## 🍕 Features
- Supports synchronous usage. No dependency on Tokio.
- Uses [@pykeio/ort](https://github.com/pykeio/ort) for performant ONNX inference.
- Uses [@huggingface/tokenizers](https://github.com/huggingface/tokenizers) for fast encodings.
- Supports batch embeddings generation with parallelism using [@rayon-rs/rayon](https://github.com/rayon-rs/rayon).
The default model is Flag Embedding, which is top of the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard.
## 🔍 Not looking for Rust?
- Python 🐍: [fastembed](https://github.com/qdrant/fastembed)
- Go 🐳: [fastembed-go](https://github.com/Anush008/fastembed-go)
- JavaScript 🌐: [fastembed-js](https://github.com/Anush008/fastembed-js)
## 🤖 Models
### Text Embedding
- [**BAAI/bge-small-en-v1.5**](https://huggingface.co/BAAI/bge-small-en-v1.5) - Default
- [**sentence-transformers/all-MiniLM-L6-v2**](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- [**mixedbread-ai/mxbai-embed-large-v1**](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
- [**Qdrant/clip-ViT-B-32-text**](https://huggingface.co/Qdrant/clip-ViT-B-32-text) - pairs with the image model clip-ViT-B-32-vision for image-to-text search
Click to see full List
- [**BAAI/bge-large-en-v1.5**](https://huggingface.co/BAAI/bge-large-en-v1.5)
- [**BAAI/bge-small-zh-v1.5**](https://huggingface.co/BAAI/bge-small-zh-v1.5)
- [**BAAI/bge-base-en-v1.5**](https://huggingface.co/BAAI/bge-base-en-v1.5)
- [**sentence-transformers/all-MiniLM-L12-v2**](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)
- [**sentence-transformers/paraphrase-MiniLM-L12-v2**](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2)
- [**sentence-transformers/paraphrase-multilingual-mpnet-base-v2**](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- [**nomic-ai/nomic-embed-text-v1**](https://huggingface.co/nomic-ai/nomic-embed-text-v1)
- [**nomic-ai/nomic-embed-text-v1.5**](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) - pairs with the image model nomic-embed-vision-v1.5 for image-to-text search
- [**intfloat/multilingual-e5-small**](https://huggingface.co/intfloat/multilingual-e5-small)
- [**intfloat/multilingual-e5-base**](https://huggingface.co/intfloat/multilingual-e5-base)
- [**intfloat/multilingual-e5-large**](https://huggingface.co/intfloat/multilingual-e5-large)
- [**Alibaba-NLP/gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)
- [**Alibaba-NLP/gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5)
### Sparse Text Embedding
- [**prithivida/Splade_PP_en_v1**](https://huggingface.co/prithivida/Splade_PP_en_v1) - Default
### Image Embedding
- [**Qdrant/clip-ViT-B-32-vision**](https://huggingface.co/Qdrant/clip-ViT-B-32-vision) - Default
- [**Qdrant/resnet50-onnx**](https://huggingface.co/Qdrant/resnet50-onnx)
- [**Qdrant/Unicom-ViT-B-16**](https://huggingface.co/Qdrant/Unicom-ViT-B-16)
- [**Qdrant/Unicom-ViT-B-32**](https://huggingface.co/Qdrant/Unicom-ViT-B-32)
- [**nomic-ai/nomic-embed-vision-v1.5**](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5)
### Reranking
- [**BAAI/bge-reranker-base**](https://huggingface.co/BAAI/bge-reranker-base)
- [**BAAI/bge-reranker-v2-m3**](https://huggingface.co/BAAI/bge-reranker-v2-m3)
- [**jinaai/jina-reranker-v1-turbo-en**](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
- [**jinaai/jina-reranker-v2-base-multiligual**](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual)
## 🚀 Installation
Run the following command in your project directory:
```bash
cargo add fastembed
```
Or add the following line to your Cargo.toml:
```toml
[dependencies]
fastembed = "3"
```
## 📖 Usage
### Text Embeddings
```rust
use fastembed::{TextEmbedding, InitOptions, EmbeddingModel};
// With default InitOptions
let model = TextEmbedding::try_new(Default::default())?;
// With custom InitOptions
let model = TextEmbedding::try_new(
InitOptions::new(EmbeddingModel::AllMiniLML6V2).with_show_download_progress(true),
)?;
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
```
### Image Embeddings
```rust
use fastembed::{ImageEmbedding, ImageInitOptions, ImageEmbeddingModel};
// With default InitOptions
let model = ImageEmbedding::try_new(Default::default())?;
// With custom InitOptions
let model = ImageEmbedding::try_new(
ImageInitOptions::new(ImageEmbeddingModel::ClipVitB32).with_show_download_progress(true),
)?;
let images = vec!["assets/image_0.png", "assets/image_1.png"];
// Generate embeddings with the default batch size, 256
let embeddings = model.embed(images, None)?;
println!("Embeddings length: {}", embeddings.len()); // -> Embeddings length: 2
println!("Embedding dimension: {}", embeddings[0].len()); // -> Embedding dimension: 512
```
### Candidates Reranking
```rust
use fastembed::{TextRerank, RerankInitOptions, RerankerModel};
let model = TextRerank::try_new(
RerankInitOptions::new(RerankerModel::BGERerankerBase).with_show_download_progress(true),
)?;
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, local model files can be used for inference via the `try_new_from_user_defined(...)` methods of respective structs.
## ✊ Support
To support the library, please consider donating to our primary upstream dependency, [`ort`](https://github.com/pykeio/ort?tab=readme-ov-file#-sponsor-ort) - The Rust wrapper for the ONNX runtime.
## ⚙️ Under the hood
It's important we justify the "fast" in FastEmbed. FastEmbed is fast because of:
1. Quantized model weights.
2. ONNX Runtime which allows for inference on CPU, GPU, and other dedicated runtimes.
3. No hidden dependencies via Huggingface Transformers.
## 📄 LICENSE
Apache 2.0 © [2024](https://github.com/Anush008/fastembed-rs/blob/main/LICENSE)
