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index dc64e0e77c..a573b9daa5 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,7 +7,6 @@
 import logging
 from typing import TYPE_CHECKING
 
-import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -18,6 +17,8 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
+import graphrag.api as api
+
 if TYPE_CHECKING:
     import pandas as pd
 

From fde14b63e5f6c38826bdc868887e765dccb92fa9 Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Tue, 30 Dec 2025 18:08:51 -0600
Subject: [PATCH 34/40] Mismatch between header in community report generation
 prompt examples and input data (id vs human_readable_id) (#2161)

* fix issue #860 for mismatch in prompts and input

* fix format

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 .../prompt_tune/template/community_report_summarization.py    | 4 ++--
 packages/graphrag/graphrag/prompts/index/community_report.py  | 4 ++--
 tests/fixtures/text/prompts/community_report.txt              | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/packages/graphrag/graphrag/prompt_tune/template/community_report_summarization.py b/packages/graphrag/graphrag/prompt_tune/template/community_report_summarization.py
index b0d037ab6b..32888fb137 100644
--- a/packages/graphrag/graphrag/prompt_tune/template/community_report_summarization.py
+++ b/packages/graphrag/graphrag/prompt_tune/template/community_report_summarization.py
@@ -56,13 +56,13 @@
 
 Entities
 
-id,entity,description
+human_readable_id,title,description
 5,VERDANT OASIS PLAZA,Verdant Oasis Plaza is the location of the Unity March
 6,HARMONY ASSEMBLY,Harmony Assembly is an organization that is holding a march at Verdant Oasis Plaza
 
 Relationships
 
-id,source,target,description
+human_readable_id,source,target,description
 37,VERDANT OASIS PLAZA,UNITY MARCH,Verdant Oasis Plaza is the location of the Unity March
 38,VERDANT OASIS PLAZA,HARMONY ASSEMBLY,Harmony Assembly is holding a march at Verdant Oasis Plaza
 39,VERDANT OASIS PLAZA,UNITY MARCH,The Unity March is taking place at Verdant Oasis Plaza
diff --git a/packages/graphrag/graphrag/prompts/index/community_report.py b/packages/graphrag/graphrag/prompts/index/community_report.py
index c3a7702ba0..b1f0f8abe2 100644
--- a/packages/graphrag/graphrag/prompts/index/community_report.py
+++ b/packages/graphrag/graphrag/prompts/index/community_report.py
@@ -59,13 +59,13 @@
 
 Entities
 
-id,entity,description
+human_readable_id,title,description
 5,VERDANT OASIS PLAZA,Verdant Oasis Plaza is the location of the Unity March
 6,HARMONY ASSEMBLY,Harmony Assembly is an organization that is holding a march at Verdant Oasis Plaza
 
 Relationships
 
-id,source,target,description
+human_readable_id,source,target,description
 37,VERDANT OASIS PLAZA,UNITY MARCH,Verdant Oasis Plaza is the location of the Unity March
 38,VERDANT OASIS PLAZA,HARMONY ASSEMBLY,Harmony Assembly is holding a march at Verdant Oasis Plaza
 39,VERDANT OASIS PLAZA,UNITY MARCH,The Unity March is taking place at Verdant Oasis Plaza
diff --git a/tests/fixtures/text/prompts/community_report.txt b/tests/fixtures/text/prompts/community_report.txt
index f939001c5b..4d78837774 100644
--- a/tests/fixtures/text/prompts/community_report.txt
+++ b/tests/fixtures/text/prompts/community_report.txt
@@ -35,12 +35,12 @@ Text:
 
 Entities
 
-id,entity,description
+human_readable_id,title,description
 5,ABILA CITY PARK,Abila City Park is the location of the POK rally
 
 Relationships
 
-id,source,target,description
+human_readable_id,source,target,description
 37,ABILA CITY PARK,POK RALLY,Abila City Park is the location of the POK rally
 38,ABILA CITY PARK,POK,POK is holding a rally in Abila City Park
 39,ABILA CITY PARK,POKRALLY,The POKRally is taking place at Abila City Park

From 8fd7730067b9a0b777fc1dbf47e2ef73e16f009a Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 6 Jan 2026 15:39:44 -0800
Subject: [PATCH 35/40] Chunker factory (#2156)

* Delete NoopTextSplitter

* Delete unused check_token_limit

* Add base chunking factory and migrate workflow to use it

* Split apart chunker module

* Co-locate chunking/splitting

* Collapse token splitting functionality into one class/function

* Restore create_base_text_units parameterization

* Move Tokenizer base class to common package

* Move pre-pending into chunkers

* Streamline config

* Fix defaults construction

* Add prepending tests

* Remove chunk_size_includes_metadata config

* Revert ChunkingDocument interface

* Move metadata prepending to a util

* Move Tokenizer back to GR core

* Fix tokenizer removal from chunker

* Set defaults for chunking config

* Move chunking to monorepo package

* Format

* Typo

* Add ChunkResult model

* Streamline chunking config

* Add missing version updates for graphrag_chunking
---
 docs/config/yaml.md                           |    3 +-
 docs/index/inputs.md                          |   52 +-
 packages/graphrag-chunking/README.md          |   32 +
 .../graphrag_chunking}/__init__.py            |    2 +
 .../graphrag_chunking/add_metadata.py         |   19 +
 .../graphrag_chunking/bootstrap_nltk.py}      |    0
 .../graphrag_chunking/chunk_result.py         |   17 +
 .../graphrag_chunking/chunk_strategy_type.py  |   13 +
 .../graphrag_chunking/chunker.py              |   21 +
 .../graphrag_chunking/chunker_factory.py      |   77 +
 .../graphrag_chunking/chunking_config.py      |   36 +
 .../graphrag_chunking/create_chunk_results.py |   30 +
 .../graphrag_chunking/sentence_chunker.py     |   44 +
 .../graphrag_chunking/token_chunker.py        |   67 +
 packages/graphrag-chunking/pyproject.toml     |   43 +
 packages/graphrag/graphrag/api/prompt_tune.py |    9 -
 packages/graphrag/graphrag/cli/main.py        |    8 +-
 packages/graphrag/graphrag/cli/prompt_tune.py |   10 +-
 packages/graphrag/graphrag/config/defaults.py |   15 +-
 packages/graphrag/graphrag/config/enums.py    |   11 -
 .../graphrag/graphrag/config/init_content.py  |    8 +-
 .../graphrag/config/models/chunking_config.py |   38 -
 .../config/models/graph_rag_config.py         |   12 +-
 .../index/operations/chunk_text/__init__.py   |    4 -
 .../index/operations/chunk_text/chunk_text.py |  140 -
 .../index/operations/chunk_text/strategies.py |   52 -
 .../index/operations/chunk_text/typing.py     |   27 -
 .../operations/embed_text/run_embed_text.py   |   36 +-
 .../index/text_splitting/check_token_limit.py |   15 -
 .../index/text_splitting/text_splitting.py    |  113 +-
 .../index/workflows/create_base_text_units.py |   90 +-
 .../graphrag/prompt_tune/loader/input.py      |   13 +-
 .../graphrag/tokenizer/get_tokenizer.py       |    4 +-
 pyproject.toml                                |    3 +
 .../operations => chunking}/__init__.py       |    0
 tests/unit/chunking/test_chunker.py           |  186 ++
 tests/unit/chunking/test_prepend_metadata.py  |   43 +
 tests/unit/config/utils.py                    |    7 +-
 .../operations/chunk_text/test_chunk_text.py  |  180 --
 .../operations/chunk_text/test_strategies.py  |  127 -
 .../unit/indexing/text_splitting/__init__.py  |    2 -
 .../text_splitting/test_text_splitting.py     |  169 --
 tests/verbs/test_create_base_text_units.py    |   21 +-
 uv.lock                                       | 2522 +++++++++--------
 44 files changed, 2005 insertions(+), 2316 deletions(-)
 create mode 100644 packages/graphrag-chunking/README.md
 rename {tests/unit/indexing/operations/chunk_text => packages/graphrag-chunking/graphrag_chunking}/__init__.py (66%)
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/add_metadata.py
 rename packages/{graphrag/graphrag/index/operations/chunk_text/bootstrap.py => graphrag-chunking/graphrag_chunking/bootstrap_nltk.py} (100%)
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/chunk_result.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/chunker.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/chunker_factory.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/chunking_config.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/token_chunker.py
 create mode 100644 packages/graphrag-chunking/pyproject.toml
 delete mode 100644 packages/graphrag/graphrag/config/models/chunking_config.py
 delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/__init__.py
 delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
 delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
 delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/typing.py
 delete mode 100644 packages/graphrag/graphrag/index/text_splitting/check_token_limit.py
 rename tests/unit/{indexing/operations => chunking}/__init__.py (100%)
 create mode 100644 tests/unit/chunking/test_chunker.py
 create mode 100644 tests/unit/chunking/test_prepend_metadata.py
 delete mode 100644 tests/unit/indexing/operations/chunk_text/test_chunk_text.py
 delete mode 100644 tests/unit/indexing/operations/chunk_text/test_strategies.py
 delete mode 100644 tests/unit/indexing/text_splitting/__init__.py
 delete mode 100644 tests/unit/indexing/text_splitting/test_text_splitting.py

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 768abef463..10973fd211 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -100,12 +100,11 @@ These settings configure how we parse documents into text chunks. This is necess
 
 #### Fields
 
+- `strategy` **str**[tokens|sentences] - How to chunk the text. 
 - `size` **int** - The max chunk size in tokens.
 - `overlap` **int** - The chunk overlap in tokens.
-- `strategy` **str**[tokens|sentences] - How to chunk the text. 
 - `encoding_model` **str** - The text encoding model to use for splitting on token boundaries.
 - `prepend_metadata` **bool** - Determines if metadata values should be added at the beginning of each chunk. Default=`False`.
-- `chunk_size_includes_metadata` **bool** - Specifies whether the chunk size calculation should include metadata tokens. Default=`False`.
 
 ## Outputs and Storage
 
diff --git a/docs/index/inputs.md b/docs/index/inputs.md
index b62bceacfa..7f34370a70 100644
--- a/docs/index/inputs.md
+++ b/docs/index/inputs.md
@@ -82,10 +82,9 @@ As described above, when documents are imported you can specify a list of `metad
 
 ### Chunking Config
 
-Next, the `chunks` block needs to instruct the chunker how to handle this metadata when creating text units. By default, it is ignored. We have two settings to include it:
+Next, the `chunks` block needs to instruct the chunker how to handle this metadata when creating text units. By default, it is ignored. We have the following setting to include it:
 
 - `prepend_metadata`. This instructs the importer to copy the contents of the `metadata` column for each row into the start of every single text chunk. This metadata is copied as key: value pairs on new lines.
-- `chunk_size_includes_metadata`: This tells the chunker how to compute the chunk size when metadata is included. By default, we create the text units using your specified `chunk_size` *and then* prepend the metadata. This means that the final text unit lengths may be longer than your configured `chunk_size`, and it will vary based on the length of the metadata for each document. When this setting is `True`, we will compute the raw text using the remainder after measuring the metadata length so that the resulting text units always comply with your configured `chunk_size`.
 
