GitModel is for dynamically generating high quality heirarchical topic tree representations of github repos using customizable GNN message passing layers.
- Highly customizable philospophy. Goal to support OpenAssistant, LAION OIG, Huggingface, Openai, Cohere, etc. python, js, c, c++, C#, etc
- Swap system prompt tasks(bug hunting, todo, documentation labeling, etc) for enriching semantic graph
- GNN Message Passing and Topic modeling pipeline as an inductive bias (GRNN)
- SVD for componenent analysis
- BERTopic is highly customizable and can compose several different clustering, embedding, vectorizers, bag of words and dimensionality reduction techniques.
- Change optics by swapping categorical objects in the pipeline swap umap_hdbscan with svd_kmeans or transform adj_matrix to graph laplacian
"Memory" Tree representations can be dynamically selected and added to the system prompt augmenting text generation.
Contributions Welcome! This is a great guide for how to make a pull request
main.py
import argparse
from getpass import getpass
import openai
from src import Pipeline
if __name__ == "__main__":
argsparse = argparse.ArgumentParser()
argsparse.add_argument("--config", type=str, default="./test_config.yaml")
argsparse.add_argument("--repo", type=str, default="https://github.com/danielpatrickhug/GitModel.git")
argsparse.add_argument("--repo_name", type=str, default="gitmodel")
args = argsparse.parse_args()
openai_secret = getpass("Enter the secret key: ")
# Set up OpenAI API credentials
openai.api_key = openai_secret
print("starting pipeline")
pipeline = Pipeline.from_yaml(args.config)
gnn_head_outputs, topic_model_outputs = pipeline.run(args.repo, args.repo_name)
for i, topic_model_output in enumerate(topic_model_outputs):
topic_model_output["data"].to_csv(f"context/{args.repo_name}_topic_model_outputs_{i}.csv")
topic_model_output["topic_info"].to_csv(f"context/{args.repo_name}_topic_info_{i}.csv")
with open(f"context/{args.repo_name}_tree_{i}.txt", "w", encoding="utf-8") as f:
f.write(topic_model_output["tree"])The ability to bootstrap its own codebase is a powerful feature as it allows for efficient self-improvement and expansion. It means that the codebase is designed in such a way that it can use its own output as an input to improve itself. In the context of GitModel, this feature allows for the efficient improvement and expansion of its own codebase. By using its own output to generate hierarchical topic trees of GitHub repositories, it can analyze and extract insights from its own codebase and other codebases to improve its functionality. This can lead to more efficient and effective code generation, better semantic graph generation, and improved text generation capabilities.
- https://github.com/danielpatrickhug/GitModel
- uses Deepminds clrs topic tree in system prompt during semantic graph generation
.
├─Function description and comparison including Gaussian kernel and sparse matrices____
│ ├─■──Understanding the Purpose and Handling of a Function for Sparse Matrices with Inputs, Outputs, and P ── Topic: 9
│ └─Understanding kernels and functions in the 'kernels.py' file for estimating PDF and computing simila
│ ├─■──Purpose and functions of kernel-related functions in kernels.py file of a Python program, including ── Topic: 22
│ └─■──Understanding the cos_sim and cos_sim_torch functions in kernels.py file____ ── Topic: 25
└─Graph message passing and adjacency matrix computation using embeddings____
├─k-hop message passing and cosine similarity kernel computation for graph embeddings____
│ ├─k-hop message passing with adjacency matrix and node features____
│ │ ├─Computation of Gaussian Kernel Matrix between Two Sets of Embeddings using PyTorch____
│ │ │ ├─■──Cosine Similarity with PyTorch Tensors and Functional.____ ── Topic: 1
│ │ │ └─■──Function to compute adjacency matrix for embeddings using specified kernel type and threshold value_ ── Topic: 19
│ │ └─Message Passing and K-hop Aggregation in Graphs using Sparse Matrices and Node Features____
│ │ ├─■──Document pruning and adjacency matrix recomputation using embeddings and thresholding____ ── Topic: 11
│ │ └─k-hop message passing and adjacency matrix computation in sparse graphs.____
│ │ ├─■──Computing graph laplacian and degree matrix from pairwise distances using a given function.____ ── Topic: 7
│ │ └─■──Message Passing with K-hop Adjacency and Aggregated Features in Sparse Matrices____ ── Topic: 8
│ └─"Outlier Reduction Using Count-TF-IDF and OpenAI Representation Model"____
│ ├─Topic Modeling and Outlier Reduction in Natural Language Processing (NLP)____
│ │ ├─Understanding the compose_inference function in a chatbot system.____
│ │ │ ├─■──Processing conversation transcripts with Python functions____ ── Topic: 18
│ │ │ └─Understanding the compose_inference function in a chatbot conversation with message templates____
│ │ │ ├─■──Understanding the `compose_inference` Function in Chatbot Conversation Generation with OpenAI GPT___ ── Topic: 2
│ │ │ └─■──Function to create prompt message template with role and text input parameters and validation of rol ── Topic: 17
│ │ └─Outlier Reduction with Machine Learning Models____
│ │ ├─Document processing and reduction techniques for topic modeling with various machine learning models
│ │ │ ├─MiniLM language model for sentence embedding____
│ │ │ │ ├─■──Embedding sentences using MiniLM language model with multiprocessing and GPU acceleration____ ── Topic: 15
│ │ │ │ └─■──Embedding Sentences using Pre-Trained Language Model with SentenceTransformer Library____ ── Topic: 23
│ │ │ └─■──Topic modeling algorithms and document reduction techniques____ ── Topic: 0
│ │ └─SQLalchemy migrations in online mode with engine configuration____
│ │ ├─■──Probability Density Estimation with Gaussian Kernel Density Estimator____ ── Topic: 12
│ │ └─Running database migrations with SQLAlchemy and Alembic____
│ │ ├─■──Graph network message passing & Mobile App Navigation System Design____ ── Topic: 21
│ │ └─■──Running migrations with SQLAlchemy and Alembic in online mode____ ── Topic: 6
│ └─Class Settings definition using BaseSettings and its purpose for managing configuration in a third-p
│ ├─■──Empty class definition for managing application settings using Pydantic's BaseSettings____ ── Topic: 3
│ └─■──MemoryTreeManager class implementation____ ── Topic: 16
└─Codebase decomposition and analysis with Git repository and AST nodes.____
├─Code decomposition and processing in Git repositories.____
│ ├─■──Python code parsing and analysis____ ── Topic: 4
│ └─Code decomposition in a Git repository____
│ ├─■──Decomposing Git Repositories with System Prompts.____ ── Topic: 10
│ └─Parsing and pruning files in a GitHub repository____
│ ├─■──parsing and pruning files in a local Git repository____ ── Topic: 5
│ └─■──purpose of `get_repo_contents` function in `repo_graph_generation.py` for retrieving and pruning Git ── Topic: 24
└─Analyzing chatbot main capabilities in a codebase using natural language processing and notable fram
├─■──summarizing code in a GitHub repository using ChatGPT____ ── Topic: 14
└─Understanding Codebase Structure and Functionality with Hierarchical Trees and Frameworks____
├─■──Analyzing codebase structure and functionalities using a hierarchical topic tree____ ── Topic: 13
└─■──Understanding the difference between format_system_prompts and format_system_prompts_with_tree in a ── Topic: 20
.
