https://github.com/anush008/fastembed-rs
Rust library for vector embeddings and reranking. Inspired by qdrant/fastembed.
https://github.com/anush008/fastembed-rs
embeddings fastembed rag reranker reranking retrieval retrieval-augmented-generation vector-search
Last synced: 3 months ago
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Rust library for vector embeddings and reranking. Inspired by qdrant/fastembed.
- Host: GitHub
- URL: https://github.com/anush008/fastembed-rs
- Owner: Anush008
- License: apache-2.0
- Created: 2023-10-01T16:13:02.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2026-01-19T09:35:28.000Z (5 months ago)
- Last Synced: 2026-01-19T23:41:51.180Z (5 months ago)
- Topics: embeddings, fastembed, rag, reranker, reranking, retrieval, retrieval-augmented-generation, vector-search
- Language: Rust
- Homepage: https://docs.rs/fastembed
- Size: 684 KB
- Stars: 727
- Watchers: 5
- Forks: 102
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 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.
## 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
- [**BAAI/bge-base-en-v1.5**](https://huggingface.co/BAAI/bge-base-en-v1.5)
- [**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-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5)
- [**BAAI/bge-m3**](https://huggingface.co/BAAI/bge-m3)
- [**sentence-transformers/all-MiniLM-L6-v2**](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- [**sentence-transformers/all-MiniLM-L12-v2**](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)
- [**sentence-transformers/all-mpnet-base-v2**](https://huggingface.co/sentence-transformers/all-mpnet-base-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 `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)
- [**mixedbread-ai/mxbai-embed-large-v1**](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
- [**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)
- [**lightonai/ModernBERT-embed-large**](https://huggingface.co/lightonai/modernbert-embed-large)
- [**Qdrant/clip-ViT-B-32-text**](https://huggingface.co/Qdrant/clip-ViT-B-32-text) - pairs with `clip-ViT-B-32-vision` for image-to-text search
- [**jinaai/jina-embeddings-v2-base-code**](https://huggingface.co/jinaai/jina-embeddings-v2-base-code)
- [**google/embeddinggemma-300m**](https://huggingface.co/google/embeddinggemma-300m)
- [**snowflake/snowflake-arctic-embed-xs**](https://huggingface.co/snowflake/snowflake-arctic-embed-xs)
- [**snowflake/snowflake-arctic-embed-s**](https://huggingface.co/snowflake/snowflake-arctic-embed-s)
- [**snowflake/snowflake-arctic-embed-m**](https://huggingface.co/snowflake/snowflake-arctic-embed-m)
- [**snowflake/snowflake-arctic-embed-m-long**](https://huggingface.co/snowflake/snowflake-arctic-embed-m-long)
- [**snowflake/snowflake-arctic-embed-l**](https://huggingface.co/snowflake/snowflake-arctic-embed-l)
Quantized versions are also available for several models above (append `Q` to the model enum variant, e.g., `EmbeddingModel::BGESmallENV15Q`).
### Sparse Text Embedding
- [**prithivida/Splade_PP_en_v1**](https://huggingface.co/prithivida/Splade_PP_en_v1) - Default
- [**BAAI/bge-m3**](https://huggingface.co/BAAI/bge-m3)
### 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) - Default
- [**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)
## ✊ Support
To support the library, please donate to our primary upstream dependency, [`ort`](https://github.com/pykeio/ort?tab=readme-ov-file#-sponsor-ort) - The Rust wrapper for the ONNX runtime.
## Installation
Run the following in your project directory:
```bash
cargo add fastembed
```
Or add the following line to your Cargo.toml:
```toml
[dependencies]
fastembed = "5"
```
## Usage
### Text Embeddings
```rust
use fastembed::{TextEmbedding, InitOptions, EmbeddingModel};
// With default options
let mut model = TextEmbedding::try_new(Default::default())?;
// With custom options
let mut 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
```
### Sparse Text Embeddings
```rust
use fastembed::{SparseEmbedding, SparseInitOptions, SparseModel, SparseTextEmbedding};
// With default options
let mut model = SparseTextEmbedding::try_new(Default::default())?;
// With custom options
let mut model = SparseTextEmbedding::try_new(
SparseInitOptions::new(SparseModel::SPLADEPPV1).with_show_download_progress(true),
)?;
let documents = vec![
"passage: Hello, World!",
"query: Hello, World!",
"passage: This is an example passage.",
"fastembed-rs is licensed under Apache 2.0"
];
// Generate embeddings with the default batch size, 256
let embeddings: Vec = model.embed(documents, None)?;
```
### Image Embeddings
```rust
use fastembed::{ImageEmbedding, ImageInitOptions, ImageEmbeddingModel};
// With default options
let mut model = ImageEmbedding::try_new(Default::default())?;
// With custom options
let mut 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};
// With default options
let mut model = TextRerank::try_new(Default::default())?;
// With custom options
let mut 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, 256 and return document contents
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.
## LICENSE
[Apache 2.0](https://github.com/Anush008/fastembed-rs/blob/main/LICENSE)