 ### Examples
 
@@ -124,7 +123,6 @@ chunks:
     size: 100
     overlap: 0
     prepend_metadata: true
-    chunk_size_includes_metadata: false
 ```
 
 Documents DataFrame
@@ -162,54 +160,6 @@ US to lift most federal COVID-19 vaccine mandates,WASHINGTON (AP) The Biden admi
 
 NY lawmakers begin debating budget 1 month after due date,ALBANY, N.Y. (AP) New York lawmakers began voting Monday on a $229 billion state budget due a month ago that would raise the minimum wage, crack down on illicit pot shops and ban gas stoves and furnaces in new buildings. Negotiations among Gov. Kathy Hochul and her fellow Democrats in control of the Legislature dragged on past the April 1 budget deadline, largely because of disagreements over changes to the bail law and other policy proposals included in the spending plan. Floor debates on some budget bills began Monday. State Senate Majority Leader Andrea Stewart-Cousins said she expected voting to be wrapped up Tuesday for a budget she said contains "significant wins" for New Yorkers. "I would have liked to have done this sooner. I think we would all agree to that," Cousins told reporters before voting began. "This has been a very policy-laden budget and a lot of the policies had to parsed through." Hochul was able to push through a change to the bail law that will eliminate the standard that requires judges to prescribe the "least restrictive" means to ensure defendants return to court. Hochul said judges needed the extra discretion. Some liberal lawmakers argued that it would undercut the sweeping bail reforms approved in 2019 and result in more people with low incomes and people of color in pretrial detention. Here are some other policy provisions that will be included in the budget, according to state officials. The minimum wage would be raised to $17 in New York City and some of its suburbs and $16 in the rest of the state by 2026. That's up from $15 in the city and $14.20 upstate.
 
---
-
-settings.yaml
-
-```yaml
-input:
-    file_type: csv
-    title_column: headline
-    text_column: article
-    metadata: [headline]
-
-chunks:
-    size: 50
-    overlap: 5
-    prepend_metadata: true
-    chunk_size_includes_metadata: true
-```
-
-Documents DataFrame
-
-| id                    | title                                                     | text                     | creation_date                 | metadata                                                                    |
-| --------------------- | --------------------------------------------------------- | ------------------------ | ----------------------------- | --------------------------------------------------------------------------- |
-| (generated from text) | US to lift most federal COVID-19 vaccine mandates         | (article column content) | (create date of articles.csv) | { "headline": "US to lift most federal COVID-19 vaccine mandates" }         |
-| (generated from text) | NY lawmakers begin debating budget 1 month after due date | (article column content) | (create date of articles.csv) | { "headline": "NY lawmakers begin debating budget 1 month after due date" } |
-
-Raw Text Chunks
-
-| content | length  |
-| ------- | ------: |
-| title: US to lift most federal COVID-19 vaccine mandates<br>WASHINGTON (AP) The Biden administration will end most of the last remaining federal COVID-19 vaccine requirements next week when the national public health emergency for the coronavirus ends, the White House said Monday. Vaccine requirements for federal workers and federal contractors, | 50 |
-| title: US to lift most federal COVID-19 vaccine mandates<br>federal workers and federal contractors as well as foreign air travelers to the U.S., will end May 11. The government is also beginning the process of lifting shot requirements for Head Start educators, healthcare workers, and noncitizens at U.S. land borders. | 50 |
-| title: US to lift most federal COVID-19 vaccine mandates<br>noncitizens at U.S. land borders. The requirements are among the last vestiges of some of the more coercive measures taken by the federal government to promote vaccination as the deadly virus raged, and their end marks the latest display of how | 50 |
-| title: US to lift most federal COVID-19 vaccine mandates<br>the latest display of how  President Joe Biden's administration is moving to treat COVID-19 as a routine, endemic illness. "While I believe that these vaccine mandates had a tremendous beneficial impact, we are now at a point where we think that | 50 |
-| title: US to lift most federal COVID-19 vaccine mandates<br>point where we think that it makes a lot of sense to pull these requirements down," White House COVID-19 coordinator Dr. Ashish Jha told The Associated Press on Monday. | 38 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>ALBANY, N.Y. (AP) New York lawmakers began voting Monday on a $229 billion state budget due a month ago that would raise the minimum wage, crack down on illicit pot shops and ban gas stoves and furnaces in new | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>stoves and furnaces in new buildings. Negotiations among Gov. Kathy Hochul and her fellow Democrats in control of the Legislature dragged on past the April 1 budget deadline, largely because of disagreements over changes to the bail law and | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>to the bail law and other policy proposals included in the spending plan. Floor debates on some budget bills began Monday. State Senate Majority Leader Andrea Stewart-Cousins said she expected voting to be wrapped up Tuesday for a budget | 50 |
-|title: NY lawmakers begin debating budget 1 month after due date<br>up Tuesday for a budget she said contains "significant wins" for New Yorkers. "I would have liked to have done this sooner. I think we would all agree to that," Cousins told reporters before voting began. "This has been | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>voting began. "This has been a very policy-laden budget and a lot of the policies had to parsed through." Hochul was able to push through a change to the bail law that will eliminate the standard that requires judges | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>the standard that requires judges to prescribe the "least restrictive" means to ensure defendants return to court. Hochul said judges needed the extra discretion. Some liberal lawmakers argued that it would undercut the sweeping bail reforms approved in 2019 | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>bail reforms approved in 2019 and result in more people with low incomes and people of color in pretrial detention. Here are some other policy provisions that will be included in the budget, according to state officials. The minimum | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>to state officials. The minimum  wage would be raised to $17 in be raised to $17 in New York City and some of its suburbs and $16 in the rest of the state by 2026. That's up from $15 | 50 |
-| title: NY lawmakers begin debating budget 1 month after due date<br>2026. That's up from $15 in the city and $14.20 upstate. | 22 |
-
-
-In this example we can see that the two input documents were parsed into fourteen output text chunks. The title (headline) of each document is prepended and included in the computed chunk size, so each chunk matches the configured chunk size (except the last one for each document). We've also configured some overlap in these text chunks, so the last five tokens are shared. Why would you use overlap in your text chunks? Consider that when you are splitting documents based on tokens, it is highly likely that sentences or even related concepts will be split into separate chunks. Each text chunk is processed separately by the language model, so this may result in incomplete "ideas" at the boundaries of the chunk. Overlap ensures that these split concepts are fully contained in at least one of the chunks.
-
-
 #### JSON files
 
 This final example uses a JSON file for each of the same two articles. In this example we'll set the object fields to read, but we will not add metadata to the text chunks.
diff --git a/packages/graphrag-chunking/README.md b/packages/graphrag-chunking/README.md
new file mode 100644
index 0000000000..278b8961d5
--- /dev/null
+++ b/packages/graphrag-chunking/README.md
@@ -0,0 +1,32 @@
+# GraphRAG Chunking
+
+This package contains a collection of text chunkers, a core config model, and a factory for acquiring instances.
+
+## Examples
+
+Basic sentence chunking with nltk
+```python
+chunker = SentenceChunker()
+chunks = chunker.chunk("This is a test. Another sentence.")
+print(chunks) # ["This is a test.", "Another sentence."]
+```
+
+Token chunking
+```python
+tokenizer = tiktoken.get_encoding("o200k_base")
+chunker = TokenChunker(size=3, overlap=0, encode=tokenizer.encode, decode=tokenizer.decode)
+chunks = chunker.chunk("This is a random test fragment of some text")
+print(chunks) # ["This is a", " random test fragment", " of some text"]
+```
+
+Using the factory via helper util
+```python
+tokenizer = tiktoken.get_encoding("o200k_base")
+config = ChunkingConfig(
+    strategy="tokens",
+    size=3,
+    overlap=0
+)
+chunker = create_chunker(config, tokenizer.encode, tokenizer.decode)
+...
+```
\ No newline at end of file
diff --git a/tests/unit/indexing/operations/chunk_text/__init__.py b/packages/graphrag-chunking/graphrag_chunking/__init__.py
similarity index 66%
rename from tests/unit/indexing/operations/chunk_text/__init__.py
rename to packages/graphrag-chunking/graphrag_chunking/__init__.py
index 0a3e38adfb..6610d3a82f 100644
--- a/tests/unit/indexing/operations/chunk_text/__init__.py
+++ b/packages/graphrag-chunking/graphrag_chunking/__init__.py
@@ -1,2 +1,4 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
+
+"""System-level chunking package."""
diff --git a/packages/graphrag-chunking/graphrag_chunking/add_metadata.py b/packages/graphrag-chunking/graphrag_chunking/add_metadata.py
new file mode 100644
index 0000000000..3b02994d4c
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/add_metadata.py
@@ -0,0 +1,19 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'prepend_metadata' function."""
+
+
+def add_metadata(
+    text: str,
+    metadata: dict,
+    delimiter: str = ": ",
+    line_delimiter: str = "\n",
+    append: bool = False,
+) -> str:
+    """Add metadata to the given text, prepending by default. This utility writes the dict as rows of key/value pairs."""
+    metadata_str = (
+        line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items())
+        + line_delimiter
+    )
+    return text + metadata_str if append else metadata_str + text
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/bootstrap.py b/packages/graphrag-chunking/graphrag_chunking/bootstrap_nltk.py
similarity index 100%
rename from packages/graphrag/graphrag/index/operations/chunk_text/bootstrap.py
rename to packages/graphrag-chunking/graphrag_chunking/bootstrap_nltk.py
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunk_result.py b/packages/graphrag-chunking/graphrag_chunking/chunk_result.py
new file mode 100644
index 0000000000..d9b3177511
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/chunk_result.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The ChunkResult dataclass."""
+
+from dataclasses import dataclass
+
+
+@dataclass
+class ChunkResult:
+    """Result of chunking a document."""
+
+    text: str
+    index: int
+    start_char: int
+    end_char: int
+    token_count: int | None = None
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py b/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py
new file mode 100644
index 0000000000..5de6321519
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Chunk strategy type enumeration."""
+
+from enum import StrEnum
+
+
+class ChunkerType(StrEnum):
+    """ChunkerType class definition."""
+
+    Tokens = "tokens"
+    Sentence = "sentence"
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunker.py b/packages/graphrag-chunking/graphrag_chunking/chunker.py
new file mode 100644
index 0000000000..552b28a8a8
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/chunker.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing the 'Chunker' class."""
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+from graphrag_chunking.chunk_result import ChunkResult
+
+
+class Chunker(ABC):
+    """Abstract base class for document chunkers."""
+
+    @abstractmethod
+    def __init__(self, **kwargs: Any) -> None:
+        """Create a chunker instance."""
+
+    @abstractmethod
+    def chunk(self, text: str) -> list[ChunkResult]:
+        """Chunk method definition."""
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py b/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py
new file mode 100644
index 0000000000..636d0909f2
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py
@@ -0,0 +1,77 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'ChunkerFactory', 'register_chunker', and 'create_chunker'."""
+
+from collections.abc import Callable
+
+from graphrag_common.factory.factory import Factory, ServiceScope
+
+from graphrag_chunking.chunk_strategy_type import ChunkerType
+from graphrag_chunking.chunker import Chunker
+from graphrag_chunking.chunking_config import ChunkingConfig
+
+
+class ChunkerFactory(Factory[Chunker]):
+    """Factory for creating Chunker instances."""
+
+
+chunker_factory = ChunkerFactory()
+
+
+def register_chunker(
+    chunker_type: str,
+    chunker_initializer: Callable[..., Chunker],
+    scope: ServiceScope = "transient",
+) -> None:
+    """Register a custom chunker implementation.
+
+    Args
+    ----
+        - chunker_type: str
+            The chunker id to register.
+        - chunker_initializer: Callable[..., Chunker]
+            The chunker initializer to register.
+    """
+    chunker_factory.register(chunker_type, chunker_initializer, scope)
+
+
+def create_chunker(
+    config: ChunkingConfig,
+    encode: Callable[[str], list[int]] | None = None,
+    decode: Callable[[list[int]], str] | None = None,
+) -> Chunker:
+    """Create a chunker implementation based on the given configuration.
+
+    Args
+    ----
+        - config: ChunkingConfig
+            The chunker configuration to use.
+
+    Returns
+    -------
+        Chunker
+            The created chunker implementation.
+    """
+    config_model = config.model_dump()
+    if encode is not None:
+        config_model["encode"] = encode
+    if decode is not None:
+        config_model["decode"] = decode
+    chunker_strategy = config.type
+
+    if chunker_strategy not in chunker_factory:
+        match chunker_strategy:
+            case ChunkerType.Tokens:
+                from graphrag_chunking.token_chunker import TokenChunker
+
+                register_chunker(ChunkerType.Tokens, TokenChunker)
+            case ChunkerType.Sentence:
+                from graphrag_chunking.sentence_chunker import SentenceChunker
+
+                register_chunker(ChunkerType.Sentence, SentenceChunker)
+            case _:
+                msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}."
+                raise ValueError(msg)
+
+    return chunker_factory.create(chunker_strategy, init_args=config_model)
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py
new file mode 100644
index 0000000000..1bb5b84e44
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Parameterization settings for the default configuration."""
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from graphrag_chunking.chunk_strategy_type import ChunkerType
+
+
+class ChunkingConfig(BaseModel):
+    """Configuration section for chunking."""
+
+    model_config = ConfigDict(extra="allow")
+    """Allow extra fields to support custom cache implementations."""
+
+    type: str = Field(
+        description="The chunking type to use.",
+        default=ChunkerType.Tokens,
+    )
+    encoding_model: str | None = Field(
+        description="The encoding model to use.",
+        default=None,
+    )
+    size: int = Field(
+        description="The chunk size to use.",
+        default=1200,
+    )
+    overlap: int = Field(
+        description="The chunk overlap to use.",
+        default=100,
+    )
+    prepend_metadata: bool = Field(
+        description="Prepend metadata into each chunk.",
+        default=False,
+    )
diff --git a/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py
new file mode 100644
index 0000000000..3eb15befff
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'create_chunk_results' function."""
+
+from collections.abc import Callable
+
+from graphrag_chunking.chunk_result import ChunkResult
+
+
+def create_chunk_results(
+    chunks: list[str],
+    encode: Callable[[str], list[int]] | None = None,
+) -> list[ChunkResult]:
+    """Create chunk results from a list of text chunks. The index assignments are 0-based and assume chunks we not stripped relative to the source text."""
+    results = []
+    start_char = 0
+    for index, chunk in enumerate(chunks):
+        end_char = start_char + len(chunk) - 1  # 0-based indices
+        chunk = ChunkResult(
+            text=chunk,
+            index=index,
+            start_char=start_char,
+            end_char=end_char,
+        )
+        if encode:
+            chunk.token_count = len(encode(chunk.text))
+        results.append(chunk)
+        start_char = end_char + 1
+    return results
diff --git a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py
new file mode 100644
index 0000000000..91e68ccaf2
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py
@@ -0,0 +1,44 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'SentenceChunker' class."""
+
+from collections.abc import Callable
+from typing import Any
+
+import nltk
+
+from graphrag_chunking.bootstrap_nltk import bootstrap
+from graphrag_chunking.chunk_result import ChunkResult
+from graphrag_chunking.chunker import Chunker
+from graphrag_chunking.create_chunk_results import create_chunk_results
+
+
+class SentenceChunker(Chunker):
+    """A chunker that splits text into sentence-based chunks."""
+
+    def __init__(
+        self, encode: Callable[[str], list[int]] | None = None, **kwargs: Any
+    ) -> None:
+        """Create a sentence chunker instance."""
+        self._encode = encode
+        bootstrap()
+
+    def chunk(self, text) -> list[ChunkResult]:
+        """Chunk the text into sentence-based chunks."""
+        sentences = nltk.sent_tokenize(text.strip())
+        results = create_chunk_results(sentences, encode=self._encode)
+        # nltk sentence tokenizer may trim whitespace, so we need to adjust start/end chars
+        for index, result in enumerate(results):
+            txt = result.text
+            start = result.start_char
+            actual_start = text.find(txt, start)
+            delta = actual_start - start
+            if delta > 0:
+                result.start_char += delta
+                result.end_char += delta
+                # bump the next to keep the start check from falling too far behind
+                if index < len(results) - 1:
+                    results[index + 1].start_char += delta
+                    results[index + 1].end_char += delta
+        return results
diff --git a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py
new file mode 100644
index 0000000000..9a5e8ecfb0
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py
@@ -0,0 +1,67 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'TokenChunker' class."""
+
+from collections.abc import Callable
+from typing import Any
+
+from graphrag_chunking.chunk_result import ChunkResult
+from graphrag_chunking.chunker import Chunker
+from graphrag_chunking.create_chunk_results import create_chunk_results
+
+
+class TokenChunker(Chunker):
+    """A chunker that splits text into token-based chunks."""
+
+    def __init__(
+        self,
+        size: int,
+        overlap: int,
+        encode: Callable[[str], list[int]],
+        decode: Callable[[list[int]], str],
+        **kwargs: Any,
+    ) -> None:
+        """Create a token chunker instance."""
+        self._size = size
+        self._overlap = overlap
+        self._encode = encode
+        self._decode = decode
+
+    def chunk(self, text: str) -> list[ChunkResult]:
+        """Chunk the text into token-based chunks."""
+        chunks = split_text_on_tokens(
+            text,
+            chunk_size=self._size,
+            chunk_overlap=self._overlap,
+            encode=self._encode,
+            decode=self._decode,
+        )
+        return create_chunk_results(chunks, encode=self._encode)
+
+
+def split_text_on_tokens(
+    text: str,
+    chunk_size: int,
+    chunk_overlap: int,
+    encode: Callable[[str], list[int]],
+    decode: Callable[[list[int]], str],
+) -> list[str]:
+    """Split a single text and return chunks using the tokenizer."""
+    result = []
+    input_tokens = encode(text)
+
+    start_idx = 0
+    cur_idx = min(start_idx + chunk_size, len(input_tokens))
+    chunk_tokens = input_tokens[start_idx:cur_idx]
+
+    while start_idx < len(input_tokens):
+        chunk_text = decode(list(chunk_tokens))
+        result.append(chunk_text)  # Append chunked text as string
+        if cur_idx == len(input_tokens):
+            break
+        start_idx += chunk_size - chunk_overlap
+        cur_idx = min(start_idx + chunk_size, len(input_tokens))
+        chunk_tokens = input_tokens[start_idx:cur_idx]
+
+    return result
diff --git a/packages/graphrag-chunking/pyproject.toml b/packages/graphrag-chunking/pyproject.toml
new file mode 100644
index 0000000000..4393245bbc
--- /dev/null
+++ b/packages/graphrag-chunking/pyproject.toml
@@ -0,0 +1,43 @@
+[project]
+name = "graphrag-chunking"
+version = "2.7.0"
+description = "Chunking utilities for GraphRAG"
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.11,<3.14"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+]
+dependencies = [
+    "graphrag-common==2.7.0",
+    "pydantic~=2.10",
+]
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
+
diff --git a/packages/graphrag/graphrag/api/prompt_tune.py b/packages/graphrag/graphrag/api/prompt_tune.py
index 43fb0a5590..e051102377 100644
--- a/packages/graphrag/graphrag/api/prompt_tune.py
+++ b/packages/graphrag/graphrag/api/prompt_tune.py
@@ -12,13 +12,10 @@
 """
 
 import logging
-from typing import Annotated
 
-import annotated_types
 from pydantic import PositiveInt, validate_call
 
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
-from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.language_model.manager import ModelManager
 from graphrag.logger.standard_logging import init_loggers
@@ -55,10 +52,6 @@
 @validate_call(config={"arbitrary_types_allowed": True})
 async def generate_indexing_prompts(
     config: GraphRagConfig,
-    chunk_size: PositiveInt = graphrag_config_defaults.chunks.size,
-    overlap: Annotated[
-        int, annotated_types.Gt(-1)
-    ] = graphrag_config_defaults.chunks.overlap,
     limit: PositiveInt = 15,
     selection_method: DocSelectionType = DocSelectionType.RANDOM,
     domain: str | None = None,
@@ -100,8 +93,6 @@ async def generate_indexing_prompts(
         limit=limit,
         select_method=selection_method,
         logger=logger,
-        chunk_size=chunk_size,
-        overlap=overlap,
         n_subset_max=n_subset_max,
         k=k,
     )
diff --git a/packages/graphrag/graphrag/cli/main.py b/packages/graphrag/graphrag/cli/main.py
index 5ede2faa6a..f79517f575 100644
--- a/packages/graphrag/graphrag/cli/main.py
+++ b/packages/graphrag/graphrag/cli/main.py
@@ -306,14 +306,14 @@ def _prompt_tune_cli(
         help="The minimum number of examples to generate/include in the entity extraction prompt.",
     ),
     chunk_size: int = typer.Option(
-        graphrag_config_defaults.chunks.size,
+        graphrag_config_defaults.chunking.size,
         "--chunk-size",
-        help="The size of each example text chunk. Overrides chunks.size in the configuration file.",
+        help="The size of each example text chunk. Overrides chunking.size in the configuration file.",
     ),
     overlap: int = typer.Option(
-        graphrag_config_defaults.chunks.overlap,
+        graphrag_config_defaults.chunking.overlap,
         "--overlap",
-        help="The overlap size for chunking documents. Overrides chunks.overlap in the configuration file.",
+        help="The overlap size for chunking documents. Overrides chunking.overlap in the configuration file.",
     ),
     language: str | None = typer.Option(
         None,
diff --git a/packages/graphrag/graphrag/cli/prompt_tune.py b/packages/graphrag/graphrag/cli/prompt_tune.py
index 2ba02e0ead..249dd7cd07 100644
--- a/packages/graphrag/graphrag/cli/prompt_tune.py
+++ b/packages/graphrag/graphrag/cli/prompt_tune.py
@@ -61,11 +61,11 @@ async def prompt_tune(
     )
 
     # override chunking config in the configuration
-    if chunk_size != graph_config.chunks.size:
-        graph_config.chunks.size = chunk_size
+    if chunk_size != graph_config.chunking.size:
+        graph_config.chunking.size = chunk_size
 
-    if overlap != graph_config.chunks.overlap:
-        graph_config.chunks.overlap = overlap
+    if overlap != graph_config.chunking.overlap:
+        graph_config.chunking.overlap = overlap
 
     # configure the root logger with the specified log level
     from graphrag.logger.standard_logging import init_loggers
@@ -81,8 +81,6 @@ async def prompt_tune(
 
     prompts = await api.generate_indexing_prompts(
         config=graph_config,
-        chunk_size=chunk_size,
-        overlap=overlap,
         limit=limit,
         selection_method=selection_method,
         domain=domain,
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index e330c82449..cab8732af2 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -8,13 +8,13 @@
 from typing import ClassVar
 
 from graphrag_cache import CacheType
+from graphrag_chunking.chunk_strategy_type import ChunkerType
 from graphrag_storage import StorageType
 
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
     AsyncType,
     AuthType,
-    ChunkStrategyType,
     InputFileType,
     ModelType,
     NounPhraseExtractorType,
@@ -57,15 +57,14 @@ class BasicSearchDefaults:
 
 
 @dataclass
-class ChunksDefaults:
-    """Default values for chunks."""
+class ChunkingDefaults:
+    """Default values for chunking."""
 
+    type: str = ChunkerType.Tokens
     size: int = 1200
     overlap: int = 100
-    strategy: ClassVar[ChunkStrategyType] = ChunkStrategyType.tokens
     encoding_model: str = ENCODING_MODEL
     prepend_metadata: bool = False
-    chunk_size_includes_metadata: bool = False
 
 
 @dataclass
@@ -127,7 +126,6 @@ class EmbedTextDefaults:
     batch_size: int = 16
     batch_max_tokens: int = 8191
     names: list[str] = field(default_factory=lambda: default_embeddings)
-    strategy: None = None
 
 
 @dataclass
@@ -140,7 +138,6 @@ class ExtractClaimsDefaults:
         "Any claims or facts that could be relevant to information discovery."
     )
     max_gleanings: int = 1
-    strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
     model_instance_name: str = "extract_claims"
 
@@ -154,7 +151,6 @@ class ExtractGraphDefaults:
         default_factory=lambda: ["organization", "person", "geo", "event"]
     )
     max_gleanings: int = 1
-    strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
     model_instance_name: str = "extract_graph"
 
@@ -361,7 +357,6 @@ class SummarizeDescriptionsDefaults:
     prompt: None = None
     max_length: int = 500
     max_input_tokens: int = 4_000
-    strategy: None = None
     model_id: str = DEFAULT_CHAT_MODEL_ID
     model_instance_name: str = "summarize_descriptions"
 
@@ -402,7 +397,7 @@ class GraphRagConfigDefaults:
     cache: CacheDefaults = field(default_factory=CacheDefaults)
     input: InputDefaults = field(default_factory=InputDefaults)
     embed_text: EmbedTextDefaults = field(default_factory=EmbedTextDefaults)
-    chunks: ChunksDefaults = field(default_factory=ChunksDefaults)
+    chunking: ChunkingDefaults = field(default_factory=ChunkingDefaults)
     snapshots: SnapshotsDefaults = field(default_factory=SnapshotsDefaults)
     extract_graph: ExtractGraphDefaults = field(default_factory=ExtractGraphDefaults)
     extract_graph_nlp: ExtractGraphNLPDefaults = field(
diff --git a/packages/graphrag/graphrag/config/enums.py b/packages/graphrag/graphrag/config/enums.py
index 4835763afc..855f062f27 100644
--- a/packages/graphrag/graphrag/config/enums.py
+++ b/packages/graphrag/graphrag/config/enums.py
@@ -112,17 +112,6 @@ class AsyncType(str, Enum):
     Threaded = "threaded"
 
 
-class ChunkStrategyType(str, Enum):
-    """ChunkStrategy class definition."""
-
-    tokens = "tokens"
-    sentence = "sentence"
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
-
-
 class SearchMethod(Enum):
     """The type of search to run."""
 
diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py
index 4567406820..086228bf55 100644
--- a/packages/graphrag/graphrag/config/init_content.py
+++ b/packages/graphrag/graphrag/config/init_content.py
@@ -54,9 +54,11 @@
     base_dir: "{graphrag_config_defaults.input.storage.base_dir}"
   file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json]
 
-chunks:
-  size: {graphrag_config_defaults.chunks.size}
-  overlap: {graphrag_config_defaults.chunks.overlap}
+chunking:
+  type: {graphrag_config_defaults.chunking.type}
+  size: {graphrag_config_defaults.chunking.size}
+  overlap: {graphrag_config_defaults.chunking.overlap}
+  encoding_model: {graphrag_config_defaults.chunking.encoding_model}
 
 ### Output/storage settings ###
 ## If blob storage is specified in the following four sections,
diff --git a/packages/graphrag/graphrag/config/models/chunking_config.py b/packages/graphrag/graphrag/config/models/chunking_config.py
deleted file mode 100644
index 4d1214241c..0000000000
--- a/packages/graphrag/graphrag/config/models/chunking_config.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from pydantic import BaseModel, Field
-
-from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.enums import ChunkStrategyType
-
-
-class ChunkingConfig(BaseModel):
-    """Configuration section for chunking."""
-
-    size: int = Field(
-        description="The chunk size to use.",
-        default=graphrag_config_defaults.chunks.size,
-    )
-    overlap: int = Field(
-        description="The chunk overlap to use.",
-        default=graphrag_config_defaults.chunks.overlap,
-    )
-    strategy: ChunkStrategyType = Field(
-        description="The chunking strategy to use.",
-        default=graphrag_config_defaults.chunks.strategy,
-    )
-    encoding_model: str = Field(
-        description="The encoding model to use.",
-        default=graphrag_config_defaults.chunks.encoding_model,
-    )
-    prepend_metadata: bool = Field(
-        description="Prepend metadata into each chunk.",
-        default=graphrag_config_defaults.chunks.prepend_metadata,
-    )
-    chunk_size_includes_metadata: bool = Field(
-        description="Count metadata in max tokens.",
-        default=graphrag_config_defaults.chunks.chunk_size_includes_metadata,
-    )
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 162fe12567..f79537cd04 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -8,6 +8,7 @@
 
 from devtools import pformat
 from graphrag_cache import CacheConfig
+from graphrag_chunking.chunking_config import ChunkingConfig
 from graphrag_storage import StorageConfig, StorageType
 from pydantic import BaseModel, Field, model_validator
 
@@ -15,7 +16,6 @@
 from graphrag.config.defaults import graphrag_config_defaults
 from graphrag.config.enums import VectorStoreType
 from graphrag.config.models.basic_search_config import BasicSearchConfig
-from graphrag.config.models.chunking_config import ChunkingConfig
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
@@ -125,9 +125,15 @@ def _validate_input_base_dir(self) -> None:
                 Path(self.input.storage.base_dir).resolve()
             )
 
-    chunks: ChunkingConfig = Field(
+    chunking: ChunkingConfig = Field(
         description="The chunking configuration to use.",
-        default=ChunkingConfig(),
+        default=ChunkingConfig(
+            type=graphrag_config_defaults.chunking.type,
+            size=graphrag_config_defaults.chunking.size,
+            overlap=graphrag_config_defaults.chunking.overlap,
+            encoding_model=graphrag_config_defaults.chunking.encoding_model,
+            prepend_metadata=graphrag_config_defaults.chunking.prepend_metadata,
+        ),
     )
     """The chunking configuration to use."""
 
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/__init__.py b/packages/graphrag/graphrag/index/operations/chunk_text/__init__.py
deleted file mode 100644
index 1e000e6aa7..0000000000
--- a/packages/graphrag/graphrag/index/operations/chunk_text/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The Indexing Engine text chunk package root."""
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py b/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
deleted file mode 100644
index cd63fec455..0000000000
--- a/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing chunk_text method definitions."""
-
-from typing import Any, cast
-
-import pandas as pd
-
-from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.models.chunking_config import ChunkingConfig, ChunkStrategyType
-from graphrag.index.operations.chunk_text.typing import (
-    ChunkInput,
-    ChunkStrategy,
-)
-from graphrag.logger.progress import ProgressTicker, progress_ticker
-
-
-def chunk_text(
-    input: pd.DataFrame,
-    column: str,
-    size: int,
-    overlap: int,
-    encoding_model: str,
-    strategy: ChunkStrategyType,
-    callbacks: WorkflowCallbacks,
-) -> pd.Series:
-    """
-    Chunk a piece of text into smaller pieces.
-
-    ## Usage
-    ```yaml
-    args:
-        column: <column name> # The name of the column containing the text to chunk, this can either be a column with text, or a column with a list[tuple[doc_id, str]]
-        strategy: <strategy config> # The strategy to use to chunk the text, see below for more details
-    ```
-
-    ## Strategies
-    The text chunk verb uses a strategy to chunk the text. The strategy is an object which defines the strategy to use. The following strategies are available:
-
-    ### tokens
-    This strategy uses the [tokens] library to chunk a piece of text. The strategy config is as follows:
-
-    ```yaml
-    strategy: tokens
-    size: 1200 # Optional, The chunk size to use, default: 1200
-    overlap: 100 # Optional, The chunk overlap to use, default: 100
-    ```
-
-    ### sentence
-    This strategy uses the nltk library to chunk a piece of text into sentences. The strategy config is as follows:
-
-    ```yaml
-    strategy: sentence
-    ```
-    """
-    strategy_exec = _load_strategy(strategy)
-
-    num_total = _get_num_total(input, column)
-    tick = progress_ticker(callbacks.progress, num_total)
-
-    # collapse the config back to a single object to support "polymorphic" function call
-    config = ChunkingConfig(size=size, overlap=overlap, encoding_model=encoding_model)
-
-    return cast(
-        "pd.Series",
-        input.apply(
-            cast(
-                "Any",
-                lambda x: _run_strategy(
-                    strategy_exec,
-                    x[column],
-                    config,
-                    tick,
-                ),
-            ),
-            axis=1,
-        ),
-    )
-
-
-def _run_strategy(
-    strategy_exec: ChunkStrategy,
-    input: ChunkInput,
-    config: ChunkingConfig,
-    tick: ProgressTicker,
-) -> list[str | tuple[list[str] | None, str, int]]:
-    """Run strategy method definition."""
-    if isinstance(input, str):
-        return [item.text_chunk for item in strategy_exec([input], config, tick)]
-
-    # We can work with both just a list of text content
-    # or a list of tuples of (document_id, text content)
-    # text_to_chunk = '''
-    texts = [item if isinstance(item, str) else item[1] for item in input]
-
-    strategy_results = strategy_exec(texts, config, tick)
-
-    results = []
-    for strategy_result in strategy_results:
-        doc_indices = strategy_result.source_doc_indices
-        if isinstance(input[doc_indices[0]], str):
-            results.append(strategy_result.text_chunk)
-        else:
-            doc_ids = [input[doc_idx][0] for doc_idx in doc_indices]
-            results.append((
-                doc_ids,
-                strategy_result.text_chunk,
-                strategy_result.n_tokens,
-            ))
-    return results
-
-
-def _load_strategy(strategy: ChunkStrategyType) -> ChunkStrategy:
-    """Load strategy method definition."""
-    match strategy:
-        case ChunkStrategyType.tokens:
-            from graphrag.index.operations.chunk_text.strategies import run_tokens
-
-            return run_tokens
-        case ChunkStrategyType.sentence:
-            # NLTK
-            from graphrag.index.operations.chunk_text.bootstrap import bootstrap
-            from graphrag.index.operations.chunk_text.strategies import run_sentences
-
-            bootstrap()
-            return run_sentences
-        case _:
-            msg = f"Unknown strategy: {strategy}"
-            raise ValueError(msg)
-
-
-def _get_num_total(output: pd.DataFrame, column: str) -> int:
-    num_total = 0
-    for row in output[column]:
-        if isinstance(row, str):
-            num_total += 1
-        else:
-            num_total += len(row)
-    return num_total
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py b/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
deleted file mode 100644
index 85a1537e3f..0000000000
--- a/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing run_tokens and run_sentences methods."""
-
-from collections.abc import Iterable
-
-import nltk
-
-from graphrag.config.models.chunking_config import ChunkingConfig
-from graphrag.index.operations.chunk_text.typing import TextChunk
-from graphrag.index.text_splitting.text_splitting import (
-    TokenChunkerOptions,
-    split_multiple_texts_on_tokens,
-)
-from graphrag.logger.progress import ProgressTicker
-from graphrag.tokenizer.get_tokenizer import get_tokenizer
-
-
-def run_tokens(
-    input: list[str],
-    config: ChunkingConfig,
-    tick: ProgressTicker,
-) -> Iterable[TextChunk]:
-    """Chunks text into chunks based on encoding tokens."""
-    tokens_per_chunk = config.size
-    chunk_overlap = config.overlap
-    tokenizer = get_tokenizer(encoding_model=config.encoding_model)
-    return split_multiple_texts_on_tokens(
-        input,
-        TokenChunkerOptions(
-            chunk_overlap=chunk_overlap,
-            tokens_per_chunk=tokens_per_chunk,
-            encode=tokenizer.encode,
-            decode=tokenizer.decode,
-        ),
-        tick,
-    )
-
-
-def run_sentences(
-    input: list[str], _config: ChunkingConfig, tick: ProgressTicker
-) -> Iterable[TextChunk]:
-    """Chunks text into multiple parts by sentence."""
-    for doc_idx, text in enumerate(input):
-        sentences = nltk.sent_tokenize(text)
-        for sentence in sentences:
-            yield TextChunk(
-                text_chunk=sentence,
-                source_doc_indices=[doc_idx],
-            )
-        tick(1)
diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/typing.py b/packages/graphrag/graphrag/index/operations/chunk_text/typing.py
deleted file mode 100644
index bf58ef5ec1..0000000000
--- a/packages/graphrag/graphrag/index/operations/chunk_text/typing.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'TextChunk' model."""
-
-from collections.abc import Callable, Iterable
-from dataclasses import dataclass
-
-from graphrag.config.models.chunking_config import ChunkingConfig
-from graphrag.logger.progress import ProgressTicker
-
-
-@dataclass
-class TextChunk:
-    """Text chunk class definition."""
-
-    text_chunk: str
-    source_doc_indices: list[int]
-    n_tokens: int | None = None
-
-
-ChunkInput = str | list[str] | list[tuple[str, str]]
-"""Input to a chunking strategy. Can be a string, a list of strings, or a list of tuples of (id, text)."""
-
-ChunkStrategy = Callable[
-    [list[str], ChunkingConfig, ProgressTicker], Iterable[TextChunk]
-]
diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
index cb8b2016f5..43ed669567 100644
--- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
+++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py
@@ -8,9 +8,9 @@
 from dataclasses import dataclass
 
 import numpy as np
+from graphrag_chunking.token_chunker import split_text_on_tokens
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.index.text_splitting.text_splitting import TokenTextSplitter
 from graphrag.index.utils.is_null import is_null
 from graphrag.language_model.protocol.base import EmbeddingModel
 from graphrag.logger.progress import ProgressTicker, progress_ticker
@@ -39,17 +39,15 @@ async def run_embed_text(
     if is_null(input):
         return TextEmbeddingResult(embeddings=None)
 
-    splitter = _get_splitter(tokenizer, batch_max_tokens)
-
     semaphore: asyncio.Semaphore = asyncio.Semaphore(num_threads)
 
     # Break up the input texts. The sizes here indicate how many snippets are in each input text
-    texts, input_sizes = _prepare_embed_texts(input, splitter)
+    texts, input_sizes = _prepare_embed_texts(input, tokenizer, batch_max_tokens)
     text_batches = _create_text_batches(
         texts,
+        tokenizer,
         batch_size,
         batch_max_tokens,
-        splitter,
     )
     logger.info(
         "embedding %d inputs via %d snippets using %d batches. max_batch_size=%d, batch_max_tokens=%d",
@@ -72,13 +70,6 @@ async def run_embed_text(
     return TextEmbeddingResult(embeddings=embeddings)
 
 
-def _get_splitter(tokenizer: Tokenizer, batch_max_tokens: int) -> TokenTextSplitter:
-    return TokenTextSplitter(
-        tokenizer=tokenizer,
-        chunk_size=batch_max_tokens,
-    )
-
-
 async def _execute(
     model: EmbeddingModel,
     chunks: list[list[str]],
@@ -100,9 +91,9 @@ async def embed(chunk: list[str]):
 
 def _create_text_batches(
     texts: list[str],
+    tokenizer: Tokenizer,
     max_batch_size: int,
     max_batch_tokens: int,
-    splitter: TokenTextSplitter,
 ) -> list[list[str]]:
     """Create batches of texts to embed."""
     # https://learn.microsoft.com/en-us/azure/ai-services/openai/reference
@@ -112,7 +103,7 @@ def _create_text_batches(
     current_batch_tokens = 0
 
     for text in texts:
-        token_count = splitter.num_tokens(text)
+        token_count = tokenizer.num_tokens(text)
         if (
             len(current_batch) >= max_batch_size
             or current_batch_tokens + token_count > max_batch_tokens
@@ -131,18 +122,23 @@ def _create_text_batches(
 
 
 def _prepare_embed_texts(
-    input: list[str], splitter: TokenTextSplitter
+    input: list[str],
+    tokenizer: Tokenizer,
+    batch_max_tokens: int = 8191,
+    chunk_overlap: int = 100,
 ) -> tuple[list[str], list[int]]:
     sizes: list[int] = []
     snippets: list[str] = []
 
     for text in input:
-        # Split the input text and filter out any empty content
-        split_texts = splitter.split_text(text)
-        if split_texts is None:
-            continue
+        split_texts = split_text_on_tokens(
+            text,
+            chunk_size=batch_max_tokens,
+            chunk_overlap=chunk_overlap,
+            encode=tokenizer.encode,
+            decode=tokenizer.decode,
+        )
         split_texts = [text for text in split_texts if len(text) > 0]
-
         sizes.append(len(split_texts))
         snippets.extend(split_texts)
 
diff --git a/packages/graphrag/graphrag/index/text_splitting/check_token_limit.py b/packages/graphrag/graphrag/index/text_splitting/check_token_limit.py
deleted file mode 100644
index 7b6a139e02..0000000000
--- a/packages/graphrag/graphrag/index/text_splitting/check_token_limit.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Token limit method definition."""
-
-from graphrag.index.text_splitting.text_splitting import TokenTextSplitter
-
-
-def check_token_limit(text, max_token):
-    """Check token limit."""
-    text_splitter = TokenTextSplitter(chunk_size=max_token, chunk_overlap=0)
-    docs = text_splitter.split_text(text)
-    if len(docs) > 1:
-        return 0
-    return 1
diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py
index cb46510afe..7be6c538ae 100644
--- a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py
+++ b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py
@@ -1,18 +1,15 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-"""A module containing 'TokenChunkerOptions', 'TextSplitter', 'NoopTextSplitter', 'TokenTextSplitter', 'split_single_text_on_tokens', and 'split_multiple_texts_on_tokens'."""
+"""A module containing 'TokenTextSplitter' class and 'split_single_text_on_tokens' function."""
 
 import logging
-from abc import ABC, abstractmethod
-from collections.abc import Callable, Iterable
-from dataclasses import dataclass
-from typing import Any, cast
+from abc import ABC
+from collections.abc import Callable
+from typing import cast
 
 import pandas as pd
 
-from graphrag.index.operations.chunk_text.typing import TextChunk
-from graphrag.logger.progress import ProgressTicker
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.tokenizer.tokenizer import Tokenizer
 
@@ -24,22 +21,8 @@
 logger = logging.getLogger(__name__)
 
 
-@dataclass(frozen=True)
-class TokenChunkerOptions:
-    """TokenChunkerOptions data class."""
-
-    chunk_overlap: int
-    """Overlap in tokens between chunks"""
-    tokens_per_chunk: int
-    """Maximum number of tokens per chunk"""
-    decode: DecodeFn
-    """ Function to decode a list of token ids to a string"""
-    encode: EncodeFn
-    """ Function to encode a string to a list of token ids"""
-
-
-class TextSplitter(ABC):
-    """Text splitter class definition."""
+class TokenTextSplitter(ABC):
+    """Token text splitter class definition."""
 
     _chunk_size: int
     _chunk_overlap: int
@@ -58,6 +41,7 @@ def __init__(
         keep_separator: bool = False,
         add_start_index: bool = False,
         strip_whitespace: bool = True,
+        tokenizer: Tokenizer | None = None,
     ):
         """Init method definition."""
         self._chunk_size = chunk_size
@@ -66,30 +50,6 @@ def __init__(
         self._keep_separator = keep_separator
         self._add_start_index = add_start_index
         self._strip_whitespace = strip_whitespace
-
-    @abstractmethod
-    def split_text(self, text: str | list[str]) -> Iterable[str]:
-        """Split text method definition."""
-
-
-class NoopTextSplitter(TextSplitter):
-    """Noop text splitter class definition."""
-
-    def split_text(self, text: str | list[str]) -> Iterable[str]:
-        """Split text method definition."""
-        return [text] if isinstance(text, str) else text
-
-
-class TokenTextSplitter(TextSplitter):
-    """Token text splitter class definition."""
-
-    def __init__(
-        self,
-        tokenizer: Tokenizer | None = None,
-        **kwargs: Any,
-    ):
-        """Init method definition."""
-        super().__init__(**kwargs)
         self._tokenizer = tokenizer or get_tokenizer()
 
     def num_tokens(self, text: str) -> int:
@@ -106,68 +66,37 @@ def split_text(self, text: str | list[str]) -> list[str]:
             msg = f"Attempting to split a non-string value, actual is {type(text)}"
             raise TypeError(msg)
 
-        token_chunker_options = TokenChunkerOptions(
+        return split_single_text_on_tokens(
+            text,
             chunk_overlap=self._chunk_overlap,
             tokens_per_chunk=self._chunk_size,
             decode=self._tokenizer.decode,
             encode=self._tokenizer.encode,
         )
 
-        return split_single_text_on_tokens(text=text, tokenizer=token_chunker_options)
-
 
-def split_single_text_on_tokens(text: str, tokenizer: TokenChunkerOptions) -> list[str]:
+def split_single_text_on_tokens(
+    text: str,
+    tokens_per_chunk: int,
+    chunk_overlap: int,
+    encode: EncodeFn,
+    decode: DecodeFn,
+) -> list[str]:
     """Split a single text and return chunks using the tokenizer."""
     result = []
-    input_ids = tokenizer.encode(text)
+    input_ids = encode(text)
 
     start_idx = 0
-    cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
+    cur_idx = min(start_idx + tokens_per_chunk, len(input_ids))
     chunk_ids = input_ids[start_idx:cur_idx]
 
     while start_idx < len(input_ids):
-        chunk_text = tokenizer.decode(list(chunk_ids))
+        chunk_text = decode(list(chunk_ids))
         result.append(chunk_text)  # Append chunked text as string
         if cur_idx == len(input_ids):
             break
-        start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
-        cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
-        chunk_ids = input_ids[start_idx:cur_idx]
-
-    return result
-
-
-# Adapted from - https://github.com/langchain-ai/langchain/blob/77b359edf5df0d37ef0d539f678cf64f5557cb54/libs/langchain/langchain/text_splitter.py#L471
-# So we could have better control over the chunking process
-def split_multiple_texts_on_tokens(
-    texts: list[str], tokenizer: TokenChunkerOptions, tick: ProgressTicker
-) -> list[TextChunk]:
-    """Split multiple texts and return chunks with metadata using the tokenizer."""
-    result = []
-    mapped_ids = []
-
-    for source_doc_idx, text in enumerate(texts):
-        encoded = tokenizer.encode(text)
-        if tick:
-            tick(1)  # Track progress if tick callback is provided
-        mapped_ids.append((source_doc_idx, encoded))
-
-    input_ids = [
-        (source_doc_idx, id) for source_doc_idx, ids in mapped_ids for id in ids
-    ]
-
-    start_idx = 0
-    cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
-    chunk_ids = input_ids[start_idx:cur_idx]
-
-    while start_idx < len(input_ids):
-        chunk_text = tokenizer.decode([id for _, id in chunk_ids])
-        doc_indices = list({doc_idx for doc_idx, _ in chunk_ids})