├─Purpose and Attributes of the `Net` Class in Graph Neural Networks____
│ ├─Graph Attention Networks and DAG Shortest Paths in JAX.____
│ │ ├─Graph Attention Networks (GAT and GATv2) code implementation____
│ │ │ ├─Code for DAG shortest path and depth-first search algorithms____
│ │ │ │ ├─■──String Matching and Maximum Subarray____ ── Topic: 10
│ │ │ │ └─Depth-First Search and DAG Shortest Path Algorithms implemented in Python____
│ │ │ │ ├─■──Description of string probe functions in probing.py file for Hash Table probing.____ ── Topic: 1
│ │ │ │ └─Graph Algorithms - DFS and DAG Shortest Paths____
│ │ │ │ ├─■──Graph algorithms (DFS and DAG shortest path) in Python____ ── Topic: 0
│ │ │ │ └─■──Functions for decoding diff and graph features in PyTorch graph neural networks.____ ── Topic: 6
│ │ │ └─■──Graph Attention Networks (GAT and GATv2)____ ── Topic: 20
│ │ └─■──Message Passing with _MessagePassingScanState, _MessagePassingOutputChunked and MessagePassingStateC ── Topic: 17
│ └─Implementing a Baseline Model with Selectable Message Passing Algorithm and its Dataset Sampler.____
│ ├─Handling of untrained parameters in optimization updates____
│ │ ├─■──Updating parameters with filtered gradients from multiple algorithms.____ ── Topic: 8
│ │ └─■──Processing trajectory hints with variable-length time dimension using batching.____ ── Topic: 9
│ └─Processing time-chunked data with batched samplers and message passing nets.____
│ ├─Model processing of time-chunked data with dataset sampling and batch processing____
│ │ ├─■──CLRS dataset download and URL retrieval on Google Cloud Platform____ ── Topic: 13
│ │ └─Chunked data and dataset sampling with JAX.____
│ │ ├─■──JAX functions for reshaping and restacking data for pmap computation____ ── Topic: 4
│ │ └─Data chunking with batched sampling and message passing in neural networks.____
│ │ ├─Processing time-chunked data with batch samplers and a NetChunked class____
│ │ │ ├─■──Time-chunked data processing using BaselineModelChunked and NetChunked in TensorFlow.____ ── Topic: 2
│ │ │ └─■──Creating samplers for training data.____ ── Topic: 11
│ │ └─■──Documented code for sampling algorithms using randomized position generation.____ ── Topic: 3
│ └─■──Point Sampling and Convex Hull Computation____ ── Topic: 18
└─Loss functions for training with time-chunked data____
├─Loss calculation for time-chunked and full-sample training.____
│ ├─Code functions for evaluating predictions using permutations and masking____
│ │ ├─■──Functions for Evaluating Predictions in Probing Tasks.____ ── Topic: 7
│ │ └─■──permutation pointer manipulation and reduction in predictions____ ── Topic: 16
│ └─Loss calculation and decoder output postprocessing in neural networks.____
│ ├─■──Postprocessing with Sinkhorn operator in log space____ ── Topic: 15
│ └─■──Loss calculation methods for training with time and full samples____ ── Topic: 19
└─Functions for expanding and broadcasting JAX arrays____
├─Description and input/output parameters of _expand_to and _is_not_done_broadcast functions____
│ ├─■──Array expansion and broadcasting techniques____ ── Topic: 21
│ └─■──Purpose and Functionality of _is_not_done_broadcast Function____ ── Topic: 14
└─Sampler classes and associated data generation types____
├─■──Understanding Parameters and Expected Input/Output of Various Functions (including mst_prim, floyd_w ── Topic: 5
└─■──Sampling classes and their data generation purpose____ ── Topic: 12
- recurrent generation augmented with the above topic tree in system prompt
.
