FastEmbed-rs 🦀

Rust implementation of @Qdrant/fastembed

🍕 Features

Supports synchronous usage. No dependency on Tokio.
Uses @pykeio/ort for performant ONNX inference.
Uses @huggingface/tokenizers for fast encodings.
Supports batch embedddings generation with parallelism using @rayon-rs/rayon.

The default model is Flag Embedding, which is top of the MTEB leaderboard.

🔍 Not looking for Rust?

Python 🐍: fastembed
Go 🐳: fastembed-go
JavaScript 🌐: fastembed-js

🤖 Models

🚀 Installation

Run the following command in your project directory:

cargo add fastembed

Or add the following line to your Cargo.toml:

[dependencies]
fastembed = "3"

📖 Usage

use fastembed::{TextEmbedding, InitOptions, EmbeddingModel};

// With default InitOptions
let model = TextEmbedding::try_new(Default::default())?;

// With custom InitOptions
let model = TextEmbedding::try_new(InitOptions {
    model_name: EmbeddingModel::AllMiniLML6V2,
    show_download_progress: true,
    ..Default::default()
})?;

let documents = vec![
    "passage: Hello, World!",
    "query: Hello, World!",
    "passage: This is an example passage.",
    // You can leave out the prefix but it's recommended
    "fastembed-rs is licensed under Apache  2.0"
    ];

 // Generate embeddings with the default batch size, 256
 let embeddings = model.embed(documents, None)?;

 println!("Embeddings length: {}", embeddings.len()); // -> Embeddings length: 4
 println!("Embedding dimension: {}", embeddings[0].len()); // -> Embedding dimension: 384

🚒 Under the hood

Why fast?

It's important we justify the "fast" in FastEmbed. FastEmbed is fast because:

Quantized model weights
ONNX Runtime which allows for inference on CPU, GPU, and other dedicated runtimes

prattcmp/fastembed-rs