Crate embedcache 

EmbedCache - High-performance text embedding library with caching capabilities

This library provides functionality for generating text embeddings with various state-of-the-art models and caching the results for improved performance.

Features

• Multiple embedding models (BGE, MiniLM, Nomic, etc.)
• Modular text chunking strategies with extensible trait-based architecture
• SQLite-based caching
• Asynchronous operation

Installation

Add this to your Cargo.toml:

[dependencies]
embedcache = "0.1.0"

Examples

Using as a library

use embedcache::{FastEmbedder, Embedder};
use fastembed::{TextEmbedding, InitOptions, EmbeddingModel};

let model = TextEmbedding::try_new(InitOptions {
    model_name: EmbeddingModel::BGESmallENV15,
    show_download_progress: true,
    ..Default::default()
})?;

let embedder = FastEmbedder {
    options: InitOptions::new(EmbeddingModel::BGESmallENV15),
};

let texts = vec![
    "This is an example sentence.".to_string(),
    "Another example sentence for embedding.".to_string(),
];

let embeddings = embedder.embed(&texts).await?;

Implementing custom chunking strategies

use embedcache::{ContentChunker};
use async_trait::async_trait;

struct MyCustomChunker;

#[async_trait]
impl ContentChunker for MyCustomChunker {
    async fn chunk(&self, content: &str, size: usize) -> Vec<String> {
        // Your custom chunking logic here
        vec![content.to_string()]
    }
     
    fn name(&self) -> &str {
        "my-custom-chunker"
    }
}

Running the embedcache service

The library also includes a binary that can be run as a standalone service:

cargo install embedcache
embedcache

Re-exports

pub use crate::core::*;

Modules

core