├─DFS and DAG Shortest Paths Algorithm Implementation with Probing____
│ ├─■──Bipartite matching-based flow networks____ ── Topic: 34
│ └─Search and Shortest Path Algorithms____
│ ├─DAG shortest path algorithm with probing and initialization____
│ │ ├─■──Strongly Connected Components Algorithm with Kosaraju's Implementation____ ── Topic: 37
│ │ └─Graph Sampling and DAG Shortest Path Algorithm____
│ │ ├─■──Bipartite matching using Edmonds-Karp algorithm____ ── Topic: 18
│ │ └─■──Random graph generation using Bellman-Ford algorithm in Python____ ── Topic: 0
│ └─Graham scan convex hull algorithm implementation in Python____
│ ├─■──Maximum subarray algorithm implementation____ ── Topic: 6
│ └─■──Graham scan convex hull algorithm implementation____ ── Topic: 12
└─Postprocessing Decoder Output for Chunked Data Processing Net____
├─Postprocessing Decoder Output with Chunked Data in JAX____
│ ├─Functions and Files in Probing.py Explained____
│ │ ├─Functions and techniques for data splitting and replication in probing and pmap computation.____
│ │ │ ├─Understanding the strings_pair_cat function and split_stages function in probing.py file____
│ │ │ │ ├─TFDS CLRSDataset Command-Line Tool for Sampling Datasets____
│ │ │ │ │ ├─■──CLRS30 dataset and related functions explanation____ ── Topic: 5
│ │ │ │ │ └─■──TFDS CLRSDataset Builder Implementation____ ── Topic: 16
│ │ │ │ └─Functions and Probing in Python Code____
│ │ │ │ ├─Purpose of the `split_stages` function in `probing.py` and related functions for evaluating `ProbesD
│ │ │ │ │ ├─Functions for evaluating hint and output predictions using permutation objects and dictionaries.____
│ │ │ │ │ │ ├─Processing randomized `pos` input in a sampler with pointers and permutations.____
│ │ │ │ │ │ │ ├─■──Process randomization of `pos` input in algorithms including string algorithms____ ── Topic: 29
│ │ │ │ │ │ │ └─■──A function to replace should-be permutations with proper permutation pointers using a sample iterato ── Topic: 19
│ │ │ │ │ │ └─Function for Evaluating Permutation Predictions using Hint Data____
│ │ │ │ │ │ ├─■──Function to Reduce Permutations in a Dictionary of Result Objects____ ── Topic: 11
│ │ │ │ │ │ └─■──Function to evaluate hint predictions with tuple and list inputs____ ── Topic: 17
│ │ │ │ │ └─Understanding probing functions in Hash Table implementation____
│ │ │ │ │ ├─Hash Table Probing Functions in probing.py File____
│ │ │ │ │ │ ├─■──Splitting ProbesDict into DataPoints by stage in Python____ ── Topic: 14
│ │ │ │ │ │ └─■──Understanding Hash Table Probing Functions (strings_pi, strings_pos, strings_pair_cat) in Python's ` ── Topic: 1
│ │ │ │ │ └─■──Functions for Checking Input Dimensions in Machine Learning Models____ ── Topic: 15
│ │ │ │ └─JAX pmap reshaping and computation functions (_pmap_reshape, _maybe_pmap_reshape, _maybe_pmap_data)_
│ │ │ │ ├─JAX pmap computation and pytree reshaping____
│ │ │ │ │ ├─■──Purpose and attributes of the Stage and OutputClass classes____ ── Topic: 22
│ │ │ │ │ └─■──JAX tree reshaping for pmap computation with _pmap_reshape and _maybe_pmap_reshape functions____ ── Topic: 3
│ │ │ │ └─Numpy array copying functions with assertions____
│ │ │ │ ├─■──Functions for copying data between numpy arrays in Python____ ── Topic: 21
│ │ │ │ └─■──Function Purpose and Parameters Analysis in Codebase____ ── Topic: 9
│ │ │ └─Trajectory Batching with Variable-Length Time Dimension____
│ │ │ ├─■──Trajectory Batching and Concatenation____ ── Topic: 35
│ │ │ └─■──Batch processing of variable-length hint trajectories.____ ── Topic: 31
│ │ └─Understanding the `_is_not_done_broadcast` function and its input/output parameters.____
│ │ ├─■──Understanding the _is_not_done_broadcast function in JAX array for sequence completion.____ ── Topic: 8
│ │ └─■──Array broadcasting and expansion with _expand_and_broadcast_to and _expand_to functions____ ── Topic: 27
│ └─Postprocessing Decoder Output with Sinkhorn Algorithm and Hard Categorization____
│ ├─Node Feature Decoding with Encoders and Decoders____
│ │ ├─■──Position Encoding Function for Natural Language Processing____ ── Topic: 23
│ │ └─Node feature decoding using decoders and edge features____
│ │ ├─■──Creating Encoders with Xavier Initialization and Truncated Normal Distribution for Encoding Categori ── Topic: 33
│ │ └─Node feature decoding with decoders and edge features____
│ │ ├─■──Node feature decoding and encoding with decoders and edge features____ ── Topic: 2
│ │ └─■──Graph diff decoders____ ── Topic: 32
│ └─Postprocessing of decoder output in graph neural networks.____
│ ├─Decoder Output Postprocessing with Sinkhorn Algorithm and Cross-Entropy Loss____
│ │ ├─Message Passing Net with Time-Chunked Data Processing____
│ │ │ ├─■──Python Class for Message Passing Model with Selectable Algorithm____ ── Topic: 26
│ │ │ └─■──NetChunked message passing operation with LSTM states for time-chunked data____ ── Topic: 7
│ │ └─Loss calculation for time-chunked training with scalar truth data.____
│ │ ├─Loss calculation function for time-chunked training with scalar truth data.____
│ │ │ ├─■──Loss calculation for time-chunked training data____ ── Topic: 4
│ │ │ └─■──Logarithmic Sinkhorn Operator for Permutation Pointer Logits____ ── Topic: 10
│ │ └─■──Decoder postprocessing with Sinkhorn operator____ ── Topic: 28
│ └─Gradient Filtering for Optimizer Updates____
│ ├─■──Filtering processor parameters in Haiku models____ ── Topic: 30
│ └─■──Filtering null gradients for untrained parameters during optimization.____ ── Topic: 24
└─PGN with Jax implementation and NeurIPS 2020 paper____
├─Message-Passing Neural Network (MPNN) for Graph Convolutional Networks (GCNs)____
│ ├─■──"Applying Triplet Message Passing with HK Transforms in MPNN for Graph Neural Networks"____ ── Topic: 20
│ └─■──Implementation of Deep Sets (Zaheer et al., NeurIPS 2017) using adjacency matrices and memory networ ── Topic: 13
└─GATv2 Graph Attention Network with adjustable sizes of multi-head attention and residual connections
├─■──Graph Attention Network v2 architecture with adjustable head number and output size.____ ── Topic: 36
└─■──Processor factory with various models and configurations____ ── Topic: 25
.
