FastEmbed-js ⚡️

Typescript/NodeJS implementation of @Qdrant/fastembed

## 🍕 Features * Supports CommonJS and ESM. * Uses [@anush008/tokenizers](https://github.com/Anush008/tokenizers) multi-arch native bindings for [@huggingface/tokenizers](https://github.com/huggingface/tokenizers). * Supports batch embedddings with generators. The default model is Flag Embedding, which is top of the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. ## 🔍 Not looking for Javascript? - Python 🐍: [fastembed](https://github.com/qdrant/fastembed) - Rust 🦀: [fastembed-rs](https://github.com/Anush008/fastembed-rs) - Go 🐳: [fastembed-go](https://github.com/Anush008/fastembed-go) ## 🤖 Models - [**BAAI/bge-base-en**](https://huggingface.co/BAAI/bge-base-en) - [**BAAI/bge-base-en-v1.5**](https://huggingface.co/BAAI/bge-base-en-v1.5) - [**BAAI/bge-small-en**](https://huggingface.co/BAAI/bge-small-en) - [**BAAI/bge-small-en-v1.5**](https://huggingface.co/BAAI/bge-small-en-v1.5) - Default - [**BAAI/bge-base-zh-v1.5**](https://huggingface.co/BAAI/bge-base-zh-v1.5) - [**sentence-transformers/all-MiniLM-L6-v2**](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - [**intfloat/multilingual-e5-large**](https://huggingface.co/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 ### Why fast? 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 ### Why light? 1. No hidden dependencies via Huggingface Transformers ### Why accurate? 1. Better than OpenAI Ada-002 2. Top of the Embedding leaderboards e.g. [MTEB](https://huggingface.co/spaces/mteb/leaderboard) ## © LICENSE MIT © [2023](https://github.com/Anush008/fastembed-js/blob/main/LICENSE)