-        result.append(TextChunk(chunk_text, doc_indices, len(chunk_ids)))
-        if cur_idx == len(input_ids):
-            break
-        start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
-        cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
+        start_idx += tokens_per_chunk - chunk_overlap
+        cur_idx = min(start_idx + tokens_per_chunk, len(input_ids))
         chunk_ids = input_ids[start_idx:cur_idx]
 
     return result
diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
index b80cf6d9f4..fce186e0c6 100644
--- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
+++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
@@ -8,15 +8,18 @@
 from typing import Any, cast
 
 import pandas as pd
+from graphrag_chunking.add_metadata import add_metadata
+from graphrag_chunking.chunker import Chunker
+from graphrag_chunking.chunker_factory import create_chunker
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.models.chunking_config import ChunkStrategyType
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.index.operations.chunk_text.chunk_text import chunk_text
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.utils.hashing import gen_sha512_hash
+from graphrag.logger.progress import progress_ticker
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
+from graphrag.tokenizer.tokenizer import Tokenizer
 from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
 
 logger = logging.getLogger(__name__)
@@ -30,17 +33,14 @@ async def run_workflow(
     logger.info("Workflow started: create_base_text_units")
     documents = await load_table_from_storage("documents", context.output_storage)
 
-    chunks = config.chunks
-
+    tokenizer = get_tokenizer(encoding_model=config.chunking.encoding_model)
+    chunker = create_chunker(config.chunking, tokenizer.encode, tokenizer.decode)
     output = create_base_text_units(
         documents,
         context.callbacks,
-        chunks.size,
-        chunks.overlap,
-        chunks.encoding_model,
-        strategy=chunks.strategy,
-        prepend_metadata=chunks.prepend_metadata,
-        chunk_size_includes_metadata=chunks.chunk_size_includes_metadata,
+        tokenizer=tokenizer,
+        chunker=chunker,
+        prepend_metadata=config.chunking.prepend_metadata,
     )
 
     await write_table_to_storage(output, "text_units", context.output_storage)
@@ -52,70 +52,32 @@ async def run_workflow(
 def create_base_text_units(
     documents: pd.DataFrame,
     callbacks: WorkflowCallbacks,
-    size: int,
-    overlap: int,
-    encoding_model: str,
-    strategy: ChunkStrategyType,
-    prepend_metadata: bool,
-    chunk_size_includes_metadata: bool,
+    tokenizer: Tokenizer,
+    chunker: Chunker,
+    prepend_metadata: bool | None = False,
 ) -> pd.DataFrame:
     """All the steps to transform base text_units."""
     documents.sort_values(by=["id"], ascending=[True], inplace=True)
 
-    tokenizer = get_tokenizer(encoding_model=encoding_model)
-
-    def chunker(row: pd.Series) -> Any:
-        line_delimiter = ".\n"
-        metadata_str = ""
-        metadata_tokens = 0
-
-        if prepend_metadata and "metadata" in row:
-            metadata = row["metadata"]
-            if isinstance(metadata, str):
-                metadata = json.loads(metadata)
-            if isinstance(metadata, dict):
-                metadata_str = (
-                    line_delimiter.join(f"{k}: {v}" for k, v in metadata.items())
-                    + line_delimiter
-                )
-
-            if chunk_size_includes_metadata:
-                metadata_tokens = len(tokenizer.encode(metadata_str))
-                if metadata_tokens >= size:
-                    message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk."
-                    raise ValueError(message)
-
-        chunked = chunk_text(
-            pd.DataFrame([row]).reset_index(drop=True),
-            column="text",
-            size=size - metadata_tokens,
-            overlap=overlap,
-            encoding_model=encoding_model,
-            strategy=strategy,
-            callbacks=callbacks,
-        )[0]
-
-        if prepend_metadata:
-            for index, chunk in enumerate(chunked):
-                if isinstance(chunk, str):
-                    chunked[index] = metadata_str + chunk
-                else:
-                    chunked[index] = (
-                        (chunk[0], metadata_str + chunk[1], chunk[2]) if chunk else None
-                    )
-
-        row["chunks"] = chunked
-        return row
+    total_rows = len(documents)
+    tick = progress_ticker(callbacks.progress, total_rows)
 
     # Track progress of row-wise apply operation
-    total_rows = len(documents)
     logger.info("Starting chunking process for %d documents", total_rows)
 
     def chunker_with_logging(row: pd.Series, row_index: int) -> Any:
-        """Add logging to chunker execution."""
-        result = chunker(row)
+        row["chunks"] = [chunk.text for chunk in chunker.chunk(row["text"])]
+
+        metadata = row.get("metadata", None)
+        if prepend_metadata and metadata is not None:
+            metadata = json.loads(metadata) if isinstance(metadata, str) else metadata
+            row["chunks"] = [
+                add_metadata(chunk, metadata, line_delimiter=".\n")
+                for chunk in row["chunks"]
+            ]
+        tick()
         logger.info("chunker progress:  %d/%d", row_index + 1, total_rows)
-        return result
+        return row
 
     text_units = documents.apply(
         lambda row: chunker_with_logging(row, row.name), axis=1
diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py
index 556f1dd615..a9ef358a30 100644
--- a/packages/graphrag/graphrag/prompt_tune/loader/input.py
+++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py
@@ -9,6 +9,7 @@
 import numpy as np
 import pandas as pd
 from graphrag_cache.noop_cache import NoopCache
+from graphrag_chunking.chunker_factory import create_chunker
 from graphrag_storage import create_storage
 
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
@@ -46,8 +47,6 @@ async def load_docs_in_chunks(
     select_method: DocSelectionType,
     limit: int,
     logger: logging.Logger,
-    chunk_size: int,
-    overlap: int,
     n_subset_max: int = N_SUBSET_MAX,
     k: int = K,
 ) -> list[str]:
@@ -63,22 +62,18 @@ async def load_docs_in_chunks(
         cache=NoopCache(),
     )
     tokenizer = get_tokenizer(embeddings_llm_settings)
+    chunker = create_chunker(config.chunking, tokenizer.encode, tokenizer.decode)
     input_storage = create_storage(config.input.storage)
     input_reader = InputReaderFactory().create(
         config.input.file_type,
         {"storage": input_storage, "config": config.input},
     )
     dataset = await input_reader.read_files()
-    chunk_config = config.chunks
     chunks_df = create_base_text_units(
         documents=dataset,
         callbacks=NoopWorkflowCallbacks(),
-        size=chunk_size,
-        overlap=overlap,
-        encoding_model=chunk_config.encoding_model,
-        strategy=chunk_config.strategy,
-        prepend_metadata=chunk_config.prepend_metadata,
-        chunk_size_includes_metadata=chunk_config.chunk_size_includes_metadata,
+        tokenizer=tokenizer,
+        chunker=chunker,
     )
 
     # Depending on the select method, build the dataset
diff --git a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py
index 5d1ef40f0c..d1bac40a1a 100644
--- a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py
+++ b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py
@@ -12,7 +12,7 @@
 
 def get_tokenizer(
     model_config: LanguageModelConfig | None = None,
-    encoding_model: str = ENCODING_MODEL,
+    encoding_model: str | None = None,
 ) -> Tokenizer:
     """
     Get the tokenizer for the given model configuration or fallback to a tiktoken based tokenizer.
@@ -38,4 +38,6 @@ def get_tokenizer(
 
         return LitellmTokenizer(model_name=model_config.model)
 
+    if encoding_model is None:
+        encoding_model = ENCODING_MODEL
     return TiktokenTokenizer(encoding_name=encoding_model)
diff --git a/pyproject.toml b/pyproject.toml
index 433e45ebee..1f4ab1d8c7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,7 @@ package = false
 members = ["packages/*"]
 
 [tool.uv.sources]
+graphrag-chunking = { workspace = true }
 graphrag-common = { workspace = true }
 graphrag-storage = { workspace = true }
 graphrag-cache = { workspace = true }
@@ -70,6 +71,7 @@ _semversioner_release = "semversioner release"
 _semversioner_changelog = "semversioner changelog > CHANGELOG.md"
 # Add more update toml tasks as packages are added
 _semversioner_update_graphrag_toml_version = "update-toml update --file packages/graphrag/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_graphrag_chunking_toml_version = "update-toml update --file packages/graphrag-chunking/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_common_toml_version = "update-toml update --file packages/graphrag-common/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_storage_toml_version = "update-toml update --file packages/graphrag-storage/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_cache_toml_version = "update-toml update --file packages/graphrag-cache/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
@@ -107,6 +109,7 @@ sequence = [
     # Add more update toml tasks as packages are added
     '_semversioner_update_graphrag_toml_version',
     '_semversioner_update_graphrag_common_toml_version',
+    '_semversioner_update_graphrag_chunking_toml_version',
     '_semversioner_update_graphrag_storage_toml_version',
     '_semversioner_update_graphrag_cache_toml_version',
     '_semversioner_update_workspace_dependency_versions',
diff --git a/tests/unit/indexing/operations/__init__.py b/tests/unit/chunking/__init__.py
similarity index 100%
rename from tests/unit/indexing/operations/__init__.py
rename to tests/unit/chunking/__init__.py
diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py
new file mode 100644
index 0000000000..4dbefa4cf3
--- /dev/null
+++ b/tests/unit/chunking/test_chunker.py
@@ -0,0 +1,186 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+from unittest.mock import Mock, patch
+
+from graphrag.tokenizer.get_tokenizer import get_tokenizer
+from graphrag.tokenizer.tokenizer import Tokenizer
+from graphrag_chunking.bootstrap_nltk import bootstrap
+from graphrag_chunking.chunk_strategy_type import ChunkerType
+from graphrag_chunking.chunker_factory import create_chunker
+from graphrag_chunking.chunking_config import ChunkingConfig
+from graphrag_chunking.token_chunker import (
+    split_text_on_tokens,
+)
+
+
+class MockTokenizer(Tokenizer):
+    def encode(self, text) -> list[int]:
+        return [ord(char) for char in text]
+
+    def decode(self, tokens) -> str:
+        return "".join(chr(id) for id in tokens)
+
+
+class TestRunSentences:
+    def setup_method(self, method):
+        bootstrap()
+
+    def test_basic_functionality(self):
+        """Test basic sentence splitting without metadata"""
+        input = "This is a test. Another sentence. And a third one!"
+        chunker = create_chunker(ChunkingConfig(type=ChunkerType.Sentence))
+        chunks = chunker.chunk(input)
+
+        assert len(chunks) == 3
+        assert chunks[0].text == "This is a test."
+        assert chunks[0].index == 0
+        assert chunks[0].start_char == 0
+        assert chunks[0].end_char == 14
+
+        assert chunks[1].text == "Another sentence."
+        assert chunks[1].index == 1
+        assert chunks[1].start_char == 16
+        assert chunks[1].end_char == 32
+
+        assert chunks[2].text == "And a third one!"
+        assert chunks[2].index == 2
+        assert chunks[2].start_char == 34
+        assert chunks[2].end_char == 49
+
+    def test_mixed_whitespace_handling(self):
+        """Test input with irregular whitespace"""
+        input = "   Sentence with spaces. Another one!   "
+        chunker = create_chunker(ChunkingConfig(type=ChunkerType.Sentence))
+        chunks = chunker.chunk(input)
+
+        assert len(chunks) == 2
+        assert chunks[0].text == "Sentence with spaces."
+        assert chunks[0].index == 0
+        assert chunks[0].start_char == 3
+        assert chunks[0].end_char == 23
+
+        assert chunks[1].text == "Another one!"
+        assert chunks[1].index == 1
+        assert chunks[1].start_char == 25
+        assert chunks[1].end_char == 36
+
+
+class TestRunTokens:
+    @patch("tiktoken.get_encoding")
+    def test_basic_functionality(self, mock_get_encoding):
+        mock_encoder = Mock()
+        mock_encoder.encode.side_effect = lambda x: list(x.encode())
+        mock_encoder.decode.side_effect = lambda x: bytes(x).decode()
+        mock_get_encoding.return_value = mock_encoder
+
+        input = "Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to."
+
+        config = ChunkingConfig(
+            size=5,
+            overlap=1,
+            encoding_model="fake-encoding",
+            type=ChunkerType.Tokens,
+        )
+
+        chunker = create_chunker(config, mock_encoder.encode, mock_encoder.decode)
+        chunks = chunker.chunk(input)
+
+        assert len(chunks) > 0
+
+
+def test_split_text_str_empty():
+    tokenizer = get_tokenizer()
+    result = split_text_on_tokens(
+        "",
+        chunk_size=5,
+        chunk_overlap=2,
+        encode=tokenizer.encode,
+        decode=tokenizer.decode,
+    )
+    assert result == []
+
+
+def test_split_text_on_tokens():
+    text = "This is a test text, meaning to be taken seriously by this test only."
+    mocked_tokenizer = MockTokenizer()
+    expected_splits = [
+        "This is a ",
+        "is a test ",
+        "test text,",
+        "text, mean",
+        " meaning t",
+        "ing to be ",
+        "o be taken",
+        "taken seri",  # cspell:disable-line
+        " seriously",
+        "ously by t",  # cspell:disable-line
+        " by this t",
+        "his test o",
+        "est only.",
+    ]
+
+    result = split_text_on_tokens(
+        text=text,
+        chunk_overlap=5,
+        chunk_size=10,
+        decode=mocked_tokenizer.decode,
+        encode=lambda text: mocked_tokenizer.encode(text),
+    )
+    assert result == expected_splits
+
+
+def test_split_text_on_tokens_one_overlap():
+    text = "This is a test text, meaning to be taken seriously by this test only."
+    tokenizer = get_tokenizer(encoding_model="o200k_base")
+
+    expected_splits = [
+        "This is",
+        " is a",
+        " a test",
+        " test text",
+        " text,",
+        ", meaning",
+        " meaning to",
+        " to be",
+        " be taken",
+        " taken seriously",
+        " seriously by",
+        " by this",
+        " this test",
+        " test only",
+        " only.",
+    ]
+
+    result = split_text_on_tokens(
+        text=text,
+        chunk_size=2,
+        chunk_overlap=1,
+        decode=tokenizer.decode,
+        encode=tokenizer.encode,
+    )
+    assert result == expected_splits
+
+
+def test_split_text_on_tokens_no_overlap():
+    text = "This is a test text, meaning to be taken seriously by this test only."
+    tokenizer = get_tokenizer(encoding_model="o200k_base")
+
+    expected_splits = [
+        "This is a",
+        " test text,",
+        " meaning to be",
+        " taken seriously by",
+        " this test only",
+        ".",
+    ]
+
+    result = split_text_on_tokens(
+        text=text,
+        chunk_size=3,
+        chunk_overlap=0,
+        decode=tokenizer.decode,
+        encode=tokenizer.encode,
+    )
+
+    assert result == expected_splits
diff --git a/tests/unit/chunking/test_prepend_metadata.py b/tests/unit/chunking/test_prepend_metadata.py
new file mode 100644
index 0000000000..99f99c3093
--- /dev/null
+++ b/tests/unit/chunking/test_prepend_metadata.py
@@ -0,0 +1,43 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+from graphrag_chunking.add_metadata import add_metadata
+
+
+def test_add_metadata_one_row():
+    """Test prepending metadata to chunks"""
+    chunks = ["This is a test.", "Another sentence."]
+    metadata = {"message": "hello"}
+    results = [add_metadata(chunk, metadata) for chunk in chunks]
+    assert results[0] == "message: hello\nThis is a test."
+    assert results[1] == "message: hello\nAnother sentence."
+
+
+def test_add_metadata_one_row_append():
+    """Test prepending metadata to chunks"""
+    chunks = ["This is a test.", "Another sentence."]
+    metadata = {"message": "hello"}
+    results = [add_metadata(chunk, metadata, append=True) for chunk in chunks]
+    assert results[0] == "This is a test.message: hello\n"
+    assert results[1] == "Another sentence.message: hello\n"
+
+
+def test_add_metadata_multiple_rows():
+    """Test prepending metadata to chunks"""
+    chunks = ["This is a test.", "Another sentence."]
+    metadata = {"message": "hello", "tag": "first"}
+    results = [add_metadata(chunk, metadata) for chunk in chunks]
+    assert results[0] == "message: hello\ntag: first\nThis is a test."
+    assert results[1] == "message: hello\ntag: first\nAnother sentence."
+
+
+def test_add_metadata_custom_delimiters():
+    """Test prepending metadata to chunks"""
+    chunks = ["This is a test.", "Another sentence."]
+    metadata = {"message": "hello", "tag": "first"}
+    results = [
+        add_metadata(chunk, metadata, delimiter="-", line_delimiter="_")
+        for chunk in chunks
+    ]
+    assert results[0] == "message-hello_tag-first_This is a test."
+    assert results[1] == "message-hello_tag-first_Another sentence."
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 1315907e59..586cd1e18e 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -5,7 +5,6 @@
 
 import graphrag.config.defaults as defs
 from graphrag.config.models.basic_search_config import BasicSearchConfig
-from graphrag.config.models.chunking_config import ChunkingConfig
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
@@ -29,6 +28,7 @@
 )
 from graphrag.config.models.vector_store_config import VectorStoreConfig
 from graphrag_cache import CacheConfig
+from graphrag_chunking.chunking_config import ChunkingConfig
 from graphrag_storage import StorageConfig
 from pydantic import BaseModel
 
@@ -164,10 +164,9 @@ def assert_text_embedding_configs(
 def assert_chunking_configs(actual: ChunkingConfig, expected: ChunkingConfig) -> None:
     assert actual.size == expected.size
     assert actual.overlap == expected.overlap
-    assert actual.strategy == expected.strategy
+    assert actual.type == expected.type
     assert actual.encoding_model == expected.encoding_model
     assert actual.prepend_metadata == expected.prepend_metadata
-    assert actual.chunk_size_includes_metadata == expected.chunk_size_includes_metadata
 
 
 def assert_snapshots_configs(
@@ -345,7 +344,7 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) ->
     assert_cache_configs(actual.cache, expected.cache)
     assert_input_configs(actual.input, expected.input)
     assert_text_embedding_configs(actual.embed_text, expected.embed_text)
-    assert_chunking_configs(actual.chunks, expected.chunks)
+    assert_chunking_configs(actual.chunking, expected.chunking)
     assert_snapshots_configs(actual.snapshots, expected.snapshots)
     assert_extract_graph_configs(actual.extract_graph, expected.extract_graph)
     assert_extract_graph_nlp_configs(
diff --git a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py b/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
deleted file mode 100644
index 029d34fa48..0000000000
--- a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-
-from unittest import mock
-from unittest.mock import ANY, Mock
-
-import pandas as pd
-import pytest
-from graphrag.config.enums import ChunkStrategyType
-from graphrag.index.operations.chunk_text.chunk_text import (
-    _get_num_total,
-    _load_strategy,
-    _run_strategy,
-    chunk_text,
-)
-from graphrag.index.operations.chunk_text.typing import (
-    TextChunk,
-)
-
-
-def test_get_num_total_default():
-    output = pd.DataFrame({"column": ["a", "b", "c"]})
-
-    total = _get_num_total(output, "column")
-    assert total == 3
-
-
-def test_get_num_total_array():
-    output = pd.DataFrame({"column": [["a", "b", "c"], ["x", "y"]]})
-
-    total = _get_num_total(output, "column")
-    assert total == 5
-
-
-def test_load_strategy_tokens():
-    strategy_type = ChunkStrategyType.tokens
-
-    strategy_loaded = _load_strategy(strategy_type)
-
-    assert strategy_loaded.__name__ == "run_tokens"
-
-
-def test_load_strategy_sentence():
-    strategy_type = ChunkStrategyType.sentence
-
-    strategy_loaded = _load_strategy(strategy_type)
-
-    assert strategy_loaded.__name__ == "run_sentences"
-
-
-def test_load_strategy_none():
-    strategy_type = ChunkStrategyType
-
-    with pytest.raises(
-        ValueError, match="Unknown strategy: <enum 'ChunkStrategyType'>"
-    ):
-        _load_strategy(strategy_type)  # type: ignore
-
-
-def test_run_strategy_str():
-    input = "text test for run strategy"
-    config = Mock()
-    tick = Mock()
-    strategy_mocked = Mock()
-
-    strategy_mocked.return_value = [
-        TextChunk(
-            text_chunk="text test for run strategy",
-            source_doc_indices=[0],
-        )
-    ]
-
-    runned = _run_strategy(strategy_mocked, input, config, tick)
-    assert runned == ["text test for run strategy"]
-
-
-def test_run_strategy_arr_str():
-    input = ["text test for run strategy", "use for strategy"]
-    config = Mock()
-    tick = Mock()
-    strategy_mocked = Mock()
-
-    strategy_mocked.return_value = [
-        TextChunk(
-            text_chunk="text test for run strategy", source_doc_indices=[0], n_tokens=5
-        ),
-        TextChunk(text_chunk="use for strategy", source_doc_indices=[1], n_tokens=3),
-    ]
-
-    expected = [
-        "text test for run strategy",
-        "use for strategy",
-    ]
-
-    runned = _run_strategy(strategy_mocked, input, config, tick)
-    assert runned == expected
-
-
-def test_run_strategy_arr_tuple():
-    input = [("text test for run strategy", "3"), ("use for strategy", "5")]
-    config = Mock()
-    tick = Mock()
-    strategy_mocked = Mock()
-
-    strategy_mocked.return_value = [
-        TextChunk(
-            text_chunk="text test for run strategy", source_doc_indices=[0], n_tokens=5
-        ),
-        TextChunk(text_chunk="use for strategy", source_doc_indices=[1], n_tokens=3),
-    ]
-
-    expected = [
-        (
-            ["text test for run strategy"],
-            "text test for run strategy",
-            5,
-        ),
-        (
-            ["use for strategy"],
-            "use for strategy",
-            3,
-        ),
-    ]
-
-    runned = _run_strategy(strategy_mocked, input, config, tick)
-    assert runned == expected
-
-
-def test_run_strategy_arr_tuple_same_doc():
-    input = [("text test for run strategy", "3"), ("use for strategy", "5")]
-    config = Mock()
-    tick = Mock()
-    strategy_mocked = Mock()
-
-    strategy_mocked.return_value = [
-        TextChunk(
-            text_chunk="text test for run strategy", source_doc_indices=[0], n_tokens=5
-        ),
-        TextChunk(text_chunk="use for strategy", source_doc_indices=[0], n_tokens=3),
-    ]
-
-    expected = [
-        (
-            ["text test for run strategy"],
-            "text test for run strategy",
-            5,
-        ),
-        (
-            ["text test for run strategy"],
-            "use for strategy",
-            3,
-        ),
-    ]
-
-    runned = _run_strategy(strategy_mocked, input, config, tick)
-    assert runned == expected
-
-
-@mock.patch("graphrag.index.operations.chunk_text.chunk_text._load_strategy")
-@mock.patch("graphrag.index.operations.chunk_text.chunk_text._run_strategy")
-@mock.patch("graphrag.index.operations.chunk_text.chunk_text.progress_ticker")
-def test_chunk_text(mock_progress_ticker, mock_run_strategy, mock_load_strategy):
-    input_data = pd.DataFrame({"name": ["The Shining"]})
-    column = "name"
-    size = 10
-    overlap = 2
-    encoding_model = "model"
-    strategy = ChunkStrategyType.sentence
-    callbacks = Mock()
-    callbacks.progress = Mock()
-
-    mock_load_strategy.return_value = Mock()
-    mock_progress_ticker.return_value = Mock()
-
-    chunk_text(input_data, column, size, overlap, encoding_model, strategy, callbacks)
-
-    mock_run_strategy.assert_called_with(
-        mock_load_strategy(), "The Shining", ANY, mock_progress_ticker.return_value
-    )
diff --git a/tests/unit/indexing/operations/chunk_text/test_strategies.py b/tests/unit/indexing/operations/chunk_text/test_strategies.py
deleted file mode 100644
index 3eae0d74d9..0000000000
--- a/tests/unit/indexing/operations/chunk_text/test_strategies.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-from unittest.mock import Mock, patch
-
-from graphrag.config.models.chunking_config import ChunkingConfig
-from graphrag.index.operations.chunk_text.bootstrap import bootstrap
-from graphrag.index.operations.chunk_text.strategies import (
-    run_sentences,
-    run_tokens,
-)
-from graphrag.index.operations.chunk_text.typing import TextChunk
-from graphrag.tokenizer.get_tokenizer import get_tokenizer
-
-
-class TestRunSentences:
-    def setup_method(self, method):
-        bootstrap()
-
-    def test_basic_functionality(self):
-        """Test basic sentence splitting without metadata"""
-        input = ["This is a test. Another sentence."]
-        tick = Mock()
-        chunks = list(run_sentences(input, ChunkingConfig(), tick))
-
-        assert len(chunks) == 2
-        assert chunks[0].text_chunk == "This is a test."
-        assert chunks[1].text_chunk == "Another sentence."
-        assert all(c.source_doc_indices == [0] for c in chunks)
-        tick.assert_called_once_with(1)
-
-    def test_multiple_documents(self):
-        """Test processing multiple input documents"""
-        input = ["First. Document.", "Second. Doc."]
-        tick = Mock()
-        chunks = list(run_sentences(input, ChunkingConfig(), tick))
-
-        assert len(chunks) == 4
-        assert chunks[0].source_doc_indices == [0]
-        assert chunks[2].source_doc_indices == [1]
-        assert tick.call_count == 2
-
-    def test_mixed_whitespace_handling(self):
-        """Test input with irregular whitespace"""
-        input = ["   Sentence with spaces.  Another one!   "]
-        chunks = list(run_sentences(input, ChunkingConfig(), Mock()))
-        assert chunks[0].text_chunk == "   Sentence with spaces."
-        assert chunks[1].text_chunk == "Another one!"
-
-
-class TestRunTokens:
-    @patch("tiktoken.get_encoding")
-    def test_basic_functionality(self, mock_get_encoding):
-        mock_encoder = Mock()
-        mock_encoder.encode.side_effect = lambda x: list(x.encode())
-        mock_encoder.decode.side_effect = lambda x: bytes(x).decode()
-        mock_get_encoding.return_value = mock_encoder
-
-        # Input and config
-        input = [
-            "Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to."
-        ]
-        config = ChunkingConfig(size=5, overlap=1, encoding_model="fake-encoding")
-        tick = Mock()
-
-        # Run the function
-        chunks = list(run_tokens(input, config, tick))
-
-        # Verify output
-        assert len(chunks) > 0
-        assert all(isinstance(chunk, TextChunk) for chunk in chunks)
-        tick.assert_called_once_with(1)
-
-    @patch("tiktoken.get_encoding")
-    def test_non_string_input(self, mock_get_encoding):
-        """Test handling of non-string input (e.g., numbers)."""
-        mock_encoder = Mock()
-        mock_encoder.encode.side_effect = lambda x: list(str(x).encode())
-        mock_encoder.decode.side_effect = lambda x: bytes(x).decode()
-        mock_get_encoding.return_value = mock_encoder
-
-        input = [123]  # Non-string input
-        config = ChunkingConfig(size=5, overlap=1, encoding_model="fake-encoding")
-        tick = Mock()
-
-        chunks = list(run_tokens(input, config, tick))  # type: ignore
-
-        # Verify non-string input is handled
-        assert len(chunks) > 0
-        assert "123" in chunks[0].text_chunk
-
-
-@patch("tiktoken.get_encoding")
-def test_get_encoding_fn_encode(mock_get_encoding):
-    # Create a mock encoding object with encode and decode methods
-    mock_encoding = Mock()
-    mock_encoding.encode = Mock(return_value=[1, 2, 3])
-    mock_encoding.decode = Mock(return_value="decoded text")
-
-    # Configure the mock_get_encoding to return the mock encoding object
-    mock_get_encoding.return_value = mock_encoding
-
-    # Call the function to get encode and decode functions
-    tokenizer = get_tokenizer(encoding_model="mock_encoding")
-
-    # Test the encode function
-    encoded_text = tokenizer.encode("test text")
-    assert encoded_text == [1, 2, 3]
-    mock_encoding.encode.assert_called_once_with("test text")
-
-
-@patch("tiktoken.get_encoding")
-def test_get_encoding_fn_decode(mock_get_encoding):
-    # Create a mock encoding object with encode and decode methods
-    mock_encoding = Mock()
-    mock_encoding.encode = Mock(return_value=[1, 2, 3])
-    mock_encoding.decode = Mock(return_value="decoded text")
-
-    # Configure the mock_get_encoding to return the mock encoding object
-    mock_get_encoding.return_value = mock_encoding
-
-    # Call the function to get encode and decode functions
-    tokenizer = get_tokenizer(encoding_model="mock_encoding")
-
-    decoded_text = tokenizer.decode([1, 2, 3])
-    assert decoded_text == "decoded text"
-    mock_encoding.decode.assert_called_once_with([1, 2, 3])
diff --git a/tests/unit/indexing/text_splitting/__init__.py b/tests/unit/indexing/text_splitting/__init__.py
deleted file mode 100644
index 0a3e38adfb..0000000000
--- a/tests/unit/indexing/text_splitting/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/indexing/text_splitting/test_text_splitting.py
deleted file mode 100644
index eb5e72d649..0000000000
--- a/tests/unit/indexing/text_splitting/test_text_splitting.py
+++ /dev/null
@@ -1,169 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-from unittest import mock
-from unittest.mock import MagicMock
-
-import pytest
-import tiktoken
-from graphrag.index.text_splitting.text_splitting import (
-    NoopTextSplitter,
-    TokenChunkerOptions,
-    TokenTextSplitter,
-    split_multiple_texts_on_tokens,
-    split_single_text_on_tokens,
-)
-
-
-def test_noop_text_splitter() -> None:
-    splitter = NoopTextSplitter()
-
-    assert list(splitter.split_text("some text")) == ["some text"]
-    assert list(splitter.split_text(["some", "text"])) == ["some", "text"]
-
-
-class MockTokenizer:
-    def encode(self, text):
-        return [ord(char) for char in text]
-
-    def decode(self, token_ids):
-        return "".join(chr(id) for id in token_ids)
-
-
-def test_split_text_str_empty():
-    splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2)
-    result = splitter.split_text("")
-
-    assert result == []
-
-
-def test_split_text_str_bool():
-    splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2)
-    result = splitter.split_text(None)  # type: ignore
-
-    assert result == []
-
-
-def test_split_text_str_int():
-    splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2)
-    with pytest.raises(TypeError):
-        splitter.split_text(123)  # type: ignore
-
-
-@mock.patch("graphrag.index.text_splitting.text_splitting.split_single_text_on_tokens")
-def test_split_text_large_input(mock_split):
-    large_text = "a" * 10_000
-    mock_split.return_value = ["chunk"] * 2_000
-    splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2)
-
-    result = splitter.split_text(large_text)
-
-    assert len(result) == 2_000, "Large input was not split correctly"
-    mock_split.assert_called_once()
-
-
-@mock.patch("graphrag.index.text_splitting.text_splitting.split_single_text_on_tokens")
-@mock.patch("graphrag.index.text_splitting.text_splitting.TokenChunkerOptions")
-def test_token_text_splitter(mock_tokenizer, mock_split_text):
-    text = "chunk1 chunk2 chunk3"
-    expected_chunks = ["chunk1", "chunk2", "chunk3"]
-
-    mocked_tokenizer = MagicMock()
-    mock_tokenizer.return_value = mocked_tokenizer
-    mock_split_text.return_value = expected_chunks
-
-    splitter = TokenTextSplitter()
-
-    splitter.split_text(["chunk1", "chunk2", "chunk3"])
-
-    mock_split_text.assert_called_once_with(text=text, tokenizer=mocked_tokenizer)
-
-
-def test_split_single_text_on_tokens():
-    text = "This is a test text, meaning to be taken seriously by this test only."
-    mocked_tokenizer = MockTokenizer()
-    tokenizer = TokenChunkerOptions(
-        chunk_overlap=5,
-        tokens_per_chunk=10,
-        decode=mocked_tokenizer.decode,
-        encode=lambda text: mocked_tokenizer.encode(text),
-    )
-
-    expected_splits = [
-        "This is a ",
-        "is a test ",
-        "test text,",
-        "text, mean",
-        " meaning t",
-        "ing to be ",
-        "o be taken",
-        "taken seri",  # cspell:disable-line
-        " seriously",
-        "ously by t",  # cspell:disable-line
-        " by this t",
-        "his test o",
-        "est only.",
-    ]
-
-    result = split_single_text_on_tokens(text=text, tokenizer=tokenizer)
-    assert result == expected_splits
-
-
-def test_split_multiple_texts_on_tokens():
-    texts = [
-        "This is a test text, meaning to be taken seriously by this test only.",
-        "This is th second text, meaning to be taken seriously by this test only.",
-    ]
-
-    mocked_tokenizer = MockTokenizer()
-    mock_tick = MagicMock()
-    tokenizer = TokenChunkerOptions(
-        chunk_overlap=5,
-        tokens_per_chunk=10,
-        decode=mocked_tokenizer.decode,
-        encode=lambda text: mocked_tokenizer.encode(text),
-    )
-
-    split_multiple_texts_on_tokens(texts, tokenizer, tick=mock_tick)
-    mock_tick.assert_called()
-
-
-def test_split_single_text_on_tokens_no_overlap():
-    text = "This is a test text, meaning to be taken seriously by this test only."
-    enc = tiktoken.get_encoding("cl100k_base")
-
-    def encode(text: str) -> list[int]:
-        if not isinstance(text, str):
-            text = f"{text}"
-        return enc.encode(text)
-
-    def decode(tokens: list[int]) -> str:
-        return enc.decode(tokens)
-
-    tokenizer = TokenChunkerOptions(
-        chunk_overlap=1,
-        tokens_per_chunk=2,
-        decode=decode,
-        encode=lambda text: encode(text),
-    )
-
-    expected_splits = [
-        "This is",
-        " is a",
-        " a test",
-        " test text",
-        " text,",
-        ", meaning",
-        " meaning to",
-        " to be",
-        " be taken",  # cspell:disable-line
-        " taken seriously",  # cspell:disable-line
-        " seriously by",
-        " by this",  # cspell:disable-line
-        " this test",
-        " test only",
-        " only.",
-    ]
-
-    result = split_single_text_on_tokens(text=text, tokenizer=tokenizer)
-    assert result == expected_splits
diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py
index 73b72fc2cb..89018e1609 100644
--- a/tests/verbs/test_create_base_text_units.py
+++ b/tests/verbs/test_create_base_text_units.py
@@ -35,7 +35,7 @@ async def test_create_base_text_units_metadata():
 
     config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
     config.input.metadata = ["title"]
-    config.chunks.prepend_metadata = True
+    config.chunking.prepend_metadata = True
 
     await update_document_metadata(config.input.metadata, context)
 
@@ -43,22 +43,3 @@ async def test_create_base_text_units_metadata():
 
     actual = await load_table_from_storage("text_units", context.output_storage)
     compare_outputs(actual, expected, ["text", "document_id", "n_tokens"])
-
-
-async def test_create_base_text_units_metadata_included_in_chunk():
-    expected = load_test_table("text_units_metadata_included_chunk")
-
-    context = await create_test_context()
-
-    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
-    config.input.metadata = ["title"]
-    config.chunks.prepend_metadata = True
-    config.chunks.chunk_size_includes_metadata = True
-
-    await update_document_metadata(config.input.metadata, context)
-
-    await run_workflow(config, context)
-
-    actual = await load_table_from_storage("text_units", context.output_storage)
-    # only check the columns from the base workflow - our expected table is the final and will have more
-    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])
diff --git a/uv.lock b/uv.lock
index 8072353c3a..94c8626cc5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.11, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -10,6 +10,7 @@ resolution-markers = [
 members = [
     "graphrag",
     "graphrag-cache",
+    "graphrag-chunking",
     "graphrag-common",
     "graphrag-monorepo",
     "graphrag-storage",
@@ -19,18 +20,18 @@ members = [
 name = "aiofiles"
 version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
 ]
 
 [[package]]
@@ -46,42 +47,42 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/62/f1/8515650ac3121a9e55c7b217c60e7fae3e0134b5acfe65691781b5356929/aiohttp-3.13.0.tar.gz", hash = "sha256:378dbc57dd8cf341ce243f13fa1fa5394d68e2e02c15cd5f28eae35a70ec7f67", size = 7832348, upload-time = "2025-10-06T19:58:48.089Z" }
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From 710fdad6f089957944e0eaa5f322a2d78341cc54 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Mon, 12 Jan 2026 12:47:57 -0800
Subject: [PATCH 36/40] Input factory (#2168)

* Update input factory to match other factories

* Move input config alongside input readers

* Move file pattern logic into InputReader

* Set encoding default

* Clean up optional column configs

* Combine structured data extraction

* Remove pandas from input loading

* Throw if empty documents

* Add json lines (jsonl) input support

* Store raw data

* Fix merge imports

* Move metadata handling entirely to chunking

* Nicer automatic title

* Typo

* Add get_property utility for nested dictionary access with dot notation

* Update structured_file_reader to use get_property utility

* Extract input module into new graphrag-input monorepo package

- Create new graphrag-input package with input loading utilities
- Move InputConfig, InputFileType, InputReader, TextDocument, and file readers (CSV, JSON, JSONL, Text)
- Add get_property utility for nested dictionary access with dot notation
- Include hashing utility for document ID generation
- Update all imports throughout codebase to use graphrag_input
- Add package to workspace configuration and release tasks
- Remove old graphrag.index.input module

* Rename ChunkResult to TextChunk and add transformer support

- Rename chunk_result.py to text_chunk.py with ChunkResult -> TextChunk
- Add 'original' field to TextChunk to track pre-transform text
- Add optional transform callback to chunker.chunk() method
- Add add_metadata transformer for prepending metadata to chunks
- Update create_chunk_results to apply transforms and populate original
- Update sentence_chunker and token_chunker with transform support
- Refactor create_base_text_units to use new transformer pattern
- Rename pluck_metadata to get/collect methods on TextDocument

* Back-compat comment

* Align input config type name with other factory configs

* Add MarkItDown support

* Remove pattern default from MarkItDown reader

* Remove plugins flag (implicit disabled)

* Format

* Update verb tests

* Separate storage from input config

* Add empty objects for NaN raw_data

* Fix smoke tests

* Fix BOM in csv smoke

* Format
---
 docs/config/yaml.md                           |   4 +-
 docs/examples_notebooks/api_overview.ipynb    |   5 +-
 docs/examples_notebooks/input_documents.ipynb |   5 +-
 docs/index/inputs.md                          |   4 +-
 .../graphrag_cache/cache_factory.py           |   2 +-
 packages/graphrag-cache/pyproject.toml        |   4 +-
 .../graphrag_chunking/add_metadata.py         |  19 -
 .../graphrag_chunking/chunk_result.py         |  17 -
 .../graphrag_chunking/chunker.py              |   7 +-
 .../graphrag_chunking/chunking_config.py      |   6 +-
 .../graphrag_chunking/create_chunk_results.py |  16 +-
 .../graphrag_chunking/sentence_chunker.py     |  10 +-
 .../graphrag_chunking/text_chunk.py           |  29 +
 .../graphrag_chunking/token_chunker.py        |   8 +-
 .../graphrag_chunking/transformers.py         |  25 +
 packages/graphrag-input/README.md             |  72 ++
 .../graphrag-input/graphrag_input/__init__.py |  20 +
 packages/graphrag-input/graphrag_input/csv.py |  38 +
 .../graphrag_input/get_property.py            |  36 +
 .../graphrag-input/graphrag_input/hashing.py  |  27 +
 .../graphrag_input/input_config.py            |  40 +
 .../graphrag_input/input_reader.py            |  75 ++
 .../graphrag_input/input_reader_factory.py    |  90 +++
 .../graphrag_input/input_type.py              |  25 +
 .../graphrag-input/graphrag_input/json.py     |  38 +
 .../graphrag-input/graphrag_input/jsonl.py    |  38 +
 .../graphrag_input/markitdown.py              |  49 ++
 .../graphrag_input/structured_file_reader.py  |  65 ++
 .../graphrag-input/graphrag_input/text.py     |  43 ++
 .../graphrag_input/text_document.py           |  59 ++
 packages/graphrag-input/pyproject.toml        |  44 ++
 packages/graphrag/graphrag/cli/index.py       |   6 +-
 packages/graphrag/graphrag/cli/initialize.py  |   2 +-
 packages/graphrag/graphrag/config/defaults.py |  40 +-
 packages/graphrag/graphrag/config/enums.py    |  51 --
 .../graphrag/graphrag/config/init_content.py  |  29 +-
 .../config/models/graph_rag_config.py         |  52 +-
 .../graphrag/config/models/input_config.py    |  46 --
 .../graphrag/graphrag/data_model/schemas.py   |   4 +-
 .../graphrag/graphrag/index/input/__init__.py |   4 -
 packages/graphrag/graphrag/index/input/csv.py |  35 -
 .../graphrag/graphrag/index/input/factory.py  |  26 -
 .../graphrag/index/input/input_reader.py      |  85 ---
 .../graphrag/graphrag/index/input/json.py     |  39 -
 .../graphrag/graphrag/index/input/text.py     |  35 -
 .../graphrag/graphrag/index/input/util.py     |  46 --
 .../graphrag/index/operations/prune_graph.py  |   3 +-
 .../graphrag/index/run/run_pipeline.py        |   4 +-
 packages/graphrag/graphrag/index/run/utils.py |   2 +-
 .../index/workflows/create_base_text_units.py |  36 +-
 .../index/workflows/create_final_documents.py |   4 +-
 .../index/workflows/load_input_documents.py   |  15 +-
 .../index/workflows/load_update_documents.py  |  11 +-
 .../graphrag/index/workflows/prune_graph.py   |   1 +
 .../graphrag/prompt_tune/loader/input.py      |  11 +-
 packages/graphrag/pyproject.toml              |   1 +
 pyproject.toml                                |   3 +
 tests/fixtures/azure/config.json              |   2 +-
 tests/fixtures/azure/settings.yml             |   2 +-
 tests/fixtures/min-csv/config.json            |   4 +-
 tests/fixtures/min-csv/settings.yml           |   3 +-
 tests/fixtures/text/config.json               |   4 +-
 tests/smoke/test_fixtures.py                  |   6 +-
 tests/unit/chunking/test_chunker.py           |   2 +-
 tests/unit/chunking/test_prepend_metadata.py  |  17 +-
 tests/unit/config/utils.py                    |  10 +-
 .../indexing/input/data/one-html/input.html   |   8 +
 .../indexing/input/data/one-jsonl/input.jsonl |   3 +
 tests/unit/indexing/input/test_csv_loader.py  |  88 +--
 tests/unit/indexing/input/test_json_loader.py | 109 +--
 .../unit/indexing/input/test_jsonl_loader.py  |  42 ++
 .../indexing/input/test_markitdown_loader.py  |  26 +
 .../unit/indexing/input/test_text_document.py |  76 ++
 tests/unit/indexing/input/test_text_loader.py |  36 +
 tests/unit/indexing/input/test_txt_loader.py  |  62 --
 tests/verbs/data/communities.parquet          | Bin 39880 -> 71503 bytes
 tests/verbs/data/community_reports.parquet    | Bin 334388 -> 894887 bytes
 tests/verbs/data/covariates.parquet           | Bin 70374 -> 137091 bytes
 tests/verbs/data/documents.parquet            | Bin 126826 -> 125443 bytes
 tests/verbs/data/entities.parquet             | Bin 60987 -> 119565 bytes
 tests/verbs/data/relationships.parquet        | Bin 73213 -> 126576 bytes
 tests/verbs/data/text_units.parquet           | Bin 163602 -> 202236 bytes
 tests/verbs/data/text_units_metadata.parquet  | Bin 163313 -> 0 bytes
 ...text_units_metadata_included_chunk.parquet | Bin 163634 -> 0 bytes
 tests/verbs/test_create_base_text_units.py    |  25 +-
 tests/verbs/test_create_community_reports.py  |   9 +-
 tests/verbs/test_create_final_documents.py    |  24 -
 tests/verbs/test_prune_graph.py               |   2 +-
 tests/verbs/util.py                           |  11 +-
 unified-search-app/app/app_logic.py           |   3 +-
 uv.lock                                       | 689 +++++++++++++++++-
 91 files changed, 1956 insertions(+), 818 deletions(-)
 delete mode 100644 packages/graphrag-chunking/graphrag_chunking/add_metadata.py
 delete mode 100644 packages/graphrag-chunking/graphrag_chunking/chunk_result.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/text_chunk.py
 create mode 100644 packages/graphrag-chunking/graphrag_chunking/transformers.py
 create mode 100644 packages/graphrag-input/README.md
 create mode 100644 packages/graphrag-input/graphrag_input/__init__.py
 create mode 100644 packages/graphrag-input/graphrag_input/csv.py
 create mode 100644 packages/graphrag-input/graphrag_input/get_property.py
 create mode 100644 packages/graphrag-input/graphrag_input/hashing.py
 create mode 100644 packages/graphrag-input/graphrag_input/input_config.py
 create mode 100644 packages/graphrag-input/graphrag_input/input_reader.py
 create mode 100644 packages/graphrag-input/graphrag_input/input_reader_factory.py
 create mode 100644 packages/graphrag-input/graphrag_input/input_type.py
 create mode 100644 packages/graphrag-input/graphrag_input/json.py
 create mode 100644 packages/graphrag-input/graphrag_input/jsonl.py
 create mode 100644 packages/graphrag-input/graphrag_input/markitdown.py
 create mode 100644 packages/graphrag-input/graphrag_input/structured_file_reader.py
 create mode 100644 packages/graphrag-input/graphrag_input/text.py
 create mode 100644 packages/graphrag-input/graphrag_input/text_document.py
 create mode 100644 packages/graphrag-input/pyproject.toml
 delete mode 100644 packages/graphrag/graphrag/config/models/input_config.py
 delete mode 100644 packages/graphrag/graphrag/index/input/__init__.py
 delete mode 100644 packages/graphrag/graphrag/index/input/csv.py
 delete mode 100644 packages/graphrag/graphrag/index/input/factory.py
 delete mode 100644 packages/graphrag/graphrag/index/input/input_reader.py
 delete mode 100644 packages/graphrag/graphrag/index/input/json.py
 delete mode 100644 packages/graphrag/graphrag/index/input/text.py
 delete mode 100644 packages/graphrag/graphrag/index/input/util.py
 create mode 100644 tests/unit/indexing/input/data/one-html/input.html
 create mode 100644 tests/unit/indexing/input/data/one-jsonl/input.jsonl
 create mode 100644 tests/unit/indexing/input/test_jsonl_loader.py
 create mode 100644 tests/unit/indexing/input/test_markitdown_loader.py
 create mode 100644 tests/unit/indexing/input/test_text_document.py
 create mode 100644 tests/unit/indexing/input/test_text_loader.py
 delete mode 100644 tests/unit/indexing/input/test_txt_loader.py
 delete mode 100644 tests/verbs/data/text_units_metadata.parquet
 delete mode 100644 tests/verbs/data/text_units_metadata_included_chunk.parquet

diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 10973fd211..1885db1fe5 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -87,9 +87,9 @@ Our pipeline can ingest .csv, .txt, or .json data from an input location. See th
   - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
   - `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
   - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
-- `file_type` **text|csv|json** - The type of input data to load. Default is `text`
+- `type` **text|csv|json** - The type of input data to load. Default is `text`
 - `encoding` **str** - The encoding of the input file. Default is `utf-8`
-- `file_pattern` **str** - A regex to match input files. Default is `.*\.csv$`, `.*\.txt$`, or `.*\.json$` depending on the specified `file_type`, but you can customize it if needed.
+- `file_pattern` **str** - A regex to match input files. Default is `.*\.csv$`, `.*\.txt$`, or `.*\.json$` depending on the specified `type`, but you can customize it if needed.
 - `text_column` **str** - (CSV/JSON only) The text column name. If unset we expect a column named `text`.
 - `title_column` **str** - (CSV/JSON only) The title column name, filename will be used if unset.
 - `metadata` **list[str]** - (CSV/JSON only) The additional document attributes fields to keep.
diff --git a/docs/examples_notebooks/api_overview.ipynb b/docs/examples_notebooks/api_overview.ipynb
index abcd7832fc..2a0c0f15de 100644
--- a/docs/examples_notebooks/api_overview.ipynb
+++ b/docs/examples_notebooks/api_overview.ipynb
@@ -28,11 +28,10 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
+    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
-    "\n",
-    "import graphrag.api as api"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
   },
   {
diff --git a/docs/examples_notebooks/input_documents.ipynb b/docs/examples_notebooks/input_documents.ipynb
index 505c0fe1f3..5657770eaf 100644
--- a/docs/examples_notebooks/input_documents.ipynb
+++ b/docs/examples_notebooks/input_documents.ipynb
@@ -30,11 +30,10 @@
     "from pathlib import Path\n",
     "from pprint import pprint\n",
     "\n",
+    "import graphrag.api as api\n",
     "import pandas as pd\n",
     "from graphrag.config.load_config import load_config\n",
-    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult\n",
-    "\n",
-    "import graphrag.api as api"
+    "from graphrag.index.typing.pipeline_run_result import PipelineRunResult"
    ]
   },
   {
diff --git a/docs/index/inputs.md b/docs/index/inputs.md
index 7f34370a70..458306bf49 100644
--- a/docs/index/inputs.md
+++ b/docs/index/inputs.md
@@ -116,7 +116,7 @@ settings.yaml
 