├─Combining documents with different chain types and LLM chains____
│ ├─MapReduce Chain Loading and Combining____
│ │ ├─Question answering chain with sources loading and combining____
│ │ │ ├─■──Loading question answering with sources chain with multiple loader mappings and chains.____ ── Topic: 53
│ │ │ └─■──Loading and Combining Documents with Language Models for Summarizing and QA____ ── Topic: 71
│ │ └─Map Reduce Chain Loading Function____
│ │ ├─Document Refinement using LLM Chains____
│ │ │ ├─■──Combining Documents with Stuffing and LLM Chain in Python____ ── Topic: 97
│ │ │ └─BaseQAWithSourcesChain document handling and processing.____
│ │ │ ├─■──Question Answering with Sources over Documents Chain____ ── Topic: 60
│ │ │ └─■──Python class for chatbot with vector database and question generation____ ── Topic: 16
│ │ └─MapReduce chain implementation____
│ │ ├─■──MapReduceDocumentsChain document combination with chaining and mapping____ ── Topic: 12
│ │ └─■──MapReduce Chain Loading Function____ ── Topic: 95
│ └─LLMBashChain document examples and related keywords____
│ ├─Bash operations and language modeling chain implementation____
│ │ ├─LLMSummarizationCheckerChain document samples____
│ │ │ ├─■──Working with SQL databases in Python using SQLDatabaseChain____ ── Topic: 46
│ │ │ └─Document processing with LLMSummarizationCheckerChain____
│ │ │ ├─■──Implementation of Program-Aided Language Models with PALChain class and related prompts and assertio ── Topic: 31
│ │ │ └─■──LLMSummarizationCheckerChain class and its functionality____ ── Topic: 93
│ │ └─LLMBashChain - interpreting prompts and executing bash code____
│ │ ├─■──LLMMathChain - Python code execution for math prompts____ ── Topic: 92
│ │ └─■──Bash execution with LLMBashChain____ ── Topic: 80
│ └─■──MRKLChain implementation with ChainConfig and API integration____ ── Topic: 59
└─Code organization and structure in Python including several classes related to self-hosted embedding
├─Code organization and improvement suggestions for a class definition.____
│ ├─Code Loading and Organization Best Practices____
│ │ ├─Web scraping Hacker News webpage titles____
│ │ │ ├─Loading files using unstructured in Python____
│ │ │ │ ├─Unstructured file loading with retry and partitioning capabilities.____
│ │ │ │ │ ├─■──Retry Decorator for OpenAI API Calls____ ── Topic: 45
│ │ │ │ │ └─Unstructured File Loading and Partitioning____
│ │ │ │ │ ├─■──Unstructured File Loader for Partitioning Files in Various Formats____ ── Topic: 25
│ │ │ │ │ └─■──Loading files with Unstructured package in different modes (Python code).____ ── Topic: 26
│ │ │ │ └─PDF manipulation in Python with pypdf, pdfminer, fitz and pymupdf libraries____
│ │ │ │ ├─■──PDF file loading and text extraction using PyMuPDF and PDFMiner____ ── Topic: 69
│ │ │ │ └─■──Extracting Text from Paged PDF using PyPDF and PDFMiner____ ── Topic: 96
│ │ │ └─Extracting Hacker News Webpage Information using WebBaseLoader and BeautifulSoup.____
│ │ │ ├─■──Web scraping Hacker News with BeautifulSoup and WebBaseLoader____ ── Topic: 21
│ │ │ └─■──Web Scraping for College Confidential and Lyrics Websites____ ── Topic: 76
│ │ └─Code organization and structure in various Python modules____
│ │ ├─Compliments on clear and structured codebase with good use of type hints for memory handling and con
│ │ │ ├─Implementation of ReAct paper using ReActChain with examples in Python____
│ │ │ │ ├─■──Implementation of ReAct paper in ReActChain agent with OpenAI LLC model and tools____ ── Topic: 101
│ │ │ │ └─In-memory Docstore for Efficient Lookup and Exploration____
│ │ │ │ ├─■──Document Store Exploration with DocstoreExplorer____ ── Topic: 87
│ │ │ │ └─■──InMemoryDocstore for Storing and Searching Documents with AddableMixin____ ── Topic: 61
│ │ │ └─Compliments on Code Readability and Organization in Python Codebase.____
│ │ │ ├─Memory Handling and Conversation Management____
│ │ │ │ ├─Memory Conversation Summarizer Implementation____
│ │ │ │ │ ├─Memory and Conversation Summarization in AI-assisted dialogues.____
│ │ │ │ │ │ ├─■──Purpose of ChatPromptValue class in chat.py____ ── Topic: 30
│ │ │ │ │ │ └─■──Memory management and conversation summarization in AI chatbot system.____ ── Topic: 6
│ │ │ │ │ └─■──Implementation of Chain class with CallbackManager and Memory attributes.____ ── Topic: 52
│ │ │ │ └─Potential bugs and suggestions for loading LLM, few-shot prompts, and examples from JSON and YAML fi
│ │ │ │ ├─Code structure and organization tips for loading examples and templates from files in Python.____
│ │ │ │ │ ├─Compliments on code structure and organization____
│ │ │ │ │ │ ├─■──Loading few-shot prompts from config with prefix and suffix templates____ ── Topic: 34
│ │ │ │ │ │ └─Code organization and structure for creating chat prompt templates____
│ │ │ │ │ │ ├─■──Chat prompt template and message prompt templates for generating chatbot prompts.____ ── Topic: 8
│ │ │ │ │ │ └─■──Purpose of `_load_prompt_from_file` function in loading.py module.____ ── Topic: 13
│ │ │ │ │ └─■──Function for Loading a Chain of LLM Checkers from a Configuration Dictionary.____ ── Topic: 3
│ │ │ │ └─Documented class definitions for tools used in handling API requests, including OpenSearchVectorSear
│ │ │ │ ├─Handling API requests using tools such as RequestsPostTool and OpenSearchVectorSearch____
│ │ │ │ │ ├─Python requests wrapper for making HTTP requests with various tools and methods____
│ │ │ │ │ │ ├─■──DeepInfra API token and text generation model wrapper____ ── Topic: 41
│ │ │ │ │ │ └─RequestsWrapper and BaseRequestsTool for making HTTP requests (POST, GET, PATCH, DELETE) to API endp
│ │ │ │ │ │ ├─■──Checking Validity of Template Strings with Input Variables and Formatter Mapping____ ── Topic: 14
│ │ │ │ │ │ └─■──Requests tools for making HTTP requests with Python____ ── Topic: 10
│ │ │ │ │ └─Code organization and positive feedback____
│ │ │ │ │ ├─Bing Search API Wrapper and Handler Classes____
