fastembed 3.6.0

🍕 Features

Supports synchronous usage. No dependency on Tokio.
Uses @pykeio/ort for performant ONNX inference.
Uses @huggingface/tokenizers for fast encodings.
Supports batch embeddings 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

Text Embedding

Reranking

BAAI/bge-reranker-base

🚀 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

Generating Text Embeddings

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

Candidates Reranking

use fastembed::{TextRerank, RerankInitOptions, RerankerModel};

let model = TextRerank::try_new(RerankInitOptions {
    model_name: RerankerModel::BGERerankerBase,
    show_download_progress: true,
    ..Default::default()
})
.unwrap();

let documents = vec![
    "hi",
    "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear, is a bear species endemic to China.",
    "panda is animal",
    "i dont know",
    "kind of mammal",
];

// Rerank with the default batch size
let results = model.rerank("what is panda?", documents, true, None);
println!("Rerank result: {:?}", results);

Alternatively, raw .onnx files can be loaded through the UserDefinedEmbeddingModel struct (for "bring your own" text embedding models) using TextEmbedding::try_new_from_user_defined(...).

🚒 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