 ```yaml
 input:
-    file_type: text
+    type: text
     metadata: [title]
 
 chunks:
@@ -194,7 +194,7 @@ settings.yaml
 
 ```yaml
 input:
-    file_type: json
+    type: json
     title_column: headline
     text_column: content
 
diff --git a/packages/graphrag-cache/graphrag_cache/cache_factory.py b/packages/graphrag-cache/graphrag_cache/cache_factory.py
index f8bbd65dcc..6e4bc32813 100644
--- a/packages/graphrag-cache/graphrag_cache/cache_factory.py
+++ b/packages/graphrag-cache/graphrag_cache/cache_factory.py
@@ -26,7 +26,7 @@ def register_cache(
     cache_initializer: Callable[..., Cache],
     scope: ServiceScope = "transient",
 ) -> None:
-    """Register a custom storage implementation.
+    """Register a custom cache implementation.
 
     Args
     ----
diff --git a/packages/graphrag-cache/pyproject.toml b/packages/graphrag-cache/pyproject.toml
index e87f5d81e0..69b79de36b 100644
--- a/packages/graphrag-cache/pyproject.toml
+++ b/packages/graphrag-cache/pyproject.toml
@@ -23,12 +23,12 @@ authors = [
 license = "MIT"
 readme = "README.md"
 license-files = ["LICENSE"]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
 classifiers = [
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
     "graphrag-common==2.7.0",
diff --git a/packages/graphrag-chunking/graphrag_chunking/add_metadata.py b/packages/graphrag-chunking/graphrag_chunking/add_metadata.py
deleted file mode 100644
index 3b02994d4c..0000000000
--- a/packages/graphrag-chunking/graphrag_chunking/add_metadata.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'prepend_metadata' function."""
-
-
-def add_metadata(
-    text: str,
-    metadata: dict,
-    delimiter: str = ": ",
-    line_delimiter: str = "\n",
-    append: bool = False,
-) -> str:
-    """Add metadata to the given text, prepending by default. This utility writes the dict as rows of key/value pairs."""
-    metadata_str = (
-        line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items())
-        + line_delimiter
-    )
-    return text + metadata_str if append else metadata_str + text
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunk_result.py b/packages/graphrag-chunking/graphrag_chunking/chunk_result.py
deleted file mode 100644
index d9b3177511..0000000000
--- a/packages/graphrag-chunking/graphrag_chunking/chunk_result.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The ChunkResult dataclass."""
-
-from dataclasses import dataclass
-
-
-@dataclass
-class ChunkResult:
-    """Result of chunking a document."""
-
-    text: str
-    index: int
-    start_char: int
-    end_char: int
-    token_count: int | None = None
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunker.py b/packages/graphrag-chunking/graphrag_chunking/chunker.py
index 552b28a8a8..3e44ce46f4 100644
--- a/packages/graphrag-chunking/graphrag_chunking/chunker.py
+++ b/packages/graphrag-chunking/graphrag_chunking/chunker.py
@@ -4,9 +4,10 @@
 """A module containing the 'Chunker' class."""
 
 from abc import ABC, abstractmethod
+from collections.abc import Callable
 from typing import Any
 
-from graphrag_chunking.chunk_result import ChunkResult
+from graphrag_chunking.text_chunk import TextChunk
 
 
 class Chunker(ABC):
@@ -17,5 +18,7 @@ def __init__(self, **kwargs: Any) -> None:
         """Create a chunker instance."""
 
     @abstractmethod
-    def chunk(self, text: str) -> list[ChunkResult]:
+    def chunk(
+        self, text: str, transform: Callable[[str], str] | None = None
+    ) -> list[TextChunk]:
         """Chunk method definition."""
diff --git a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py
index 1bb5b84e44..69db1260a1 100644
--- a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py
+++ b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py
@@ -30,7 +30,7 @@ class ChunkingConfig(BaseModel):
         description="The chunk overlap to use.",
         default=100,
     )
-    prepend_metadata: bool = Field(
-        description="Prepend metadata into each chunk.",
-        default=False,
+    prepend_metadata: list[str] | None = Field(
+        description="Metadata fields from the source document to prepend on each chunk.",
+        default=None,
     )
diff --git a/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py
index 3eb15befff..780de6a0bc 100644
--- a/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py
+++ b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py
@@ -5,26 +5,28 @@
 
 from collections.abc import Callable
 
-from graphrag_chunking.chunk_result import ChunkResult
+from graphrag_chunking.text_chunk import TextChunk
 
 
 def create_chunk_results(
     chunks: list[str],
+    transform: Callable[[str], str] | None = None,
     encode: Callable[[str], list[int]] | None = None,
-) -> list[ChunkResult]:
-    """Create chunk results from a list of text chunks. The index assignments are 0-based and assume chunks we not stripped relative to the source text."""
+) -> list[TextChunk]:
+    """Create chunk results from a list of text chunks. The index assignments are 0-based and assume chunks were not stripped relative to the source text."""
     results = []
     start_char = 0
     for index, chunk in enumerate(chunks):
         end_char = start_char + len(chunk) - 1  # 0-based indices
-        chunk = ChunkResult(
-            text=chunk,
+        result = TextChunk(
+            original=chunk,
+            text=transform(chunk) if transform else chunk,
             index=index,
             start_char=start_char,
             end_char=end_char,
         )
         if encode:
-            chunk.token_count = len(encode(chunk.text))
-        results.append(chunk)
+            result.token_count = len(encode(result.text))
+        results.append(result)
         start_char = end_char + 1
     return results
diff --git a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py
index 91e68ccaf2..42d8a9a211 100644
--- a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py
+++ b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py
@@ -9,9 +9,9 @@
 import nltk
 
 from graphrag_chunking.bootstrap_nltk import bootstrap
-from graphrag_chunking.chunk_result import ChunkResult
 from graphrag_chunking.chunker import Chunker
 from graphrag_chunking.create_chunk_results import create_chunk_results
+from graphrag_chunking.text_chunk import TextChunk
 
 
 class SentenceChunker(Chunker):
@@ -24,10 +24,14 @@ def __init__(
         self._encode = encode
         bootstrap()
 
-    def chunk(self, text) -> list[ChunkResult]:
+    def chunk(
+        self, text: str, transform: Callable[[str], str] | None = None
+    ) -> list[TextChunk]:
         """Chunk the text into sentence-based chunks."""
         sentences = nltk.sent_tokenize(text.strip())
-        results = create_chunk_results(sentences, encode=self._encode)
+        results = create_chunk_results(
+            sentences, transform=transform, encode=self._encode
+        )
         # nltk sentence tokenizer may trim whitespace, so we need to adjust start/end chars
         for index, result in enumerate(results):
             txt = result.text
diff --git a/packages/graphrag-chunking/graphrag_chunking/text_chunk.py b/packages/graphrag-chunking/graphrag_chunking/text_chunk.py
new file mode 100644
index 0000000000..876832f734
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/text_chunk.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The TextChunk dataclass."""
+
+from dataclasses import dataclass
+
+
+@dataclass
+class TextChunk:
+    """Result of chunking a document."""
+
+    original: str
+    """Raw original text chunk before any transformation."""
+
+    text: str
+    """The final text content of this chunk."""
+
+    index: int
+    """Zero-based index of this chunk within the source document."""
+
+    start_char: int
+    """Character index where the raw chunk text begins in the source document."""
+
+    end_char: int
+    """Character index where the raw chunk text ends in the source document."""
+
+    token_count: int | None = None
+    """Number of tokens in the final chunk text, if computed."""
diff --git a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py
index 9a5e8ecfb0..41921a0415 100644
--- a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py
+++ b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py
@@ -6,9 +6,9 @@
 from collections.abc import Callable
 from typing import Any
 
-from graphrag_chunking.chunk_result import ChunkResult
 from graphrag_chunking.chunker import Chunker
 from graphrag_chunking.create_chunk_results import create_chunk_results
+from graphrag_chunking.text_chunk import TextChunk
 
 
 class TokenChunker(Chunker):
@@ -28,7 +28,9 @@ def __init__(
         self._encode = encode
         self._decode = decode
 
-    def chunk(self, text: str) -> list[ChunkResult]:
+    def chunk(
+        self, text: str, transform: Callable[[str], str] | None = None
+    ) -> list[TextChunk]:
         """Chunk the text into token-based chunks."""
         chunks = split_text_on_tokens(
             text,
@@ -37,7 +39,7 @@ def chunk(self, text: str) -> list[ChunkResult]:
             encode=self._encode,
             decode=self._decode,
         )
-        return create_chunk_results(chunks, encode=self._encode)
+        return create_chunk_results(chunks, transform=transform, encode=self._encode)
 
 
 def split_text_on_tokens(
diff --git a/packages/graphrag-chunking/graphrag_chunking/transformers.py b/packages/graphrag-chunking/graphrag_chunking/transformers.py
new file mode 100644
index 0000000000..5d6f11e0e6
--- /dev/null
+++ b/packages/graphrag-chunking/graphrag_chunking/transformers.py
@@ -0,0 +1,25 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A collection of useful built-in transformers you can use for chunking."""
+
+from collections.abc import Callable
+from typing import Any
+
+
+def add_metadata(
+    metadata: dict[str, Any],
+    delimiter: str = ": ",
+    line_delimiter: str = "\n",
+    append: bool = False,
+) -> Callable[[str], str]:
+    """Add metadata to the given text, prepending by default. This utility writes the dict as rows of key/value pairs."""
+
+    def transformer(text: str) -> str:
+        metadata_str = (
+            line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items())
+            + line_delimiter
+        )
+        return text + metadata_str if append else metadata_str + text
+
+    return transformer
diff --git a/packages/graphrag-input/README.md b/packages/graphrag-input/README.md
new file mode 100644
index 0000000000..5dd6cb1497
--- /dev/null
+++ b/packages/graphrag-input/README.md
@@ -0,0 +1,72 @@
+# GraphRAG Inputs
+
+This package provides input document loading utilities for GraphRAG, supporting multiple file formats including CSV, JSON, JSON Lines, and plain text.
+
+## Supported File Types
+
+The following four standard file formats are supported out of the box:
+
+- **CSV** - Tabular data with configurable column mappings
+- **JSON** - JSON files with configurable property paths
+- **JSON Lines** - Line-delimited JSON records
+- **Text** - Plain text files
+
+### Markitdown Support
+
+Additionally, we support the `InputType.MarkItDown` format, which uses the [MarkItDown](https://github.com/microsoft/markitdown) library to import any supported file type. The MarkItDown converter can handle a wide variety of file formats including Office documents, PDFs, HTML, and more.
+
+**Note:** Additional optional dependencies may need to be installed depending on the file type you're processing. The choice of converter is determined by MarkItDowns's processing logic, which primarily uses the file extension to select the appropriate converter. Please refer to the [MarkItDown repository](https://github.com/microsoft/markitdown) for installation instructions and detailed information about supported formats.
+
+## Examples
+
+Basic usage with the factory:
+```python
+from graphrag_input import create_input_reader, InputConfig, InputType
+from graphrag_storage import StorageConfig, create_storage
+
+config = InputConfig(
+    type=InputType.Csv,
+    text_column="content",
+    title_column="title",
+)
+storage = create_storage(StorageConfig(base_dir="./input"))
+reader = create_input_reader(config, storage)
+documents = await reader.read_files()
+```
+
+Import a pdf with MarkItDown:
+
+```bash
+pip install 'markitdown[pdf]' # required dependency for pdf processing
+```
+
+```python
+from graphrag_input import create_input_reader, InputConfig, InputType
+from graphrag_storage import StorageConfig, create_storage
+
+config = InputConfig(
+    type=InputType.MarkitDown,
+    file_pattern=".*\\.pdf$"
+)
+storage = create_storage(StorageConfig(base_dir="./input"))
+reader = create_input_reader(config, storage)
+documents = await reader.read_files()
+```
+
+YAML config example for above:
+```yaml
+input:
+  type: markitdown
+  file_pattern: ".*\\.pdf$$"
+input_storage:
+    type: file
+    base_dir: "input"
+```
+
+Note that when specifying column names for data extraction, we can handle nested objects (e.g., in JSON) with dot notation:
+```python
+from graphrag_input import get_property
+
+data = {"user": {"profile": {"name": "Alice"}}}
+name = get_property(data, "user.profile.name")  # Returns "Alice"
+```
diff --git a/packages/graphrag-input/graphrag_input/__init__.py b/packages/graphrag-input/graphrag_input/__init__.py
new file mode 100644
index 0000000000..e742d7f06b
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/__init__.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""GraphRAG input document loading package."""
+
+from graphrag_input.get_property import get_property
+from graphrag_input.input_config import InputConfig
+from graphrag_input.input_reader import InputReader
+from graphrag_input.input_reader_factory import create_input_reader
+from graphrag_input.input_type import InputType
+from graphrag_input.text_document import TextDocument
+
+__all__ = [
+    "InputConfig",
+    "InputReader",
+    "InputType",
+    "TextDocument",
+    "create_input_reader",
+    "get_property",
+]
diff --git a/packages/graphrag-input/graphrag_input/csv.py b/packages/graphrag-input/graphrag_input/csv.py
new file mode 100644
index 0000000000..6c0f51dd3a
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/csv.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'CSVFileReader' model."""
+
+import csv
+import logging
+
+from graphrag_input.structured_file_reader import StructuredFileReader
+from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class CSVFileReader(StructuredFileReader):
+    """Reader implementation for csv files."""
+
+    def __init__(self, file_pattern: str | None = None, **kwargs):
+        super().__init__(
+            file_pattern=file_pattern if file_pattern is not None else ".*\\.csv$",
+            **kwargs,
+        )
+
+    async def read_file(self, path: str) -> list[TextDocument]:
+        """Read a csv file into a list of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - list with a TextDocument for each row in the file.
+        """
+        file = await self._storage.get(path, encoding=self._encoding)
+
+        reader = csv.DictReader(file.splitlines())
+        rows = list(reader)
+        return await self.process_data_columns(rows, path)
diff --git a/packages/graphrag-input/graphrag_input/get_property.py b/packages/graphrag-input/graphrag_input/get_property.py
new file mode 100644
index 0000000000..8c82d69885
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/get_property.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Utility for retrieving properties from nested dictionaries."""
+
+from typing import Any
+
+
+def get_property(data: dict[str, Any], path: str) -> Any:
+    """Retrieve a property from a dictionary using dot notation.
+
+    Parameters
+    ----------
+    data : dict[str, Any]
+        The dictionary to retrieve the property from.
+    path : str
+        A dot-separated string representing the path to the property (e.g., "foo.bar.baz").
+
+    Returns
+    -------
+    Any
+        The value at the specified path.
+
+    Raises
+    ------
+    KeyError
+        If the path does not exist in the dictionary.
+    """
+    keys = path.split(".")
+    current = data
+    for key in keys:
+        if not isinstance(current, dict) or key not in current:
+            msg = f"Property '{path}' not found"
+            raise KeyError(msg)
+        current = current[key]
+    return current
diff --git a/packages/graphrag-input/graphrag_input/hashing.py b/packages/graphrag-input/graphrag_input/hashing.py
new file mode 100644
index 0000000000..915824cd9f
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/hashing.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Hashing utilities."""
+
+from collections.abc import Iterable
+from hashlib import sha512
+from typing import Any
+
+
+def gen_sha512_hash(item: dict[str, Any], hashcode: Iterable[str]) -> str:
+    """Generate a SHA512 hash.
+
+    Parameters
+    ----------
+    item : dict[str, Any]
+        The dictionary containing values to hash.
+    hashcode : Iterable[str]
+        The keys to include in the hash.
+
+    Returns
+    -------
+    str
+        The SHA512 hash as a hexadecimal string.
+    """
+    hashed = "".join([str(item[column]) for column in hashcode])
+    return f"{sha512(hashed.encode('utf-8'), usedforsecurity=False).hexdigest()}"
diff --git a/packages/graphrag-input/graphrag_input/input_config.py b/packages/graphrag-input/graphrag_input/input_config.py
new file mode 100644
index 0000000000..792ce3f85a
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/input_config.py
@@ -0,0 +1,40 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Parameterization settings for the default configuration."""
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from graphrag_input.input_type import InputType
+
+
+class InputConfig(BaseModel):
+    """The default configuration section for Input."""
+
+    model_config = ConfigDict(extra="allow")
+    """Allow extra fields to support custom reader implementations."""
+
+    type: str = Field(
+        description="The input file type to use.",
+        default=InputType.Text,
+    )
+    encoding: str | None = Field(
+        description="The input file encoding to use.",
+        default=None,
+    )
+    file_pattern: str | None = Field(
+        description="The input file pattern to use.",
+        default=None,
+    )
+    id_column: str | None = Field(
+        description="The input ID column to use.",
+        default=None,
+    )
+    title_column: str | None = Field(
+        description="The input title column to use.",
+        default=None,
+    )
+    text_column: str | None = Field(
+        description="The input text column to use.",
+        default=None,
+    )
diff --git a/packages/graphrag-input/graphrag_input/input_reader.py b/packages/graphrag-input/graphrag_input/input_reader.py
new file mode 100644
index 0000000000..be95168336
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/input_reader.py
@@ -0,0 +1,75 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'InputReader' model."""
+
+from __future__ import annotations
+
+import logging
+import re
+from abc import ABCMeta, abstractmethod
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from graphrag_storage import Storage
+
+    from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class InputReader(metaclass=ABCMeta):
+    """Provide a cache interface for the pipeline."""
+
+    def __init__(
+        self,
+        storage: Storage,
+        file_pattern: str,
+        encoding: str = "utf-8",
+        **kwargs,
+    ):
+        self._storage = storage
+        self._encoding = encoding
+        self._file_pattern = file_pattern
+
+    async def read_files(self) -> list[TextDocument]:
+        """Load files from storage and apply a loader function based on file type. Process metadata on the results if needed."""
+        files = list(self._storage.find(re.compile(self._file_pattern)))
+        if len(files) == 0:
+            msg = f"No {self._file_pattern} matches found in storage"
+            logger.warning(msg)
+            files = []
+
+        documents: list[TextDocument] = []
+
+        for file in files:
+            try:
+                documents.extend(await self.read_file(file))
+            except Exception as e:  # noqa: BLE001 (catching Exception is fine here)
+                logger.warning("Warning! Error loading file %s. Skipping...", file)
+                logger.warning("Error: %s", e)
+
+        logger.info(
+            "Found %d %s files, loading %d",
+            len(files),
+            self._file_pattern,
+            len(documents),
+        )
+        total_files_log = (
+            f"Total number of unfiltered {self._file_pattern} rows: {len(documents)}"
+        )
+        logger.info(total_files_log)
+
+        return documents
+
+    @abstractmethod
+    async def read_file(self, path: str) -> list[TextDocument]:
+        """Read a file into a list of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - List with an entry for each document in the file.
+        """
diff --git a/packages/graphrag-input/graphrag_input/input_reader_factory.py b/packages/graphrag-input/graphrag_input/input_reader_factory.py
new file mode 100644
index 0000000000..825665fa9d
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/input_reader_factory.py
@@ -0,0 +1,90 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'InputReaderFactory' model."""
+
+import logging
+from collections.abc import Callable
+
+from graphrag_common.factory import Factory
+from graphrag_common.factory.factory import ServiceScope
+from graphrag_storage.storage import Storage
+
+from graphrag_input.input_config import InputConfig
+from graphrag_input.input_reader import InputReader
+from graphrag_input.input_type import InputType
+
+logger = logging.getLogger(__name__)
+
+
+class InputReaderFactory(Factory[InputReader]):
+    """Factory for creating Input Reader instances."""
+
+
+input_reader_factory = InputReaderFactory()
+
+
+def register_input_reader(
+    input_reader_type: str,
+    input_reader_initializer: Callable[..., InputReader],
+    scope: ServiceScope = "transient",
+) -> None:
+    """Register a custom input reader implementation.
+
+    Args
+    ----
+        - input_reader_type: str
+            The input reader id to register.
+        - input_reader_initializer: Callable[..., InputReader]
+            The input reader initializer to register.
+    """
+    input_reader_factory.register(input_reader_type, input_reader_initializer, scope)
+
+
+def create_input_reader(config: InputConfig, storage: Storage) -> InputReader:
+    """Create an input reader implementation based on the given configuration.
+
+    Args
+    ----
+        - config: InputConfig
+            The input reader configuration to use.
+        - storage: Storage | None
+            The storage implementation to use for reading the files.
+
+    Returns
+    -------
+        InputReader
+            The created input reader implementation.
+    """
+    config_model = config.model_dump()
+    input_strategy = config.type
+
+    if input_strategy not in input_reader_factory:
+        match input_strategy:
+            case InputType.Csv:
+                from graphrag_input.csv import CSVFileReader
+
+                register_input_reader(InputType.Csv, CSVFileReader)
+            case InputType.Text:
+                from graphrag_input.text import TextFileReader
+
+                register_input_reader(InputType.Text, TextFileReader)
+            case InputType.Json:
+                from graphrag_input.json import JSONFileReader
+
+                register_input_reader(InputType.Json, JSONFileReader)
+            case InputType.JsonLines:
+                from graphrag_input.jsonl import JSONLinesFileReader
+
+                register_input_reader(InputType.JsonLines, JSONLinesFileReader)
+            case InputType.MarkItDown:
+                from graphrag_input.markitdown import MarkItDownFileReader
+
+                register_input_reader(InputType.MarkItDown, MarkItDownFileReader)
+            case _:
+                msg = f"InputConfig.type '{input_strategy}' is not registered in the InputReaderFactory. Registered types: {', '.join(input_reader_factory.keys())}."
+                raise ValueError(msg)
+
+    config_model["storage"] = storage
+
+    return input_reader_factory.create(input_strategy, init_args=config_model)
diff --git a/packages/graphrag-input/graphrag_input/input_type.py b/packages/graphrag-input/graphrag_input/input_type.py
new file mode 100644
index 0000000000..39d0559c8e
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/input_type.py
@@ -0,0 +1,25 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing input file type enum."""
+
+from enum import StrEnum
+
+
+class InputType(StrEnum):
+    """The input file type for the pipeline."""
+
+    Csv = "csv"
+    """The CSV input type."""
+    Text = "text"
+    """The text input type."""
+    Json = "json"
+    """The JSON input type."""
+    JsonLines = "jsonl"
+    """The JSON Lines input type."""
+    MarkItDown = "markitdown"
+    """The MarkItDown input type."""
+
+    def __repr__(self):
+        """Get a string representation."""
+        return f'"{self.value}"'
diff --git a/packages/graphrag-input/graphrag_input/json.py b/packages/graphrag-input/graphrag_input/json.py
new file mode 100644
index 0000000000..1e4bdefe8d
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/json.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'JSONFileReader' model."""
+
+import json
+import logging
+
+from graphrag_input.structured_file_reader import StructuredFileReader
+from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class JSONFileReader(StructuredFileReader):
+    """Reader implementation for json files."""
+
+    def __init__(self, file_pattern: str | None = None, **kwargs):
+        super().__init__(
+            file_pattern=file_pattern if file_pattern is not None else ".*\\.json$",
+            **kwargs,
+        )
+
+    async def read_file(self, path: str) -> list[TextDocument]:
+        """Read a JSON file into a list of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - list with a TextDocument for each row in the file.
+        """
+        text = await self._storage.get(path, encoding=self._encoding)
+        as_json = json.loads(text)
+        # json file could just be a single object, or an array of objects
+        rows = as_json if isinstance(as_json, list) else [as_json]
+        return await self.process_data_columns(rows, path)
diff --git a/packages/graphrag-input/graphrag_input/jsonl.py b/packages/graphrag-input/graphrag_input/jsonl.py
new file mode 100644
index 0000000000..f038aafaa5
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/jsonl.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'JSONLinesFileReader' model."""
+
+import json
+import logging
+
+from graphrag_input.structured_file_reader import StructuredFileReader
+from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class JSONLinesFileReader(StructuredFileReader):
+    """Reader implementation for json lines files."""
+
+    def __init__(self, file_pattern: str | None = None, **kwargs):
+        super().__init__(
+            file_pattern=file_pattern if file_pattern is not None else ".*\\.jsonl$",
+            **kwargs,
+        )
+
+    async def read_file(self, path: str) -> list[TextDocument]:
+        """Read a JSON lines file into a list of documents.
+
+        This differs from standard JSON files in that each line is a separate JSON object.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - list with a TextDocument for each row in the file.
+        """
+        text = await self._storage.get(path, encoding=self._encoding)
+        rows = [json.loads(line) for line in text.splitlines()]
+        return await self.process_data_columns(rows, path)
diff --git a/packages/graphrag-input/graphrag_input/markitdown.py b/packages/graphrag-input/graphrag_input/markitdown.py
new file mode 100644
index 0000000000..466a2ebdd9
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/markitdown.py
@@ -0,0 +1,49 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'TextFileReader' model."""
+
+import logging
+from io import BytesIO
+from pathlib import Path
+
+from markitdown import MarkItDown, StreamInfo
+
+from graphrag_input.hashing import gen_sha512_hash
+from graphrag_input.input_reader import InputReader
+from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class MarkItDownFileReader(InputReader):
+    """Reader implementation for any file type supported by markitdown.
+
+    https://github.com/microsoft/markitdown
+    """
+
+    async def read_file(self, path: str) -> list[TextDocument]:
+        """Read a text file into a DataFrame of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - list with a TextDocument for each row in the file.
+        """
+        bytes = await self._storage.get(path, encoding=self._encoding, as_bytes=True)
+        md = MarkItDown()
+        result = md.convert_stream(
+            BytesIO(bytes), stream_info=StreamInfo(extension=Path(path).suffix)
+        )
+        text = result.markdown
+
+        document = TextDocument(
+            id=gen_sha512_hash({"text": text}, ["text"]),
+            title=result.title if result.title else str(Path(path).name),
+            text=text,
+            creation_date=await self._storage.get_creation_date(path),
+            raw_data=None,
+        )
+        return [document]
diff --git a/packages/graphrag-input/graphrag_input/structured_file_reader.py b/packages/graphrag-input/graphrag_input/structured_file_reader.py
new file mode 100644
index 0000000000..45628ff94d
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/structured_file_reader.py
@@ -0,0 +1,65 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'StructuredFileReader' model."""
+
+import logging
+from typing import Any
+
+from graphrag_input.get_property import get_property
+from graphrag_input.hashing import gen_sha512_hash
+from graphrag_input.input_reader import InputReader
+from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class StructuredFileReader(InputReader):
+    """Base reader implementation for structured files such as csv and json."""
+
+    def __init__(
+        self,
+        id_column: str | None = None,
+        title_column: str | None = None,
+        text_column: str = "text",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self._id_column = id_column
+        self._title_column = title_column
+        self._text_column = text_column
+
+    async def process_data_columns(
+        self,
+        rows: list[dict[str, Any]],
+        path: str,
+    ) -> list[TextDocument]:
+        """Process configured data columns from a list of loaded dicts."""
+        documents = []
+        for index, row in enumerate(rows):
+            # text column is required - harvest from dict
+            text = get_property(row, self._text_column)
+            # id is optional - harvest from dict or hash from text
+            id = (
+                get_property(row, self._id_column)
+                if self._id_column
+                else gen_sha512_hash({"text": text}, ["text"])
+            )
+            # title is optional - harvest from dict or use filename
+            num = f" ({index})" if len(rows) > 1 else ""
+            title = (
+                get_property(row, self._title_column)
+                if self._title_column
+                else f"{path}{num}"
+            )
+            creation_date = await self._storage.get_creation_date(path)
+            documents.append(
+                TextDocument(
+                    id=id,
+                    title=title,
+                    text=text,
+                    creation_date=creation_date,
+                    raw_data=row,
+                )
+            )
+        return documents
diff --git a/packages/graphrag-input/graphrag_input/text.py b/packages/graphrag-input/graphrag_input/text.py
new file mode 100644
index 0000000000..5d42924181
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/text.py
@@ -0,0 +1,43 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing 'TextFileReader' model."""
+
+import logging
+from pathlib import Path
+
+from graphrag_input.hashing import gen_sha512_hash
+from graphrag_input.input_reader import InputReader
+from graphrag_input.text_document import TextDocument
+
+logger = logging.getLogger(__name__)
+
+
+class TextFileReader(InputReader):
+    """Reader implementation for text files."""
+
+    def __init__(self, file_pattern: str | None = None, **kwargs):
+        super().__init__(
+            file_pattern=file_pattern if file_pattern is not None else ".*\\.txt$",
+            **kwargs,
+        )
+
+    async def read_file(self, path: str) -> list[TextDocument]:
+        """Read a text file into a list of documents.
+
+        Args:
+            - path - The path to read the file from.
+
+        Returns
+        -------
+            - output - list with a TextDocument for each row in the file.
+        """
+        text = await self._storage.get(path, encoding=self._encoding)
+        document = TextDocument(
+            id=gen_sha512_hash({"text": text}, ["text"]),
+            title=str(Path(path).name),
+            text=text,
+            creation_date=await self._storage.get_creation_date(path),
+            raw_data=None,
+        )
+        return [document]
diff --git a/packages/graphrag-input/graphrag_input/text_document.py b/packages/graphrag-input/graphrag_input/text_document.py
new file mode 100644
index 0000000000..a771f30d43
--- /dev/null
+++ b/packages/graphrag-input/graphrag_input/text_document.py
@@ -0,0 +1,59 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""TextDocument dataclass."""
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+from graphrag_input.get_property import get_property
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TextDocument:
+    """The TextDocument holds relevant content for GraphRAG indexing."""
+
+    id: str
+    """Unique identifier for the document."""
+    text: str
+    """The main text content of the document."""
+    title: str
+    """The title of the document."""
+    creation_date: str
+    """The creation date of the document, ISO-8601 format."""
+    raw_data: dict[str, Any] | None = None
+    """Raw data from source document."""
+
+    def get(self, field: str, default_value: Any = None) -> Any:
+        """
+        Get a single field from the TextDocument.
+
+        Functions like the get method on a dictionary, returning default_value if the field is not found.
+
+        Supports nested fields using dot notation.
+
+        This takes a two step approach for flexibility:
+        1. If the field is one of the standard text document fields (id, title, text, creation_date), just grab it directly. This accommodates unstructured text for example, which just has the standard fields.
+        2. Otherwise. try to extract it from the raw_data dict. This allows users to specify any column from the original input file.
+
+        """
+        if field in ["id", "title", "text", "creation_date"]:
+            return getattr(self, field)
+
+        raw = self.raw_data or {}
+        try:
+            return get_property(raw, field)
+        except KeyError:
+            return default_value
+
+    def collect(self, fields: list[str]) -> dict[str, Any]:
+        """Extract data fields from a TextDocument into a dict."""
+        data = {}
+        for field in fields:
+            value = self.get(field)
+            if value is not None:
+                data[field] = value
+        return data
diff --git a/packages/graphrag-input/pyproject.toml b/packages/graphrag-input/pyproject.toml
new file mode 100644
index 0000000000..0a7d6b269f
--- /dev/null
+++ b/packages/graphrag-input/pyproject.toml
@@ -0,0 +1,44 @@
+[project]
+name = "graphrag-input"
+version = "2.7.0"
+description = "Input document loading utilities for GraphRAG"
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.11,<3.14"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+]
+dependencies = [
+    "graphrag-common==2.7.0",
+    "graphrag-storage==2.7.0",
+    "pydantic~=2.10",
+    "markitdown~=0.1.0"
+]
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
diff --git a/packages/graphrag/graphrag/cli/index.py b/packages/graphrag/graphrag/cli/index.py
index 60b724ea98..94a40cde48 100644
--- a/packages/graphrag/graphrag/cli/index.py
+++ b/packages/graphrag/graphrag/cli/index.py
@@ -9,9 +9,11 @@
 import warnings
 from pathlib import Path
 
+from graphrag_cache.cache_type import CacheType
+
 import graphrag.api as api
 from graphrag.callbacks.console_workflow_callbacks import ConsoleWorkflowCallbacks
-from graphrag.config.enums import CacheType, IndexingMethod
+from graphrag.config.enums import IndexingMethod
 from graphrag.config.load_config import load_config
 from graphrag.index.validate_config import validate_config_names
 from graphrag.utils.cli import redact
@@ -102,7 +104,7 @@ def _run_index(
     )
 
     if not cache:
-        config.cache.type = CacheType.none
+        config.cache.type = CacheType.Noop
 
     if not skip_validation:
         validate_config_names(config)
diff --git a/packages/graphrag/graphrag/cli/initialize.py b/packages/graphrag/graphrag/cli/initialize.py
index d29866e5c4..2eb5ef785e 100644
--- a/packages/graphrag/graphrag/cli/initialize.py
+++ b/packages/graphrag/graphrag/cli/initialize.py
@@ -63,7 +63,7 @@ def initialize_project_at(
         raise ValueError(msg)
 
     input_path = (
-        root / (graphrag_config_defaults.input.storage.base_dir or "input")
+        root / (graphrag_config_defaults.input_storage.base_dir or "input")
     ).resolve()
     input_path.mkdir(parents=True, exist_ok=True)
     # using replace with custom tokens instead of format here because we have a placeholder for GRAPHRAG_API_KEY that is used later for .env overlay
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index cab8732af2..cc07438c85 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -9,13 +9,13 @@
 
 from graphrag_cache import CacheType
 from graphrag_chunking.chunk_strategy_type import ChunkerType
+from graphrag_input import InputType
 from graphrag_storage import StorageType
 
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
     AsyncType,
     AuthType,
-    InputFileType,
     ModelType,
     NounPhraseExtractorType,
     ReportingType,
@@ -64,7 +64,7 @@ class ChunkingDefaults:
     size: int = 1200
     overlap: int = 100
     encoding_model: str = ENCODING_MODEL
-    prepend_metadata: bool = False
+    prepend_metadata: None = None
 
 
 @dataclass
@@ -226,23 +226,22 @@ class StorageDefaults:
 
 
 @dataclass
-class InputStorageDefaults(StorageDefaults):
-    """Default values for input storage."""
+class InputDefaults:
+    """Default values for input."""
 
-    base_dir: str | None = DEFAULT_INPUT_BASE_DIR
+    type: ClassVar[InputType] = InputType.Text
+    encoding: str | None = None
+    file_pattern: None = None
+    id_column: None = None
+    title_column: None = None
+    text_column: None = None
 
 
 @dataclass
-class InputDefaults:
-    """Default values for input."""
+class InputStorageDefaults(StorageDefaults):
+    """Default values for input storage."""
 
-    storage: InputStorageDefaults = field(default_factory=InputStorageDefaults)
-    file_type: ClassVar[InputFileType] = InputFileType.text
-    encoding: str = "utf-8"
-    file_pattern: str = ""
-    text_column: str = "text"
-    title_column: None = None
-    metadata: None = None
+    base_dir: str | None = DEFAULT_INPUT_BASE_DIR
 
 
 @dataclass
@@ -311,7 +310,7 @@ class LocalSearchDefaults:
 
 
 @dataclass
-class OutputDefaults(StorageDefaults):
+class OutputStorageDefaults(StorageDefaults):
     """Default values for output."""
 
     base_dir: str | None = DEFAULT_OUTPUT_BASE_DIR
@@ -362,7 +361,7 @@ class SummarizeDescriptionsDefaults:
 
 
 @dataclass
-class UpdateIndexOutputDefaults(StorageDefaults):
+class UpdateOutputStorageDefaults(StorageDefaults):
     """Default values for update index output."""
 
     base_dir: str | None = DEFAULT_UPDATE_OUTPUT_BASE_DIR
@@ -389,13 +388,14 @@ class GraphRagConfigDefaults:
 
     models: dict = field(default_factory=dict)
     reporting: ReportingDefaults = field(default_factory=ReportingDefaults)
-    storage: StorageDefaults = field(default_factory=StorageDefaults)
-    output: OutputDefaults = field(default_factory=OutputDefaults)
-    update_index_output: UpdateIndexOutputDefaults = field(
-        default_factory=UpdateIndexOutputDefaults
+    input_storage: InputStorageDefaults = field(default_factory=InputStorageDefaults)
+    output_storage: OutputStorageDefaults = field(default_factory=OutputStorageDefaults)
+    update_output_storage: UpdateOutputStorageDefaults = field(
+        default_factory=UpdateOutputStorageDefaults
     )
     cache: CacheDefaults = field(default_factory=CacheDefaults)
     input: InputDefaults = field(default_factory=InputDefaults)
+
     embed_text: EmbedTextDefaults = field(default_factory=EmbedTextDefaults)
     chunking: ChunkingDefaults = field(default_factory=ChunkingDefaults)
     snapshots: SnapshotsDefaults = field(default_factory=SnapshotsDefaults)
diff --git a/packages/graphrag/graphrag/config/enums.py b/packages/graphrag/graphrag/config/enums.py
index 855f062f27..9685ebb07b 100644
--- a/packages/graphrag/graphrag/config/enums.py
+++ b/packages/graphrag/graphrag/config/enums.py
@@ -8,57 +8,6 @@
 from enum import Enum
 
 
-class CacheType(str, Enum):
-    """The cache configuration type for the pipeline."""
-
-    file = "file"
-    """The file cache configuration type."""
-    memory = "memory"
-    """The memory cache configuration type."""
-    none = "none"
-    """The none cache configuration type."""
-    blob = "blob"
-    """The blob cache configuration type."""
-    cosmosdb = "cosmosdb"
-    """The cosmosdb cache configuration type"""
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
-
-
-class InputFileType(str, Enum):
-    """The input file type for the pipeline."""
-
-    csv = "csv"
-    """The CSV input type."""
-    text = "text"
-    """The text input type."""
-    json = "json"
-    """The JSON input type."""
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
-
-
-class StorageType(str, Enum):
-    """The output type for the pipeline."""
-
-    file = "file"
-    """The file output type."""
-    memory = "memory"
-    """The memory output type."""
-    blob = "blob"
-    """The blob output type."""
-    cosmosdb = "cosmosdb"
-    """The cosmosdb output type"""
-
-    def __repr__(self):
-        """Get a string representation."""
-        return f'"{self.value}"'
-
-
 class VectorStoreType(str, Enum):
     """The supported vector store types."""
 
diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py
index 086228bf55..df075ada0e 100644
--- a/packages/graphrag/graphrag/config/init_content.py
+++ b/packages/graphrag/graphrag/config/init_content.py
@@ -46,13 +46,10 @@
     tokens_per_minute: null
     requests_per_minute: null
 
-### Input settings ###
+### Document processing settings ###
 
 input:
-  storage:
-    type: {graphrag_config_defaults.input.storage.type} # [file, blob, cosmosdb]
-    base_dir: "{graphrag_config_defaults.input.storage.base_dir}"
-  file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json]
+  type: {graphrag_config_defaults.input.type.value} # [csv, text, json, jsonl]
 
 chunking:
   type: {graphrag_config_defaults.chunking.type}
@@ -60,24 +57,28 @@
   overlap: {graphrag_config_defaults.chunking.overlap}
   encoding_model: {graphrag_config_defaults.chunking.encoding_model}
 
-### Output/storage settings ###
+### Storage settings ###
 ## If blob storage is specified in the following four sections,
 ## connection_string and container_name must be provided
 
-output:
-  type: {graphrag_config_defaults.output.type} # [file, blob, cosmosdb]
-  base_dir: "{graphrag_config_defaults.output.base_dir}"
+input_storage:
+    type: {graphrag_config_defaults.input_storage.type} # [file, blob, cosmosdb]
+    base_dir: "{graphrag_config_defaults.input_storage.base_dir}"
 
-cache:
-  type: {graphrag_config_defaults.cache.type} # [json, memory, none]
-  storage:
-    type: {graphrag_config_defaults.cache.storage.type} # [file, blob, cosmosdb]
-    base_dir: "{graphrag_config_defaults.cache.storage.base_dir}"
+output_storage:
+  type: {graphrag_config_defaults.output_storage.type} # [file, blob, cosmosdb]
+  base_dir: "{graphrag_config_defaults.output_storage.base_dir}"
 
 reporting:
   type: {graphrag_config_defaults.reporting.type.value} # [file, blob]
   base_dir: "{graphrag_config_defaults.reporting.base_dir}"
 
+cache:
+  type: {graphrag_config_defaults.cache.type} # [json, memory, none]
+  storage:
+    type: {graphrag_config_defaults.cache.storage.type} # [file, blob, cosmosdb]
+    base_dir: "{graphrag_config_defaults.cache.storage.base_dir}"
+    
 vector_store:
   type: {vector_store_defaults.type}
   db_uri: {vector_store_defaults.db_uri}
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index f79537cd04..2e6efd4461 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -9,12 +9,12 @@
 from devtools import pformat
 from graphrag_cache import CacheConfig
 from graphrag_chunking.chunking_config import ChunkingConfig
+from graphrag_input import InputConfig
 from graphrag_storage import StorageConfig, StorageType
 from pydantic import BaseModel, Field, model_validator
 
-import graphrag.config.defaults as defs
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.enums import VectorStoreType
+from graphrag.config.enums import ReportingType, VectorStoreType
 from graphrag.config.models.basic_search_config import BasicSearchConfig
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
@@ -24,7 +24,6 @@
 from graphrag.config.models.extract_graph_config import ExtractGraphConfig
 from graphrag.config.models.extract_graph_nlp_config import ExtractGraphNLPConfig
 from graphrag.config.models.global_search_config import GlobalSearchConfig
-from graphrag.config.models.input_config import InputConfig
 from graphrag.config.models.language_model_config import LanguageModelConfig
 from graphrag.config.models.local_search_config import LocalSearchConfig
 from graphrag.config.models.prune_graph_config import PruneGraphConfig
@@ -107,22 +106,22 @@ def _validate_rate_limiter_services(self) -> None:
     )
     """The input configuration."""
 
-    def _validate_input_pattern(self) -> None:
-        """Validate the input file pattern based on the specified type."""
-        if len(self.input.file_pattern) == 0:
-            if self.input.file_type == defs.InputFileType.text:
-                self.input.file_pattern = ".*\\.txt$"
-            else:
-                self.input.file_pattern = f".*\\.{self.input.file_type.value}$"
+    input_storage: StorageConfig = Field(
+        description="The input storage configuration.",
+        default=StorageConfig(
+            base_dir=graphrag_config_defaults.input_storage.base_dir,
+        ),
+    )
+    """The input storage configuration."""
 
     def _validate_input_base_dir(self) -> None:
         """Validate the input base directory."""
-        if self.input.storage.type == StorageType.File:
-            if not self.input.storage.base_dir:
+        if self.input_storage.type == StorageType.File:
+            if not self.input_storage.base_dir:
                 msg = "input storage base directory is required for file input storage. Please rerun `graphrag init` and set the input storage configuration."
                 raise ValueError(msg)
-            self.input.storage.base_dir = str(
-                Path(self.input.storage.base_dir).resolve()
+            self.input_storage.base_dir = str(
+                Path(self.input_storage.base_dir).resolve()
             )
 
     chunking: ChunkingConfig = Field(
@@ -140,7 +139,7 @@ def _validate_input_base_dir(self) -> None:
     output: StorageConfig = Field(
         description="The output configuration.",
         default=StorageConfig(
-            base_dir=graphrag_config_defaults.output.base_dir,
+            base_dir=graphrag_config_defaults.output_storage.base_dir,
         ),
     )
     """The output configuration."""
@@ -153,22 +152,22 @@ def _validate_output_base_dir(self) -> None:
                 raise ValueError(msg)
             self.output.base_dir = str(Path(self.output.base_dir).resolve())
 
-    update_index_output: StorageConfig = Field(
+    update_output_storage: StorageConfig = Field(
         description="The output configuration for the updated index.",
         default=StorageConfig(
-            base_dir=graphrag_config_defaults.update_index_output.base_dir,
+            base_dir=graphrag_config_defaults.update_output_storage.base_dir,
         ),
     )
     """The output configuration for the updated index."""
 
-    def _validate_update_index_output_base_dir(self) -> None:
-        """Validate the update index output base directory."""
-        if self.update_index_output.type == StorageType.File:
-            if not self.update_index_output.base_dir:
-                msg = "update_index_output base directory is required for file output. Please rerun `graphrag init` and set the update_index_output configuration."
+    def _validate_update_output_storage_base_dir(self) -> None:
+        """Validate the update output base directory."""
+        if self.update_output_storage.type == StorageType.File:
+            if not self.update_output_storage.base_dir:
+                msg = "update_output_storage base directory is required for file output. Please rerun `graphrag init` and set the update_output_storage configuration."
                 raise ValueError(msg)
-            self.update_index_output.base_dir = str(
-                Path(self.update_index_output.base_dir).resolve()
+            self.update_output_storage.base_dir = str(
+                Path(self.update_output_storage.base_dir).resolve()
             )
 
     cache: CacheConfig = Field(
@@ -184,7 +183,7 @@ def _validate_update_index_output_base_dir(self) -> None:
 
     def _validate_reporting_base_dir(self) -> None:
         """Validate the reporting base directory."""
-        if self.reporting.type == defs.ReportingType.file:
+        if self.reporting.type == ReportingType.file:
             if self.reporting.base_dir.strip() == "":
                 msg = "Reporting base directory is required for file reporting. Please rerun `graphrag init` and set the reporting configuration."
                 raise ValueError(msg)
@@ -318,11 +317,10 @@ def get_language_model_config(self, model_id: str) -> LanguageModelConfig:
     @model_validator(mode="after")
     def _validate_model(self):
         """Validate the model configuration."""
-        self._validate_input_pattern()
         self._validate_input_base_dir()
         self._validate_reporting_base_dir()
         self._validate_output_base_dir()
-        self._validate_update_index_output_base_dir()
+        self._validate_update_output_storage_base_dir()
         self._validate_vector_store_db_uri()
         self._validate_factories()
         return self
diff --git a/packages/graphrag/graphrag/config/models/input_config.py b/packages/graphrag/graphrag/config/models/input_config.py
deleted file mode 100644
index c3c30f6302..0000000000
--- a/packages/graphrag/graphrag/config/models/input_config.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from graphrag_storage import StorageConfig
-from pydantic import BaseModel, Field
-
-import graphrag.config.defaults as defs
-from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.enums import InputFileType
-
-
-class InputConfig(BaseModel):
-    """The default configuration section for Input."""
-
-    storage: StorageConfig = Field(
-        description="The storage configuration to use for reading input documents.",
-        default=StorageConfig(
-            base_dir=graphrag_config_defaults.input.storage.base_dir,