│ │ │ │ │ │ ├─■──Langchain callback manager and codebase organization____ ── Topic: 2
│ │ │ │ │ │ └─■──Bing Search API Wrapper and SERP API Usage in Python____ ── Topic: 1
│ │ │ │ │ └─Handling iFixit devices with models and remote hardware____
│ │ │ │ │ ├─■──Loading iFixit repair guides and device wikis with transformer model inference.____ ── Topic: 0
│ │ │ │ │ └─■──Potential Issues with Modifying Input Dictionary in a Prompt Loading Function____ ── Topic: 9
│ │ │ │ └─Implementation and Usage of SearxSearchWrapper with Environment Variables and SSL Support____
│ │ │ │ ├─Python Libraries for API Wrappers and Search Engines____
│ │ │ │ │ ├─Python packages for integrating with search engines: SearxSearchWrapper and QdrantClient.____
│ │ │ │ │ │ ├─■──Implementation of Searx API Wrapper (SearxSearchWrapper) using Python's BaseModel with QdrantClient ── Topic: 33
│ │ │ │ │ │ └─■──Handling environment variables and dictionaries with get_from_dict_or_env function____ ── Topic: 72
│ │ │ │ │ └─Purpose and Issues with `print_text` Function in `langchain` Repository's `input.py` File____
│ │ │ │ │ ├─■──Printing Highlighted Text with Options in Python____ ── Topic: 51
│ │ │ │ │ └─■──Converting Python Objects to String Representation with Nested Structures and Joining on Newline Cha ── Topic: 66
│ │ │ │ └─GitbookLoader class and its methods____
│ │ │ │ ├─■──Handling newlines recursively in data structures using pandas____ ── Topic: 29
│ │ │ │ └─GitBookLoader class for loading web pages with options to load all or single pages____
│ │ │ │ ├─■──GitbookLoader class for loading single or multiple pages from GitBook with relative paths in the nav ── Topic: 28
│ │ │ │ └─■──Length-Based Example Selection and Text Length Calculation____ ── Topic: 57
│ │ │ └─Ngram overlap score using sentence_bleu and method1 smoothing function____
│ │ │ ├─Ngram overlap score using sentence_bleu method1 smoothing function and auto reweighting____
│ │ │ │ ├─■──Code structure and organization in langchain document loaders with support for parsing comma-separat ── Topic: 70
│ │ │ │ └─Ngram overlap score using sentence_bleu and method1 smoothing function with auto reweighting in nltk
│ │ │ │ ├─■──Compliments on well-structured and organized code in different classes and methods____ ── Topic: 65
│ │ │ │ └─■──Sentence BLEU score and ngram overlap computation with method1 smoothing function and auto reweighti ── Topic: 49
│ │ │ └─Model Definition and Experimentation with Datetime and UTCNow Attributes____
│ │ │ ├─■──Data Modeling with Time Zones in Python____ ── Topic: 91
│ │ │ └─■──Constitutional Principles and Tracing in Python____ ── Topic: 68
│ │ └─Text splitting for knowledge triple extraction____
│ │ ├─Text Splitting Toolkit____
│ │ │ ├─Text splitting interface and implementation____
│ │ │ │ ├─Python REPL Tool and AST Implementation____
│ │ │ │ │ ├─Python REPL Tool Implementation____
│ │ │ │ │ │ ├─SQL database metadata retrieval tool____
│ │ │ │ │ │ │ ├─■──Python function to concatenate cell information for AI and human usage____ ── Topic: 44
│ │ │ │ │ │ │ └─SQL database metadata tool for listing table schema and metadata____
│ │ │ │ │ │ │ ├─■──SQL database metadata extraction tool for specified tables____ ── Topic: 75
│ │ │ │ │ │ │ └─■──JSON and SQL database tools for listing and getting values____ ── Topic: 15
│ │ │ │ │ │ └─Python REPL Tool using AST and Coroutine____
│ │ │ │ │ │ ├─■──Tool implementation with direct function or coroutine input and error handling.____ ── Topic: 99
│ │ │ │ │ │ └─■──Python REPL Tool with AST and version validation____ ── Topic: 74
│ │ │ │ │ └─Implementing API wrappers for news, movie information, and weather using APIChain____
│ │ │ │ │ ├─Implementing APIs for News, Weather, and Movie Information in LangChain's Load Tools Module____
│ │ │ │ │ │ ├─■──Language model for reasoning about position and color attributes of objects in weather forecasting w ── Topic: 73
│ │ │ │ │ │ └─Implementing APIs for fetching news and movies using Python____
│ │ │ │ │ │ ├─■──well-structured and readable implementation of API initialization functions in load_tools.py for Too ── Topic: 85
│ │ │ │ │ │ └─■──Working with API authentication and chaining for news and movie information retrieval (using news_ap ── Topic: 100
│ │ │ │ │ └─■──Wolfram Alpha SDK querying using WolframAlphaQueryRun class and api_wrapper attribute____ ── Topic: 89
│ │ │ │ └─TextSplitting for Vector Storage with Overlapping Chunks____
│ │ │ │ ├─Python's StrictFormatter class and its check_unused_args method for formatting and validation of inp
│ │ │ │ │ ├─L2 distance search using ndarray in Python____
│ │ │ │ │ │ ├─■──L2 search for nearest neighbors with np.linalg.norm____ ── Topic: 32
│ │ │ │ │ │ └─■──Parsing and Organizing Notes with Hashing and Embeddings____ ── Topic: 67
│ │ │ │ │ └─Python Class for Strict Formatter with Check on Unused Args____
│ │ │ │ │ ├─Vector Store Toolkit and Deployment____
│ │ │ │ │ │ ├─■──Vector Store Toolkit and Deployment with OpenAI LLM____ ── Topic: 35
│ │ │ │ │ │ └─■──Working with AirbyteJSONLoader to load local Airbyte JSON files____ ── Topic: 47
│ │ │ │ │ └─Python Formatter class with check_unused_args method and strict validation____
│ │ │ │ │ ├─Python's StrictFormatter class and its check_unused_args method for validating unused and extra argu
│ │ │ │ │ │ ├─■──Finding TODO Tasks in Code Snippets____ ── Topic: 4
│ │ │ │ │ │ └─Python Formatter and StrictFormatter with check_unused_args method____
│ │ │ │ │ │ ├─■──Color Mapping Function for Prompt Inputs with Exclusions____ ── Topic: 88
│ │ │ │ │ │ └─■──Implementing strict checking of unused and extra keys in a subclass of formatter____ ── Topic: 48
│ │ │ │ │ └─Python module for loading and manipulating language chain data with verbosity control.____
│ │ │ │ │ ├─■──Python function for getting verbosity from language chaining with Azure OpenAI and difference from O ── Topic: 64
