Show HN: Fastembed-rs – Rust library for generating vector embeddings, reranking

• Supports synchronous usage. No dependency on Tokio.

• Uses @pykeio/ortfor performant ONNX inference. - Uses @huggingface/tokenizersfor fast encodings.

• Python: fastembed - Go: fastembed-go - JavaScript: fastembed-js

Click to list models #

• DefaultBAAI/bge-small-en-v1.5BAAI/bge-base-en-v1.5BAAI/bge-large-en-v1.5BAAI/bge-small-zh-v1.5BAAI/bge-large-zh-v1.5BAAI/bge-m3sentence-transformers/all-MiniLM-L6-v2sentence-transformers/all-MiniLM-L12-v2sentence-transformers/all-mpnet-base-v2sentence-transformers/paraphrase-MiniLM-L12-v2sentence-transformers/paraphrase-multilingual-mpnet-base-v2****nomic-ai/nomic-embed-text-v1- pairs with** nomic-ai/nomic-embed-text-v1.5**nomic-embed-vision-v1.5

for image-to-text searchintfloat/multilingual-e5-smallintfloat/multilingual-e5-baseintfloat/multilingual-e5-largemixedbread-ai/mxbai-embed-large-v1Alibaba-NLP/gte-base-en-v1.5Alibaba-NLP/gte-large-en-v1.5lightonai/ModernBERT-embed-large- pairs with** Qdrant/clip-ViT-B-32-text**clip-ViT-B-32-vision

for image-to-text searchjinaai/jina-embeddings-v2-base-codejinaai/jina-embeddings-v2-base-engoogle/embeddinggemma-300m- requires** nomic-ai/nomic-embed-text-v2-moe**nomic-v2-moe

feature (candle backend)- requiresQwen/Qwen3-Embedding-0.6Bqwen3

feature (candle backend)- requiresQwen/Qwen3-Embedding-4Bqwen3

feature (candle backend)- requiresQwen/Qwen3-Embedding-8Bqwen3

feature (candle backend)- requiresQwen/Qwen3-VL-Embedding-2Bqwen3

feature (candle backend, multimodal viaQwen3VLEmbedding

)snowflake/snowflake-arctic-embed-xssnowflake/snowflake-arctic-embed-ssnowflake/snowflake-arctic-embed-msnowflake/snowflake-arctic-embed-m-longsnowflake/snowflake-arctic-embed-l

Quantized versions are also available for several models above (append Q

to the model enum variant, e.g., EmbeddingModel::BGESmallENV15Q

). EmbeddingGemma additionally ships a 4-bit build as EmbeddingModel::EmbeddingGemma300MQ4

.

Click to list models #

• Defaultprithivida/Splade_PP_en_v1****BAAI/bge-m3

Click to list models #

Click to list models #

To support the library, please donate to our primary upstream dependency, ort - The Rust wrapper for the ONNX runtime.

Run the following in your project directory:

cargo add fastembed

Or add the following line to your Cargo.toml:

[dependencies]
fastembed = "5"
use fastembed::{TextEmbedding, TextInitOptions, EmbeddingModel};

// With default options
let mut model = TextEmbedding::try_new(Default::default())?;

// With custom options
let mut model = TextEmbedding::try_new(
    TextInitOptions::new(EmbeddingModel::AllMiniLML6V2).with_show_download_progress(true).with_intra_threads(4),
)?;

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
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<SparseEmbedding> = model.embed(documents, None)?;
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
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.

Helpers in the similarity module score and rank the vectors

embed

returns, so a quick in-memory search needs no extra crate:

use fastembed::similarity::{cosine_similarity, top_k};

// `embeddings` is the Vec<Embedding> from model.embed(...)
let query = &embeddings[0];

// Score two vectors directly ([-1.0, 1.0], higher = closer)
let score = cosine_similarity(query, &embeddings[1]);

// Or rank the corpus: (index, score) pairs, best first
let hits = top_k(query, &embeddings, 5);
println!("Closest: {:?}", hits);

For larger corpora or persistence, push the vectors to a vector search engine (e.g. Qdrant) and query there.

Qwen3 embedding models are available behind the qwen3

feature flag (candle backend).