-        ),
-    )
-    file_type: InputFileType = Field(
-        description="The input file type to use.",
-        default=graphrag_config_defaults.input.file_type,
-    )
-    encoding: str = Field(
-        description="The input file encoding to use.",
-        default=defs.graphrag_config_defaults.input.encoding,
-    )
-    file_pattern: str = Field(
-        description="The input file pattern to use.",
-        default=graphrag_config_defaults.input.file_pattern,
-    )
-    text_column: str = Field(
-        description="The input text column to use.",
-        default=graphrag_config_defaults.input.text_column,
-    )
-    title_column: str | None = Field(
-        description="The input title column to use.",
-        default=graphrag_config_defaults.input.title_column,
-    )
-    metadata: list[str] | None = Field(
-        description="The document attribute columns to use.",
-        default=graphrag_config_defaults.input.metadata,
-    )
diff --git a/packages/graphrag/graphrag/data_model/schemas.py b/packages/graphrag/graphrag/data_model/schemas.py
index 93ede46191..c0926b9bb7 100644
--- a/packages/graphrag/graphrag/data_model/schemas.py
+++ b/packages/graphrag/graphrag/data_model/schemas.py
@@ -64,7 +64,7 @@
 N_TOKENS = "n_tokens"
 
 CREATION_DATE = "creation_date"
-METADATA = "metadata"
+RAW_DATA = "raw_data"
 
 # the following lists define the final content and ordering of columns in the data model parquet outputs
 ENTITIES_FINAL_COLUMNS = [
@@ -155,5 +155,5 @@
     TEXT,
     TEXT_UNIT_IDS,
     CREATION_DATE,
-    METADATA,
+    RAW_DATA,
 ]
diff --git a/packages/graphrag/graphrag/index/input/__init__.py b/packages/graphrag/graphrag/index/input/__init__.py
deleted file mode 100644
index 15177c91db..0000000000
--- a/packages/graphrag/graphrag/index/input/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""The Indexing Engine input package root."""
diff --git a/packages/graphrag/graphrag/index/input/csv.py b/packages/graphrag/graphrag/index/input/csv.py
deleted file mode 100644
index 810ffdd03c..0000000000
--- a/packages/graphrag/graphrag/index/input/csv.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'CSVFileReader' model."""
-
-import logging
-from io import BytesIO
-
-import pandas as pd
-
-from graphrag.index.input.input_reader import InputReader
-from graphrag.index.input.util import process_data_columns
-
-logger = logging.getLogger(__name__)
-
-
-class CSVFileReader(InputReader):
-    """Reader implementation for csv files."""
-
-    async def read_file(self, path: str) -> pd.DataFrame:
-        """Read a csv file into a DataFrame of documents.
-
-        Args:
-            - path - The path to read the file from.
-
-        Returns
-        -------
-            - output - DataFrame with a row for each document in the file.
-        """
-        buffer = BytesIO(await self._storage.get(path, as_bytes=True))
-        data = pd.read_csv(buffer, encoding=self._config.encoding)
-        data = process_data_columns(data, self._config, path)
-        creation_date = await self._storage.get_creation_date(path)
-        data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
-        return data
diff --git a/packages/graphrag/graphrag/index/input/factory.py b/packages/graphrag/graphrag/index/input/factory.py
deleted file mode 100644
index bc3b8112c8..0000000000
--- a/packages/graphrag/graphrag/index/input/factory.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'InputReaderFactory' model."""
-
-import logging
-
-from graphrag_common.factory import Factory
-
-from graphrag.config.enums import InputFileType
-from graphrag.index.input.csv import CSVFileReader
-from graphrag.index.input.input_reader import InputReader
-from graphrag.index.input.json import JSONFileReader
-from graphrag.index.input.text import TextFileReader
-
-logger = logging.getLogger(__name__)
-
-
-class InputReaderFactory(Factory[InputReader]):
-    """Factory for creating Input Reader instances."""
-
-
-input_reader_factory = InputReaderFactory()
-input_reader_factory.register(InputFileType.text, TextFileReader)
-input_reader_factory.register(InputFileType.csv, CSVFileReader)
-input_reader_factory.register(InputFileType.json, JSONFileReader)
diff --git a/packages/graphrag/graphrag/index/input/input_reader.py b/packages/graphrag/graphrag/index/input/input_reader.py
deleted file mode 100644
index 18dc713dfd..0000000000
--- a/packages/graphrag/graphrag/index/input/input_reader.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'InputReader' model."""
-
-from __future__ import annotations
-
-import logging
-import re
-from abc import ABCMeta, abstractmethod
-from typing import TYPE_CHECKING
-
-import pandas as pd
-
-if TYPE_CHECKING:
-    from graphrag_storage import Storage
-
-    from graphrag.config.models.input_config import InputConfig
-
-logger = logging.getLogger(__name__)
-
-
-class InputReader(metaclass=ABCMeta):
-    """Provide a cache interface for the pipeline."""
-
-    def __init__(self, storage: Storage, config: InputConfig, **kwargs):
-        self._storage = storage
-        self._config = config
-
-    async def read_files(self) -> pd.DataFrame:
-        """Load files from storage and apply a loader function based on file type. Process metadata on the results if needed."""
-        files = list(self._storage.find(re.compile(self._config.file_pattern)))
-
-        if len(files) == 0:
-            msg = f"No {self._config.file_type} files found in {self._config.storage.base_dir}"
-            raise ValueError(msg)
-
-        files_loaded = []
-
-        for file in files:
-            try:
-                files_loaded.append(await self.read_file(file))
-            except Exception as e:  # noqa: BLE001 (catching Exception is fine here)
-                logger.warning("Warning! Error loading file %s. Skipping...", file)
-                logger.warning("Error: %s", e)
-
-        logger.info(
-            "Found %d %s files, loading %d",
-            len(files),
-            self._config.file_type,
-            len(files_loaded),
-        )
-        result = pd.concat(files_loaded)
-        total_files_log = (
-            f"Total number of unfiltered {self._config.file_type} rows: {len(result)}"
-        )
-        logger.info(total_files_log)
-        # Convert metadata columns to strings and collapse them into a JSON object
-        if self._config.metadata:
-            if all(col in result.columns for col in self._config.metadata):
-                # Collapse the metadata columns into a single JSON object column
-                result["metadata"] = result[self._config.metadata].apply(
-                    lambda row: row.to_dict(), axis=1
-                )
-            else:
-                value_error_msg = (
-                    "One or more metadata columns not found in the DataFrame."
-                )
-                raise ValueError(value_error_msg)
-
-            result[self._config.metadata] = result[self._config.metadata].astype(str)
-
-        return result
-
-    @abstractmethod
-    async def read_file(self, path: str) -> pd.DataFrame:
-        """Read a file into a DataFrame of documents.
-
-        Args:
-            - path - The path to read the file from.
-
-        Returns
-        -------
-            - output - DataFrame with a row for each document in the file.
-        """
diff --git a/packages/graphrag/graphrag/index/input/json.py b/packages/graphrag/graphrag/index/input/json.py
deleted file mode 100644
index e9e1a7ab04..0000000000
--- a/packages/graphrag/graphrag/index/input/json.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'JSONFileReader' model."""
-
-import json
-import logging
-
-import pandas as pd
-
-from graphrag.index.input.input_reader import InputReader
-from graphrag.index.input.util import process_data_columns
-
-logger = logging.getLogger(__name__)
-
-
-class JSONFileReader(InputReader):
-    """Reader implementation for json files."""
-
-    async def read_file(self, path: str) -> pd.DataFrame:
-        """Read a JSON file into a DataFrame of documents.
-
-        Args:
-            - path - The path to read the file from.
-
-        Returns
-        -------
-            - output - DataFrame with a row for each document in the file.
-        """
-        text = await self._storage.get(path, encoding=self._config.encoding)
-        as_json = json.loads(text)
-        # json file could just be a single object, or an array of objects
-        rows = as_json if isinstance(as_json, list) else [as_json]
-        data = pd.DataFrame(rows)
-        data = process_data_columns(data, self._config, path)
-        creation_date = await self._storage.get_creation_date(path)
-        data["creation_date"] = data.apply(lambda _: creation_date, axis=1)
-
-        return data
diff --git a/packages/graphrag/graphrag/index/input/text.py b/packages/graphrag/graphrag/index/input/text.py
deleted file mode 100644
index f9e2d7779e..0000000000
--- a/packages/graphrag/graphrag/index/input/text.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A module containing 'TextFileReader' model."""
-
-import logging
-from pathlib import Path
-
-import pandas as pd
-
-from graphrag.index.input.input_reader import InputReader
-from graphrag.index.utils.hashing import gen_sha512_hash
-
-logger = logging.getLogger(__name__)
-
-
-class TextFileReader(InputReader):
-    """Reader implementation for text files."""
-
-    async def read_file(self, path: str) -> pd.DataFrame:
-        """Read a text file into a DataFrame of documents.
-
-        Args:
-            - path - The path to read the file from.
-
-        Returns
-        -------
-            - output - DataFrame with a row for each document in the file.
-        """
-        text = await self._storage.get(path, encoding=self._config.encoding)
-        new_item = {"text": text}
-        new_item["id"] = gen_sha512_hash(new_item, new_item.keys())
-        new_item["title"] = str(Path(path).name)
-        new_item["creation_date"] = await self._storage.get_creation_date(path)
-        return pd.DataFrame([new_item])
diff --git a/packages/graphrag/graphrag/index/input/util.py b/packages/graphrag/graphrag/index/input/util.py
deleted file mode 100644
index 2780909167..0000000000
--- a/packages/graphrag/graphrag/index/input/util.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Shared column processing for structured input files."""
-
-import logging
-
-import pandas as pd
-
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.utils.hashing import gen_sha512_hash
-
-logger = logging.getLogger(__name__)
-
-
-def process_data_columns(
-    documents: pd.DataFrame, config: InputConfig, path: str
-) -> pd.DataFrame:
-    """Process configured data columns of a DataFrame."""
-    if "id" not in documents.columns:
-        documents["id"] = documents.apply(
-            lambda x: gen_sha512_hash(x, x.keys()), axis=1
-        )
-    if config.text_column is not None and "text" not in documents.columns:
-        if config.text_column not in documents.columns:
-            logger.warning(
-                "text_column %s not found in csv file %s",
-                config.text_column,
-                path,
-            )
-        else:
-            documents["text"] = documents.apply(lambda x: x[config.text_column], axis=1)
-    if config.title_column is not None:
-        if config.title_column not in documents.columns:
-            logger.warning(
-                "title_column %s not found in csv file %s",
-                config.title_column,
-                path,
-            )
-        else:
-            documents["title"] = documents.apply(
-                lambda x: x[config.title_column], axis=1
-            )
-    else:
-        documents["title"] = documents.apply(lambda _: path, axis=1)
-    return documents
diff --git a/packages/graphrag/graphrag/index/operations/prune_graph.py b/packages/graphrag/graphrag/index/operations/prune_graph.py
index 6eb6b60065..11a5335b4e 100644
--- a/packages/graphrag/graphrag/index/operations/prune_graph.py
+++ b/packages/graphrag/graphrag/index/operations/prune_graph.py
@@ -50,7 +50,8 @@ def prune_graph(
     graph.remove_nodes_from([
         node
         for node, data in graph.nodes(data=True)
-        if data[schemas.NODE_FREQUENCY] < min_node_freq
+        if schemas.NODE_FREQUENCY not in data
+        or data[schemas.NODE_FREQUENCY] < min_node_freq
     ])
     if max_node_freq_std is not None:
         upper_threshold = _get_upper_threshold_by_std(
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index d4f0af20b6..f82e774423 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -35,7 +35,7 @@ async def run_pipeline(
     input_documents: pd.DataFrame | None = None,
 ) -> AsyncIterable[PipelineRunResult]:
     """Run all workflows using a simplified pipeline."""
-    input_storage = create_storage(config.input.storage)
+    input_storage = create_storage(config.input_storage)
     output_storage = create_storage(config.output)
     cache = create_cache(config.cache)
 
@@ -49,7 +49,7 @@ async def run_pipeline(
     if is_update_run:
         logger.info("Running incremental indexing.")
 
-        update_storage = create_storage(config.update_index_output)
+        update_storage = create_storage(config.update_output_storage)
         # we use this to store the new subset index, and will merge its content with the previous index
         update_timestamp = time.strftime("%Y%m%d-%H%M%S")
         timestamped_storage = update_storage.child(update_timestamp)
diff --git a/packages/graphrag/graphrag/index/run/utils.py b/packages/graphrag/graphrag/index/run/utils.py
index 9c77d16de6..040b77a45c 100644
--- a/packages/graphrag/graphrag/index/run/utils.py
+++ b/packages/graphrag/graphrag/index/run/utils.py
@@ -53,7 +53,7 @@ def get_update_storages(
 ) -> tuple[Storage, Storage, Storage]:
     """Get storage objects for the update index run."""
     output_storage = create_storage(config.output)
-    update_storage = create_storage(config.update_index_output)
+    update_storage = create_storage(config.update_output_storage)
     timestamped_storage = update_storage.child(timestamp)
     delta_storage = timestamped_storage.child("delta")
     previous_storage = timestamped_storage.child("previous")
diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
index fce186e0c6..1b1d9b48a9 100644
--- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
+++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py
@@ -3,14 +3,14 @@
 
 """A module containing run_workflow method definition."""
 
-import json
 import logging
 from typing import Any, cast
 
 import pandas as pd
-from graphrag_chunking.add_metadata import add_metadata
 from graphrag_chunking.chunker import Chunker
 from graphrag_chunking.chunker_factory import create_chunker
+from graphrag_chunking.transformers import add_metadata
+from graphrag_input import TextDocument
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
@@ -54,7 +54,7 @@ def create_base_text_units(
     callbacks: WorkflowCallbacks,
     tokenizer: Tokenizer,
     chunker: Chunker,
-    prepend_metadata: bool | None = False,
+    prepend_metadata: list[str] | None = None,
 ) -> pd.DataFrame:
     """All the steps to transform base text_units."""
     documents.sort_values(by=["id"], ascending=[True], inplace=True)
@@ -66,15 +66,27 @@ def create_base_text_units(
     logger.info("Starting chunking process for %d documents", total_rows)
 
     def chunker_with_logging(row: pd.Series, row_index: int) -> Any:
-        row["chunks"] = [chunk.text for chunk in chunker.chunk(row["text"])]
-
-        metadata = row.get("metadata", None)
-        if prepend_metadata and metadata is not None:
-            metadata = json.loads(metadata) if isinstance(metadata, str) else metadata
-            row["chunks"] = [
-                add_metadata(chunk, metadata, line_delimiter=".\n")
-                for chunk in row["chunks"]
-            ]
+        if prepend_metadata:
+            # create a standard text document for metadata plucking
+            # ignore any additional fields in case the input dataframe has extra columns
+            document = TextDocument(
+                id=row["id"],
+                title=row["title"],
+                text=row["text"],
+                creation_date=row["creation_date"],
+                raw_data=row["raw_data"],
+            )
+            metadata = document.collect(prepend_metadata)
+            transformer = add_metadata(
+                metadata=metadata, line_delimiter=".\n"
+            )  # delim with . for back-compat older indexes
+        else:
+            transformer = None
+
+        row["chunks"] = [
+            chunk.text for chunk in chunker.chunk(row["text"], transform=transformer)
+        ]
+
         tick()
         logger.info("chunker progress:  %d/%d", row_index + 1, total_rows)
         return row
diff --git a/packages/graphrag/graphrag/index/workflows/create_final_documents.py b/packages/graphrag/graphrag/index/workflows/create_final_documents.py
index f12560bab6..554fbc4254 100644
--- a/packages/graphrag/graphrag/index/workflows/create_final_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/create_final_documents.py
@@ -67,7 +67,7 @@ def create_final_documents(
     rejoined["id"] = rejoined["id"].astype(str)
     rejoined["human_readable_id"] = rejoined.index
 
-    if "metadata" not in rejoined.columns:
-        rejoined["metadata"] = pd.Series(dtype="object")
+    if "raw_data" not in rejoined.columns:
+        rejoined["raw_data"] = pd.Series(dtype="object")
 
     return rejoined.loc[:, DOCUMENTS_FINAL_COLUMNS]
diff --git a/packages/graphrag/graphrag/index/workflows/load_input_documents.py b/packages/graphrag/graphrag/index/workflows/load_input_documents.py
index 228ca4d0c4..0a5aa65454 100644
--- a/packages/graphrag/graphrag/index/workflows/load_input_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/load_input_documents.py
@@ -6,10 +6,9 @@
 import logging
 
 import pandas as pd
+from graphrag_input import InputReader, create_input_reader
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.index.input.factory import InputReaderFactory
-from graphrag.index.input.input_reader import InputReader
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.utils.storage import write_table_to_storage
@@ -22,13 +21,15 @@ async def run_workflow(
     context: PipelineRunContext,
 ) -> WorkflowFunctionOutput:
     """Load and parse input documents into a standard format."""
-    input_reader = InputReaderFactory().create(
-        config.input.file_type,
-        {"storage": context.input_storage, "config": config.input},
-    )
+    input_reader = create_input_reader(config.input, context.input_storage)
 
     output = await load_input_documents(input_reader)
 
+    if len(output) == 0:
+        msg = "Error reading documents, please see logs."
+        logger.error(msg)
+        raise ValueError(msg)
+
     logger.info("Final # of rows loaded: %s", len(output))
     context.stats.num_documents = len(output)
 
@@ -39,4 +40,4 @@ async def run_workflow(
 
 async def load_input_documents(input_reader: InputReader) -> pd.DataFrame:
     """Load and parse input documents into a standard format."""
-    return await input_reader.read_files()
+    return pd.DataFrame(await input_reader.read_files())
diff --git a/packages/graphrag/graphrag/index/workflows/load_update_documents.py b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
index e68471eae7..1cab6cabfe 100644
--- a/packages/graphrag/graphrag/index/workflows/load_update_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/load_update_documents.py
@@ -6,11 +6,11 @@
 import logging
 
 import pandas as pd
+from graphrag_input.input_reader import InputReader
+from graphrag_input.input_reader_factory import create_input_reader
 from graphrag_storage import Storage
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.index.input.factory import InputReaderFactory
-from graphrag.index.input.input_reader import InputReader
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
 from graphrag.index.update.incremental_index import get_delta_docs
@@ -24,10 +24,7 @@ async def run_workflow(
     context: PipelineRunContext,
 ) -> WorkflowFunctionOutput:
     """Load and parse update-only input documents into a standard format."""
-    input_reader = InputReaderFactory().create(
-        config.input.file_type,
-        {"storage": context.input_storage, "config": config.input},
-    )
+    input_reader = create_input_reader(config.input, context.input_storage)
     output = await load_update_documents(
         input_reader,
         context.previous_storage,
@@ -50,7 +47,7 @@ async def load_update_documents(
     previous_storage: Storage,
 ) -> pd.DataFrame:
     """Load and parse update-only input documents into a standard format."""
-    input_documents = await input_reader.read_files()
+    input_documents = pd.DataFrame(await input_reader.read_files())
     # previous storage is the output of the previous run
     # we'll use this to diff the input from the prior
     delta_documents = await get_delta_docs(input_documents, previous_storage)
diff --git a/packages/graphrag/graphrag/index/workflows/prune_graph.py b/packages/graphrag/graphrag/index/workflows/prune_graph.py
index da4e2bc835..5653eef49b 100644
--- a/packages/graphrag/graphrag/index/workflows/prune_graph.py
+++ b/packages/graphrag/graphrag/index/workflows/prune_graph.py
@@ -58,6 +58,7 @@ def prune_graph(
     """Prune a full graph based on graph statistics."""
     # create a temporary graph to prune, then turn it back into dataframes
     graph = create_graph(relationships, edge_attr=["weight"], nodes=entities)
+
     pruned = prune_graph_operation(
         graph,
         min_node_freq=pruning_config.min_node_freq,
diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py
index a9ef358a30..2b4e2b1861 100644
--- a/packages/graphrag/graphrag/prompt_tune/loader/input.py
+++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py
@@ -10,11 +10,11 @@
 import pandas as pd
 from graphrag_cache.noop_cache import NoopCache
 from graphrag_chunking.chunker_factory import create_chunker
+from graphrag_input import create_input_reader
 from graphrag_storage import create_storage
 
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.index.input.factory import InputReaderFactory
 from graphrag.index.operations.embed_text.run_embed_text import (
     run_embed_text,
 )
@@ -63,14 +63,11 @@ async def load_docs_in_chunks(
     )
     tokenizer = get_tokenizer(embeddings_llm_settings)
     chunker = create_chunker(config.chunking, tokenizer.encode, tokenizer.decode)
-    input_storage = create_storage(config.input.storage)
-    input_reader = InputReaderFactory().create(
-        config.input.file_type,
-        {"storage": input_storage, "config": config.input},
-    )
+    input_storage = create_storage(config.input_storage)
+    input_reader = create_input_reader(config.input, input_storage)
     dataset = await input_reader.read_files()
     chunks_df = create_base_text_units(
-        documents=dataset,
+        documents=pd.DataFrame(dataset),
         callbacks=NoopWorkflowCallbacks(),
         tokenizer=tokenizer,
         chunker=chunker,
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index 5c2cb687d7..769634bcb3 100644
--- a/packages/graphrag/pyproject.toml
+++ b/packages/graphrag/pyproject.toml
@@ -41,6 +41,7 @@ dependencies = [
     "environs~=11.0",
     "graphrag-cache==2.7.0",
     "graphrag-common==2.7.0",
+    "graphrag-input==2.7.0",
     "graphrag-storage==2.7.0",
     "graspologic-native~=1.2",
     "json-repair~=0.30",
diff --git a/pyproject.toml b/pyproject.toml
index 1f4ab1d8c7..b2ec49f4c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,7 @@ members = ["packages/*"]
 [tool.uv.sources]
 graphrag-chunking = { workspace = true }
 graphrag-common = { workspace = true }
+graphrag-input = { workspace = true }
 graphrag-storage = { workspace = true }
 graphrag-cache = { workspace = true }
 
@@ -75,6 +76,7 @@ _semversioner_update_graphrag_chunking_toml_version = "update-toml update --file
 _semversioner_update_graphrag_common_toml_version = "update-toml update --file packages/graphrag-common/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_storage_toml_version = "update-toml update --file packages/graphrag-storage/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_cache_toml_version = "update-toml update --file packages/graphrag-cache/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_graphrag_input_toml_version = "update-toml update --file packages/graphrag-input/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_workspace_dependency_versions = "python -m scripts.update_workspace_dependency_versions"
 semversioner_add = "semversioner add-change"
 coverage_report = 'coverage report --omit "**/tests/**" --show-missing'
@@ -110,6 +112,7 @@ sequence = [
     '_semversioner_update_graphrag_toml_version',
     '_semversioner_update_graphrag_common_toml_version',
     '_semversioner_update_graphrag_chunking_toml_version',
+    '_semversioner_update_graphrag_input_toml_version',
     '_semversioner_update_graphrag_storage_toml_version',
     '_semversioner_update_graphrag_cache_toml_version',
     '_semversioner_update_workspace_dependency_versions',
diff --git a/tests/fixtures/azure/config.json b/tests/fixtures/azure/config.json
index 91d9fc51d0..774228d0ad 100644
--- a/tests/fixtures/azure/config.json
+++ b/tests/fixtures/azure/config.json
@@ -1,6 +1,6 @@
 {
     "input_path": "./tests/fixtures/azure",
-    "input_file_type": "text",
+    "input_type": "text",
     "index_method": "standard",
     "workflow_config": {
         "skip_assert": true,
diff --git a/tests/fixtures/azure/settings.yml b/tests/fixtures/azure/settings.yml
index 80ba02e56b..b220786d88 100644
--- a/tests/fixtures/azure/settings.yml
+++ b/tests/fixtures/azure/settings.yml
@@ -14,7 +14,7 @@ input:
     connection_string: ${LOCAL_BLOB_STORAGE_CONNECTION_STRING}
     container_name: azurefixture
     base_dir: input
-  file_type: text
+  type: text
   
 
 cache:
diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json
index 88d38f1e61..90b7c26061 100644
--- a/tests/fixtures/min-csv/config.json
+++ b/tests/fixtures/min-csv/config.json
@@ -1,6 +1,6 @@
 {
     "input_path": "./tests/fixtures/min-csv",
-    "input_file_type": "text",
+    "input_type": "text",
     "index_method": "standard",
     "workflow_config": {
         "load_input_documents": {
@@ -73,7 +73,7 @@
                 15
             ],
             "nan_allowed_columns": [
-                "metadata"
+                "raw_data"
             ],
             "max_runtime": 30,
             "expected_artifacts": ["documents.parquet"]
diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml
index 82066e753c..c6ebb2248e 100644
--- a/tests/fixtures/min-csv/settings.yml
+++ b/tests/fixtures/min-csv/settings.yml
@@ -35,7 +35,8 @@ vector_store:
     overwrite: True
 
 input:
-  file_type: csv
+  type: csv
+  encoding: utf-8-sig
 
 snapshots:
   embeddings: true
\ No newline at end of file
diff --git a/tests/fixtures/text/config.json b/tests/fixtures/text/config.json
index 6248a08044..cc69e523ec 100644
--- a/tests/fixtures/text/config.json
+++ b/tests/fixtures/text/config.json
@@ -1,6 +1,6 @@
 {
     "input_path": "./tests/fixtures/text",
-    "input_file_type": "text",
+    "input_type": "text",
     "index_method": "fast",
     "workflow_config": {
         "load_input_documents": {
@@ -72,7 +72,7 @@
                 1
             ],
             "nan_allowed_columns": [
-                "metadata"
+                "raw_data"
             ],
             "max_runtime": 30,
             "expected_artifacts": ["documents.parquet"]
diff --git a/tests/smoke/test_fixtures.py b/tests/smoke/test_fixtures.py
index 53205c7c09..7d43b0140a 100644
--- a/tests/smoke/test_fixtures.py
+++ b/tests/smoke/test_fixtures.py
@@ -126,7 +126,7 @@ class TestIndexer:
     def __run_indexer(
         self,
         root: Path,
-        input_file_type: str,
+        input_type: str,
         index_method: str,
     ):
         command = [
@@ -232,7 +232,7 @@ def __run_query(self, root: Path, query_config: dict[str, str]):
     def test_fixture(
         self,
         input_path: str,
-        input_file_type: str,
+        input_type: str,
         index_method: str,
         workflow_config: dict[str, dict[str, Any]],
         query_config: list[dict[str, str]],
@@ -248,7 +248,7 @@ def test_fixture(
             dispose = asyncio.run(prepare_azurite_data(input_path, azure))
 
         print("running indexer")
-        self.__run_indexer(root, input_file_type, index_method)
+        self.__run_indexer(root, input_type, index_method)
         print("indexer complete")
 
         if dispose is not None:
diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py
index 4dbefa4cf3..dd68b0ab6f 100644
--- a/tests/unit/chunking/test_chunker.py
+++ b/tests/unit/chunking/test_chunker.py
@@ -27,7 +27,7 @@ def setup_method(self, method):
         bootstrap()
 
     def test_basic_functionality(self):
-        """Test basic sentence splitting without metadata"""
+        """Test basic sentence splitting"""
         input = "This is a test. Another sentence. And a third one!"
         chunker = create_chunker(ChunkingConfig(type=ChunkerType.Sentence))
         chunks = chunker.chunk(input)
diff --git a/tests/unit/chunking/test_prepend_metadata.py b/tests/unit/chunking/test_prepend_metadata.py
index 99f99c3093..74f5a698e5 100644
--- a/tests/unit/chunking/test_prepend_metadata.py
+++ b/tests/unit/chunking/test_prepend_metadata.py
@@ -1,14 +1,15 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag_chunking.add_metadata import add_metadata
+from graphrag_chunking.transformers import add_metadata
 
 
 def test_add_metadata_one_row():
     """Test prepending metadata to chunks"""
     chunks = ["This is a test.", "Another sentence."]
     metadata = {"message": "hello"}
-    results = [add_metadata(chunk, metadata) for chunk in chunks]
+    transformer = add_metadata(metadata)
+    results = [transformer(chunk) for chunk in chunks]
     assert results[0] == "message: hello\nThis is a test."
     assert results[1] == "message: hello\nAnother sentence."
 
@@ -17,7 +18,8 @@ def test_add_metadata_one_row_append():
     """Test prepending metadata to chunks"""
     chunks = ["This is a test.", "Another sentence."]
     metadata = {"message": "hello"}
-    results = [add_metadata(chunk, metadata, append=True) for chunk in chunks]
+    transformer = add_metadata(metadata, append=True)
+    results = [transformer(chunk) for chunk in chunks]
     assert results[0] == "This is a test.message: hello\n"
     assert results[1] == "Another sentence.message: hello\n"
 
@@ -26,7 +28,8 @@ def test_add_metadata_multiple_rows():
     """Test prepending metadata to chunks"""
     chunks = ["This is a test.", "Another sentence."]
     metadata = {"message": "hello", "tag": "first"}
-    results = [add_metadata(chunk, metadata) for chunk in chunks]
+    transformer = add_metadata(metadata)
+    results = [transformer(chunk) for chunk in chunks]
     assert results[0] == "message: hello\ntag: first\nThis is a test."
     assert results[1] == "message: hello\ntag: first\nAnother sentence."
 
@@ -35,9 +38,7 @@ def test_add_metadata_custom_delimiters():
     """Test prepending metadata to chunks"""
     chunks = ["This is a test.", "Another sentence."]
     metadata = {"message": "hello", "tag": "first"}
-    results = [
-        add_metadata(chunk, metadata, delimiter="-", line_delimiter="_")
-        for chunk in chunks
-    ]
+    transformer = add_metadata(metadata, delimiter="-", line_delimiter="_")
+    results = [transformer(chunk) for chunk in chunks]
     assert results[0] == "message-hello_tag-first_This is a test."
     assert results[1] == "message-hello_tag-first_Another sentence."
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 586cd1e18e..c02285125a 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -17,7 +17,6 @@
 )
 from graphrag.config.models.global_search_config import GlobalSearchConfig
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.input_config import InputConfig
 from graphrag.config.models.language_model_config import LanguageModelConfig
 from graphrag.config.models.local_search_config import LocalSearchConfig
 from graphrag.config.models.prune_graph_config import PruneGraphConfig
@@ -29,6 +28,7 @@
 from graphrag.config.models.vector_store_config import VectorStoreConfig
 from graphrag_cache import CacheConfig
 from graphrag_chunking.chunking_config import ChunkingConfig
+from graphrag_input import InputConfig
 from graphrag_storage import StorageConfig
 from pydantic import BaseModel
 
@@ -143,13 +143,11 @@ def assert_cache_configs(actual: CacheConfig, expected: CacheConfig) -> None:
 
 
 def assert_input_configs(actual: InputConfig, expected: InputConfig) -> None:
-    assert_storage_config(actual.storage, expected.storage)
-    assert actual.file_type == expected.file_type
+    assert actual.type == expected.type
     assert actual.encoding == expected.encoding
     assert actual.file_pattern == expected.file_pattern
     assert actual.text_column == expected.text_column
     assert actual.title_column == expected.title_column
-    assert actual.metadata == expected.metadata
 
 
 def assert_text_embedding_configs(
@@ -338,8 +336,8 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) ->
     assert_vector_store_configs(actual.vector_store, expected.vector_store)
     assert_reporting_configs(actual.reporting, expected.reporting)
     assert_storage_config(actual.output, expected.output)
-
-    assert_storage_config(actual.update_index_output, expected.update_index_output)
+    assert_storage_config(actual.input_storage, expected.input_storage)
+    assert_storage_config(actual.update_output_storage, expected.update_output_storage)
 
     assert_cache_configs(actual.cache, expected.cache)
     assert_input_configs(actual.input, expected.input)
diff --git a/tests/unit/indexing/input/data/one-html/input.html b/tests/unit/indexing/input/data/one-html/input.html
new file mode 100644
index 0000000000..dbf8badc70
--- /dev/null
+++ b/tests/unit/indexing/input/data/one-html/input.html
@@ -0,0 +1,8 @@
+<html>
+<head>
+<title>Test</title>
+<head>
+<body>
+Hi how are you today?
+</body>
+</html>
\ No newline at end of file
diff --git a/tests/unit/indexing/input/data/one-jsonl/input.jsonl b/tests/unit/indexing/input/data/one-jsonl/input.jsonl
new file mode 100644
index 0000000000..2866aed408
--- /dev/null
+++ b/tests/unit/indexing/input/data/one-jsonl/input.jsonl
@@ -0,0 +1,3 @@
+{ "title": "Hello", "text": "Hi how are you today?"}
+{ "title": "Goodbye", "text": "I'm outta here"}
+{ "title": "Adios", "text": "See you later"}
\ No newline at end of file
diff --git a/tests/unit/indexing/input/test_csv_loader.py b/tests/unit/indexing/input/test_csv_loader.py
index b0dc645e1b..1a84d82676 100644
--- a/tests/unit/indexing/input/test_csv_loader.py
+++ b/tests/unit/indexing/input/test_csv_loader.py
@@ -1,82 +1,56 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.enums import InputFileType
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.factory import InputReaderFactory
+from graphrag_input import InputConfig, InputType, create_input_reader
 from graphrag_storage import StorageConfig, create_storage
 
 
 async def test_csv_loader_one_file():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-csv",
-        ),
-        file_type=InputFileType.csv,
+        type=InputType.Csv,
         file_pattern=".*\\.csv$",
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-csv",
+        )
     )
-    assert documents.shape == (2, 4)
-    assert documents["title"].iloc[0] == "input.csv"
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 2
+    assert documents[0].title == "input.csv (0)"
+    assert documents[0].raw_data == {
+        "title": "Hello",
+        "text": "Hi how are you today?",
+    }
+    assert documents[1].title == "input.csv (1)"
 
 
 async def test_csv_loader_one_file_with_title():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-csv",
-        ),
-        file_type=InputFileType.csv,
-        file_pattern=".*\\.csv$",
+        type=InputType.Csv,
         title_column="title",
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
-    )
-    assert documents.shape == (2, 4)
-    assert documents["title"].iloc[0] == "Hello"
-
-
-async def test_csv_loader_one_file_with_metadata():
-    config = InputConfig(
-        storage=StorageConfig(
+    storage = create_storage(
+        StorageConfig(
             base_dir="tests/unit/indexing/input/data/one-csv",
-        ),
-        file_type=InputFileType.csv,
-        file_pattern=".*\\.csv$",
-        title_column="title",
-        metadata=["title"],
-    )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
+        )
     )
-    print(documents)
-    assert documents.shape == (2, 5)
-    assert documents["metadata"][0] == {"title": "Hello"}
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 2
+    assert documents[0].title == "Hello"
 
 
 async def test_csv_loader_multiple_files():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/multiple-csvs",
-        ),
-        file_type=InputFileType.csv,
-        file_pattern=".*\\.csv$",
+        type=InputType.Csv,
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/multiple-csvs",
+        )
     )
-    assert documents.shape == (4, 4)
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 4
diff --git a/tests/unit/indexing/input/test_json_loader.py b/tests/unit/indexing/input/test_json_loader.py
index 3959096b1c..d20d64ad2d 100644
--- a/tests/unit/indexing/input/test_json_loader.py
+++ b/tests/unit/indexing/input/test_json_loader.py
@@ -1,100 +1,71 @@
 # Copyright (c) 2024 Microsoft Corporation.
 # Licensed under the MIT License
 
-from graphrag.config.enums import InputFileType
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.factory import InputReaderFactory
+from graphrag_input import InputConfig, InputType, create_input_reader
 from graphrag_storage import StorageConfig, create_storage
 
 
 async def test_json_loader_one_file_one_object():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-json-one-object",
-        ),
-        file_type=InputFileType.json,
+        type=InputType.Json,
         file_pattern=".*\\.json$",
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-json-one-object",
+        )
     )
-    assert documents.shape == (1, 4)
-    assert documents["title"].iloc[0] == "input.json"
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 1
+    assert documents[0].title == "input.json"
+    assert documents[0].raw_data == {
+        "title": "Hello",
+        "text": "Hi how are you today?",
+    }
 
 
 async def test_json_loader_one_file_multiple_objects():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-json-multiple-objects",
-        ),
-        file_type=InputFileType.json,
-        file_pattern=".*\\.json$",
+        type=InputType.Json,
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-json-multiple-objects",
+        )
     )
-    print(documents)
-    assert documents.shape == (3, 4)
-    assert documents["title"].iloc[0] == "input.json"
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 3
+    assert documents[0].title == "input.json (0)"
+    assert documents[1].title == "input.json (1)"
 
 
 async def test_json_loader_one_file_with_title():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-json-one-object",
-        ),
-        file_type=InputFileType.json,
-        file_pattern=".*\\.json$",
+        type=InputType.Json,
         title_column="title",
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
-    )
-    assert documents.shape == (1, 4)
-    assert documents["title"].iloc[0] == "Hello"
-
-
-async def test_json_loader_one_file_with_metadata():
-    config = InputConfig(
-        storage=StorageConfig(
+    storage = create_storage(
+        StorageConfig(
             base_dir="tests/unit/indexing/input/data/one-json-one-object",
-        ),
-        file_type=InputFileType.json,
-        file_pattern=".*\\.json$",
-        title_column="title",
-        metadata=["title"],
+        )
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
-    )
-    assert documents.shape == (1, 5)
-    assert documents["metadata"][0] == {"title": "Hello"}
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 1
+    assert documents[0].title == "Hello"
 
 
 async def test_json_loader_multiple_files():
     config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/multiple-jsons",
-        ),
-        file_type=InputFileType.json,
-        file_pattern=".*\\.json$",
+        type=InputType.Json,
     )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/multiple-jsons",
+        )
     )
-    assert documents.shape == (4, 4)
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 4
diff --git a/tests/unit/indexing/input/test_jsonl_loader.py b/tests/unit/indexing/input/test_jsonl_loader.py
new file mode 100644
index 0000000000..dd56094290
--- /dev/null
+++ b/tests/unit/indexing/input/test_jsonl_loader.py
@@ -0,0 +1,42 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+from graphrag_input import InputConfig, InputType, create_input_reader
+from graphrag_storage import StorageConfig, create_storage
+
+
+async def test_jsonl_loader_one_file_multiple_objects():
+    config = InputConfig(
+        type=InputType.JsonLines,
+        file_pattern=".*\\.jsonl$",
+    )
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-jsonl",
+        )
+    )
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 3
+    assert documents[0].title == "input.jsonl (0)"
+    assert documents[0].raw_data == {
+        "title": "Hello",
+        "text": "Hi how are you today?",
+    }
+    assert documents[1].title == "input.jsonl (1)"
+
+
+async def test_jsonl_loader_one_file_with_title():
+    config = InputConfig(
+        type=InputType.JsonLines,
+        title_column="title",
+    )
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-jsonl",
+        )
+    )
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 3
+    assert documents[0].title == "Hello"
diff --git a/tests/unit/indexing/input/test_markitdown_loader.py b/tests/unit/indexing/input/test_markitdown_loader.py
new file mode 100644
index 0000000000..c32e36dbb7
--- /dev/null
+++ b/tests/unit/indexing/input/test_markitdown_loader.py
@@ -0,0 +1,26 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+from graphrag_input import InputConfig, InputType, create_input_reader
+from graphrag_storage import StorageConfig, create_storage
+
+
+# these tests just confirm we can load files with MarkItDown,
+# and use html specifically because it requires no additional dependency installation
+async def test_markitdown_loader_one_file():
+    config = InputConfig(
+        type=InputType.MarkItDown,
+        file_pattern=".*\\.html$",
+    )
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-html",
+        )
+    )
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 1
+    # markitdown will extract the title and body from the HTML if present and clean them
+    assert documents[0].title == "Test"
+    assert documents[0].text == "Hi how are you today?"
+    assert documents[0].raw_data is None
diff --git a/tests/unit/indexing/input/test_text_document.py b/tests/unit/indexing/input/test_text_document.py
new file mode 100644
index 0000000000..d71c515bf5
--- /dev/null
+++ b/tests/unit/indexing/input/test_text_document.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+import pytest
+from graphrag_input import get_property
+
+
+def test_get_property_single_level():
+    data = {"foo": "bar"}
+    assert get_property(data, "foo") == "bar"
+
+
+def test_get_property_two_levels():
+    data = {"foo": {"bar": "baz"}}
+    assert get_property(data, "foo.bar") == "baz"
+
+
+def test_get_property_three_levels():
+    data = {"a": {"b": {"c": "value"}}}
+    assert get_property(data, "a.b.c") == "value"
+
+
+def test_get_property_returns_dict():
+    data = {"foo": {"bar": {"baz": "qux"}}}
+    result = get_property(data, "foo.bar")
+    assert result == {"baz": "qux"}
+
+
+def test_get_property_missing_key_raises():
+    data = {"foo": "bar"}
+    with pytest.raises(KeyError):
+        get_property(data, "missing")
+
+
+def test_get_property_missing_nested_key_raises():
+    data = {"foo": {"bar": "baz"}}
+    with pytest.raises(KeyError):
+        get_property(data, "foo.missing")
+
+
+def test_get_property_non_dict_intermediate_raises():
+    data = {"foo": "bar"}
+    with pytest.raises(KeyError):
+        get_property(data, "foo.bar")
+
+
+def test_get_property_empty_dict_raises():
+    data = {}
+    with pytest.raises(KeyError):
+        get_property(data, "foo")
+
+
+def test_get_property_with_none_value():
+    data = {"foo": None}
+    assert get_property(data, "foo") is None
+
+
+def test_get_property_with_list_value():
+    data = {"foo": [1, 2, 3]}
+    assert get_property(data, "foo") == [1, 2, 3]
+
+
+def test_get_property_list_intermediate_raises():
+    data = {"foo": [{"bar": "baz"}]}
+    with pytest.raises(KeyError):
+        get_property(data, "foo.bar")
+
+
+def test_get_property_numeric_value():
+    data = {"count": 42}
+    assert get_property(data, "count") == 42
+
+
+def test_get_property_boolean_value():
+    data = {"enabled": True}
+    assert get_property(data, "enabled") is True
diff --git a/tests/unit/indexing/input/test_text_loader.py b/tests/unit/indexing/input/test_text_loader.py
new file mode 100644
index 0000000000..482593a270
--- /dev/null
+++ b/tests/unit/indexing/input/test_text_loader.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+from graphrag_input import InputConfig, InputType, create_input_reader
+from graphrag_storage import StorageConfig, create_storage
+
+
+async def test_text_loader_one_file():
+    config = InputConfig(
+        type=InputType.Text,
+        file_pattern=".*\\.txt$",
+    )
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/one-txt",
+        )
+    )
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 1
+    assert documents[0].title == "input.txt"
+    assert documents[0].raw_data is None
+
+
+async def test_text_loader_multiple_files():
+    config = InputConfig(
+        type=InputType.Text,
+    )
+    storage = create_storage(
+        StorageConfig(
+            base_dir="tests/unit/indexing/input/data/multiple-txts",
+        )
+    )
+    reader = create_input_reader(config, storage)
+    documents = await reader.read_files()
+    assert len(documents) == 2
diff --git a/tests/unit/indexing/input/test_txt_loader.py b/tests/unit/indexing/input/test_txt_loader.py
deleted file mode 100644
index 57ded82507..0000000000
--- a/tests/unit/indexing/input/test_txt_loader.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-from graphrag.config.enums import InputFileType
-from graphrag.config.models.input_config import InputConfig
-from graphrag.index.input.factory import InputReaderFactory
-from graphrag_storage import StorageConfig, create_storage
-
-
-async def test_txt_loader_one_file():
-    config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-txt",
-        ),
-        file_type=InputFileType.text,
-        file_pattern=".*\\.txt$",
-    )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
-    )
-    assert documents.shape == (1, 4)
-    assert documents["title"].iloc[0] == "input.txt"
-
-
-async def test_txt_loader_one_file_with_metadata():
-    config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/one-txt",
-        ),
-        file_type=InputFileType.text,
-        file_pattern=".*\\.txt$",
-        metadata=["title"],
-    )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
-    )
-    assert documents.shape == (1, 5)
-    # unlike csv, we cannot set the title to anything other than the filename
-    assert documents["metadata"][0] == {"title": "input.txt"}
-
-
-async def test_txt_loader_multiple_files():
-    config = InputConfig(
-        storage=StorageConfig(
-            base_dir="tests/unit/indexing/input/data/multiple-txts",
-        ),
-        file_type=InputFileType.text,
-        file_pattern=".*\\.txt$",
-    )
-    storage = create_storage(config.storage)
-    documents = (
-        await InputReaderFactory()
-        .create(config.file_type, {"storage": storage, "config": config})
-        .read_files()
-    )
-    assert documents.shape == (2, 4)
diff --git a/tests/verbs/data/communities.parquet b/tests/verbs/data/communities.parquet
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diff --git a/tests/verbs/data/documents.parquet b/tests/verbs/data/documents.parquet
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diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py
index 89018e1609..77546c4c88 100644
--- a/tests/verbs/test_create_base_text_units.py
+++ b/tests/verbs/test_create_base_text_units.py
@@ -10,7 +10,6 @@
     compare_outputs,
     create_test_context,
     load_test_table,
-    update_document_metadata,
 )
 
 
@@ -20,26 +19,18 @@ async def test_create_base_text_units():
     context = await create_test_context()
 
     config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
+    config.chunking.prepend_metadata = ["title"]
 
     await run_workflow(config, context)
 
     actual = await load_table_from_storage("text_units", context.output_storage)
 
-    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])
-
-
-async def test_create_base_text_units_metadata():
-    expected = load_test_table("text_units_metadata")
+    print("EXPECTED")
+    print(expected.columns)
+    print(expected)
 
-    context = await create_test_context()
-
-    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
-    config.input.metadata = ["title"]
-    config.chunking.prepend_metadata = True
+    print("ACTUAL")
+    print(actual.columns)
+    print(actual)
 
-    await update_document_metadata(config.input.metadata, context)
-
-    await run_workflow(config, context)
-
-    actual = await load_table_from_storage("text_units", context.output_storage)
-    compare_outputs(actual, expected, ["text", "document_id", "n_tokens"])
+    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])
diff --git a/tests/verbs/test_create_community_reports.py b/tests/verbs/test_create_community_reports.py
index 561f54108b..d479120ce2 100644
--- a/tests/verbs/test_create_community_reports.py
+++ b/tests/verbs/test_create_community_reports.py
@@ -4,15 +4,14 @@
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
-from graphrag.index.workflows.create_community_reports import (
-    run_workflow,
-)
-from graphrag.utils.storage import load_table_from_storage
-
 from graphrag.index.operations.summarize_communities.community_reports_extractor import (
     CommunityReportResponse,
     FindingModel,
 )
+from graphrag.index.workflows.create_community_reports import (
+    run_workflow,
+)
+from graphrag.utils.storage import load_table_from_storage
 
 from .util import (
     DEFAULT_MODEL_CONFIG,
diff --git a/tests/verbs/test_create_final_documents.py b/tests/verbs/test_create_final_documents.py
index 05f4b60691..32084466e7 100644
--- a/tests/verbs/test_create_final_documents.py
+++ b/tests/verbs/test_create_final_documents.py
@@ -13,7 +13,6 @@
     compare_outputs,
     create_test_context,
     load_test_table,
-    update_document_metadata,
 )
 
 
@@ -34,26 +33,3 @@ async def test_create_final_documents():
 
     for column in DOCUMENTS_FINAL_COLUMNS:
         assert column in actual.columns
-
-
-async def test_create_final_documents_with_metadata_column():
-    context = await create_test_context(
-        storage=["text_units"],
-    )
-
-    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
-    config.input.metadata = ["title"]
-
-    # simulate the metadata construction during initial input loading
-    await update_document_metadata(config.input.metadata, context)
-
-    expected = await load_table_from_storage("documents", context.output_storage)
-
-    await run_workflow(config, context)