│ │ │ │ │ └─■──Purpose of functions in loading.py and csv toolkit of langchain repository____ ── Topic: 42
│ │ │ │ └─Text splitting using chunk size and overlap with various libraries and interfaces.____
│ │ │ │ ├─Text splitting and chunking with overlap and length functions____
│ │ │ │ │ ├─■──Developing and Maintaining Docker Compose Modules in Python____ ── Topic: 79
│ │ │ │ │ └─Text splitting and chunking using TextSplitter interface____
│ │ │ │ │ ├─Text Splitting Interface and Implementation____
│ │ │ │ │ │ ├─■──Text splitting using TokenTextSplitter class.____ ── Topic: 7
│ │ │ │ │ │ └─■──Document Loading and Splitting with Text Splitting and Callback Management.____ ── Topic: 84
│ │ │ │ │ └─■──Python code for initializing an agent with various optional arguments____ ── Topic: 18
│ │ │ │ └─Loading Google Docs from Google Drive using Credentials and Tokens with Python____
│ │ │ │ ├─Document Loading from Cloud Storage (GCS and S3) using BaseLoader Class____
│ │ │ │ │ ├─■──Online PDF loading and caching using SQLite and temporary directories____ ── Topic: 98
│ │ │ │ │ └─■──Loading documents from cloud storage using GCSFileLoader and S3FileLoader classes.____ ── Topic: 36
│ │ │ │ └─■──Google Drive Loader and Credentials for Loading Google Docs____ ── Topic: 86
│ │ │ └─StreamlitCallbackHandler for logging to streamlit in Python code____
│ │ │ ├─Streaming with LLMs and Callback Handlers____
│ │ │ │ ├─Networkx wrapper for entity graph operations with Redis caching.____
│ │ │ │ │ ├─NetworkX Entity Graph with Missing Tables and Callback Manager____
│ │ │ │ │ │ ├─■──Graph Index Creation and Operations using NetworkX Library in Python____ ── Topic: 58
│ │ │ │ │ │ └─■──NetworkxEntityGraph and entity graph operations.____ ── Topic: 20
│ │ │ │ │ └─Redis cache implementation in Python____
│ │ │ │ │ ├─■──Implementing a SQAlchemy-based cache system with missing and existing prompts for better performance ── Topic: 17
│ │ │ │ │ └─■──Implementation of a Redis cache as a backend in Python____ ── Topic: 39
│ │ │ │ └─Python Callback Handler for Streamlit Logging____
│ │ │ │ ├─■──Callback handlers for printing to standard output.____ ── Topic: 43
│ │ │ │ └─■──StreamlitCallbackHandler for logging prompts and actions to Streamlit____ ── Topic: 90
│ │ │ └─ZeroShotAgent class and observation prefix property in Python____
│ │ │ ├─Creating a JSON agent using a toolkit for zeroshot agent execution with format instructions and inpu
│ │ │ │ ├─■──Creating Pandas DataFrames using Agent Scratchpad and Python AST REPL Tool.____ ── Topic: 82
│ │ │ │ └─Creating a JSON agent with toolkit, format instructions, and prefix/suffix____
│ │ │ │ ├─■──SQL agent creation with SQLDatabaseToolkit, BaseLLM and BaseCallbackManager____ ── Topic: 11
│ │ │ │ └─■──Creating a JSON agent with OpenAPI toolkit and interacting with it using JSON tools____ ── Topic: 56
│ │ │ └─Classes for language model-driven decision making and use of "agent_scratchpad" in LLMChain prompts_
│ │ │ ├─■──Agent class and entity extraction using "agent_scratchpad" variable____ ── Topic: 38
│ │ │ └─■──Code for a text-based game-playing agent using self-ask-with-search approach in TextWorld environmen ── Topic: 102
│ │ └─Text Mapping for Approximate k-NN Search using nmslib in Python____
│ │ ├─Script Scoring with KNN Search____
│ │ │ ├─■──Document bulk-ingest function for embeddings in Elasticsearch index____ ── Topic: 23
│ │ │ └─■──Script Scoring Search with Cosine Similarity and k-Nearest Neighbors (k-NN) Algorithm____ ── Topic: 19
│ │ └─Default text mapping for Approximate k-NN Search in dense vector fields using NMSLIB engine____
│ │ ├─■──Default Mapping for Approximate k-NN Search using NMSLIB Engine____ ── Topic: 81
│ │ └─■──Elasticsearch indexing and scripting with default mappings and painless scripting____ ── Topic: 94
│ └─Tracing and Recording Runs with SharedTracer and TracerStack____
│ ├─Python classes ToolRun and ChainRun in schemas.py file with additional attributes and their purpose.
│ │ ├─■──Extracting information about ElementInViewPort instances in chainrun toolrun runs.____ ── Topic: 77
│ │ └─■──Purpose and attributes of the ChainRun class in schemas.py file____ ── Topic: 78
│ └─Tracing and thread-safe execution with SharedTracer Singleton class____
│ ├─■──Tracing Execution Order with BaseTracer in a Thread-Safe Manner____ ── Topic: 55
│ └─■──TracerStack and SharedTracer Implementation in Python____ ── Topic: 63
└─Python wrapper for OpenAI and Hugging Face language models____
├─Self-Hosted Hugging Face Instructor Embedding Models on Remote Hardware____
│ ├─HuggingFace and Sentence-Transformers Embeddings for Cohere____
│ │ ├─■──Output parsing using regular expressions and the BaseOutputParser class____ ── Topic: 54
│ │ └─NLP Embeddings using Hugging Face and Sentence Transformers____
│ │ ├─■──Neural Embeddings with Hugging Face and Cohere API____ ── Topic: 24
│ │ └─■──Loading sentence embedding model with sentence_transformers library.____ ── Topic: 27
│ └─Self-hosted HuggingFace pipeline API for running models on remote hardware____
│ ├─Self-hosted HuggingFace pipeline for remote GPU hardware inference with autolaunched instances on va
│ │ ├─■──Self-hosted HuggingFace pipeline for remote hardware with HuggingFace Transformers and AutoTokenizer ── Topic: 40
│ │ └─■──Self-hosted embeddings for sentence_transformers with remote hardware support.____ ── Topic: 22
│ └─■──Self-hosted embeddings for running custom embedding models on remote hardware____ ── Topic: 62
└─Python wrapper for OpenAI language model with API key authentication and model parameters configurat
├─OpenAI Language Model Wrapper Class with API Key Authentication and Model Parameters Configuration__
│ ├─■──StochasticAI Wrapper for Large Language Models with Environment Key Validation and PDF Partitioning_ ── Topic: 50
│ └─Integration of OpenAI Language Model with GooseAI class for Text Generation____
│ ├─■──OpenAI Chat Model Implementation____ ── Topic: 37
