🍕 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:

``bash
npm install fastembed
`

📖 Usage

`js
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
`ts
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

$3

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

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

$3

1. No hidden dependencies via Huggingface Transformers

$3

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

© LICENSE

MIT © 2023