-
-    actual = await load_table_from_storage("documents", context.output_storage)
-
-    compare_outputs(actual, expected)
-
-    for column in DOCUMENTS_FINAL_COLUMNS:
-        assert column in actual.columns
diff --git a/tests/verbs/test_prune_graph.py b/tests/verbs/test_prune_graph.py
index 426230161a..78c6391d50 100644
--- a/tests/verbs/test_prune_graph.py
+++ b/tests/verbs/test_prune_graph.py
@@ -28,4 +28,4 @@ async def test_prune_graph():
 
     nodes_actual = await load_table_from_storage("entities", context.output_storage)
 
-    assert len(nodes_actual) == 21
+    assert len(nodes_actual) == 29
diff --git a/tests/verbs/util.py b/tests/verbs/util.py
index 6dc6667210..e025dde65d 100644
--- a/tests/verbs/util.py
+++ b/tests/verbs/util.py
@@ -5,7 +5,7 @@
 import pandas as pd
 from graphrag.index.run.utils import create_run_context
 from graphrag.index.typing.context import PipelineRunContext
-from graphrag.utils.storage import load_table_from_storage, write_table_to_storage
+from graphrag.utils.storage import write_table_to_storage
 from pandas.testing import assert_series_equal
 
 pd.set_option("display.max_columns", None)
@@ -87,12 +87,3 @@ def compare_outputs(
             print("Actual:")
             print(actual[column])
             raise
-
-
-async def update_document_metadata(metadata: list[str], context: PipelineRunContext):
-    """Takes the default documents and adds the configured metadata columns for later parsing by the text units and final documents workflows."""
-    documents = await load_table_from_storage("documents", context.output_storage)
-    documents["metadata"] = documents[metadata].apply(lambda row: row.to_dict(), axis=1)
-    await write_table_to_storage(
-        documents, "documents", context.output_storage
-    )  # write to the runtime context storage only
diff --git a/unified-search-app/app/app_logic.py b/unified-search-app/app/app_logic.py
index a573b9daa5..dc64e0e77c 100644
--- a/unified-search-app/app/app_logic.py
+++ b/unified-search-app/app/app_logic.py
@@ -7,6 +7,7 @@
 import logging
 from typing import TYPE_CHECKING
 
+import graphrag.api as api
 import streamlit as st
 from knowledge_loader.data_sources.loader import (
     create_datasource,
@@ -17,8 +18,6 @@
 from state.session_variables import SessionVariables
 from ui.search import display_search_result
 
-import graphrag.api as api
-
 if TYPE_CHECKING:
     import pandas as pd
 
diff --git a/uv.lock b/uv.lock
index 94c8626cc5..72a6db6f11 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,9 +1,13 @@
 version = 1
 revision = 1
-requires-python = ">=3.11, <3.13"
+requires-python = ">=3.11, <3.14"
 resolution-markers = [
-    "python_full_version >= '3.12'",
-    "python_full_version < '3.12'",
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'win32'",
+    "python_full_version < '3.12' and sys_platform != 'win32'",
 ]
 
 [manifest]
@@ -12,6 +16,7 @@ members = [
     "graphrag-cache",
     "graphrag-chunking",
     "graphrag-common",
+    "graphrag-input",
     "graphrag-monorepo",
     "graphrag-storage",
 ]
@@ -83,6 +88,23 @@ wheels = [
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     { url = "https://files.pythonhosted.org/packages/8b/68/46dd042d7bc62eab30bafdb8569f55ef125c3a88bb174270324224f8df56/aiohttp-3.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:0a2be20eb23888df130214b91c262a90e2de1553d6fb7de9e9010cec994c0ff2", size = 451401 },
+    { url = "https://files.pythonhosted.org/packages/86/2c/ac53efdc9c10e41399acc2395af98f835b86d0141d5c3820857eb9f6a14a/aiohttp-3.13.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:00243e51f16f6ec0fb021659d4af92f675f3cf9f9b39efd142aa3ad641d8d1e6", size = 730090 },
+    { url = "https://files.pythonhosted.org/packages/13/18/1ac95683e1c1d48ef4503965c96f5401618a04c139edae12e200392daae8/aiohttp-3.13.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:059978d2fddc462e9211362cbc8446747ecd930537fa559d3d25c256f032ff54", size = 488041 },
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@@ -854,6 +1007,7 @@ dependencies = [
     { name = "fastuuid" },
     { name = "graphrag-cache" },
     { name = "graphrag-common" },
+    { name = "graphrag-input" },
     { name = "graphrag-storage" },
     { name = "graspologic-native" },
     { name = "json-repair" },
@@ -887,6 +1041,7 @@ requires-dist = [
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     { name = "graphrag-cache", editable = "packages/graphrag-cache" },
     { name = "graphrag-common", editable = "packages/graphrag-common" },
+    { name = "graphrag-input", editable = "packages/graphrag-input" },
     { name = "graphrag-storage", editable = "packages/graphrag-storage" },
     { name = "graspologic-native", specifier = "~=1.2" },
     { name = "json-repair", specifier = "~=0.30" },
@@ -952,6 +1107,25 @@ requires-dist = [
     { name = "pyyaml", specifier = "~=6.0" },
 ]
 
+[[package]]
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+version = "2.7.0"
+source = { editable = "packages/graphrag-input" }
+dependencies = [
+    { name = "graphrag-common" },
+    { name = "graphrag-storage" },
+    { name = "markitdown" },
+    { name = "pydantic" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "graphrag-common", editable = "packages/graphrag-common" },
+    { name = "graphrag-storage", editable = "packages/graphrag-storage" },
+    { name = "markitdown", specifier = "~=0.1.0" },
+    { name = "pydantic", specifier = "~=2.10" },
+]
+
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 name = "graphrag-monorepo"
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From 22a4d29a1097f504647d0b3b7b2d326419eb706d Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Tue, 13 Jan 2026 15:56:26 -0800
Subject: [PATCH 37/40] DRIFT fixes (#2171)

* Use stable ids for community reports

* Remove deprecated title from embedding flow

* Remove embedding column from df loaders

* Fix lancedb insertion

* Add drift back to smoke tests

* Fix mock embedder to match default embedding length

* Fix DRIFT notebook

* Push drift_k_followups through to prompt

* Format
---
 docs/examples_notebooks/drift_search.ipynb    |  45 ++++--------------
 .../operation dulce/communities.parquet       | Bin 13291 -> 14094 bytes
 .../operation dulce/community_reports.parquet | Bin 51718 -> 88785 bytes
 .../inputs/operation dulce/covariates.parquet | Bin 21453 -> 26494 bytes
 .../embeddings.community_full_content.parquet | Bin 0 -> 220010 bytes
 .../inputs/operation dulce/entities.parquet   | Bin 12361 -> 19483 bytes
 .../auxiliary.idx                             | Bin 0 -> 12931 bytes
 .../index.idx                                 | Bin 0 -> 12626 bytes
 ...0-a943ac34-0e87-43c2-80d0-8f83fb80f4f5.txn | Bin 0 -> 283 bytes
 ...1-ec798d7b-a8bf-4985-a5d0-784434802168.txn | Bin 0 -> 146 bytes
 ...2-78887911-d792-4dc9-b28d-f2858db1139a.txn | Bin 0 -> 64 bytes
 ...3-c16be721-5d7e-46a0-98c2-34d5d9c29383.txn | Bin 0 -> 107 bytes
 .../_versions/1.manifest                      | Bin 0 -> 358 bytes
 .../_versions/2.manifest                      | Bin 0 -> 467 bytes
 .../_versions/3.manifest                      | Bin 0 -> 294 bytes
 .../_versions/4.manifest                      | Bin 0 -> 360 bytes
 ...667cccff-01b5-4b70-a2a6-8cf4d6ada077.lance | Bin 0 -> 12853 bytes
 ...a84d995f-111c-45d1-ba5a-32b3747b8a18.lance | Bin 0 -> 124729 bytes
 ...0-2fed1d8b-daac-41b0-a93a-e115cda75be3.txn |   2 -
 ...1-61dbb7c2-aec3-4796-b223-941fc7cc93cc.txn | Bin 107 -> 0 bytes
 ...2-60012692-a153-48f9-8f4e-c479b44cbf3f.txn |   2 -
 ...3-0d2dc9a1-094f-4220-83c7-6ad6f26fac2b.txn | Bin 107 -> 0 bytes
 .../_versions/1.manifest                      | Bin 303 -> 0 bytes
 .../_versions/2.manifest                      | Bin 367 -> 0 bytes
 .../_versions/3.manifest                      | Bin 302 -> 0 bytes
 .../_versions/4.manifest                      | Bin 366 -> 0 bytes
 ...1e7b2d94-ed06-4aa0-b22e-86a71d416bc6.lance | Bin 38577 -> 0 bytes
 ...1ed9f301-ce30-46a8-8c0b-9c2a60a3cf43.lance | Bin 28592 -> 0 bytes
 ...0-92c031e5-7558-451e-9d0f-f5514db9616d.txn |   2 -
 ...1-7b3cb8d8-3512-4584-a003-91838fed8911.txn | Bin 107 -> 0 bytes
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 ...3-9ad29d69-9a69-43a8-8b26-252ea267958d.txn | Bin 107 -> 0 bytes
 .../_versions/1.manifest                      | Bin 303 -> 0 bytes
 .../_versions/2.manifest                      | Bin 367 -> 0 bytes
 .../_versions/3.manifest                      | Bin 303 -> 0 bytes
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 ...a34575c4-5260-457f-bebe-3f40bc0e2ee3.lance | Bin 120119 -> 0 bytes
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 ...3-7516fb71-9db3-4666-bdef-ea04c1eb9697.txn | Bin 107 -> 0 bytes
 .../_versions/1.manifest                      | Bin 302 -> 0 bytes
 .../_versions/2.manifest                      | Bin 366 -> 0 bytes
 .../_versions/3.manifest                      | Bin 302 -> 0 bytes
 .../_versions/4.manifest                      | Bin 366 -> 0 bytes
 ...2794bf5b-de3d-4202-ab16-e76bc27c8e6a.lance | Bin 80120 -> 0 bytes
 ...2f74c8e8-3f35-4209-889c-a13cf0780eb3.lance | Bin 80120 -> 0 bytes
 .../auxiliary.idx                             | Bin 0 -> 12931 bytes
 .../index.idx                                 | Bin 0 -> 12626 bytes
 ...0-5b75ba0a-bae9-4244-8a6b-31de09f7e03d.txn | Bin 0 -> 283 bytes
 ...1-71f2ac8d-a101-467d-b57d-2dea6d14f7a7.txn | Bin 0 -> 146 bytes
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 ...3-4ada922f-cf85-44df-bc8b-b132e35009d0.txn | Bin 0 -> 107 bytes
 ...4-dcac110d-2a49-4777-a51e-5078fed1b0df.txn |   1 +
 ...5-ec86af9f-b799-4457-b07a-24a3459dd952.txn | Bin 0 -> 107 bytes
 ...6-c2afb84c-4b3a-4ccd-8843-0deaa25bd971.txn |   1 +
 ...7-6798041f-3283-4b54-8313-54f80e00d338.txn | Bin 0 -> 107 bytes
 .../_versions/1.manifest                      | Bin 0 -> 358 bytes
 .../_versions/2.manifest                      | Bin 0 -> 467 bytes
 .../_versions/3.manifest                      | Bin 0 -> 295 bytes
 .../_versions/4.manifest                      | Bin 0 -> 361 bytes
 .../_versions/5.manifest                      | Bin 0 -> 360 bytes
 .../_versions/6.manifest                      | Bin 0 -> 426 bytes
 .../_versions/7.manifest                      | Bin 0 -> 427 bytes
 .../_versions/8.manifest                      | Bin 0 -> 493 bytes
 ...2677858d-16a4-4c0c-9515-ed5a9ee32fd7.lance | Bin 0 -> 86776 bytes
 ...4ed23c16-d11c-49b5-869d-653cfbd9c271.lance | Bin 0 -> 12853 bytes
 ...5370b2ef-efd3-434e-9745-9d046b53bb4a.lance | Bin 0 -> 197754 bytes
 ...ec59d894-a2a6-4a44-8266-d07bbd684c33.lance | Bin 0 -> 197754 bytes
 .../auxiliary.idx                             | Bin 0 -> 12931 bytes
 .../index.idx                                 | Bin 0 -> 12626 bytes
 ...0-9abee4db-9914-4c35-b600-d995235f8e27.txn | Bin 0 -> 283 bytes
 ...1-302f5740-fbd0-4887-9933-13a2842ec8e4.txn | Bin 0 -> 146 bytes
 ...2-551a917c-5ab9-46a7-9085-fd82aa879717.txn | Bin 0 -> 64 bytes
 ...3-b0b6e7bb-a152-4148-be3b-d1ab50215bda.txn | Bin 0 -> 107 bytes
 .../text_unit_text.lance/_versions/1.manifest | Bin 0 -> 357 bytes
 .../text_unit_text.lance/_versions/2.manifest | Bin 0 -> 466 bytes
 .../text_unit_text.lance/_versions/3.manifest | Bin 0 -> 294 bytes
 .../text_unit_text.lance/_versions/4.manifest | Bin 0 -> 360 bytes
 ...4105076e-7a55-4549-b86a-51c6bfa68ea5.lance | Bin 0 -> 12853 bytes
 ...a28d4629-848b-4e1d-854d-cad6297a0c1a.lance | Bin 0 -> 62584 bytes
 .../operation dulce/relationships.parquet     | Bin 13354 -> 21686 bytes
 .../inputs/operation dulce/text_units.parquet | Bin 28854 -> 30769 bytes
 .../index/operations/embed_text/embed_text.py |   7 ---
 .../operations/finalize_community_reports.py  |   7 +--
 .../query/drift_search_system_prompt.py       |   2 +-
 .../graphrag/query/indexer_adapters.py        |  28 +----------
 .../graphrag/query/input/loaders/dfs.py       |   4 --
 .../structured_search/drift_search/action.py  |  12 ++++-
 .../drift_search/drift_context.py             |   4 +-
 .../structured_search/drift_search/search.py  |  17 +++++--
 .../structured_search/local_search/search.py  |   1 +
 .../graphrag/vector_stores/cosmosdb.py        |   4 --
 .../graphrag/vector_stores/lancedb.py         |   7 +--
 tests/fixtures/min-csv/config.json            |   2 +-
 tests/mock_provider.py                        |  13 ++---
 97 files changed, 51 insertions(+), 116 deletions(-)
 create mode 100644 docs/examples_notebooks/inputs/operation dulce/embeddings.community_full_content.parquet
 create mode 100644 docs/examples_notebooks/inputs/operation dulce/lancedb/community_full_content.lance/_indices/dd5917d9-d48d-4af3-bc2e-43a53b2fdbe6/auxiliary.idx
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 create mode 100644 docs/examples_notebooks/inputs/operation dulce/lancedb/community_full_content.lance/_transactions/0-a943ac34-0e87-43c2-80d0-8f83fb80f4f5.txn
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diff --git a/docs/examples_notebooks/drift_search.ipynb b/docs/examples_notebooks/drift_search.ipynb
index e33e8bc94f..81cd193eec 100644
--- a/docs/examples_notebooks/drift_search.ipynb
+++ b/docs/examples_notebooks/drift_search.ipynb
@@ -17,7 +17,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "from pathlib import Path\n",
     "\n",
     "import pandas as pd\n",
     "from graphrag.config.enums import ModelType\n",
@@ -62,15 +61,13 @@
     "# load description embeddings to an in-memory lancedb vectorstore\n",
     "# to connect to a remote db, specify url and port values.\n",
     "description_embedding_store = LanceDBVectorStore(\n",
-    "    vector_store_schema_config=VectorStoreSchemaConfig(\n",
-    "        index_name=\"default-entity-description\"\n",
-    "    ),\n",
+    "    vector_store_schema_config=VectorStoreSchemaConfig(index_name=\"entity_description\"),\n",
     ")\n",
     "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
     "\n",
     "full_content_embedding_store = LanceDBVectorStore(\n",
     "    vector_store_schema_config=VectorStoreSchemaConfig(\n",
-    "        index_name=\"default-community-full_content\"\n",
+    "        index_name=\"community_full_content\"\n",
     "    )\n",
     ")\n",
     "full_content_embedding_store.connect(db_uri=LANCEDB_URI)\n",
@@ -88,7 +85,11 @@
     "text_units = read_indexer_text_units(text_unit_df)\n",
     "\n",
     "print(f\"Text unit records: {len(text_unit_df)}\")\n",
-    "text_unit_df.head()"
+    "text_unit_df.head()\n",
+    "\n",
+    "report_df = pd.read_parquet(f\"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet\")\n",
+    "reports = read_indexer_reports(report_df, community_df, COMMUNITY_LEVEL)\n",
+    "read_indexer_report_embeddings(reports, full_content_embedding_store)"
    ]
   },
   {
@@ -118,7 +119,7 @@
     "    api_key=api_key,\n",
     "    type=ModelType.Embedding,\n",
     "    model_provider=\"openai\",\n",
-    "    model=\"text-embedding-3-small\",\n",
+    "    model=\"text-embedding-3-large\",\n",
     "    max_retries=20,\n",
     ")\n",
     "\n",
@@ -129,31 +130,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_community_reports(\n",
-    "    input_dir: str,\n",
-    "    community_report_table: str = COMMUNITY_REPORT_TABLE,\n",
-    "):\n",
-    "    \"\"\"Embeds the full content of the community reports and saves the DataFrame with embeddings to the output path.\"\"\"\n",
-    "    input_path = Path(input_dir) / f\"{community_report_table}.parquet\"\n",
-    "    return pd.read_parquet(input_path)\n",
-    "\n",
-    "\n",
-    "report_df = read_community_reports(INPUT_DIR)\n",
-    "reports = read_indexer_reports(\n",
-    "    report_df,\n",
-    "    community_df,\n",
-    "    COMMUNITY_LEVEL,\n",
-    "    content_embedding_col=\"full_content_embeddings\",\n",
-    ")\n",
-    "read_indexer_report_embeddings(reports, full_content_embedding_store)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -161,12 +137,9 @@
    "outputs": [],
    "source": [
     "drift_params = DRIFTSearchConfig(\n",
-    "    temperature=0,\n",
-    "    max_tokens=12_000,\n",
     "    primer_folds=1,\n",
     "    drift_k_followups=3,\n",
     "    n_depth=3,\n",
-    "    n=1,\n",
     ")\n",
     "\n",
     "context_builder = DRIFTSearchContextBuilder(\n",
@@ -216,7 +189,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "graphrag",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
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diff --git a/docs/examples_notebooks/inputs/operation dulce/lancedb/default-entity-description.lance/_transactions/0-92c031e5-7558-451e-9d0f-f5514db9616d.txn b/docs/examples_notebooks/inputs/operation dulce/lancedb/default-entity-description.lance/_transactions/0-92c031e5-7558-451e-9d0f-f5514db9616d.txn
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diff --git a/docs/examples_notebooks/inputs/operation dulce/lancedb/text_unit_text.lance/data/a28d4629-848b-4e1d-854d-cad6297a0c1a.lance b/docs/examples_notebooks/inputs/operation dulce/lancedb/text_unit_text.lance/data/a28d4629-848b-4e1d-854d-cad6297a0c1a.lance
new file mode 100644
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z5C+IudOxRCAer;T<pc&#Tu{btYAsbxgcGrVX<Arf*dmH2;z_yiaCH15)AcL!b@qPb
z_|Kn<bUg3Y8DlN{)A2iFr%dWHg|RM^ySz=3cIZ=TJYUll;-xwcE``%I#-<GY@XCb@
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hf^T^7crV}WKFhYczy5(w&=39{&F~9VZ}E$T{{j+hFslFn

diff --git a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
index 08f941f908..7ca6c80920 100644
--- a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
+++ b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
@@ -28,18 +28,11 @@ async def embed_text(
     num_threads: int,
     vector_store: BaseVectorStore,
     id_column: str = "id",
-    title_column: str | None = None,
 ):
     """Embed a piece of text into a vector space. The operation outputs a new column containing a mapping between doc_id and vector."""
     if embed_column not in input.columns:
         msg = f"Column {embed_column} not found in input dataframe with columns {input.columns}"
         raise ValueError(msg)
-    title = title_column or embed_column
-    if title not in input.columns:
-        msg = (
-            f"Column {title} not found in input dataframe with columns {input.columns}"
-        )
-        raise ValueError(msg)
     if id_column not in input.columns:
         msg = f"Column {id_column} not found in input dataframe with columns {input.columns}"
         raise ValueError(msg)
diff --git a/packages/graphrag/graphrag/index/operations/finalize_community_reports.py b/packages/graphrag/graphrag/index/operations/finalize_community_reports.py
index 124e17e430..4446e3ee8f 100644
--- a/packages/graphrag/graphrag/index/operations/finalize_community_reports.py
+++ b/packages/graphrag/graphrag/index/operations/finalize_community_reports.py
@@ -3,11 +3,10 @@
 
 """All the steps to transform final entities."""
 
-from uuid import uuid4
-
 import pandas as pd
 
 from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
+from graphrag.index.utils.hashing import gen_sha512_hash
 
 
 def finalize_community_reports(
@@ -25,7 +24,9 @@ def finalize_community_reports(
 
     community_reports["community"] = community_reports["community"].astype(int)
     community_reports["human_readable_id"] = community_reports["community"]
-    community_reports["id"] = [uuid4().hex for _ in range(len(community_reports))]
+    community_reports["id"] = community_reports.apply(
+        lambda row: gen_sha512_hash(row, ["full_content"]), axis=1
+    )
 
     return community_reports.loc[
         :,
diff --git a/packages/graphrag/graphrag/prompts/query/drift_search_system_prompt.py b/packages/graphrag/graphrag/prompts/query/drift_search_system_prompt.py
index 3faae89a0e..4cb220f69d 100644
--- a/packages/graphrag/graphrag/prompts/query/drift_search_system_prompt.py
+++ b/packages/graphrag/graphrag/prompts/query/drift_search_system_prompt.py
@@ -65,7 +65,7 @@
 
 Add sections and commentary to the response as appropriate for the length and format.
 
-Additionally provide a score between 0 and 100 representing how well the response addresses the overall research question: {global_query}. Based on your response, suggest up to five follow-up questions that could be asked to further explore the topic as it relates to the overall research question. Do not include scores or follow up questions in the 'response' field of the JSON, add them to the respective 'score' and 'follow_up_queries' keys of the JSON output. Format your response in JSON with the following keys and values:
+Additionally provide a score between 0 and 100 representing how well the response addresses the overall research question: {global_query}. Based on your response, suggest up to {followups} follow-up questions that could be asked to further explore the topic as it relates to the overall research question. Do not include scores or follow up questions in the 'response' field of the JSON, add them to the respective 'score' and 'follow_up_queries' keys of the JSON output. Format your response in JSON with the following keys and values:
 
 {{'response': str, Put your answer, formatted in markdown, here. Do not answer the global query in this section.
 'score': int,
diff --git a/packages/graphrag/graphrag/query/indexer_adapters.py b/packages/graphrag/graphrag/query/indexer_adapters.py
index 0c6e54a8af..56064d8c2a 100644
--- a/packages/graphrag/graphrag/query/indexer_adapters.py
+++ b/packages/graphrag/graphrag/query/indexer_adapters.py
@@ -11,14 +11,12 @@
 
 import pandas as pd
 
-from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.community import Community
 from graphrag.data_model.community_report import CommunityReport
 from graphrag.data_model.covariate import Covariate
 from graphrag.data_model.entity import Entity
 from graphrag.data_model.relationship import Relationship
 from graphrag.data_model.text_unit import TextUnit
-from graphrag.language_model.manager import ModelManager
 from graphrag.language_model.protocol.base import EmbeddingModel
 from graphrag.query.input.loaders.dfs import (
     read_communities,
@@ -76,8 +74,6 @@ def read_indexer_reports(
     final_communities: pd.DataFrame,
     community_level: int | None,
     dynamic_community_selection: bool = False,
-    content_embedding_col: str = "full_content_embedding",
-    config: GraphRagConfig | None = None,
 ) -> list[CommunityReport]:
     """Read in the Community Reports from the raw indexing outputs.
 
@@ -102,29 +98,7 @@ def read_indexer_reports(
             filtered_community_df, on="community", how="inner"
         )
 
-    if config and (
-        content_embedding_col not in reports_df.columns
-        or reports_df.loc[:, content_embedding_col].isna().any()
-    ):
-        # TODO: Find a way to retrieve the right embedding model id.
-        embedding_model_settings = config.get_language_model_config(
-            "default_embedding_model"
-        )
-        embedder = ModelManager().get_or_create_embedding_model(
-            name="default_embedding",
-            model_type=embedding_model_settings.type,
-            config=embedding_model_settings,
-        )
-        reports_df = embed_community_reports(
-            reports_df, embedder, embedding_col=content_embedding_col
-        )
-
-    return read_community_reports(
-        df=reports_df,
-        id_col="id",
-        short_id_col="community",
-        content_embedding_col=content_embedding_col,
-    )
+    return read_community_reports(df=reports_df, id_col="id", short_id_col="community")
 
 
 def read_indexer_report_embeddings(
diff --git a/packages/graphrag/graphrag/query/input/loaders/dfs.py b/packages/graphrag/graphrag/query/input/loaders/dfs.py
index 17aeb604a2..a2b636f1dd 100644
--- a/packages/graphrag/graphrag/query/input/loaders/dfs.py
+++ b/packages/graphrag/graphrag/query/input/loaders/dfs.py
@@ -197,7 +197,6 @@ def read_community_reports(
     summary_col: str = "summary",
     content_col: str = "full_content",
     rank_col: str | None = "rank",
-    content_embedding_col: str | None = "full_content_embedding",
     attributes_cols: list[str] | None = None,
 ) -> list[CommunityReport]:
     """Read community reports from a dataframe using pre-converted records."""
@@ -213,9 +212,6 @@ def read_community_reports(
             summary=to_str(row, summary_col),
             full_content=to_str(row, content_col),
             rank=to_optional_float(row, rank_col),
-            full_content_embedding=to_optional_list(
-                row, content_embedding_col, item_type=float
-            ),
             attributes=(
                 {col: row.get(col) for col in attributes_cols}
                 if attributes_cols
diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/action.py b/packages/graphrag/graphrag/query/structured_search/drift_search/action.py
index 8f1da3d721..23cc811a39 100644
--- a/packages/graphrag/graphrag/query/structured_search/drift_search/action.py
+++ b/packages/graphrag/graphrag/query/structured_search/drift_search/action.py
@@ -50,7 +50,13 @@ def is_complete(self) -> bool:
         """Check if the action is complete (i.e., an answer is available)."""
         return self.answer is not None
 
-    async def search(self, search_engine: Any, global_query: str, scorer: Any = None):
+    async def search(
+        self,
+        search_engine: Any,
+        global_query: str,
+        k_followups: int,
+        scorer: Any = None,
+    ):
         """
         Execute an asynchronous search using the search engine, and update the action with the results.
 
@@ -71,7 +77,9 @@ async def search(self, search_engine: Any, global_query: str, scorer: Any = None
             return self
 
         search_result = await search_engine.search(
-            drift_query=global_query, query=self.query
+            query=self.query,
+            drift_query=global_query,
+            k_followups=k_followups,
         )
 
         # Do not launch exception as it will roll up with other steps
diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
index 4b4325ae2e..9e1e9c317a 100644
--- a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
+++ b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
@@ -40,6 +40,7 @@ class DRIFTSearchContextBuilder(DRIFTContextBuilder):
     def __init__(
         self,
         model: ChatModel,
+        config: DRIFTSearchConfig,
         text_embedder: EmbeddingModel,
         entities: list[Entity],
         entity_text_embeddings: BaseVectorStore,
@@ -49,14 +50,13 @@ def __init__(
         covariates: dict[str, list[Covariate]] | None = None,
         tokenizer: Tokenizer | None = None,
         embedding_vectorstore_key: str = EntityVectorStoreKey.ID,
-        config: DRIFTSearchConfig | None = None,
         local_system_prompt: str | None = None,
         local_mixed_context: LocalSearchMixedContext | None = None,
         reduce_system_prompt: str | None = None,
         response_type: str | None = None,
     ):
         """Initialize the DRIFT search context builder with necessary components."""
-        self.config = config or DRIFTSearchConfig()
+        self.config = config
         self.model = model
         self.text_embedder = text_embedder
         self.tokenizer = tokenizer or get_tokenizer()
diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/search.py b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py
index 64a8e52b43..14f208c77a 100644
--- a/packages/graphrag/graphrag/query/structured_search/drift_search/search.py
+++ b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py
@@ -156,7 +156,11 @@ def _process_primer_results(
         raise ValueError(error_msg)
 
     async def _search_step(
-        self, global_query: str, search_engine: LocalSearch, actions: list[DriftAction]
+        self,
+        global_query: str,
+        k_followups: int,
+        search_engine: LocalSearch,
+        actions: list[DriftAction],
     ) -> list[DriftAction]:
         """
         Perform an asynchronous search step by executing each DriftAction asynchronously.
@@ -171,7 +175,11 @@ async def _search_step(
         list[DriftAction]: The results from executing the search actions asynchronously.
         """
         tasks = [
-            action.search(search_engine=search_engine, global_query=global_query)
+            action.search(
+                search_engine=search_engine,
+                global_query=global_query,
+                k_followups=k_followups,
+            )
             for action in actions
         ]
         return await tqdm_asyncio.gather(*tasks, leave=False)
@@ -241,7 +249,10 @@ async def search(
             )
             # Process actions
             results = await self._search_step(
-                global_query=query, search_engine=self.local_search, actions=actions
+                global_query=query,
+                k_followups=self.context_builder.config.drift_k_followups,
+                search_engine=self.local_search,
+                actions=actions,
             )
 
             # Update query state
diff --git a/packages/graphrag/graphrag/query/structured_search/local_search/search.py b/packages/graphrag/graphrag/query/structured_search/local_search/search.py
index fdd72949da..64fc884213 100644
--- a/packages/graphrag/graphrag/query/structured_search/local_search/search.py
+++ b/packages/graphrag/graphrag/query/structured_search/local_search/search.py
@@ -76,6 +76,7 @@ async def search(
                     context_data=context_result.context_chunks,
                     response_type=self.response_type,
                     global_query=drift_query,
+                    followups=kwargs.get("k_followups", 0),
                 )
             else:
                 search_prompt = self.system_prompt.format(
diff --git a/packages/graphrag/graphrag/vector_stores/cosmosdb.py b/packages/graphrag/graphrag/vector_stores/cosmosdb.py
index 23b2c8f821..7ad06950c2 100644
--- a/packages/graphrag/graphrag/vector_stores/cosmosdb.py
+++ b/packages/graphrag/graphrag/vector_stores/cosmosdb.py
@@ -156,14 +156,10 @@ def load_documents(self, documents: list[VectorStoreDocument]) -> None:
         # Upload documents to CosmosDB
         for doc in documents:
             if doc.vector is not None:
-                print("Document to store:")  # noqa: T201
-                print(doc)  # noqa: T201
                 doc_json = {
                     self.id_field: doc.id,
                     self.vector_field: doc.vector,
                 }
-                print("Storing document in CosmosDB:")  # noqa: T201
-                print(doc_json)  # noqa: T201
                 self._container_client.upsert_item(doc_json)
 
     def similarity_search_by_vector(
diff --git a/packages/graphrag/graphrag/vector_stores/lancedb.py b/packages/graphrag/graphrag/vector_stores/lancedb.py
index 2b589d5bb0..bddc83cab2 100644
--- a/packages/graphrag/graphrag/vector_stores/lancedb.py
+++ b/packages/graphrag/graphrag/vector_stores/lancedb.py
@@ -84,12 +84,7 @@ def load_documents(self, documents: list[VectorStoreDocument]) -> None:
             })
 
             if data:
-                self.document_collection = self.db_connection.create_table(
-                    self.index_name if self.index_name else "",
-                    data=data,
-                    mode="overwrite",
-                    schema=data.schema,
-                )
+                self.document_collection.add(data)
 
     def similarity_search_by_vector(
         self, query_embedding: list[float] | np.ndarray, k: int = 10
diff --git a/tests/fixtures/min-csv/config.json b/tests/fixtures/min-csv/config.json
index 90b7c26061..99291c05cb 100644
--- a/tests/fixtures/min-csv/config.json
+++ b/tests/fixtures/min-csv/config.json
@@ -98,7 +98,7 @@
         },
         {
             "query": "What is the major conflict in this story and who are the protagonist and antagonist?",
-            "method": "global"
+            "method": "drift"
         }
     ],
     "slow": false
diff --git a/tests/mock_provider.py b/tests/mock_provider.py
index 9ffb52fb48..97b7aa9076 100644
--- a/tests/mock_provider.py
+++ b/tests/mock_provider.py
@@ -100,25 +100,22 @@ def __init__(self, **kwargs: Any):
         self.config = LanguageModelConfig(
             type=ModelType.MockEmbedding, model="text-embedding-3-large", api_key="mock"
         )
+        self.vectors = 3072 * [1.0]
 
     def embed_batch(self, text_list: list[str], **kwargs: Any) -> list[list[float]]:
         """Generate an embedding for the input text."""
-        if isinstance(text_list, str):
-            return [[1.0, 1.0, 1.0]]
-        return [[1.0, 1.0, 1.0] for _ in text_list]
+        return [self.vectors for _ in text_list]
 
     def embed(self, text: str, **kwargs: Any) -> list[float]:
         """Generate an embedding for the input text."""
-        return [1.0, 1.0, 1.0]
+        return self.vectors
 
     async def aembed(self, text: str, **kwargs: Any) -> list[float]:
         """Generate an embedding for the input text."""
-        return [1.0, 1.0, 1.0]
+        return self.vectors
 
     async def aembed_batch(
         self, text_list: list[str], **kwargs: Any
     ) -> list[list[float]]:
         """Generate an embedding for the input text."""
-        if isinstance(text_list, str):
-            return [[1.0, 1.0, 1.0]]
-        return [[1.0, 1.0, 1.0] for _ in text_list]
+        return [self.vectors for _ in text_list]

From b05709fc4c344a91842a721a637ead5aeb5feebe Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Wed, 14 Jan 2026 16:16:58 -0800
Subject: [PATCH 38/40] Vector package (#2172)

* Extract graphrag-vectors package

* Simplify vector factory usage and config defaults

* Update factory integ initializers

* Fix mock patch

* Format

* Register vector stores in tests

* Set a default vector store name

* Update vector readme

* Remove impls from init

* Move some validation into impls

* Remove index_prefix

* Move duplicate method to base class
---
 breaking-changes.md                           |   2 +-
 docs/config/yaml.md                           |   6 +-
 .../custom_vector_store.ipynb                 | 234 ++++++++----------
 docs/examples_notebooks/drift_search.ipynb    |  15 +-
 docs/examples_notebooks/local_search.ipynb    |   7 +-
 packages/graphrag-vectors/README.md           | 109 ++++++++
 .../graphrag_vectors/__init__.py              |  34 +++
 .../graphrag_vectors}/azure_ai_search.py      |  87 ++++---
 .../graphrag_vectors}/cosmosdb.py             |  81 +++---
 .../graphrag_vectors/index_schema.py}         |   8 +-
 .../graphrag_vectors}/lancedb.py              |  50 ++--
 .../graphrag_vectors/types.py                 |   8 +
 .../graphrag_vectors/vector_store.py}         |  37 ++-
 .../graphrag_vectors/vector_store_config.py   |  53 ++++
 .../graphrag_vectors/vector_store_factory.py  |  99 ++++++++
 .../graphrag_vectors/vector_store_type.py     |  14 ++
 packages/graphrag-vectors/pyproject.toml      |  49 ++++
 packages/graphrag/graphrag/api/query.py       |   8 +-
 packages/graphrag/graphrag/config/defaults.py |   9 +-
 .../graphrag/graphrag/config/embeddings.py    |  22 --
 packages/graphrag/graphrag/config/enums.py    |   8 -
 .../config/models/graph_rag_config.py         |  24 +-
 .../config/models/vector_store_config.py      | 106 --------
 .../index/operations/embed_text/embed_text.py |   4 +-
 .../workflows/generate_text_embeddings.py     |  68 +----
 .../context_builder/entity_extraction.py      |   5 +-
 packages/graphrag/graphrag/query/factory.py   |   9 +-
 .../graphrag/query/indexer_adapters.py        |   4 +-
 .../basic_search/basic_context.py             |   4 +-
 .../drift_search/drift_context.py             |   4 +-
 .../local_search/mixed_context.py             |   4 +-
 packages/graphrag/graphrag/utils/api.py       |  45 +---
 .../graphrag/vector_stores/__init__.py        |   4 -
 .../graphrag/vector_stores/factory.py         |  33 ---
 packages/graphrag/pyproject.toml              |   1 +
 pyproject.toml                                |   2 +
 tests/fixtures/min-csv/settings.yml           |   1 -
 .../vector_stores/test_azure_ai_search.py     |  44 ++--
 .../vector_stores/test_cosmosdb.py            |  42 ++--
 .../integration/vector_stores/test_factory.py |  64 +++--
 .../integration/vector_stores/test_lancedb.py |  38 ++-
 tests/unit/config/utils.py                    |   3 +-
 .../context_builder/test_entity_extraction.py |  17 +-
 tests/unit/utils/test_embeddings.py           |  20 --
 uv.lock                                       |  32 +++
 45 files changed, 791 insertions(+), 727 deletions(-)
 create mode 100644 packages/graphrag-vectors/README.md
 create mode 100644 packages/graphrag-vectors/graphrag_vectors/__init__.py
 rename packages/{graphrag/graphrag/vector_stores => graphrag-vectors/graphrag_vectors}/azure_ai_search.py (71%)
 rename packages/{graphrag/graphrag/vector_stores => graphrag-vectors/graphrag_vectors}/cosmosdb.py (81%)
 rename packages/{graphrag/graphrag/config/models/vector_store_schema_config.py => graphrag-vectors/graphrag_vectors/index_schema.py} (90%)
 rename packages/{graphrag/graphrag/vector_stores => graphrag-vectors/graphrag_vectors}/lancedb.py (74%)
 create mode 100644 packages/graphrag-vectors/graphrag_vectors/types.py
 rename packages/{graphrag/graphrag/vector_stores/base.py => graphrag-vectors/graphrag_vectors/vector_store.py} (63%)
 create mode 100644 packages/graphrag-vectors/graphrag_vectors/vector_store_config.py
 create mode 100644 packages/graphrag-vectors/graphrag_vectors/vector_store_factory.py
 create mode 100644 packages/graphrag-vectors/graphrag_vectors/vector_store_type.py
 create mode 100644 packages/graphrag-vectors/pyproject.toml
 delete mode 100644 packages/graphrag/graphrag/config/models/vector_store_config.py
 delete mode 100644 packages/graphrag/graphrag/vector_stores/__init__.py
 delete mode 100644 packages/graphrag/graphrag/vector_stores/factory.py
 delete mode 100644 tests/unit/utils/test_embeddings.py

diff --git a/breaking-changes.md b/breaking-changes.md
index b07aa6fca8..93430cbfe4 100644
--- a/breaking-changes.md
+++ b/breaking-changes.md
@@ -34,7 +34,7 @@ This is a summary of changes:
 - Collapsed the `vector_store` dict into a single root-level object. This is because we no longer support multi-search, and this dict required a lot of downstream complexity for that single use case.
 - Removed the `outputs` block that was also only used for multi-search.
 - Most workflows had an undocumented `strategy` config dict that allowed fine tuning of internal settings. These fine tunings are never used and had associated complexity, so we removed it.
-- Vector store configuration now allows custom schema per embedded field. This overrides the need for the `container_name` prefix, which caused confusion anyway. Now, the default container name will simply be the embedded field name - if you need something custom, add the `embeddings_schema` block and populate as needed.
+- Vector store configuration now allows custom schema per embedded field. This overrides the need for the `container_name` prefix, which caused confusion anyway. Now, the default container name will simply be the embedded field name - if you need something custom, add the `index_schema` block and populate as needed.
 - We previously supported the ability to embed any text field in the data model. However, we only ever use text_unit_text, entity_description, and community_full_content, so all others have been removed.
 - Removed the `umap` and `embed_graph` blocks which were only used to add x/y fields to the entities. This fixed a long-standing dependency issue with graspologic. If you need x/y positions, see the [visualization guide](https://microsoft.github.io/graphrag/visualization_guide/) for using gephi.
 - Removed file filtering from input document loading. This was essentially unused.
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 1885db1fe5..0f67390833 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -172,9 +172,8 @@ Where to put all vectors for the system. Configured for lancedb by default. This
 - `url` **str** (only for AI Search) - AI Search endpoint
 - `api_key` **str** (optional - only for AI Search) - The AI Search api key to use.
 - `audience` **str** (only for AI Search) - Audience for managed identity token if managed identity authentication is used.
-- `index_prefix` **str** - (optional) A prefix for the indexes you will create for embeddings. This stores all indexes (tables) for a given dataset ingest.
 - `database_name` **str** - (cosmosdb only) Name of the database.
-- `embeddings_schema` **dict[str, dict[str, str]]** (optional) - Enables customization for each of your embeddings. 
+- `index_schema` **dict[str, dict[str, str]]** (optional) - Enables customization for each of your embeddings. 
   - `<supported_embedding>`: 
     - `index_name` **str**: (optional) - Name for the specific embedding index table.
     - `id_field` **str**: (optional) - Field name to be used as id. Default=`id`
@@ -193,8 +192,7 @@ For example:
 vector_store:
   type: lancedb
   db_uri: output/lancedb
-  index_prefix: "christmas-carol"
-  embeddings_schema:
+  index_schema:
     text_unit_text:
       index_name: "text-unit-embeddings"
       id_field: "id_custom"
diff --git a/docs/examples_notebooks/custom_vector_store.ipynb b/docs/examples_notebooks/custom_vector_store.ipynb
index 60be6c1fb5..2e79c66d86 100644
--- a/docs/examples_notebooks/custom_vector_store.ipynb
+++ b/docs/examples_notebooks/custom_vector_store.ipynb
@@ -28,7 +28,7 @@
     "\n",
     "### What You'll Learn\n",
     "\n",
-    "1. Understanding the `BaseVectorStore` interface\n",
+    "1. Understanding the `VectorStore` interface\n",
     "2. Implementing a custom vector store class\n",
     "3. Registering your vector store with the `VectorStoreFactory`\n",
     "4. Testing and validating your implementation\n",
@@ -50,35 +50,13 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Any\n",
-    "\n",
-    "import numpy as np\n",
-    "import yaml\n",
-    "from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig\n",
-    "from graphrag.data_model.types import TextEmbedder\n",
-    "\n",
-    "# GraphRAG vector store components\n",
-    "from graphrag.vector_stores.base import (\n",
-    "    BaseVectorStore,\n",
-    "    VectorStoreDocument,\n",
-    "    VectorStoreSearchResult,\n",
-    ")\n",
-    "from graphrag.vector_stores.factory import VectorStoreFactory"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2: Understand the BaseVectorStore Interface\n",
+    "## Step 2: Understand the VectorStore Interface\n",
     "\n",
-    "Before using a custom vector store, let's examine the `BaseVectorStore` interface to understand what methods need to be implemented."
+    "Before using a custom vector store, let's examine the `VectorStore` interface to understand what methods need to be implemented."
    ]
   },
   {
@@ -87,18 +65,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Let's inspect the BaseVectorStore class to understand the required methods\n",
     "import inspect\n",
     "\n",
-    "print(\"BaseVectorStore Abstract Methods:\")\n",
-    "print(\"=\" * 40)\n",
+    "# Let's inspect the VectorStore class to understand the required methods\n",
+    "from typing import Any\n",
+    "\n",
+    "import numpy as np\n",
+    "import yaml\n",
+    "from graphrag_vectors import (\n",
+    "    IndexSchema,\n",
+    "    TextEmbedder,\n",
+    "    VectorStore,\n",
+    "    VectorStoreDocument,\n",
+    "    VectorStoreFactory,\n",
+    "    VectorStoreSearchResult,\n",
+    ")\n",
+    "\n",
+    "print(\"VectorStore Abstract Methods:\")\n",
+    "print(\"=\" * 80)\n",
     "\n",
     "abstract_methods = []\n",
-    "for name, method in inspect.getmembers(BaseVectorStore, predicate=inspect.isfunction):\n",
+    "for name, method in inspect.getmembers(VectorStore, predicate=inspect.isfunction):\n",
     "    if getattr(method, \"__isabstractmethod__\", False):\n",
-    "        signature = inspect.signature(method)\n",
-    "        abstract_methods.append(f\"• {name}{signature}\")\n",
-    "        print(f\"• {name}{signature}\")\n",
+    "        abstract_methods.append(name)\n",
+    "        print(f\"\\n{name}:\")\n",
+    "        print(f\"  {inspect.signature(method)}\")\n",
     "\n",
     "print(f\"\\nTotal abstract methods to implement: {len(abstract_methods)}\")"
    ]
@@ -112,7 +103,7 @@
     "Now let's implement a simple in-memory vector store as an example. This vector store will:\n",
     "\n",
     "- Store documents and vectors in memory using Python data structures\n",
-    "- Support all required BaseVectorStore methods\n",
+    "- Support all required VectorStore methods\n",
     "\n",
     "**Note**: This is a simplified example for demonstration. Production vector stores would typically use optimized libraries like FAISS, more sophisticated indexing, and persistent storage."
    ]
@@ -123,7 +114,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class SimpleInMemoryVectorStore(BaseVectorStore):\n",
+    "class SimpleInMemoryVectorStore(VectorStore):\n",
     "    \"\"\"A simple in-memory vector store implementation for demonstration purposes.\n",
     "\n",
     "    This vector store stores documents and their embeddings in memory and provides\n",
@@ -147,112 +138,90 @@
     "        self.vectors: dict[str, np.ndarray] = {}\n",
     "        self.connected = False\n",
     "\n",
-    "        print(f\"🚀 SimpleInMemoryVectorStore initialized for index: {self.index_name}\")\n",
-    "\n",
     "    def connect(self, **kwargs: Any) -> None:\n",
-    "        \"\"\"Connect to the vector storage (no-op for in-memory store).\"\"\"\n",
+    "        \"\"\"Connect to the vector store (simulated for in-memory store).\"\"\"\n",
+    "        print(\"Connecting to in-memory vector store...\")\n",
     "        self.connected = True\n",
-    "        print(f\"✅ Connected to in-memory vector store: {self.index_name}\")\n",
-    "\n",
-    "    def create_index(self) -> None:\n",
-    "        \"\"\"Create index in the vector store (no-op for in-memory store).\"\"\"\n",
-    "        self.documents.clear()\n",
-    "        self.vectors.clear()\n",
-    "\n",
-    "        print(f\"✅ Index '{self.index_name}' is ready in in-memory vector store\")\n",
-    "\n",
-    "    def load_documents(self, documents: list[VectorStoreDocument]) -> None:\n",
+    "        print(\"Connected successfully!\")\n",
+    "\n",
+    "    def create_index(self, **kwargs: Any) -> None:\n",
+    "        \"\"\"Create an index (simulated for in-memory store).\n",
+    "\n",
+    "        In a real vector database, this would create the necessary data structures\n",
+    "        and indexes for efficient vector search.\n",
+    "        \"\"\"\n",
+    "        print(f\"Creating index: {self.index_name}\")\n",
+    "        # For in-memory store, we just ensure our storage dictionaries are ready\n",
+    "        if not isinstance(self.documents, dict):\n",
+    "            self.documents = {}\n",
+    "        if not isinstance(self.vectors, dict):\n",
+    "            self.vectors = {}\n",
+    "        print(\"Index created successfully!\")\n",
+    "\n",
+    "    def load_documents(\n",
+    "        self, documents: list[VectorStoreDocument], overwrite: bool = False\n",
+    "    ) -> None:\n",
     "        \"\"\"Load documents into the vector store.\"\"\"\n",
-    "        if not self.connected:\n",
-    "            msg = \"Vector store not connected. Call connect() first.\"\n",
-    "            raise RuntimeError(msg)\n",
+    "        if overwrite:\n",
+    "            print(\"Clearing existing documents...\")\n",
+    "            self.documents.clear()\n",
+    "            self.vectors.clear()\n",
     "\n",
-    "        loaded_count = 0\n",
+    "        print(f\"Loading {len(documents)} documents...\")\n",
     "        for doc in documents:\n",
-    "            if doc.vector is not None:\n",
-    "                doc_id = str(doc.id)\n",
-    "                self.documents[doc_id] = doc\n",
-    "                self.vectors[doc_id] = np.array(doc.vector, dtype=np.float32)\n",
-    "                loaded_count += 1\n",
+    "            self.documents[doc.id] = doc\n",
+    "            if doc.vector:\n",
+    "                self.vectors[doc.id] = np.array(doc.vector)\n",
     "\n",
-    "        print(f\"📚 Loaded {loaded_count} documents into vector store\")\n",
-    "\n",
-    "    def _cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:\n",
-    "        \"\"\"Calculate cosine similarity between two vectors.\"\"\"\n",
-    "        # Normalize vectors\n",
-    "        norm1 = np.linalg.norm(vec1)\n",
-    "        norm2 = np.linalg.norm(vec2)\n",
-    "\n",
-    "        if norm1 == 0 or norm2 == 0:\n",
-    "            return 0.0\n",
-    "\n",
-    "        return float(np.dot(vec1, vec2) / (norm1 * norm2))\n",
+    "        print(f\"Successfully loaded {len(documents)} documents!\")\n",
     "\n",
     "    def similarity_search_by_vector(\n",
     "        self, query_embedding: list[float], k: int = 10, **kwargs: Any\n",
     "    ) -> list[VectorStoreSearchResult]:\n",
-    "        \"\"\"Perform similarity search using a query vector.\"\"\"\n",
-    "        if not self.connected:\n",
-    "            msg = \"Vector store not connected. Call connect() first.\"\n",
-    "            raise RuntimeError(msg)\n",
-    "\n",
+    "        \"\"\"Search for similar documents using a query vector.\"\"\"\n",
     "        if not self.vectors:\n",
     "            return []\n",
     "\n",
-    "        query_vec = np.array(query_embedding, dtype=np.float32)\n",
-    "        similarities = []\n",
+    "        query_vector = np.array(query_embedding)\n",
     "\n",
-    "        # Calculate similarity with all stored vectors\n",
-    "        for doc_id, stored_vec in self.vectors.items():\n",
-    "            similarity = self._cosine_similarity(query_vec, stored_vec)\n",
+    "        # Calculate cosine similarity for all documents\n",
+    "        similarities = []\n",
+    "        for doc_id, doc_vector in self.vectors.items():\n",
+    "            # Cosine similarity\n",
+    "            similarity = np.dot(query_vector, doc_vector) / (\n",
+    "                np.linalg.norm(query_vector) * np.linalg.norm(doc_vector)\n",
+    "            )\n",
     "            similarities.append((doc_id, similarity))\n",
     "\n",
-    "        # Sort by similarity (descending) and take top k\n",
+    "        # Sort by similarity (highest first) and take top k\n",
     "        similarities.sort(key=lambda x: x[1], reverse=True)\n",
-    "        top_k = similarities[:k]\n",
+    "        top_results = similarities[:k]\n",
     "\n",
-    "        # Create search results\n",
+    "        # Convert to search results\n",
     "        results = []\n",
-    "        for doc_id, score in top_k:\n",
-    "            document = self.documents[doc_id]\n",
-    "            result = VectorStoreSearchResult(document=document, score=score)\n",
-    "            results.append(result)\n",
+    "        for doc_id, score in top_results:\n",
+    "            doc = self.documents[doc_id]\n",
+    "            results.append(VectorStoreSearchResult(document=doc, score=float(score)))\n",
     "\n",
     "        return results\n",
     "\n",
     "    def similarity_search_by_text(\n",
-    "        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any\n",
+    "        self,\n",
+    "        text: str,\n",
+    "        text_embedder: TextEmbedder,\n",
+    "        k: int = 10,\n",
+    "        **kwargs: Any,\n",
     "    ) -> list[VectorStoreSearchResult]:\n",
-    "        \"\"\"Perform similarity search using text (which gets embedded first).\"\"\"\n",
-    "        # Embed the text first\n",
+    "        \"\"\"Search for similar documents using a text query.\"\"\"\n",
+    "        # Embed the query text\n",
     "        query_embedding = text_embedder(text)\n",
     "\n",
-    "        # Use vector search with the embedding\n",
+    "        # Use vector search\n",