│ └─■──Python Wrapper for OpenAI Language Models____ ── Topic: 5
└─■──Anthropic Large Language Models and API Usage in AtlasDB Project Management____ ── Topic: 83
└─Calculation of Alexander polynomial for knots in Python and Mathematica.____
├─Alexander polynomial calculation using Mathematica process and knot routing algorithm with various p
│ ├─Calculation of Alexander polynomial for knots using Python and Mathematica representations____
│ │ ├─CellKnot object initialization and properties with sin, cos, linspace, phi, psi, theta, rotation, pe
│ │ │ ├─Mollweide projection and spherical coordinates____
│ │ │ │ ├─Rotation of Spheres using Rotation Matrices____
│ │ │ │ │ ├─■──Rotation of sphere to align given positions at the top____ ── Topic: 41
│ │ │ │ │ └─■──Rotation matrix computation and manipulation using iterable angles.____ ── Topic: 18
│ │ │ │ └─Mollweide projection and conversion of spherical coordinates____
│ │ │ │ ├─Mollweide projection and spherical coordinates conversion____
│ │ │ │ │ ├─■──Vector magnitude calculation, Mollweide projection, and well-written code in Python.____ ── Topic: 51
│ │ │ │ │ └─■──"Mollweide projection and spherical coordinate conversion"____ ── Topic: 30
│ │ │ │ └─■──Verbose printing function for Pyknotid counters.____ ── Topic: 10
│ │ │ └─CellKnot class and points folding____
│ │ │ ├─CellKnot and Knot Folding____
│ │ │ │ ├─■──Understanding the "cell_trefoil" function and the "aperiodic_trefoil" function for creating interpol ── Topic: 37
│ │ │ │ └─■──CellKnot class and related methods____ ── Topic: 33
│ │ │ └─3D geometric scaling with numpy and crossing signs____
│ │ │ ├─Geometric Transformation with Crossing Signs____
│ │ │ │ ├─■──Numpy arrays for creating and perturbing a simple link using sin and cos in Python code.____ ── Topic: 15
│ │ │ │ └─■──Geometric transformation with crossing signs and np array____ ── Topic: 3
│ │ │ └─■──3D point scaling helper functions in p4_3__1, p4_4__1, p4_5__1_false, p5_3__1 and p5_4__1.____ ── Topic: 47
│ │ └─Knot representations and calculation of Alexander polynomial using Python and Mathematica____
│ │ ├─Line Segment Open by Distance Generator____
│ │ │ ├─Issues with missing function definitions and potential bugs in serialisation functions.____
│ │ │ │ ├─■──JSON and polynomial serialisation with potential implementation issues____ ── Topic: 17
│ │ │ │ └─■──Issues with incomplete function for serialising Jones polynomials in Python____ ── Topic: 36
│ │ │ └─Line vectors open by distance fraction with seed and number of segments as parameters.____
│ │ │ ├─Line segment manipulation and generation____
│ │ │ │ ├─Line Segments and Open/Closed Loop Detection____
│ │ │ │ │ ├─■──Open and closed line segments generation with distance constraint.____ ── Topic: 5
│ │ │ │ │ └─■──Writing Mathematica code to file and running it using MathKernel____ ── Topic: 28
│ │ │ │ └─Loading and manipulating CSV files with Pandas and saving to JSON.____
│ │ │ │ ├─■──Writing and loading data in json format with numpy and handling file paths (filenotfounderror explan ── Topic: 14
│ │ │ │ └─■──Parsing CSV data using pandas in Python____ ── Topic: 19
│ │ │ └─Downloading Knots Database with Pyknotid Library.____
│ │ │ ├─Knots database download and management____
│ │ │ │ ├─■──Downloading Knots Database using Pyknotid Library____ ── Topic: 23
│ │ │ │ └─■──Deleting old versions of database files in specific format using Python.____ ── Topic: 44
│ │ │ └─■──Recursive file inclusion using fnmatch patterns in Python____ ── Topic: 43
│ │ └─Alexander polynomial computation using Mathematica for knot representations____
│ │ ├─Calculation of Alexander polynomial using Python and Mathematica code snippets.____
│ │ │ ├─MeshCollectionVisual class and associated methods for vertex colors and shading in mesh visualizatio
│ │ │ │ ├─Code Refactoring and Todo Tasks with Comments and Unit Tests____
│ │ │ │ │ ├─■──Classes and functionality for handling periodic boundary conditions in a 2D space.____ ── Topic: 39
│ │ │ │ │ └─■──Code Refactoring and Unit Testing____ ── Topic: 4
│ │ │ │ └─MeshCollectionVisual class and vertex colors in 3D mesh visualization.____
│ │ │ │ ├─Signal Smoothing with Window Functions____
│ │ │ │ │ ├─■──Testing vector intersection in a dp/dq region using do_vectors_intersect function and obtaining bool ── Topic: 20
│ │ │ │ │ └─signal smoothing with different windows and sizes____
│ │ │ │ │ ├─■──Signal Smoothing using Different Windows____ ── Topic: 49
│ │ │ │ │ └─■──Code organization and readability of periodic_vassiliev_degree_2 function in adherence with PEP 8 gu ── Topic: 26
│ │ │ │ └─MeshCollectionVisual class and related methods____
│ │ │ │ ├─■──MeshCollectionVisual class and its methods for mesh visualization and handling vertex colors and sha ── Topic: 9
│ │ │ │ └─■──Cell object for lines with periodic boundary conditions____ ── Topic: 45
│ │ │ └─Alexander polynomial calculation using Mathematica____
│ │ │ ├─Calculating the Alexander polynomial of knots using various representations____
│ │ │ │ ├─Gauss code conversion to crossing indices____
│ │ │ │ │ ├─Recommendations for the "mag" function implementation in periodic.py____
│ │ │ │ │ │ ├─■──Implementing vector magnitude using dot product in Pyknotid____ ── Topic: 8
│ │ │ │ │ │ └─■──Improving code with imports and using numpy.zeros instead of n.zeros.____ ── Topic: 50
│ │ │ │ │ └─■──Converting Gauss code to crossings in a crossing object____ ── Topic: 22
│ │ │ │ └─Calculation of Alexander polynomial using Mathematica for knot representations____
│ │ │ │ ├─Knot theory and Alexander polynomial calculation using Mathematica____
│ │ │ │ │ ├─■──BoundingBox class implementation in Python with numpy and axis manipulation____ ── Topic: 1
│ │ │ │ │ └─■──Calculation of Alexander polynomial for knot representations using Mathematica____ ── Topic: 0
│ │ │ │ └─3D sphere plotting with Mollweide projection using VisPy____
