https://github.com/Anush008/fastembed-js
Library to generate vector embeddings in NodeJS
https://github.com/Anush008/fastembed-js
embeddings rag retrieval retrieval-augmented-generation vector-search
Last synced: 3 months ago
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Library to generate vector embeddings in NodeJS
- Host: GitHub
- URL: https://github.com/Anush008/fastembed-js
- Owner: Anush008
- License: mit
- Created: 2023-09-17T07:00:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-17T17:27:48.000Z (7 months ago)
- Last Synced: 2025-02-26T05:54:46.324Z (3 months ago)
- Topics: embeddings, rag, retrieval, retrieval-augmented-generation, vector-search
- Language: TypeScript
- Homepage: https://www.npmjs.com/package/fastembed/
- Size: 1.09 MB
- Stars: 105
- Watchers: 4
- Forks: 7
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 🍕 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)