[dependencies]
fastembed = { version = "5", features = ["qwen3"] }
js
use candle_core::{DType, Device};
use fastembed::Qwen3TextEmbedding;

let device = Device::Cpu;
let model = Qwen3TextEmbedding::from_hf(
    "Qwen/Qwen3-Embedding-0.6B",
    &device,
    DType::F32,
    512,
)?;

// Text-only usage with the Qwen3-VL embedding checkpoint is also supported:
// let model = Qwen3TextEmbedding::from_hf("Qwen/Qwen3-VL-Embedding-2B", &device, DType::F32, 512)?;

let embeddings = model.embed(&["query: ...", "passage: ..."])?;
println!("Embeddings length: {}", embeddings.len());

For multimodal text/image usage with Qwen/Qwen3-VL-Embedding-2B

:

use candle_core::{DType, Device};
use fastembed::Qwen3VLEmbedding;

let device = Device::Cpu;
let model = Qwen3VLEmbedding::from_hf(
    "Qwen/Qwen3-VL-Embedding-2B",
    &device,
    DType::F32,
    2048,
)?;

let image_embeddings = model.embed_images(&["tests/assets/image_0.png", "tests/assets/image_1.png"])?;
let text_embeddings = model.embed_texts(&["query: blue cat", "query: red cat"])?;

println!("Image embeddings: {}", image_embeddings.len());
println!("Text embeddings: {}", text_embeddings.len());

The nomic-embed-text-v2-moe model is available behind the nomic-v2-moe

feature flag (candle backend). First general-purpose MoE embedding model with 100+ language support.

[dependencies]
fastembed = { version = "5", features = ["nomic-v2-moe"] }
js
use candle_core::{DType, Device};
use fastembed::NomicV2MoeTextEmbedding;

let device = Device::Cpu;
let model = NomicV2MoeTextEmbedding::from_hf(
    "nomic-ai/nomic-embed-text-v2-moe",
    &device,
    DType::F32,
    512,
)?;

let embeddings = model.embed(&["search_query: ...", "search_document: ..."])?;
println!("Embeddings length: {}", embeddings.len());

The BGE-M3 model produces dense, sparse, and ColBERT embeddings simultaneously in a single forward pass.

use fastembed::{Bgem3Embedding, Bgem3InitOptions, Bgem3Model};

// With default options
let mut model = Bgem3Embedding::try_new(Default::default())?;

// With custom options (supporting custom max length up to 8192 tokens)
let mut model = Bgem3Embedding::try_new(
    Bgem3InitOptions::new(Bgem3Model::BGEM3Q)
        .with_max_length(1024)
        .with_show_download_progress(true),
)?;

let documents = vec![
    "Hello, World!",
    "This is an example passage.",
    "fastembed-rs is licensed under Apache 2.0",
    "i dont know"
];

// Generate all three representations in a single forward pass
let output = model.embed(documents, None)?;

println!("Dense dimension: {}", output.dense[0].len()); // -> Dense dimension: 1024

let sparse_emb = &output.sparse[0];
println!("Sparse non-zero tokens: {}", sparse_emb.indices.len());

println!("ColBERT token count: {}", output.colbert[0].len());

Note

The default quantized model (BGEM3Q

) is optimized for CPUs; passing a GPU execution provider (like CUDA) will fail. For GPU inference or custom requirements, you can export your own custom model (FP32, FP16, or INT8) using the ONNX export script from hf gpahal/bge-m3-onnx-int8

and load it via try_new_from_path

.

Models download on first use and load from cache afterwards (no network needed at runtime once cached).

FASTEMBED_CACHE_DIR

— cache location (default:.fastembed_cache

). Equivalent toTextInitOptions::with_cache_dir

.HF_HOME

— if set, takes precedence over the above.HF_ENDPOINT

— Hugging Face mirror base URL, for restricted networks.

To run models on a GPU via DirectML on Windows, enable the directml

feature:

[dependencies]
fastembed = { version = "5", features = ["directml"] }

Then pass a DirectML execution provider when initializing a model:

use fastembed::{TextEmbedding, TextInitOptions, EmbeddingModel};
use ort::ep::DirectML;

let model = TextEmbedding::try_new(
    TextInitOptions::new(EmbeddingModel::AllMiniLML6V2)
        .with_execution_providers(vec![DirectML::default().into()]),
)?;

When DirectML is detected, fastembed automatically disables memory pattern optimization and parallel execution on the ONNX Runtime session, as required by the DirectML execution provider.