     "        return self.similarity_search_by_vector(query_embedding, k, **kwargs)\n",
     "\n",
-    "    def search_by_id(self, id: str) -> VectorStoreDocument:\n",
-    "        \"\"\"Search for a document by id.\"\"\"\n",
-    "        doc_id = str(id)\n",
-    "        if doc_id not in self.documents:\n",
-    "            msg = f\"Document with id '{id}' not found\"\n",
-    "            raise KeyError(msg)\n",
-    "\n",
-    "        return self.documents[doc_id]\n",
-    "\n",
-    "    def get_stats(self) -> dict[str, Any]:\n",
-    "        \"\"\"Get statistics about the vector store (custom method).\"\"\"\n",
-    "        return {\n",
-    "            \"index_name\": self.index_name,\n",
-    "            \"document_count\": len(self.documents),\n",
-    "            \"vector_count\": len(self.vectors),\n",
-    "            \"connected\": self.connected,\n",
-    "            \"vector_dimension\": len(next(iter(self.vectors.values())))\n",
-    "            if self.vectors\n",
-    "            else 0,\n",
-    "        }\n",
-    "\n",
-    "\n",
-    "print(\"✅ SimpleInMemoryVectorStore class defined!\")"
+    "    def search_by_id(self, id: str) -> VectorStoreDocument | None:\n",
+    "        \"\"\"Retrieve a document by its ID.\"\"\"\n",
+    "        return self.documents.get(id)"
    ]
   },
   {
@@ -337,17 +306,24 @@
    "outputs": [],
    "source": [
     "# Test creating vector store using the factory\n",
-    "schema = VectorStoreSchemaConfig(index_name=\"test_collection\")\n",
+    "schema = IndexSchema(index_name=\"test_collection\")\n",
     "\n",
     "# Create vector store instance using factory\n",
     "vector_store = VectorStoreFactory().create(\n",
-    "    CUSTOM_VECTOR_STORE_TYPE, {\"vector_store_schema_config\": schema}\n",
+    "    CUSTOM_VECTOR_STORE_TYPE, {\"index_schema\": schema}\n",
     ")\n",
     "\n",
     "print(f\"✅ Created vector store instance: {type(vector_store).__name__}\")\n",
     "print(f\"📊 Initial stats: {vector_store.get_stats()}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -464,11 +440,11 @@
     "    print(\"🚀 Simulating GraphRAG pipeline with custom vector store...\\n\")\n",
     "\n",
     "    # 1. GraphRAG creates vector store using factory\n",
-    "    schema = VectorStoreSchemaConfig(index_name=\"graphrag_entities\")\n",
+    "    schema = IndexSchema(index_name=\"graphrag_entities\")\n",
     "\n",
     "    store = VectorStoreFactory().create(\n",
     "        CUSTOM_VECTOR_STORE_TYPE,\n",
-    "        {\"vector_store_schema_config\": schema, \"similarity_threshold\": 0.3},\n",
+    "        {\"index_schema\": schema, \"similarity_threshold\": 0.3},\n",
     "    )\n",
     "    store.connect()\n",
     "    store.create_index()\n",
@@ -529,7 +505,7 @@
     "    print(\"Test 1: Basic functionality\")\n",
     "    store = VectorStoreFactory().create(\n",
     "        CUSTOM_VECTOR_STORE_TYPE,\n",
-    "        {\"vector_store_schema_config\": VectorStoreSchemaConfig(index_name=\"test\")},\n",
+    "        {\"index_schema\": IndexSchema(index_name=\"test\")},\n",
     "    )\n",
     "    store.connect()\n",
     "    store.create_index()\n",
@@ -572,7 +548,7 @@
     "    print(\"\\nTest 5: Error handling\")\n",
     "    disconnected_store = VectorStoreFactory().create(\n",
     "        CUSTOM_VECTOR_STORE_TYPE,\n",
-    "        {\"vector_store_schema_config\": VectorStoreSchemaConfig(index_name=\"test2\")},\n",
+    "        {\"index_schema\": IndexSchema(index_name=\"test2\")},\n",
     "    )\n",
     "\n",
     "    try:\n",
@@ -611,16 +587,24 @@
     "- ✅ **Configuration Examples**: Learned how to configure GraphRAG to use your vector store\n",
     "\n",
     "### Key Takeaways\n",
-    "1. **Interface Compliance**: Always implement all methods from `BaseVectorStore`\n",
+    "1. **Interface Compliance**: Always implement all methods from `VectorStore`\n",
     "2. **Factory Pattern**: Use `VectorStoreFactory.register()` to make your vector store available\n",
-    "3. **Configuration**: Vector stores are configured in GraphRAG settings files\n",
-    "4. **Testing**: Thoroughly test all functionality before deploying\n",
-    "\n",
-    "### Next Steps\n",
-    "Check out the API Overview notebook to learn how to index and query data via the graphrag API.\n",
+    "3. **Testing**: Validate your implementation thoroughly before production use\n",
+    "4. **Configuration**: Use YAML or environment variables for flexible configuration\n",
+    "\n",
+    "### Production Considerations\n",
+    "For production use, consider:\n",
+    "- **Persistence**: Add data persistence mechanisms\n",
+    "- **Scalability**: Use optimized vector search libraries (FAISS, HNSW)\n",
+    "- **Error Handling**: Implement robust error handling and logging\n",
+    "- **Performance**: Add caching, batching, and connection pooling\n",
+    "- **Security**: Implement authentication and authorization\n",
+    "- **Monitoring**: Add metrics and health checks\n",
     "\n",
     "### Resources\n",
     "- [GraphRAG Documentation](https://microsoft.github.io/graphrag/)\n",
+    "- [Vector Store Examples](https://github.com/microsoft/graphrag/tree/main/packages/graphrag-vectors)\n",
+    "- [GraphRAG GitHub Repository](https://github.com/microsoft/graphrag)\n",
     "\n",
     "Happy building! 🚀"
    ]
@@ -628,7 +612,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "graphrag",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/examples_notebooks/drift_search.ipynb b/docs/examples_notebooks/drift_search.ipynb
index 81cd193eec..8d53c7d9cc 100644
--- a/docs/examples_notebooks/drift_search.ipynb
+++ b/docs/examples_notebooks/drift_search.ipynb
@@ -22,7 +22,6 @@
     "from graphrag.config.enums import ModelType\n",
     "from graphrag.config.models.drift_search_config import DRIFTSearchConfig\n",
     "from graphrag.config.models.language_model_config import LanguageModelConfig\n",
-    "from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig\n",
     "from graphrag.language_model.manager import ModelManager\n",
     "from graphrag.query.indexer_adapters import (\n",
     "    read_indexer_entities,\n",
@@ -36,7 +35,7 @@
     ")\n",
     "from graphrag.query.structured_search.drift_search.search import DRIFTSearch\n",
     "from graphrag.tokenizer.get_tokenizer import get_tokenizer\n",
-    "from graphrag.vector_stores.lancedb import LanceDBVectorStore\n",
+    "from graphrag_vectors.lancedb import LanceDBVectorStore\n",
     "\n",
     "INPUT_DIR = \"./inputs/operation dulce\"\n",
     "LANCEDB_URI = f\"{INPUT_DIR}/lancedb\"\n",
@@ -61,16 +60,16 @@
     "# load description embeddings to an in-memory lancedb vectorstore\n",
     "# to connect to a remote db, specify url and port values.\n",
     "description_embedding_store = LanceDBVectorStore(\n",
-    "    vector_store_schema_config=VectorStoreSchemaConfig(index_name=\"entity_description\"),\n",
+    "    db_uri=LANCEDB_URI,\n",
+    "    index_name=\"entity_description\",\n",
     ")\n",
-    "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
+    "description_embedding_store.connect()\n",
     "\n",
     "full_content_embedding_store = LanceDBVectorStore(\n",
-    "    vector_store_schema_config=VectorStoreSchemaConfig(\n",
-    "        index_name=\"community_full_content\"\n",
-    "    )\n",
+    "    db_uri=LANCEDB_URI,\n",
+    "    index_name=\"community_full_content\",\n",
     ")\n",
-    "full_content_embedding_store.connect(db_uri=LANCEDB_URI)\n",
+    "full_content_embedding_store.connect()\n",
     "\n",
     "print(f\"Entity count: {len(entity_df)}\")\n",
     "entity_df.head()\n",
diff --git a/docs/examples_notebooks/local_search.ipynb b/docs/examples_notebooks/local_search.ipynb
index 4a5497a824..f7f0c5a54b 100644
--- a/docs/examples_notebooks/local_search.ipynb
+++ b/docs/examples_notebooks/local_search.ipynb
@@ -19,7 +19,6 @@
     "import os\n",
     "\n",
     "import pandas as pd\n",
-    "from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig\n",
     "from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey\n",
     "from graphrag.query.indexer_adapters import (\n",
     "    read_indexer_covariates,\n",
@@ -33,7 +32,7 @@
     "    LocalSearchMixedContext,\n",
     ")\n",
     "from graphrag.query.structured_search.local_search.search import LocalSearch\n",
-    "from graphrag.vector_stores.lancedb import LanceDBVectorStore"
+    "from graphrag_vectors import IndexSchema, LanceDBVectorStore"
    ]
   },
   {
@@ -101,9 +100,7 @@
     "# load description embeddings to an in-memory lancedb vectorstore\n",
     "# to connect to a remote db, specify url and port values.\n",
     "description_embedding_store = LanceDBVectorStore(\n",
-    "    vector_store_schema_config=VectorStoreSchemaConfig(\n",
-    "        index_name=\"default-entity-description\"\n",
-    "    )\n",
+    "    index_schema=IndexSchema(index_name=\"default-entity-description\")\n",
     ")\n",
     "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
     "\n",
diff --git a/packages/graphrag-vectors/README.md b/packages/graphrag-vectors/README.md
new file mode 100644
index 0000000000..1966c442e8
--- /dev/null
+++ b/packages/graphrag-vectors/README.md
@@ -0,0 +1,109 @@
+# GraphRAG Vectors
+
+Vector store implementations for GraphRAG.
+
+## Basic Usage
+
+### Using the utility function (recommended)
+
+```python
+from graphrag_vectors import (
+    create_vector_store,
+    VectorStoreType,
+    IndexSchema,
+)
+
+# Create a vector store using the convenience function
+store_config = VectorStoreConfig(
+    type="lancedb",
+    db_uri="lance"
+)
+
+schema_config = IndexSchema(
+    index_name="my_index",
+    vector_size=1536,
+)
+
+vector_store = create_vector_store(
+    config=store_config
+    index_schema=schema_config,
+)
+
+vector_store.connect()
+vector_store.create_index()
+```
+
+### Using the factory directly
+
+```python
+from graphrag_vectors import (
+    VectorStoreFactory,
+    vector_store_factory,
+    VectorStoreType,
+    IndexSchema,
+)
+
+# Create a vector store using the factory
+schema_config = IndexSchema(
+    index_name="my_index",
+    vector_size=1536,
+)
+
+vector_store = vector_store_factory.create(
+    VectorStoreType.LanceDB,
+    {
+        "index_schema": schema_config,
+        "db_uri": "./lancedb"
+    }
+)
+
+vector_store.connect()
+vector_store.create_index()
+```
+
+## Supported Vector Stores
+
+- **LanceDB**: Local vector database
+- **Azure AI Search**: Azure's managed search service with vector capabilities
+- **Azure Cosmos DB**: Azure's NoSQL database with vector search support
+
+## Custom Vector Store
+
+You can register custom vector store implementations:
+
+```python
+from graphrag_vectors import VectorStore, register_vector_store, create_vector_store
+
+class MyCustomVectorStore(VectorStore):
+    def __init__(self, my_param):
+        self.my_param = my_param
+
+    def connect(self):
+        # Implementation
+        pass
+    
+    def create_index(self):
+        # Implementation
+        pass
+    
+    # ... implement other required methods
+
+# Register your custom implementation
+register_vector_store("my_custom_store", MyCustomVectorStore)
+
+# Use your custom vector store
+config = VectorStoreConfig(
+    type="my_custom_store",
+    my_param="something"
+)
+custom_store = create_vector_store(
+    config=config,
+    index_schema=schema_config,
+)
+```
+
+## Configuration
+
+Vector stores are configured using:
+- `VectorStoreConfig`: baseline parameters for the store
+- `IndexSchema`: Schema configuration for the specific index to create/connect to (index name, field names, vector size)
diff --git a/packages/graphrag-vectors/graphrag_vectors/__init__.py b/packages/graphrag-vectors/graphrag_vectors/__init__.py
new file mode 100644
index 0000000000..915d1f0cd1
--- /dev/null
+++ b/packages/graphrag-vectors/graphrag_vectors/__init__.py
@@ -0,0 +1,34 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""GraphRAG vector store implementations."""
+
+from graphrag_vectors.index_schema import IndexSchema
+from graphrag_vectors.types import TextEmbedder
+from graphrag_vectors.vector_store import (
+    VectorStore,
+    VectorStoreDocument,
+    VectorStoreSearchResult,
+)
+from graphrag_vectors.vector_store_config import VectorStoreConfig
+from graphrag_vectors.vector_store_factory import (
+    VectorStoreFactory,
+    create_vector_store,
+    register_vector_store,
+    vector_store_factory,
+)
+from graphrag_vectors.vector_store_type import VectorStoreType
+
+__all__ = [
+    "IndexSchema",
+    "TextEmbedder",
+    "VectorStore",
+    "VectorStoreConfig",
+    "VectorStoreDocument",
+    "VectorStoreFactory",
+    "VectorStoreSearchResult",
+    "VectorStoreType",
+    "create_vector_store",
+    "register_vector_store",
+    "vector_store_factory",
+]
diff --git a/packages/graphrag/graphrag/vector_stores/azure_ai_search.py b/packages/graphrag-vectors/graphrag_vectors/azure_ai_search.py
similarity index 71%
rename from packages/graphrag/graphrag/vector_stores/azure_ai_search.py
rename to packages/graphrag-vectors/graphrag_vectors/azure_ai_search.py
index e6193e09d9..fcbb5e48ae 100644
--- a/packages/graphrag/graphrag/vector_stores/azure_ai_search.py
+++ b/packages/graphrag-vectors/graphrag_vectors/azure_ai_search.py
@@ -22,49 +22,59 @@
 )
 from azure.search.documents.models import VectorizedQuery
 
-from graphrag.data_model.types import TextEmbedder
-from graphrag.vector_stores.base import (
-    BaseVectorStore,
+from graphrag_vectors.vector_store import (
+    VectorStore,
     VectorStoreDocument,
     VectorStoreSearchResult,
 )
 
 
-class AzureAISearchVectorStore(BaseVectorStore):
+class AzureAISearchVectorStore(VectorStore):
     """Azure AI Search vector storage implementation."""
 
     index_client: SearchIndexClient
 
-    def connect(self, **kwargs: Any) -> Any:
+    def __init__(
+        self,
+        url: str,
+        api_key: str | None = None,
+        audience: str | None = None,
+        vector_search_profile_name: str = "vectorSearchProfile",
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        if not url:
+            msg = "url must be provided for Azure AI Search."
+            raise ValueError(msg)
+        self.url = url
+        self.api_key = api_key
+        self.audience = audience
+        self.vector_search_profile_name = vector_search_profile_name
+
+    def connect(self) -> Any:
         """Connect to AI search vector storage."""
-        url = kwargs["url"]
-        api_key = kwargs.get("api_key")
-        audience = kwargs.get("audience")
-
-        self.vector_search_profile_name = kwargs.get(
-            "vector_search_profile_name", "vectorSearchProfile"
+        audience_arg = (
+            {"audience": self.audience} if self.audience and not self.api_key else {}
+        )
+        self.db_connection = SearchClient(
+            endpoint=self.url,
+            index_name=self.index_name,
+            credential=(
+                AzureKeyCredential(self.api_key)
+                if self.api_key
+                else DefaultAzureCredential()
+            ),
+            **audience_arg,
+        )
+        self.index_client = SearchIndexClient(
+            endpoint=self.url,
+            credential=(
+                AzureKeyCredential(self.api_key)
+                if self.api_key
+                else DefaultAzureCredential()
+            ),
+            **audience_arg,
         )
-
-        if url:
-            audience_arg = {"audience": audience} if audience and not api_key else {}
-            self.db_connection = SearchClient(
-                endpoint=url,
-                index_name=self.index_name if self.index_name else "",
-                credential=(
-                    AzureKeyCredential(api_key) if api_key else DefaultAzureCredential()
-                ),
-                **audience_arg,
-            )
-            self.index_client = SearchIndexClient(
-                endpoint=url,
-                credential=(
-                    AzureKeyCredential(api_key) if api_key else DefaultAzureCredential()
-                ),
-                **audience_arg,
-            )
-        else:
-            not_supported_error = "Azure AI Search expects `url`."
-            raise ValueError(not_supported_error)
 
     def create_index(self) -> None:
         """Load documents into an Azure AI Search index."""
@@ -93,7 +103,7 @@ def create_index(self) -> None:
         )
         # Configure the index
         index = SearchIndex(
-            name=self.index_name if self.index_name else "",
+            name=self.index_name,
             fields=[
                 SimpleField(
                     name=self.id_field,
@@ -154,17 +164,6 @@ def similarity_search_by_vector(
             for doc in response
         ]
 
-    def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10
-    ) -> list[VectorStoreSearchResult]:
-        """Perform a text-based similarity search."""
-        query_embedding = text_embedder(text)
-        if query_embedding:
-            return self.similarity_search_by_vector(
-                query_embedding=query_embedding, k=k
-            )
-        return []
-
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""
         response = self.db_connection.get_document(id)
diff --git a/packages/graphrag/graphrag/vector_stores/cosmosdb.py b/packages/graphrag-vectors/graphrag_vectors/cosmosdb.py
similarity index 81%
rename from packages/graphrag/graphrag/vector_stores/cosmosdb.py
rename to packages/graphrag-vectors/graphrag_vectors/cosmosdb.py
index 7ad06950c2..fcff47e782 100644
--- a/packages/graphrag/graphrag/vector_stores/cosmosdb.py
+++ b/packages/graphrag-vectors/graphrag_vectors/cosmosdb.py
@@ -10,67 +10,71 @@
 from azure.cosmos.partition_key import PartitionKey
 from azure.identity import DefaultAzureCredential
 
-from graphrag.data_model.types import TextEmbedder
-from graphrag.vector_stores.base import (
-    BaseVectorStore,
+from graphrag_vectors.vector_store import (
+    VectorStore,
     VectorStoreDocument,
     VectorStoreSearchResult,
 )
 
 
-class CosmosDBVectorStore(BaseVectorStore):
+class CosmosDBVectorStore(VectorStore):
     """Azure CosmosDB vector storage implementation."""
 
     _cosmos_client: CosmosClient
     _database_client: DatabaseProxy
     _container_client: ContainerProxy
 
-    def connect(self, **kwargs: Any) -> Any:
+    def __init__(
+        self,
+        database_name: str,
+        connection_string: str | None = None,
+        url: str | None = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if self.id_field != "id":
+            msg = "CosmosDB requires the id_field to be 'id'."
+            raise ValueError(msg)
+        if not connection_string and not url:
+            msg = "Either connection_string or url must be provided for CosmosDB."
+            raise ValueError(msg)
+
+        self.database_name = database_name
+        self.connection_string = connection_string
+        self.url = url
+
+    def connect(self) -> Any:
         """Connect to CosmosDB vector storage."""
-        connection_string = kwargs.get("connection_string")
-        if connection_string:
-            self._cosmos_client = CosmosClient.from_connection_string(connection_string)
+        if self.connection_string:
+            self._cosmos_client = CosmosClient.from_connection_string(
+                self.connection_string
+            )
         else:
-            url = kwargs.get("url")
-            if not url:
-                msg = "Either connection_string or url must be provided."
-                raise ValueError(msg)
             self._cosmos_client = CosmosClient(
-                url=url, credential=DefaultAzureCredential()
+                url=self.url, credential=DefaultAzureCredential()
             )
 
-        database_name = kwargs.get("database_name")
-        if database_name is None:
-            msg = "Database name must be provided."
-            raise ValueError(msg)
-        self._database_name = database_name
-        if self.index_name is None:
-            msg = "Index name is empty or not provided."
-            raise ValueError(msg)
-        self._container_name = self.index_name
-
-        self.vector_size = self.vector_size
         self._create_database()
         self._create_container()
 
     def _create_database(self) -> None:
         """Create the database if it doesn't exist."""
-        self._cosmos_client.create_database_if_not_exists(id=self._database_name)
+        self._cosmos_client.create_database_if_not_exists(id=self.database_name)
         self._database_client = self._cosmos_client.get_database_client(
-            self._database_name
+            self.database_name
         )
 
     def _delete_database(self) -> None:
         """Delete the database if it exists."""
         if self._database_exists():
-            self._cosmos_client.delete_database(self._database_name)
+            self._cosmos_client.delete_database(self.database_name)
 
     def _database_exists(self) -> bool:
         """Check if the database exists."""
         existing_database_names = [
             database["id"] for database in self._cosmos_client.list_databases()
         ]
-        return self._database_name in existing_database_names
+        return self.database_name in existing_database_names
 
     def _create_container(self) -> None:
         """Create the container if it doesn't exist."""
@@ -108,7 +112,7 @@ def _create_container(self) -> None:
 
             # Create the container and container client
             self._database_client.create_container_if_not_exists(
-                id=self._container_name,
+                id=self.index_name,
                 partition_key=partition_key,
                 indexing_policy=indexing_policy,
                 vector_embedding_policy=vector_embedding_policy,
@@ -119,27 +123,27 @@ def _create_container(self) -> None:
 
             # Create the container with compatible indexing policy
             self._database_client.create_container_if_not_exists(
-                id=self._container_name,
+                id=self.index_name,
                 partition_key=partition_key,
                 indexing_policy=indexing_policy,
                 vector_embedding_policy=vector_embedding_policy,
             )
 
         self._container_client = self._database_client.get_container_client(
-            self._container_name
+            self.index_name
         )
 
     def _delete_container(self) -> None:
         """Delete the vector store container in the database if it exists."""
         if self._container_exists():
-            self._database_client.delete_container(self._container_name)
+            self._database_client.delete_container(self.index_name)
 
     def _container_exists(self) -> bool:
         """Check if the container name exists in the database."""
         existing_container_names = [
             container["id"] for container in self._database_client.list_containers()
         ]
-        return self._container_name in existing_container_names
+        return self.index_name in existing_container_names
 
     def create_index(self) -> None:
         """Load documents into CosmosDB."""
@@ -222,17 +226,6 @@ def cosine_similarity(a, b):
             for item in items
         ]
 
-    def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10
-    ) -> list[VectorStoreSearchResult]:
-        """Perform a text-based similarity search."""
-        query_embedding = text_embedder(text)
-        if query_embedding:
-            return self.similarity_search_by_vector(
-                query_embedding=query_embedding, k=k
-            )
-        return []
-
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""
         if self._container_client is None:
diff --git a/packages/graphrag/graphrag/config/models/vector_store_schema_config.py b/packages/graphrag-vectors/graphrag_vectors/index_schema.py
similarity index 90%
rename from packages/graphrag/graphrag/config/models/vector_store_schema_config.py
rename to packages/graphrag-vectors/graphrag_vectors/index_schema.py
index ccb91b3cbb..2eacc3f0c0 100644
--- a/packages/graphrag/graphrag/config/models/vector_store_schema_config.py
+++ b/packages/graphrag-vectors/graphrag_vectors/index_schema.py
@@ -17,9 +17,13 @@ def is_valid_field_name(field: str) -> bool:
     return bool(VALID_IDENTIFIER_REGEX.match(field))
 
 
-class VectorStoreSchemaConfig(BaseModel):
+class IndexSchema(BaseModel):
     """The default configuration section for Vector Store Schema."""
 
+    index_name: str = Field(
+        description="The index name to use.", default="vector_index"
+    )
+
     id_field: str = Field(
         description="The ID field to use.",
         default="id",
@@ -35,8 +39,6 @@ class VectorStoreSchemaConfig(BaseModel):
         default=DEFAULT_VECTOR_SIZE,
     )
 
-    index_name: str | None = Field(description="The index name to use.", default=None)
-
     def _validate_schema(self) -> None:
         """Validate the schema."""
         for field in [
diff --git a/packages/graphrag/graphrag/vector_stores/lancedb.py b/packages/graphrag-vectors/graphrag_vectors/lancedb.py
similarity index 74%
rename from packages/graphrag/graphrag/vector_stores/lancedb.py
rename to packages/graphrag-vectors/graphrag_vectors/lancedb.py
index bddc83cab2..724885d34b 100644
--- a/packages/graphrag/graphrag/vector_stores/lancedb.py
+++ b/packages/graphrag-vectors/graphrag_vectors/lancedb.py
@@ -9,20 +9,23 @@
 import numpy as np
 import pyarrow as pa
 
-from graphrag.data_model.types import TextEmbedder
-from graphrag.vector_stores.base import (
-    BaseVectorStore,
+from graphrag_vectors.vector_store import (
+    VectorStore,
     VectorStoreDocument,
     VectorStoreSearchResult,
 )
 
 
-class LanceDBVectorStore(BaseVectorStore):
+class LanceDBVectorStore(VectorStore):
     """LanceDB vector storage implementation."""
 
-    def connect(self, **kwargs: Any) -> Any:
+    def __init__(self, db_uri: str = "lancedb", **kwargs: Any):
+        super().__init__(**kwargs)
+        self.db_uri = db_uri
+
+    def connect(self) -> Any:
         """Connect to the vector storage."""
-        self.db_connection = lancedb.connect(kwargs["db_uri"])
+        self.db_connection = lancedb.connect(self.db_uri)
 
         if self.index_name and self.index_name in self.db_connection.table_names():
             self.document_collection = self.db_connection.open_table(self.index_name)
@@ -90,25 +93,15 @@ def similarity_search_by_vector(
         self, query_embedding: list[float] | np.ndarray, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
-        if self.query_filter:
-            docs = (
-                self.document_collection.search(
-                    query=query_embedding, vector_column_name=self.vector_field
-                )
-                .where(self.query_filter, prefilter=True)
-                .limit(k)
-                .to_list()
-            )
-        else:
-            query_embedding = np.array(query_embedding, dtype=np.float32)
-
-            docs = (
-                self.document_collection.search(
-                    query=query_embedding, vector_column_name=self.vector_field
-                )
-                .limit(k)
-                .to_list()
+        query_embedding = np.array(query_embedding, dtype=np.float32)
+
+        docs = (
+            self.document_collection.search(
+                query=query_embedding, vector_column_name=self.vector_field
             )
+            .limit(k)
+            .to_list()
+        )
         return [
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
@@ -120,15 +113,6 @@ def similarity_search_by_vector(
             for doc in docs
         ]
 
-    def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10
-    ) -> list[VectorStoreSearchResult]:
-        """Perform a similarity search using a given input text."""
-        query_embedding = text_embedder(text)
-        if query_embedding:
-            return self.similarity_search_by_vector(query_embedding, k)
-        return []
-
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""
         doc = (
diff --git a/packages/graphrag-vectors/graphrag_vectors/types.py b/packages/graphrag-vectors/graphrag_vectors/types.py
new file mode 100644
index 0000000000..63b032486c
--- /dev/null
+++ b/packages/graphrag-vectors/graphrag_vectors/types.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Common types for vector stores."""
+
+from collections.abc import Callable
+
+TextEmbedder = Callable[[str], list[float]]
diff --git a/packages/graphrag/graphrag/vector_stores/base.py b/packages/graphrag-vectors/graphrag_vectors/vector_store.py
similarity index 63%
rename from packages/graphrag/graphrag/vector_stores/base.py
rename to packages/graphrag-vectors/graphrag_vectors/vector_store.py
index eda7e189b6..de19bb5a4c 100644
--- a/packages/graphrag/graphrag/vector_stores/base.py
+++ b/packages/graphrag-vectors/graphrag_vectors/vector_store.py
@@ -7,8 +7,7 @@
 from dataclasses import dataclass
 from typing import Any
 
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.data_model.types import TextEmbedder
+from graphrag_vectors.types import TextEmbedder
 
 
 @dataclass
@@ -32,29 +31,24 @@ class VectorStoreSearchResult:
     """Similarity score between -1 and 1. Higher is more similar."""
 
 
-class BaseVectorStore(ABC):
+class VectorStore(ABC):
     """The base class for vector storage data-access classes."""
 
     def __init__(
         self,
-        vector_store_schema_config: VectorStoreSchemaConfig,
-        db_connection: Any | None = None,
-        document_collection: Any | None = None,
-        query_filter: Any | None = None,
+        index_name: str = "vector_index",
+        id_field: str = "id",
+        vector_field: str = "vector",
+        vector_size: int = 3072,
         **kwargs: Any,
     ):
-        self.db_connection = db_connection
-        self.document_collection = document_collection
-        self.query_filter = query_filter
-        self.kwargs = kwargs
-
-        self.index_name = vector_store_schema_config.index_name
-        self.id_field = vector_store_schema_config.id_field
-        self.vector_field = vector_store_schema_config.vector_field
-        self.vector_size = vector_store_schema_config.vector_size
+        self.index_name = index_name
+        self.id_field = id_field
+        self.vector_field = vector_field
+        self.vector_size = vector_size
 
     @abstractmethod
-    def connect(self, **kwargs: Any) -> None:
+    def connect(self) -> None:
         """Connect to vector storage."""
 
     @abstractmethod
@@ -71,11 +65,16 @@ def similarity_search_by_vector(
     ) -> list[VectorStoreSearchResult]:
         """Perform ANN search by vector."""
 
-    @abstractmethod
     def similarity_search_by_text(
         self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
-        """Perform ANN search by text."""
+        """Perform a text-based similarity search."""
+        query_embedding = text_embedder(text)
+        if query_embedding:
+            return self.similarity_search_by_vector(
+                query_embedding=query_embedding, k=k
+            )
+        return []
 
     @abstractmethod
     def search_by_id(self, id: str) -> VectorStoreDocument:
diff --git a/packages/graphrag-vectors/graphrag_vectors/vector_store_config.py b/packages/graphrag-vectors/graphrag_vectors/vector_store_config.py
new file mode 100644
index 0000000000..17f70af86d
--- /dev/null
+++ b/packages/graphrag-vectors/graphrag_vectors/vector_store_config.py
@@ -0,0 +1,53 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Parameterization settings for the default configuration."""
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from graphrag_vectors.index_schema import IndexSchema
+from graphrag_vectors.vector_store_type import VectorStoreType
+
+
+class VectorStoreConfig(BaseModel):
+    """The default configuration section for Vector Store."""
+
+    model_config = ConfigDict(extra="allow")
+    """Allow extra fields to support custom vector implementations."""
+
+    type: str = Field(
+        description="The vector store type to use.",
+        default=VectorStoreType.LanceDB,
+    )
+
+    db_uri: str | None = Field(
+        description="The database URI to use (only used by lancedb for built-in stores).",
+        default=None,
+    )
+
+    url: str | None = Field(
+        description="The database URL when type == azure_ai_search or cosmosdb.",
+        default=None,
+    )
+
+    api_key: str | None = Field(
+        description="The database API key when type == azure_ai_search.",
+        default=None,
+    )
+
+    audience: str | None = Field(
+        description="The database audience when type == azure_ai_search.",
+        default=None,
+    )
+
+    connection_string: str | None = Field(
+        description="The connection string when type == cosmosdb.",
+        default=None,
+    )
+
+    database_name: str | None = Field(
+        description="The database name to use when type == cosmosdb.",
+        default=None,
+    )
+
+    index_schema: dict[str, IndexSchema] = {}
diff --git a/packages/graphrag-vectors/graphrag_vectors/vector_store_factory.py b/packages/graphrag-vectors/graphrag_vectors/vector_store_factory.py
new file mode 100644
index 0000000000..6d94fa63a9
--- /dev/null
+++ b/packages/graphrag-vectors/graphrag_vectors/vector_store_factory.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Factory functions for creating a vector store."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from graphrag_common.factory import Factory, ServiceScope
+
+from graphrag_vectors.vector_store import VectorStore
+from graphrag_vectors.vector_store_type import VectorStoreType
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from graphrag_vectors.index_schema import IndexSchema
+    from graphrag_vectors.vector_store_config import VectorStoreConfig
+
+
+class VectorStoreFactory(Factory[VectorStore]):
+    """A factory for vector stores.
+
+    Includes a method for users to register a custom vector store implementation.
+
+    Configuration arguments are passed to each vector store implementation as kwargs
+    for individual enforcement of required/optional arguments.
+    """
+
+
+vector_store_factory = VectorStoreFactory()
+
+
+def register_vector_store(
+    vector_store_type: str,
+    vector_store_initializer: Callable[..., VectorStore],
+    scope: ServiceScope = "transient",
+) -> None:
+    """Register a custom vector store implementation.
+
+    Args
+    ----
+        - vector_store_type: str
+            The vector store id to register.
+        - vector_store_initializer: Callable[..., VectorStore]
+            The vector store initializer to register.
+        - scope: ServiceScope
+            The service scope for the vector store (default: "transient").
+    """
+    vector_store_factory.register(vector_store_type, vector_store_initializer, scope)
+
+
+def create_vector_store(
+    config: VectorStoreConfig, index_schema: IndexSchema
+) -> VectorStore:
+    """Create a vector store implementation based on the given type and configuration.
+
+    Args
+    ----
+        - config: VectorStoreConfig
+            The base vector store configuration.
+        - index_schema: IndexSchema
+            The index schema configuration for the vector store instance - i.e., for the specific table we are reading/writing.
+
+    Returns
+    -------
+        VectorStore
+            The created vector store implementation.
+    """
+    strategy = config.type
+
+    # Lazy load built-in implementations
+    if strategy not in vector_store_factory:
+        match strategy:
+            case VectorStoreType.LanceDB:
+                from graphrag_vectors.lancedb import LanceDBVectorStore
+
+                register_vector_store(VectorStoreType.LanceDB, LanceDBVectorStore)
+            case VectorStoreType.AzureAISearch:
+                from graphrag_vectors.azure_ai_search import AzureAISearchVectorStore
+
+                register_vector_store(
+                    VectorStoreType.AzureAISearch, AzureAISearchVectorStore
+                )
+            case VectorStoreType.CosmosDB:
+                from graphrag_vectors.cosmosdb import CosmosDBVectorStore
+
+                register_vector_store(VectorStoreType.CosmosDB, CosmosDBVectorStore)
+            case _:
+                msg = f"Vector store type '{strategy}' is not registered in the VectorStoreFactory. Registered types: {', '.join(vector_store_factory.keys())}."
+                raise ValueError(msg)
+
+    # collapse the base config and specific index config into a single dict for the initializer
+    config_model = config.model_dump()
+    index_model = index_schema.model_dump()
+    return vector_store_factory.create(
+        strategy, init_args={**config_model, **index_model}
+    )
diff --git a/packages/graphrag-vectors/graphrag_vectors/vector_store_type.py b/packages/graphrag-vectors/graphrag_vectors/vector_store_type.py
new file mode 100644
index 0000000000..86b60bf2fc
--- /dev/null
+++ b/packages/graphrag-vectors/graphrag_vectors/vector_store_type.py
@@ -0,0 +1,14 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Vector store type enum."""
+
+from enum import StrEnum
+
+
+class VectorStoreType(StrEnum):
+    """The supported vector store types."""
+
+    LanceDB = "lancedb"
+    AzureAISearch = "azure_ai_search"
+    CosmosDB = "cosmosdb"
diff --git a/packages/graphrag-vectors/pyproject.toml b/packages/graphrag-vectors/pyproject.toml
new file mode 100644
index 0000000000..e851f244d2
--- /dev/null
+++ b/packages/graphrag-vectors/pyproject.toml
@@ -0,0 +1,49 @@
+[project]
+name = "graphrag-vectors"
+version = "2.7.0"
+description = "GraphRAG vector store package."
+authors = [
+    {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"},
+    {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"},
+    {name = "Chris Trevino", email = "chtrevin@microsoft.com"},
+    {name = "David Tittsworth", email = "datittsw@microsoft.com"},
+    {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"},
+    {name = "Derek Worthen", email = "deworthe@microsoft.com"},
+    {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"},
+    {name = "Ha Trinh", email = "trinhha@microsoft.com"},
+    {name = "Jonathan Larson", email = "jolarso@microsoft.com"},
+    {name = "Josh Bradley", email = "joshbradley@microsoft.com"},
+    {name = "Kate Lytvynets", email = "kalytv@microsoft.com"},
+    {name = "Kenny Zhang", email = "zhangken@microsoft.com"},
+    {name = "Mónica Carvajal"},
+    {name = "Nathan Evans", email = "naevans@microsoft.com"},
+    {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"},
+    {name = "Sarah Smith", email = "smithsarah@microsoft.com"},
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.11,<3.14"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+]
+dependencies = [
+    "azure-core~=1.32",
+    "azure-cosmos~=4.9",
+    "azure-identity~=1.19",
+    "azure-search-documents~=11.6",
+    "graphrag-common==2.7.0",
+    "lancedb~=0.24.1",
+    "numpy~=2.2",
+    "pyarrow~=22.0",
+    "pydantic~=2.10",
+]
+
+[project.urls]
+Source = "https://github.com/microsoft/graphrag"
+
+[build-system]
+requires = ["hatchling>=1.27.0,<2.0.0"]
+build-backend = "hatchling.build"
diff --git a/packages/graphrag/graphrag/api/query.py b/packages/graphrag/graphrag/api/query.py
index f573559bcb..fe50d56a45 100644
--- a/packages/graphrag/graphrag/api/query.py
+++ b/packages/graphrag/graphrag/api/query.py
@@ -294,7 +294,7 @@ def local_search_streaming(
     logger.debug(msg)
 
     description_embedding_store = get_embedding_store(
-        store=config.vector_store.model_dump(),
+        config=config.vector_store,
         embedding_name=entity_description_embedding,
     )
 
@@ -419,12 +419,12 @@ def drift_search_streaming(
     logger.debug(msg)
 
     description_embedding_store = get_embedding_store(
-        store=config.vector_store.model_dump(),
+        config=config.vector_store,
         embedding_name=entity_description_embedding,
     )
 
     full_content_embedding_store = get_embedding_store(
-        store=config.vector_store.model_dump(),
+        config=config.vector_store,
         embedding_name=community_full_content_embedding,
     )
 
@@ -528,7 +528,7 @@ def basic_search_streaming(
     logger.debug(msg)
 
     embedding_store = get_embedding_store(
-        store=config.vector_store.model_dump(),
+        config=config.vector_store,
         embedding_name=text_unit_text_embedding,
     )
 
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index cc07438c85..7f7fd3f9e8 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -11,6 +11,7 @@
 from graphrag_chunking.chunk_strategy_type import ChunkerType
 from graphrag_input import InputType
 from graphrag_storage import StorageType
+from graphrag_vectors import VectorStoreType
 
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
@@ -19,7 +20,6 @@
     ModelType,
     NounPhraseExtractorType,
     ReportingType,
-    VectorStoreType,
 )
 from graphrag.index.operations.build_noun_graph.np_extractors.stop_words import (
     EN_STOP_WORDS,
@@ -373,13 +373,6 @@ class VectorStoreDefaults:
 
     type: ClassVar[str] = VectorStoreType.LanceDB.value
     db_uri: str = str(Path(DEFAULT_OUTPUT_BASE_DIR) / "lancedb")
-    overwrite: bool = True
-    index_prefix: str = ""
-    url: None = None
-    api_key: None = None
-    audience: None = None
-    database_name: None = None
-    schema: None = None
 
 
 @dataclass
diff --git a/packages/graphrag/graphrag/config/embeddings.py b/packages/graphrag/graphrag/config/embeddings.py
index c06567acfa..7456ffca5a 100644
--- a/packages/graphrag/graphrag/config/embeddings.py
+++ b/packages/graphrag/graphrag/config/embeddings.py
@@ -17,25 +17,3 @@
     community_full_content_embedding,
     text_unit_text_embedding,
 ]
-
-
-def create_index_name(
-    index_prefix: str, embedding_name: str, validate: bool = True
-) -> str:
-    """
-    Create a index name for the embedding store.
-
-    Within any given vector store, we can have multiple sets of embeddings organized into projects.
-    The `container` param is used for this partitioning, and is added as a index_prefix to the index name for differentiation.
-
-    The embedding name is fixed, with the available list defined in graphrag.index.config.embeddings
-
-    Note that we use dot notation in our names, but many vector stores do not support this - so we convert to dashes.
-    """
-    if validate and embedding_name not in all_embeddings:
-        msg = f"Invalid embedding name: {embedding_name}"
-        raise KeyError(msg)
-
-    if index_prefix:
-        return f"{index_prefix}-{embedding_name}"
-    return embedding_name
diff --git a/packages/graphrag/graphrag/config/enums.py b/packages/graphrag/graphrag/config/enums.py
index 9685ebb07b..8389a724cd 100644
--- a/packages/graphrag/graphrag/config/enums.py
+++ b/packages/graphrag/graphrag/config/enums.py
@@ -8,14 +8,6 @@
 from enum import Enum
 
 
-class VectorStoreType(str, Enum):
-    """The supported vector store types."""
-
-    LanceDB = "lancedb"
-    AzureAISearch = "azure_ai_search"
-    CosmosDB = "cosmosdb"
-
-
 class ReportingType(str, Enum):
     """The reporting configuration type for the pipeline."""
 
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 2e6efd4461..0bb74b2385 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -11,10 +11,12 @@
 from graphrag_chunking.chunking_config import ChunkingConfig
 from graphrag_input import InputConfig
 from graphrag_storage import StorageConfig, StorageType
+from graphrag_vectors import IndexSchema, VectorStoreConfig, VectorStoreType
 from pydantic import BaseModel, Field, model_validator
 
 from graphrag.config.defaults import graphrag_config_defaults
-from graphrag.config.enums import ReportingType, VectorStoreType
+from graphrag.config.embeddings import all_embeddings
+from graphrag.config.enums import ReportingType
 from graphrag.config.models.basic_search_config import BasicSearchConfig
 from graphrag.config.models.cluster_graph_config import ClusterGraphConfig
 from graphrag.config.models.community_reports_config import CommunityReportsConfig
@@ -32,7 +34,6 @@
 from graphrag.config.models.summarize_descriptions_config import (
     SummarizeDescriptionsConfig,
 )
-from graphrag.config.models.vector_store_config import VectorStoreConfig
 from graphrag.language_model.providers.litellm.services.rate_limiter.rate_limiter_factory import (
     RateLimiterFactory,
 )
@@ -276,13 +277,26 @@ def _validate_reporting_base_dir(self) -> None:
     )
     """The basic search configuration."""
 
+    def _validate_vector_store(self) -> None:
+        """Validate the vector store configuration specifically in the GraphRAG context. This checks and sets required dynamic defaults for the embeddings we require."""
+        self._validate_vector_store_db_uri()
+        # check and insert/overlay schemas for all of the core embeddings
+        # note that this does not require that they are used, only that they have a schema
+        # the embed_text block has the list of actual embeddings
+        if not self.vector_store.index_schema:
+            self.vector_store.index_schema = {}
+        for embedding in all_embeddings:
+            if embedding not in self.vector_store.index_schema:
+                self.vector_store.index_schema[embedding] = IndexSchema(
+                    index_name=embedding,
+                )
+
     def _validate_vector_store_db_uri(self) -> None:
         """Validate the vector store configuration."""
         store = self.vector_store
         if store.type == VectorStoreType.LanceDB:
             if not store.db_uri or store.db_uri.strip == "":
-                msg = "Vector store URI is required for LanceDB. Please rerun `graphrag init` and set the vector store configuration."
-                raise ValueError(msg)
+                store.db_uri = graphrag_config_defaults.vector_store.db_uri
             store.db_uri = str(Path(store.db_uri).resolve())
 
     def _validate_factories(self) -> None:
@@ -321,6 +335,6 @@ def _validate_model(self):
         self._validate_reporting_base_dir()
         self._validate_output_base_dir()
         self._validate_update_output_storage_base_dir()
-        self._validate_vector_store_db_uri()
+        self._validate_vector_store()
         self._validate_factories()
         return self
diff --git a/packages/graphrag/graphrag/config/models/vector_store_config.py b/packages/graphrag/graphrag/config/models/vector_store_config.py
deleted file mode 100644
index c2b3e61de1..0000000000
--- a/packages/graphrag/graphrag/config/models/vector_store_config.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Parameterization settings for the default configuration."""
-
-from pydantic import BaseModel, Field, model_validator
-
-from graphrag.config.defaults import vector_store_defaults
-from graphrag.config.embeddings import all_embeddings
-from graphrag.config.enums import VectorStoreType
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-
-
-class VectorStoreConfig(BaseModel):
-    """The default configuration section for Vector Store."""
-
-    type: str = Field(
-        description="The vector store type to use.",
-        default=vector_store_defaults.type,
-    )
-
-    db_uri: str | None = Field(
-        description="The database URI to use.",
-        default=None,
-    )
-
-    def _validate_db_uri(self) -> None:
-        """Validate the database URI."""
-        if self.type == VectorStoreType.LanceDB.value and (
-            self.db_uri is None or self.db_uri.strip() == ""
-        ):
-            self.db_uri = vector_store_defaults.db_uri
-
-        if self.type != VectorStoreType.LanceDB.value and (
-            self.db_uri is not None and self.db_uri.strip() != ""
-        ):
-            msg = "vector_store.db_uri is only used when vector_store.type == lancedb. Please rerun `graphrag init` and select the correct vector store type."
-            raise ValueError(msg)
-
-    url: str | None = Field(
-        description="The database URL when type == azure_ai_search.",
-        default=vector_store_defaults.url,
-    )
-
-    def _validate_url(self) -> None:
-        """Validate the database URL."""
-        if self.type == VectorStoreType.AzureAISearch and (
-            self.url is None or self.url.strip() == ""
-        ):
-            msg = "vector_store.url is required when vector_store.type == azure_ai_search. Please rerun `graphrag init` and select the correct vector store type."
-            raise ValueError(msg)
-
-        if self.type == VectorStoreType.CosmosDB and (
-            self.url is None or self.url.strip() == ""
-        ):
-            msg = "vector_store.url is required when vector_store.type == cosmos_db. Please rerun `graphrag init` and select the correct vector store type."
-            raise ValueError(msg)
-
-        if self.type == VectorStoreType.LanceDB and (
-            self.url is not None and self.url.strip() != ""
-        ):
-            msg = "vector_store.url is only used when vector_store.type == azure_ai_search or vector_store.type == cosmos_db. Please rerun `graphrag init` and select the correct vector store type."
-            raise ValueError(msg)
-
-    api_key: str | None = Field(
-        description="The database API key when type == azure_ai_search.",
-        default=vector_store_defaults.api_key,
-    )
-
-    audience: str | None = Field(
-        description="The database audience when type == azure_ai_search.",
-        default=vector_store_defaults.audience,
-    )
-
-    index_prefix: str | None = Field(
-        description="The index prefix to use.",
-        default=vector_store_defaults.index_prefix,
-    )
-
-    database_name: str | None = Field(
-        description="The database name to use when type == cosmos_db.",
-        default=vector_store_defaults.database_name,
-    )
-
-    embeddings_schema: dict[str, VectorStoreSchemaConfig] = {}
-
-    def _validate_embeddings_schema(self) -> None:
-        """Validate the embeddings schema."""
-        for name in self.embeddings_schema:
-            if name not in all_embeddings:
-                msg = f"vector_store.embeddings_schema contains an invalid embedding schema name: {name}. Please update your settings.yaml and select the correct embedding schema names."
-                raise ValueError(msg)
-
-        if self.type == VectorStoreType.CosmosDB:
-            for id_field in self.embeddings_schema:
-                if id_field != "id":
-                    msg = "When using CosmosDB, the id_field in embeddings_schema must be 'id'. Please update your settings.yaml and set the id_field to 'id'."
-                    raise ValueError(msg)
-
-    @model_validator(mode="after")
-    def _validate_model(self):
-        """Validate the model."""
-        self._validate_db_uri()
-        self._validate_url()
-        self._validate_embeddings_schema()
-        return self
diff --git a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
index 7ca6c80920..2e075ac655 100644
--- a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
+++ b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py
@@ -7,12 +7,12 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_vectors import VectorStore, VectorStoreDocument
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.index.operations.embed_text.run_embed_text import run_embed_text
 from graphrag.language_model.protocol.base import EmbeddingModel
 from graphrag.tokenizer.tokenizer import Tokenizer
-from graphrag.vector_stores.base import BaseVectorStore, VectorStoreDocument
 
 logger = logging.getLogger(__name__)
 
@@ -26,7 +26,7 @@ async def embed_text(
     batch_size: int,
     batch_max_tokens: int,
     num_threads: int,
-    vector_store: BaseVectorStore,
+    vector_store: VectorStore,
     id_column: str = "id",
 ):
     """Embed a piece of text into a vector space. The operation outputs a new column containing a mapping between doc_id and vector."""