│ │ │ │ ├─■──"3D visualization of spherical data using VisPy and Mollweide projection"____ ── Topic: 2
│ │ │ │ └─■──Class definition of MeshCollectionVisual that creates a mesh by concatenating visuals' vertices, ind ── Topic: 12
│ │ │ └─Database objects matching invariants using Python code____
│ │ │ ├─Database objects and invariants in knot theory____
│ │ │ │ ├─■──"Database storage and manipulation of knots using Peewee and optimized Cython routines"____ ── Topic: 35
│ │ │ │ └─■──Database searching with invariants in Python____ ── Topic: 7
│ │ │ └─■──OpenKnot class for holding vertices of an open curve in spacecurves module____ ── Topic: 38
│ │ └─Parsing data files and visualizing with matplotlib and mayavi/vispy.____
│ │ ├─Code for drawing bounding boxes in 3D using VisPy____
│ │ │ ├─Plotting Lissajous Conformation with Vispy and Mayavi Toolkits____
│ │ │ │ ├─■──Plotting Lissajous conformations with plot_cell using Vispy and Mayavi toolkits____ ── Topic: 13
│ │ │ │ └─■──Understanding the plot_line() function in pyknotid's visualise.py and its 3D plotting toolkits (Maya ── Topic: 27
│ │ │ └─■──Bounding box visualization with VisPy____ ── Topic: 32
│ │ └─Analyzing knot type of curve in a data file using argparse parser and VisPy canvas plotting.____
│ │ ├─■──Plotting 2D projections with optional markers in Python using pyplot____ ── Topic: 48
│ │ └─Analysis of knot types in data files using argparse and vispy_canvas.____
│ │ ├─■──Working with VisPy Canvas and Scene objects____ ── Topic: 40
│ │ └─■──Parsing and analyzing knot types in data files using argparse____ ── Topic: 42
│ └─Alternative Periodic Vassiliev Function with Conway Notation (Degree 4, Z4 Coefficients) and Core Cr
│ ├─■──Calculation of writhing numbers using Arrow diagrams and Gauss codes____ ── Topic: 16
│ └─Alternative periodic Vassiliev function for Conway notation with z4 coefficients and related invaria
│ ├─■──Arnold's invariants and their calculation by transforming representation into an unknot____ ── Topic: 6
│ └─■──Alternative periodic Vassiliev function in pyknotid with Conway notation and Z4 coefficients____ ── Topic: 11
└─"Calculating higher order writhe integrals using numpy and cython"____
├─Calculation of Higher Order Writhe Integral with NumPy and Cython Implementation.____
│ ├─Calculation of higher order writhe integral using points and order contributions____
│ │ ├─■──higher_order_writhe_integral function implementation with numpy____ ── Topic: 29
│ │ └─■──Writhing matrix and coefficient calculations for points in 3-dimensional space____ ── Topic: 46
│ └─■──Calculation of Writhe and Average Crossing Number using Integral____ ── Topic: 21
└─■──Distance Quantity Calculation from Curve Integral____ ── Topic: 31
.
├─Updating Interpretations Graph with Nodes and Edges____
│ ├─Analysis of Python code implementing a graph data structure and functions to add nodes and edges, us
│ │ ├─■──Code Refactoring and Commenting, Handling None Values in Python Code____ ── Topic: 10
│ │ └─Code analysis and review of a function for adding edges and nodes to a graph, including checking for
│ │ ├─■──Positive aspects of a codebase with clear and descriptive function names and variable names.____ ── Topic: 4
│ │ └─■──Methods for adding edges to a graph with considerations for existing nodes and labels.____ ── Topic: 11
│ └─Updating nodes and edges in a graph with rule trace and atom trace, using Numba for optimization____
│ ├─Python functions for updating rule traces with graph attributes____
│ │ ├─■──Updating nodes with rules and traces in a converging system.____ ── Topic: 15
│ │ └─■──Interpretation of Graph Labels Using Numba in Python____ ── Topic: 5
│ └─analysis of profiling data for program optimization____
│ ├─■──Parsing YAML to create a list of rules____ ── Topic: 9
│ └─■──Parsing and Profiling Data from CSV files in Python____ ── Topic: 1
└─Python Object Getter Implementation for Fact Objects____
├─Python code structure and implementation in pyreason's numba_wrapper lib____
│ ├─Functions and objects in pyreason's numba_wrapper module that return nested functions and implement
│ │ ├─■──Function for getting time lower and upper bounds from a fact object____ ── Topic: 14
│ │ └─Higher-order functions in rule_type.py for accessing fields of a rule object in pyreason/pyreason/sc
│ │ ├─Python classes and object-oriented programming concepts with "Fact" class examples.____
│ │ │ ├─■──Nested function type checking with isinstance in Python____ ── Topic: 8
│ │ │ └─■──Class Fact and its attributes and methods.____ ── Topic: 7
│ │ └─Numba implementation functions for label and world types in PyReason____
│ │ ├─Higher-order functions for getting attributes of rules in PyReason's numba_types module____
│ │ │ ├─■──Code structure and naming conventions in pyreason's numba_wrapper/numpy_types directory____ ── Topic: 6
│ │ │ └─■──Implementation details of the `get_target_criteria` function and `unbox_rule` in rule_type.py____ ── Topic: 0
│ │ └─■──Implementation of interval methods in pyreason using numba_wrapper____ ── Topic: 2
│ └─Compliments on codebase functions for calculating minimum, maximum, average, and average lower using
│ ├─■──Implementing a reset function to update the bounds of an interval object in Python____ ── Topic: 18
│ └─■──Compliments on Function Names and Docstrings for Array Calculation Functions____ ── Topic: 13
└─Working with pyapi and objects in Python code____
├─Understanding a Python code snippet for unboxing facts with structmodels____
│ ├─■──Object Unboxing and Attribute Retrieval in Python with PyAPI____ ── Topic: 17
│ └─Numba code for creating and boxing a struct model instance____
│ ├─■──Code snippet for creating a struct proxy and boxing its components in Numba.____ ── Topic: 3
│ └─■──Python class RuleModel with labeled attributes and types____ ── Topic: 12
└─■──Functions for creating and boxing objects in a Python-C++ interface____ ── Topic: 16