fastembed

FastEmbed-js ⚡️

Typescript/NodeJS implementation of @Qdrant/fastembed

🍕 Features

• Supports CommonJS and ESM.
• Uses @anush008/tokenizers multi-arch native bindings for @huggingface/tokenizers.
• Supports batch embedddings with generators.

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

🔍 Not looking for Javascript?

• Python 🐍: fastembed
• Rust 🦀: fastembed-rs
• Go 🐳: fastembed-go

🤖 Models

• BAAI/bge-base-en
• BAAI/bge-base-en-v1.5
• BAAI/bge-small-en
• BAAI/bge-small-en-v1.5 - Default
• BAAI/bge-base-zh-v1.5
• sentence-transformers/all-MiniLM-L6-v2
• intfloat/multilingual-e5-large

🚀 Installation

To install the FastEmbed library, npm works:

npm install fastembed

📖 Usage

import { EmbeddingModel, FlagEmbedding } from "fastembed";
// For CommonJS
// const { EmbeddingModel, FlagEmbedding } = require("fastembed)

const embeddingModel = await FlagEmbedding.init({
    model: EmbeddingModel.BGEBaseEN
});

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

const embeddings = embeddingModel.embed(documents, 2); //Optional batch size. Defaults to 256

for await (const batch of embeddings) {
    // batch is list of Float32 embeddings(number[][]) with length 2
    console.log(batch);
}

Supports passage and query embeddings for more accurate results

const embeddings = embeddingModel.passageEmbed(listOfLongTexts, 10); //Optional batch size. Defaults to 256

for await (const batch of embeddings) {
    // batch is list of Float32 passage embeddings(number[][]) with length 10
    console.log(batch);
}

const queryEmbeddings: number[] = await embeddingModel.queryEmbed(userQuery);
console.log(queryEmbeddings)

🚒 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

Why light?

• No hidden dependencies via Huggingface Transformers

Why accurate?

• Better than OpenAI Ada-002
• Top of the Embedding leaderboards e.g. MTEB

© LICENSE

MIT © 2023