diff --git a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
index ef24d3e348..104631c451 100644
--- a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
+++ b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py
@@ -6,17 +6,18 @@
 import logging
 
 import pandas as pd
+from graphrag_vectors import (
+    VectorStoreConfig,
+    create_vector_store,
+)
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.embeddings import (
     community_full_content_embedding,
-    create_index_name,
     entity_description_embedding,
     text_unit_text_embedding,
 )
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.vector_store_config import VectorStoreConfig
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.index.operations.embed_text.embed_text import embed_text
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
@@ -28,8 +29,6 @@
     load_table_from_storage,
     write_table_to_storage,
 )
-from graphrag.vector_stores.base import BaseVectorStore
-from graphrag.vector_stores.factory import VectorStoreFactory
 
 logger = logging.getLogger(__name__)
 
@@ -163,8 +162,10 @@ async def _run_embeddings(
     vector_store_config: VectorStoreConfig,
 ) -> pd.DataFrame:
     """All the steps to generate single embedding."""
-    index_name = _get_index_name(vector_store_config, name)
-    vector_store = _create_vector_store(vector_store_config, index_name, name)
+    vector_store = create_vector_store(
+        vector_store_config, vector_store_config.index_schema[name]
+    )
+    vector_store.connect()
 
     data["embedding"] = await embed_text(
         input=data,
@@ -179,56 +180,3 @@ async def _run_embeddings(
     )
 
     return data.loc[:, ["id", "embedding"]]
-
-
-def _create_vector_store(
-    vector_store_config: VectorStoreConfig,
-    index_name: str,
-    embedding_name: str | None = None,
-) -> BaseVectorStore:
-    embeddings_schema: dict[str, VectorStoreSchemaConfig] = (
-        vector_store_config.embeddings_schema
-    )
-
-    single_embedding_config: VectorStoreSchemaConfig = VectorStoreSchemaConfig()
-
-    if (
-        embeddings_schema is not None
-        and embedding_name is not None
-        and embedding_name in embeddings_schema
-    ):
-        raw_config = embeddings_schema[embedding_name]
-        if isinstance(raw_config, dict):
-            single_embedding_config = VectorStoreSchemaConfig(**raw_config)
-        else:
-            single_embedding_config = raw_config
-
-    if (
-        single_embedding_config.index_name is not None
-        and vector_store_config.index_prefix
-    ):
-        single_embedding_config.index_name = (
-            f"{vector_store_config.index_prefix}-{single_embedding_config.index_name}"
-        )
-
-    if single_embedding_config.index_name is None:
-        single_embedding_config.index_name = index_name
-
-    args = vector_store_config.model_dump()
-    args["vector_store_schema_config"] = single_embedding_config
-    vector_store = VectorStoreFactory().create(
-        vector_store_config.type,
-        args,
-    )
-
-    vector_store.connect(**args)
-    return vector_store
-
-
-def _get_index_name(vector_store_config: VectorStoreConfig, embedding_name: str) -> str:
-    index_prefix = vector_store_config.index_prefix or ""
-    index_name = create_index_name(index_prefix, embedding_name)
-
-    msg = f"using vector store {vector_store_config.type} with index prefix {index_prefix} for embedding {embedding_name}: {index_name}"
-    logger.info(msg)
-    return index_name
diff --git a/packages/graphrag/graphrag/query/context_builder/entity_extraction.py b/packages/graphrag/graphrag/query/context_builder/entity_extraction.py
index 0dd03ba281..289b3b7ea0 100644
--- a/packages/graphrag/graphrag/query/context_builder/entity_extraction.py
+++ b/packages/graphrag/graphrag/query/context_builder/entity_extraction.py
@@ -5,6 +5,8 @@
 
 from enum import Enum
 
+from graphrag_vectors import VectorStore
+
 from graphrag.data_model.entity import Entity
 from graphrag.data_model.relationship import Relationship
 from graphrag.language_model.protocol.base import EmbeddingModel
@@ -13,7 +15,6 @@
     get_entity_by_key,
     get_entity_by_name,
 )
-from graphrag.vector_stores.base import BaseVectorStore
 
 
 class EntityVectorStoreKey(str, Enum):
@@ -36,7 +37,7 @@ def from_string(value: str) -> "EntityVectorStoreKey":
 
 def map_query_to_entities(
     query: str,
-    text_embedding_vectorstore: BaseVectorStore,
+    text_embedding_vectorstore: VectorStore,
     text_embedder: EmbeddingModel,
     all_entities_dict: dict[str, Entity],
     embedding_vectorstore_key: str = EntityVectorStoreKey.ID,
diff --git a/packages/graphrag/graphrag/query/factory.py b/packages/graphrag/graphrag/query/factory.py
index 3f73c8c1ab..4ff36100cc 100644
--- a/packages/graphrag/graphrag/query/factory.py
+++ b/packages/graphrag/graphrag/query/factory.py
@@ -3,6 +3,8 @@
 
 """Query Factory methods to support CLI."""
 
+from graphrag_vectors import VectorStore
+
 from graphrag.callbacks.query_callbacks import QueryCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag.data_model.community import Community
@@ -33,7 +35,6 @@
 )
 from graphrag.query.structured_search.local_search.search import LocalSearch
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
-from graphrag.vector_stores.base import BaseVectorStore
 
 
 def get_local_search_engine(
@@ -44,7 +45,7 @@ def get_local_search_engine(
     relationships: list[Relationship],
     covariates: dict[str, list[Covariate]],
     response_type: str,
-    description_embedding_store: BaseVectorStore,
+    description_embedding_store: VectorStore,
     system_prompt: str | None = None,
     callbacks: list[QueryCallbacks] | None = None,
 ) -> LocalSearch:
@@ -198,7 +199,7 @@ def get_drift_search_engine(
     text_units: list[TextUnit],
     entities: list[Entity],
     relationships: list[Relationship],
-    description_embedding_store: BaseVectorStore,
+    description_embedding_store: VectorStore,
     response_type: str,
     local_system_prompt: str | None = None,
     reduce_system_prompt: str | None = None,
@@ -249,7 +250,7 @@ def get_drift_search_engine(
 
 def get_basic_search_engine(
     text_units: list[TextUnit],
-    text_unit_embeddings: BaseVectorStore,
+    text_unit_embeddings: VectorStore,
     config: GraphRagConfig,
     response_type: str,
     system_prompt: str | None = None,
diff --git a/packages/graphrag/graphrag/query/indexer_adapters.py b/packages/graphrag/graphrag/query/indexer_adapters.py
index 56064d8c2a..f0d5ff7dae 100644
--- a/packages/graphrag/graphrag/query/indexer_adapters.py
+++ b/packages/graphrag/graphrag/query/indexer_adapters.py
@@ -10,6 +10,7 @@
 from typing import cast
 
 import pandas as pd
+from graphrag_vectors import VectorStore
 
 from graphrag.data_model.community import Community
 from graphrag.data_model.community_report import CommunityReport
@@ -26,7 +27,6 @@
     read_relationships,
     read_text_units,
 )
-from graphrag.vector_stores.base import BaseVectorStore
 
 logger = logging.getLogger(__name__)
 
@@ -103,7 +103,7 @@ def read_indexer_reports(
 
 def read_indexer_report_embeddings(
     community_reports: list[CommunityReport],
-    embeddings_store: BaseVectorStore,
+    embeddings_store: VectorStore,
 ):
     """Read in the Community Reports from the raw indexing outputs."""
     for report in community_reports:
diff --git a/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py
index b7390017fc..57bb16efca 100644
--- a/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py
+++ b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py
@@ -7,6 +7,7 @@
 from typing import cast
 
 import pandas as pd
+from graphrag_vectors import VectorStore
 
 from graphrag.data_model.text_unit import TextUnit
 from graphrag.language_model.protocol.base import EmbeddingModel
@@ -17,7 +18,6 @@
 from graphrag.query.context_builder.conversation_history import ConversationHistory
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.tokenizer.tokenizer import Tokenizer
-from graphrag.vector_stores.base import BaseVectorStore
 
 logger = logging.getLogger(__name__)
 
@@ -28,7 +28,7 @@ class BasicSearchContext(BasicContextBuilder):
     def __init__(
         self,
         text_embedder: EmbeddingModel,
-        text_unit_embeddings: BaseVectorStore,
+        text_unit_embeddings: VectorStore,
         text_units: list[TextUnit] | None = None,
         tokenizer: Tokenizer | None = None,
         embedding_vectorstore_key: str = "id",
diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
index 9e1e9c317a..41649f2f6c 100644
--- a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
+++ b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py
@@ -9,6 +9,7 @@
 
 import numpy as np
 import pandas as pd
+from graphrag_vectors import VectorStore
 
 from graphrag.config.models.drift_search_config import DRIFTSearchConfig
 from graphrag.data_model.community_report import CommunityReport
@@ -29,7 +30,6 @@
 )
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.tokenizer.tokenizer import Tokenizer
-from graphrag.vector_stores.base import BaseVectorStore
 
 logger = logging.getLogger(__name__)
 
@@ -43,7 +43,7 @@ def __init__(
         config: DRIFTSearchConfig,
         text_embedder: EmbeddingModel,
         entities: list[Entity],
-        entity_text_embeddings: BaseVectorStore,
+        entity_text_embeddings: VectorStore,
         text_units: list[TextUnit] | None = None,
         reports: list[CommunityReport] | None = None,
         relationships: list[Relationship] | None = None,
diff --git a/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py
index b91272d164..a3540dfcb2 100644
--- a/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py
+++ b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py
@@ -7,6 +7,7 @@
 from typing import Any
 
 import pandas as pd
+from graphrag_vectors import VectorStore
 
 from graphrag.data_model.community_report import CommunityReport
 from graphrag.data_model.covariate import Covariate
@@ -42,7 +43,6 @@
 from graphrag.query.structured_search.base import LocalContextBuilder
 from graphrag.tokenizer.get_tokenizer import get_tokenizer
 from graphrag.tokenizer.tokenizer import Tokenizer
-from graphrag.vector_stores.base import BaseVectorStore
 
 logger = logging.getLogger(__name__)
 
@@ -53,7 +53,7 @@ class LocalSearchMixedContext(LocalContextBuilder):
     def __init__(
         self,
         entities: list[Entity],
-        entity_text_embeddings: BaseVectorStore,
+        entity_text_embeddings: VectorStore,
         text_embedder: EmbeddingModel,
         text_units: list[TextUnit] | None = None,
         community_reports: list[CommunityReport] | None = None,
diff --git a/packages/graphrag/graphrag/utils/api.py b/packages/graphrag/graphrag/utils/api.py
index 2aef931170..980997db06 100644
--- a/packages/graphrag/graphrag/utils/api.py
+++ b/packages/graphrag/graphrag/utils/api.py
@@ -4,48 +4,21 @@
 """API functions for the GraphRAG module."""
 
 from pathlib import Path
-from typing import Any
 
-from graphrag.config.embeddings import create_index_name
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.vector_stores.base import (
-    BaseVectorStore,
+from graphrag_vectors import (
+    VectorStore,
+    VectorStoreConfig,
+    create_vector_store,
 )
-from graphrag.vector_stores.factory import VectorStoreFactory
 
 
 def get_embedding_store(
-    store: dict[str, Any],
+    config: VectorStoreConfig,
     embedding_name: str,
-) -> BaseVectorStore:
-    """Get the embedding description store."""
-    vector_store_type = store["type"]
-    index_name = create_index_name(store.get("index_prefix", ""), embedding_name)
-
-    embeddings_schema: dict[str, VectorStoreSchemaConfig] = store.get(
-        "embeddings_schema", {}
-    )
-    embedding_config: VectorStoreSchemaConfig = VectorStoreSchemaConfig()
-
-    if (
-        embeddings_schema is not None
-        and embedding_name is not None
-        and embedding_name in embeddings_schema
-    ):
-        raw_config = embeddings_schema[embedding_name]
-        if isinstance(raw_config, dict):
-            embedding_config = VectorStoreSchemaConfig(**raw_config)
-        else:
-            embedding_config = raw_config
-
-    if embedding_config.index_name is None:
-        embedding_config.index_name = index_name
-
-    embedding_store = VectorStoreFactory().create(
-        vector_store_type,
-        {**store, "vector_store_schema_config": embedding_config},
-    )
-    embedding_store.connect(**store)
+) -> VectorStore:
+    """Get the embedding store."""
+    embedding_store = create_vector_store(config, config.index_schema[embedding_name])
+    embedding_store.connect()
 
     return embedding_store
 
diff --git a/packages/graphrag/graphrag/vector_stores/__init__.py b/packages/graphrag/graphrag/vector_stores/__init__.py
deleted file mode 100644
index 4f137d07bb..0000000000
--- a/packages/graphrag/graphrag/vector_stores/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""A package containing vector store implementations."""
diff --git a/packages/graphrag/graphrag/vector_stores/factory.py b/packages/graphrag/graphrag/vector_stores/factory.py
deleted file mode 100644
index 00a451771c..0000000000
--- a/packages/graphrag/graphrag/vector_stores/factory.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-"""Factory functions for creating a vector store."""
-
-from __future__ import annotations
-
-from graphrag_common.factory import Factory
-
-from graphrag.config.enums import VectorStoreType
-from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
-from graphrag.vector_stores.base import BaseVectorStore
-from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
-from graphrag.vector_stores.lancedb import LanceDBVectorStore
-
-
-class VectorStoreFactory(Factory[BaseVectorStore]):
-    """A factory for vector stores.
-
-    Includes a method for users to register a custom vector store implementation.
-
-    Configuration arguments are passed to each vector store implementation as kwargs
-    for individual enforcement of required/optional arguments.
-    """
-
-
-# --- register built-in vector store implementations ---
-vector_store_factory = VectorStoreFactory()
-vector_store_factory.register(VectorStoreType.LanceDB.value, LanceDBVectorStore)
-vector_store_factory.register(
-    VectorStoreType.AzureAISearch.value, AzureAISearchVectorStore
-)
-vector_store_factory.register(VectorStoreType.CosmosDB.value, CosmosDBVectorStore)
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index 769634bcb3..2db8c4f835 100644
--- a/packages/graphrag/pyproject.toml
+++ b/packages/graphrag/pyproject.toml
@@ -43,6 +43,7 @@ dependencies = [
     "graphrag-common==2.7.0",
     "graphrag-input==2.7.0",
     "graphrag-storage==2.7.0",
+    "graphrag-vectors==2.7.0",
     "graspologic-native~=1.2",
     "json-repair~=0.30",
     "lancedb~=0.24.1",
diff --git a/pyproject.toml b/pyproject.toml
index b2ec49f4c1..a14efa5ab0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,6 +58,7 @@ graphrag-common = { workspace = true }
 graphrag-input = { workspace = true }
 graphrag-storage = { workspace = true }
 graphrag-cache = { workspace = true }
+graphrag-vectors = { workspace = true }
 
 # Keep poethepoet for task management to minimize changes
 [tool.poe.tasks]
@@ -77,6 +78,7 @@ _semversioner_update_graphrag_common_toml_version = "update-toml update --file p
 _semversioner_update_graphrag_storage_toml_version = "update-toml update --file packages/graphrag-storage/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_cache_toml_version = "update-toml update --file packages/graphrag-cache/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_graphrag_input_toml_version = "update-toml update --file packages/graphrag-input/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
+_semversioner_update_graphrag_vectors_toml_version = "update-toml update --file packages/graphrag-vectors/pyproject.toml --path project.version --value $(uv run semversioner current-version)"
 _semversioner_update_workspace_dependency_versions = "python -m scripts.update_workspace_dependency_versions"
 semversioner_add = "semversioner add-change"
 coverage_report = 'coverage report --omit "**/tests/**" --show-missing'
diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml
index c6ebb2248e..bf7aa59644 100644
--- a/tests/fixtures/min-csv/settings.yml
+++ b/tests/fixtures/min-csv/settings.yml
@@ -32,7 +32,6 @@ vector_store:
     type: "lancedb"
     db_uri: "./tests/fixtures/min-csv/lancedb"
     container_name: "lancedb_ci"
-    overwrite: True
 
 input:
   type: csv
diff --git a/tests/integration/vector_stores/test_azure_ai_search.py b/tests/integration/vector_stores/test_azure_ai_search.py
index 9f35f62fbd..ffd445508c 100644
--- a/tests/integration/vector_stores/test_azure_ai_search.py
+++ b/tests/integration/vector_stores/test_azure_ai_search.py
@@ -7,9 +7,10 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
-from graphrag.vector_stores.base import VectorStoreDocument
+from graphrag_vectors import (
+    VectorStoreDocument,
+)
+from graphrag_vectors.azure_ai_search import AzureAISearchVectorStore
 
 TEST_AZURE_AI_SEARCH_URL = os.environ.get(
     "TEST_AZURE_AI_SEARCH_URL", "https://test-url.search.windows.net"
@@ -23,58 +24,49 @@ class TestAzureAISearchVectorStore:
     @pytest.fixture
     def mock_search_client(self):
         """Create a mock Azure AI Search client."""
-        with patch(
-            "graphrag.vector_stores.azure_ai_search.SearchClient"
-        ) as mock_client:
+        with patch("graphrag_vectors.azure_ai_search.SearchClient") as mock_client:
             yield mock_client.return_value
 
     @pytest.fixture
     def mock_index_client(self):
         """Create a mock Azure AI Search index client."""
-        with patch(
-            "graphrag.vector_stores.azure_ai_search.SearchIndexClient"
-        ) as mock_client:
+        with patch("graphrag_vectors.azure_ai_search.SearchIndexClient") as mock_client:
             yield mock_client.return_value
 
     @pytest.fixture
     def vector_store(self, mock_search_client, mock_index_client):
         """Create an Azure AI Search vector store instance."""
         vector_store = AzureAISearchVectorStore(
-            vector_store_schema_config=VectorStoreSchemaConfig(
-                index_name="test_vectors", vector_size=5
-            ),
+            url=TEST_AZURE_AI_SEARCH_URL,
+            api_key=TEST_AZURE_AI_SEARCH_KEY,
+            index_name="test_vectors",
+            vector_size=5,
         )
 
         # Create the necessary mocks first
         vector_store.db_connection = mock_search_client
         vector_store.index_client = mock_index_client
 
-        vector_store.connect(
-            url=TEST_AZURE_AI_SEARCH_URL,
-            api_key=TEST_AZURE_AI_SEARCH_KEY,
-        )
+        vector_store.connect()
         return vector_store
 
     @pytest.fixture
     def vector_store_custom(self, mock_search_client, mock_index_client):
         """Create an Azure AI Search vector store instance."""
         vector_store = AzureAISearchVectorStore(
-            vector_store_schema_config=VectorStoreSchemaConfig(
-                index_name="test_vectors",
-                id_field="id_custom",
-                vector_field="vector_custom",
-                vector_size=5,
-            ),
+            url=TEST_AZURE_AI_SEARCH_URL,
+            api_key=TEST_AZURE_AI_SEARCH_KEY,
+            index_name="test_vectors",
+            id_field="id_custom",
+            vector_field="vector_custom",
+            vector_size=5,
         )
 
         # Create the necessary mocks first
         vector_store.db_connection = mock_search_client
         vector_store.index_client = mock_index_client
 
-        vector_store.connect(
-            url=TEST_AZURE_AI_SEARCH_URL,
-            api_key=TEST_AZURE_AI_SEARCH_KEY,
-        )
+        vector_store.connect()
         return vector_store
 
     @pytest.fixture
diff --git a/tests/integration/vector_stores/test_cosmosdb.py b/tests/integration/vector_stores/test_cosmosdb.py
index 331ef1f297..de30bb6c50 100644
--- a/tests/integration/vector_stores/test_cosmosdb.py
+++ b/tests/integration/vector_stores/test_cosmosdb.py
@@ -7,9 +7,10 @@
 
 import numpy as np
 import pytest
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.vector_stores.base import VectorStoreDocument
-from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
+from graphrag_vectors import (
+    VectorStoreDocument,
+)
+from graphrag_vectors.cosmosdb import CosmosDBVectorStore
 
 # cspell:disable-next-line well-known-key
 WELL_KNOWN_COSMOS_CONNECTION_STRING = "AccountEndpoint=https://127.0.0.1:8081/;AccountKey=C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw=="
@@ -24,14 +25,13 @@
 def test_vector_store_operations():
     """Test basic vector store operations with CosmosDB."""
     vector_store = CosmosDBVectorStore(
-        vector_store_schema_config=VectorStoreSchemaConfig(index_name="testvector"),
+        connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
+        database_name="test_db",
+        index_name="testvector",
     )
 
     try:
-        vector_store.connect(
-            connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-            database_name="test_db",
-        )
+        vector_store.connect()
 
         docs = [
             VectorStoreDocument(
@@ -72,13 +72,12 @@ def mock_embedder(text: str) -> list[float]:
 def test_clear():
     """Test clearing the vector store."""
     vector_store = CosmosDBVectorStore(
-        vector_store_schema_config=VectorStoreSchemaConfig(index_name="testclear"),
+        connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
+        database_name="testclear",
+        index_name="testclear",
     )
     try:
-        vector_store.connect(
-            connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-            database_name="testclear",
-        )
+        vector_store.connect()
 
         doc = VectorStoreDocument(
             id="test",
@@ -100,19 +99,16 @@ def test_clear():
 def test_vector_store_customization():
     """Test vector store customization with CosmosDB."""
     vector_store = CosmosDBVectorStore(
-        vector_store_schema_config=VectorStoreSchemaConfig(
-            index_name="text-embeddings",
-            id_field="id",
-            vector_field="vector_custom",
-            vector_size=5,
-        ),
+        connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
+        database_name="test_db",
+        index_name="text-embeddings",
+        id_field="id",
+        vector_field="vector_custom",
+        vector_size=5,
     )
 
     try:
-        vector_store.connect(
-            connection_string=WELL_KNOWN_COSMOS_CONNECTION_STRING,
-            database_name="test_db",
-        )
+        vector_store.connect()
 
         docs = [
             VectorStoreDocument(
diff --git a/tests/integration/vector_stores/test_factory.py b/tests/integration/vector_stores/test_factory.py
index a8b797dbb2..ceb2a84c84 100644
--- a/tests/integration/vector_stores/test_factory.py
+++ b/tests/integration/vector_stores/test_factory.py
@@ -6,28 +6,28 @@
 """
 
 import pytest
-from graphrag.config.enums import VectorStoreType
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.vector_stores.azure_ai_search import AzureAISearchVectorStore
-from graphrag.vector_stores.base import BaseVectorStore
-from graphrag.vector_stores.cosmosdb import CosmosDBVectorStore
-from graphrag.vector_stores.factory import VectorStoreFactory
-from graphrag.vector_stores.lancedb import LanceDBVectorStore
+from graphrag_vectors import (
+    VectorStore,
+    VectorStoreFactory,
+    VectorStoreType,
+)
+from graphrag_vectors.azure_ai_search import AzureAISearchVectorStore
+from graphrag_vectors.cosmosdb import CosmosDBVectorStore
+from graphrag_vectors.lancedb import LanceDBVectorStore
+
+# register the defaults, since they are lazily registered
+VectorStoreFactory().register(VectorStoreType.LanceDB, LanceDBVectorStore)
+VectorStoreFactory().register(VectorStoreType.AzureAISearch, AzureAISearchVectorStore)
+VectorStoreFactory().register(VectorStoreType.CosmosDB, CosmosDBVectorStore)
 
 
 def test_create_lancedb_vector_store():
     kwargs = {
         "db_uri": "/tmp/lancedb",
-        "vector_store_schema_config": VectorStoreSchemaConfig(
-            index_name="test_collection"
-        ),
     }
-    vector_store = VectorStoreFactory().create(
-        VectorStoreType.LanceDB.value,
-        kwargs,
-    )
+    vector_store = VectorStoreFactory().create(VectorStoreType.LanceDB, kwargs)
     assert isinstance(vector_store, LanceDBVectorStore)
-    assert vector_store.index_name == "test_collection"
+    assert vector_store.index_name == "vector_index"
 
 
 @pytest.mark.skip(reason="Azure AI Search requires credentials and setup")
@@ -35,12 +35,10 @@ def test_create_azure_ai_search_vector_store():
     kwargs = {
         "url": "https://test.search.windows.net",
         "api_key": "test_key",
-        "vector_store_schema_config": VectorStoreSchemaConfig(
-            index_name="test_collection"
-        ),
+        "index_name": "test_collection",
     }
     vector_store = VectorStoreFactory().create(
-        VectorStoreType.AzureAISearch.value,
+        VectorStoreType.AzureAISearch,
         kwargs,
     )
     assert isinstance(vector_store, AzureAISearchVectorStore)
@@ -51,13 +49,11 @@ def test_create_cosmosdb_vector_store():
     kwargs = {
         "connection_string": "AccountEndpoint=https://test.documents.azure.com:443/;AccountKey=test_key==",
         "database_name": "test_db",
-        "vector_store_schema_config": VectorStoreSchemaConfig(
-            index_name="test_collection"
-        ),
+        "index_name": "test_collection",
     }
 
     vector_store = VectorStoreFactory().create(
-        VectorStoreType.CosmosDB.value,
+        VectorStoreType.CosmosDB,
         kwargs,
     )
 
@@ -68,8 +64,8 @@ def test_register_and_create_custom_vector_store():
     """Test registering and creating a custom vector store type."""
     from unittest.mock import MagicMock
 
-    # Create a mock that satisfies the BaseVectorStore interface
-    custom_vector_store_class = MagicMock(spec=BaseVectorStore)
+    # Create a mock that satisfies the VectorStore interface
+    custom_vector_store_class = MagicMock(spec=VectorStore)
     # Make the mock return a mock instance when instantiated
     instance = MagicMock()
     instance.initialized = True
@@ -79,9 +75,7 @@ def test_register_and_create_custom_vector_store():
         "custom", lambda **kwargs: custom_vector_store_class(**kwargs)
     )
 
-    vector_store = VectorStoreFactory().create(
-        "custom", {"vector_store_schema_config": VectorStoreSchemaConfig()}
-    )
+    vector_store = VectorStoreFactory().create("custom", {})
 
     assert custom_vector_store_class.called
     assert vector_store is instance
@@ -99,9 +93,9 @@ def test_create_unknown_vector_store():
 
 def test_is_supported_type():
     # Test built-in types
-    assert VectorStoreType.LanceDB.value in VectorStoreFactory()
-    assert VectorStoreType.AzureAISearch.value in VectorStoreFactory()
-    assert VectorStoreType.CosmosDB.value in VectorStoreFactory()
+    assert VectorStoreType.LanceDB in VectorStoreFactory()
+    assert VectorStoreType.AzureAISearch in VectorStoreFactory()
+    assert VectorStoreType.CosmosDB in VectorStoreFactory()
 
     # Test unknown type
     assert "unknown" not in VectorStoreFactory()
@@ -109,9 +103,9 @@ def test_is_supported_type():
 
 def test_register_class_directly_works():
     """Test that registering a class directly works."""
-    from graphrag.vector_stores.base import BaseVectorStore
+    from graphrag_vectors import VectorStore
 
-    class CustomVectorStore(BaseVectorStore):
+    class CustomVectorStore(VectorStore):
         def __init__(self, **kwargs):
             super().__init__(**kwargs)
 
@@ -131,7 +125,7 @@ def similarity_search_by_text(self, text, text_embedder, k=10, **kwargs):
             return []
 
         def search_by_id(self, id):
-            from graphrag.vector_stores.base import VectorStoreDocument
+            from graphrag_vectors import VectorStoreDocument
 
             return VectorStoreDocument(id=id, vector=None)
 
@@ -144,7 +138,7 @@ def search_by_id(self, id):
     # Test creating an instance
     vector_store = VectorStoreFactory().create(
         "custom_class",
-        {"vector_store_schema_config": VectorStoreSchemaConfig()},
+        {},
     )
 
     assert isinstance(vector_store, CustomVectorStore)
diff --git a/tests/integration/vector_stores/test_lancedb.py b/tests/integration/vector_stores/test_lancedb.py
index 628c89f012..dbd1198556 100644
--- a/tests/integration/vector_stores/test_lancedb.py
+++ b/tests/integration/vector_stores/test_lancedb.py
@@ -8,9 +8,10 @@
 
 import numpy as np
 import pytest
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
-from graphrag.vector_stores.base import VectorStoreDocument
-from graphrag.vector_stores.lancedb import LanceDBVectorStore
+from graphrag_vectors import (
+    VectorStoreDocument,
+)
+from graphrag_vectors.lancedb import LanceDBVectorStore
 
 
 class TestLanceDBVectorStore:
@@ -58,11 +59,9 @@ def test_vector_store_operations(self, sample_documents):
         temp_dir = tempfile.mkdtemp()
         try:
             vector_store = LanceDBVectorStore(
-                vector_store_schema_config=VectorStoreSchemaConfig(
-                    index_name="test_collection", vector_size=5
-                )
+                db_uri=temp_dir, index_name="test_collection", vector_size=5
             )
-            vector_store.connect(db_uri=temp_dir)
+            vector_store.connect()
             vector_store.create_index()
             vector_store.load_documents(sample_documents[:2])
 
@@ -111,11 +110,9 @@ def test_empty_collection(self):
         temp_dir = tempfile.mkdtemp()
         try:
             vector_store = LanceDBVectorStore(
-                vector_store_schema_config=VectorStoreSchemaConfig(
-                    index_name="empty_collection", vector_size=5
-                )
+                db_uri=temp_dir, index_name="empty_collection", vector_size=5
             )
-            vector_store.connect(db_uri=temp_dir)
+            vector_store.connect()
 
             # Load the vector store with a document, then delete it
             sample_doc = VectorStoreDocument(
@@ -154,12 +151,10 @@ def test_filter_search(self, sample_documents_categories):
         temp_dir = tempfile.mkdtemp()
         try:
             vector_store = LanceDBVectorStore(
-                vector_store_schema_config=VectorStoreSchemaConfig(
-                    index_name="filter_collection", vector_size=5
-                )
+                db_uri=temp_dir, index_name="filter_collection", vector_size=5
             )
 
-            vector_store.connect(db_uri=temp_dir)
+            vector_store.connect()
             vector_store.create_index()
             vector_store.load_documents(sample_documents_categories)
 
@@ -181,14 +176,13 @@ def test_vector_store_customization(self, sample_documents):
         temp_dir = tempfile.mkdtemp()
         try:
             vector_store = LanceDBVectorStore(
-                vector_store_schema_config=VectorStoreSchemaConfig(
-                    index_name="text-embeddings",
-                    id_field="id_custom",
-                    vector_field="vector_custom",
-                    vector_size=5,
-                ),
+                db_uri=temp_dir,
+                index_name="text-embeddings",
+                id_field="id_custom",
+                vector_field="vector_custom",
+                vector_size=5,
             )
-            vector_store.connect(db_uri=temp_dir)
+            vector_store.connect()
             vector_store.create_index()
             vector_store.load_documents(sample_documents[:2])
 
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index c02285125a..813502d548 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -25,11 +25,11 @@
 from graphrag.config.models.summarize_descriptions_config import (
     SummarizeDescriptionsConfig,
 )
-from graphrag.config.models.vector_store_config import VectorStoreConfig
 from graphrag_cache import CacheConfig
 from graphrag_chunking.chunking_config import ChunkingConfig
 from graphrag_input import InputConfig
 from graphrag_storage import StorageConfig
+from graphrag_vectors import VectorStoreConfig
 from pydantic import BaseModel
 
 FAKE_API_KEY = "NOT_AN_API_KEY"
@@ -112,7 +112,6 @@ def assert_vector_store_configs(
     assert actual.url == expected.url
     assert actual.api_key == expected.api_key
     assert actual.audience == expected.audience
-    assert actual.index_prefix == expected.index_prefix
     assert actual.database_name == expected.database_name
 
 
diff --git a/tests/unit/query/context_builder/test_entity_extraction.py b/tests/unit/query/context_builder/test_entity_extraction.py
index 1fd5e41474..0d1c7c0018 100644
--- a/tests/unit/query/context_builder/test_entity_extraction.py
+++ b/tests/unit/query/context_builder/test_entity_extraction.py
@@ -3,26 +3,23 @@
 
 from typing import Any
 
-from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.data_model.entity import Entity
-from graphrag.data_model.types import TextEmbedder
 from graphrag.language_model.manager import ModelManager
 from graphrag.query.context_builder.entity_extraction import (
     EntityVectorStoreKey,
     map_query_to_entities,
 )
-from graphrag.vector_stores.base import (
-    BaseVectorStore,
+from graphrag_vectors import (
+    TextEmbedder,
+    VectorStore,
     VectorStoreDocument,
     VectorStoreSearchResult,
 )
 
 
-class MockBaseVectorStore(BaseVectorStore):
+class MockVectorStore(VectorStore):
     def __init__(self, documents: list[VectorStoreDocument]) -> None:
-        super().__init__(
-            vector_store_schema_config=VectorStoreSchemaConfig(index_name="mock")
-        )
+        super().__init__(index_name="mock")
         self.documents = documents
 
     def connect(self, **kwargs: Any) -> None:
@@ -92,7 +89,7 @@ def test_map_query_to_entities():
 
     assert map_query_to_entities(
         query="t22",
-        text_embedding_vectorstore=MockBaseVectorStore([
+        text_embedding_vectorstore=MockVectorStore([
             VectorStoreDocument(id=entity.title, vector=None) for entity in entities
         ]),
         text_embedder=ModelManager().get_or_create_embedding_model(
@@ -113,7 +110,7 @@ def test_map_query_to_entities():
 
     assert map_query_to_entities(
         query="",
-        text_embedding_vectorstore=MockBaseVectorStore([
+        text_embedding_vectorstore=MockVectorStore([
             VectorStoreDocument(id=entity.id, vector=None) for entity in entities
         ]),
         text_embedder=ModelManager().get_or_create_embedding_model(
diff --git a/tests/unit/utils/test_embeddings.py b/tests/unit/utils/test_embeddings.py
deleted file mode 100644
index 63d0619c1d..0000000000
--- a/tests/unit/utils/test_embeddings.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) 2024 Microsoft Corporation.
-# Licensed under the MIT License
-
-import pytest
-from graphrag.config.embeddings import create_index_name
-
-
-def test_create_index_name():
-    collection = create_index_name("default", "entity_description")
-    assert collection == "default-entity_description"
-
-
-def test_create_index_name_invalid_embedding_throws():
-    with pytest.raises(KeyError):
-        create_index_name("default", "invalid.name")
-
-
-def test_create_index_name_invalid_embedding_does_not_throw():
-    collection = create_index_name("default", "invalid_name", validate=False)
-    assert collection == "default-invalid_name"
diff --git a/uv.lock b/uv.lock
index 72a6db6f11..ddff9f008b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -19,6 +19,7 @@ members = [
     "graphrag-input",
     "graphrag-monorepo",
     "graphrag-storage",
+    "graphrag-vectors",
 ]
 
 [[package]]
@@ -1009,6 +1010,7 @@ dependencies = [
     { name = "graphrag-common" },
     { name = "graphrag-input" },
     { name = "graphrag-storage" },
+    { name = "graphrag-vectors" },
     { name = "graspologic-native" },
     { name = "json-repair" },
     { name = "lancedb" },
@@ -1043,6 +1045,7 @@ requires-dist = [
     { name = "graphrag-common", editable = "packages/graphrag-common" },
     { name = "graphrag-input", editable = "packages/graphrag-input" },
     { name = "graphrag-storage", editable = "packages/graphrag-storage" },
+    { name = "graphrag-vectors", editable = "packages/graphrag-vectors" },
     { name = "graspologic-native", specifier = "~=1.2" },
     { name = "json-repair", specifier = "~=0.30" },
     { name = "lancedb", specifier = "~=0.24.1" },
@@ -1204,6 +1207,35 @@ requires-dist = [
     { name = "pydantic", specifier = "~=2.10" },
 ]
 
+[[package]]
+name = "graphrag-vectors"
+version = "2.7.0"
+source = { editable = "packages/graphrag-vectors" }
+dependencies = [
+    { name = "azure-core" },
+    { name = "azure-cosmos" },
+    { name = "azure-identity" },
+    { name = "azure-search-documents" },
+    { name = "graphrag-common" },
+    { name = "lancedb" },
+    { name = "numpy" },
+    { name = "pyarrow" },
+    { name = "pydantic" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "azure-core", specifier = "~=1.32" },
+    { name = "azure-cosmos", specifier = "~=4.9" },
+    { name = "azure-identity", specifier = "~=1.19" },
+    { name = "azure-search-documents", specifier = "~=11.6" },
+    { name = "graphrag-common", editable = "packages/graphrag-common" },
+    { name = "lancedb", specifier = "~=0.24.1" },
+    { name = "numpy", specifier = "~=2.2" },
+    { name = "pyarrow", specifier = "~=22.0" },
+    { name = "pydantic", specifier = "~=2.10" },
+]
+
 [[package]]
 name = "graspologic-native"
 version = "1.2.5"

From d7bd7c62b1ddb3e97efa40971e638673b870ca21 Mon Sep 17 00:00:00 2001
From: Nathan Evans <github@talkswithnumbers.com>
Date: Fri, 16 Jan 2026 15:03:47 -0800
Subject: [PATCH 39/40] Fix smoke vector config

---
 tests/fixtures/azure/settings.yml   | 9 ++++-----
 tests/fixtures/min-csv/settings.yml | 7 +++----
 tests/fixtures/text/settings.yml    | 9 ++++-----
 3 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/tests/fixtures/azure/settings.yml b/tests/fixtures/azure/settings.yml
index b220786d88..961037cffa 100644
--- a/tests/fixtures/azure/settings.yml
+++ b/tests/fixtures/azure/settings.yml
@@ -2,11 +2,10 @@ extract_claims:
   enabled: true
 
 vector_store:
-  default_vector_store:
-    type: "azure_ai_search"
-    url: ${AZURE_AI_SEARCH_URL_ENDPOINT}
-    api_key: ${AZURE_AI_SEARCH_API_KEY}
-    container_name: "azure_ci"
+  type: "azure_ai_search"
+  url: ${AZURE_AI_SEARCH_URL_ENDPOINT}
+  api_key: ${AZURE_AI_SEARCH_API_KEY}
+  container_name: "azure_ci"
 
 input:
   storage:
diff --git a/tests/fixtures/min-csv/settings.yml b/tests/fixtures/min-csv/settings.yml
index bf7aa59644..27cff53522 100644
--- a/tests/fixtures/min-csv/settings.yml
+++ b/tests/fixtures/min-csv/settings.yml
@@ -28,10 +28,9 @@ models:
     concurrent_requests: 25
 
 vector_store:
-  default_vector_store:
-    type: "lancedb"
-    db_uri: "./tests/fixtures/min-csv/lancedb"
-    container_name: "lancedb_ci"
+  type: "lancedb"
+  db_uri: "./tests/fixtures/min-csv/lancedb"
+  container_name: "lancedb_ci"
 
 input:
   type: csv
diff --git a/tests/fixtures/text/settings.yml b/tests/fixtures/text/settings.yml
index 24e59b5942..9eb1cd3c08 100644
--- a/tests/fixtures/text/settings.yml
+++ b/tests/fixtures/text/settings.yml
@@ -28,11 +28,10 @@ models:
     concurrent_requests: 25
 
 vector_store:
-  default_vector_store:
-    type: "azure_ai_search"
-    url: ${AZURE_AI_SEARCH_URL_ENDPOINT}
-    api_key: ${AZURE_AI_SEARCH_API_KEY}
-    container_name: "simple_text_ci"
+  type: "azure_ai_search"
+  url: ${AZURE_AI_SEARCH_URL_ENDPOINT}
+  api_key: ${AZURE_AI_SEARCH_API_KEY}
+  container_name: "simple_text_ci"
 
 community_reports:
   prompt: "prompts/community_report.txt"

From fc98a1d4fa478227236bfc1d99da49c2fa89bfb2 Mon Sep 17 00:00:00 2001
From: gaudyb <85708998+gaudyb@users.noreply.github.com>
Date: Fri, 16 Jan 2026 23:23:03 -0600
Subject: [PATCH 40/40] Update index bug (#2173)

* fix update index bug

* blob storage bug fix

---------

Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
---
 .vscode/launch.json                                | 12 ++++++++++++
 docs/config/yaml.md                                | 12 ++++++------
 .../graphrag_storage/azure_blob_storage.py         | 10 +++++-----
 packages/graphrag/graphrag/config/defaults.py      |  2 +-
 .../graphrag/config/models/reporting_config.py     |  2 +-
 .../graphrag/graphrag/index/run/run_pipeline.py    |  2 +-
 .../graphrag/logger/blob_workflow_logger.py        | 14 +++++++-------
 packages/graphrag/graphrag/logger/factory.py       |  2 +-
 tests/unit/config/utils.py                         |  2 +-
 9 files changed, 35 insertions(+), 23 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index 9f949cb12f..3d563d6f74 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -14,6 +14,18 @@
 			],
 			"console": "integratedTerminal"
 		},
+		{
+			"name": "Update",
+			"type": "debugpy",
+			"request": "launch",
+			"module": "graphrag",
+			"args": [
+				"update",
+				"--root", 
+				"${input:root_folder}"
+			],
+			"console": "integratedTerminal"
+		},
 		{
 			"name": "Query",
 			"type": "debugpy",
diff --git a/docs/config/yaml.md b/docs/config/yaml.md
index 0f67390833..43d15e9349 100644
--- a/docs/config/yaml.md
+++ b/docs/config/yaml.md
@@ -85,7 +85,7 @@ Our pipeline can ingest .csv, .txt, or .json data from an input location. See th
   - `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
   - `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
   - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
-  - `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
+  - `account_url` **str** - (blob only) The storage account blob URL to use.
   - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
 - `type` **text|csv|json** - The type of input data to load. Default is `text`
 - `encoding` **str** - The encoding of the input file. Default is `utf-8`
@@ -118,10 +118,10 @@ This section controls the storage mechanism used by the pipeline used for export
 - `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
 - `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
 - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
-- `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
+- `account_url` **str** - (blob only) The storage account blob URL to use.
 - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
 
-### update_index_output
+### update_output_storage
 
 The section defines a secondary storage location for running incremental indexing, to preserve your original outputs.
 
@@ -131,7 +131,7 @@ The section defines a secondary storage location for running incremental indexin
 - `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
 - `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
 - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
-- `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
+- `account_url` **str** - (blob only) The storage account blob URL to use.
 - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
 
 ### cache
@@ -146,7 +146,7 @@ This section controls the cache mechanism used by the pipeline. This is used to
   - `base_dir` **str** - The base directory to write output artifacts to, relative to the root.
   - `connection_string` **str** - (blob/cosmosdb only) The Azure Storage connection string.
   - `container_name` **str** - (blob/cosmosdb only) The Azure Storage container name.
-  - `storage_account_blob_url` **str** - (blob only) The storage account blob URL to use.
+  - `account_url` **str** - (blob only) The storage account blob URL to use.
   - `cosmosdb_account_blob_url` **str** - (cosmosdb only) The CosmosDB account blob URL to use.
 
 ### reporting
@@ -159,7 +159,7 @@ This section controls the reporting mechanism used by the pipeline, for common e
 - `base_dir` **str** - The base directory to write reports to, relative to the root.
 - `connection_string` **str** - (blob only) The Azure Storage connection string.
 - `container_name` **str** - (blob only) The Azure Storage container name.
-- `storage_account_blob_url` **str** - The storage account blob URL to use.
+- `account_url` **str** - The storage account blob URL to use.
 
 ### vector_store
 
diff --git a/packages/graphrag-storage/graphrag_storage/azure_blob_storage.py b/packages/graphrag-storage/graphrag_storage/azure_blob_storage.py
index bec1bdb465..68cca2c017 100644
--- a/packages/graphrag-storage/graphrag_storage/azure_blob_storage.py
+++ b/packages/graphrag-storage/graphrag_storage/azure_blob_storage.py
@@ -27,7 +27,7 @@ class AzureBlobStorage(Storage):
     _container_name: str
     _base_dir: str | None
     _encoding: str
-    _storage_account_blob_url: str | None
+    _account_url: str | None
     _blob_service_client: BlobServiceClient
     _storage_account_name: str | None
 
@@ -42,7 +42,7 @@ def __init__(
     ) -> None:
         """Create a new BlobStorage instance."""
         if connection_string is not None and account_url is not None:
-            msg = "AzureBlobStorage requires only one of connection_string or storage_account_blob_url to be specified, not both."
+            msg = "AzureBlobStorage requires only one of connection_string or account_url to be specified, not both."
             logger.error(msg)
             raise ValueError(msg)
 
@@ -63,7 +63,7 @@ def __init__(
                 credential=DefaultAzureCredential(),
             )
         else:
-            msg = "AzureBlobStorage requires either a connection_string or storage_account_blob_url to be specified."
+            msg = "AzureBlobStorage requires either a connection_string or account_url to be specified."
             logger.error(msg)
             raise ValueError(msg)
 
@@ -71,7 +71,7 @@ def __init__(
         self._container_name = container_name
         self._connection_string = connection_string
         self._base_dir = base_dir
-        self._storage_account_blob_url = account_url
+        self._account_url = account_url
         self._storage_account_name = (
             account_url.split("//")[1].split(".")[0] if account_url else None
         )
@@ -225,7 +225,7 @@ def child(self, name: str | None) -> "Storage":
             container_name=self._container_name,
             encoding=self._encoding,
             base_dir=path,
-            account_url=self._storage_account_blob_url,
+            account_url=self._account_url,
         )
 
     def keys(self) -> list[str]:
diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py
index 7f7fd3f9e8..bb2e2fd947 100644
--- a/packages/graphrag/graphrag/config/defaults.py
+++ b/packages/graphrag/graphrag/config/defaults.py
@@ -221,7 +221,7 @@ class StorageDefaults:
     base_dir: str | None = None
     azure_connection_string: None = None
     azure_container_name: None = None
-    azure_storage_account_blob_url: None = None
+    azure_account_url: None = None
     azure_cosmosdb_account_url: None = None
 
 
diff --git a/packages/graphrag/graphrag/config/models/reporting_config.py b/packages/graphrag/graphrag/config/models/reporting_config.py
index 0e33736058..7443ea2961 100644
--- a/packages/graphrag/graphrag/config/models/reporting_config.py
+++ b/packages/graphrag/graphrag/config/models/reporting_config.py
@@ -28,7 +28,7 @@ class ReportingConfig(BaseModel):
         description="The reporting container name to use.",
         default=graphrag_config_defaults.reporting.container_name,
     )
-    storage_account_blob_url: str | None = Field(
+    account_url: str | None = Field(
         description="The storage account blob url to use.",
         default=graphrag_config_defaults.reporting.storage_account_blob_url,
     )
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index f82e774423..d552acecac 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -160,6 +160,6 @@ async def _copy_previous_output(
     copy_storage: Storage,
 ):
     for file in storage.find(re.compile(r"\.parquet$")):
-        base_name = file[0].replace(".parquet", "")
+        base_name = file.replace(".parquet", "")
         table = await load_table_from_storage(base_name, storage)
         await write_table_to_storage(table, base_name, copy_storage)
diff --git a/packages/graphrag/graphrag/logger/blob_workflow_logger.py b/packages/graphrag/graphrag/logger/blob_workflow_logger.py
index ae4893c6e8..fd7ccac4fe 100644
--- a/packages/graphrag/graphrag/logger/blob_workflow_logger.py
+++ b/packages/graphrag/graphrag/logger/blob_workflow_logger.py
@@ -26,7 +26,7 @@ def __init__(
         container_name: str | None,
         blob_name: str = "",
         base_dir: str | None = None,
-        storage_account_blob_url: str | None = None,
+        account_url: str | None = None,
         level: int = logging.NOTSET,
     ):
         """Create a new instance of the BlobWorkflowLogger class."""
@@ -35,24 +35,24 @@ def __init__(
         if container_name is None:
             msg = "No container name provided for blob storage."
             raise ValueError(msg)
-        if connection_string is None and storage_account_blob_url is None:
+        if connection_string is None and account_url is None:
             msg = "No storage account blob url provided for blob storage."
             raise ValueError(msg)
 
         self._connection_string = connection_string
-        self._storage_account_blob_url = storage_account_blob_url
+        self.account_url = account_url
 
         if self._connection_string:
             self._blob_service_client = BlobServiceClient.from_connection_string(
                 self._connection_string
             )
         else:
-            if storage_account_blob_url is None:
-                msg = "Either connection_string or storage_account_blob_url must be provided."
+            if account_url is None:
+                msg = "Either connection_string or account_url must be provided."
                 raise ValueError(msg)
 
             self._blob_service_client = BlobServiceClient(
-                storage_account_blob_url,
+                account_url,
                 credential=DefaultAzureCredential(),
             )
 
@@ -107,7 +107,7 @@ def _write_log(self, log: dict[str, Any]):
             self.__init__(
                 self._connection_string,
                 self._container_name,
-                storage_account_blob_url=self._storage_account_blob_url,
+                account_url=self.account_url,
             )
 
         blob_client = self._blob_service_client.get_blob_client(
diff --git a/packages/graphrag/graphrag/logger/factory.py b/packages/graphrag/graphrag/logger/factory.py
index 1094d080f3..7b5d28ef5e 100644
--- a/packages/graphrag/graphrag/logger/factory.py
+++ b/packages/graphrag/graphrag/logger/factory.py
@@ -54,7 +54,7 @@ def create_blob_logger(**kwargs) -> logging.Handler:
         connection_string=kwargs["connection_string"],
         container_name=kwargs["container_name"],
         base_dir=kwargs["base_dir"],
-        storage_account_blob_url=kwargs["storage_account_blob_url"],
+        account_url=kwargs["account_url"],
     )
 
 
diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py
index 813502d548..4410618777 100644
--- a/tests/unit/config/utils.py
+++ b/tests/unit/config/utils.py
@@ -122,7 +122,7 @@ def assert_reporting_configs(
     assert actual.base_dir == expected.base_dir
     assert actual.connection_string == expected.connection_string
     assert actual.container_name == expected.container_name
-    assert actual.storage_account_blob_url == expected.storage_account_blob_url
+    assert actual.account_url == expected.account_url
 
 
 def assert_storage_config(actual: StorageConfig, expected: StorageConfig) -> None: