Back to directory

FastEmbed-rs

FastEmbed-rs is a Rust library for generating text and image vector embeddings and for reranking search results locally, using ONNX Runtime for inference and Hugging Face tokenizers for encoding, with synchronous use and no dependency on Tokio.

FastEmbed-rs

FastEmbed-rs is a Rust library for generating vector embeddings and reranking search results locally. It supports synchronous usage with no dependency on Tokio, uses the ort crate for ONNX inference, and uses Hugging Face's tokenizers crate for fast text encoding.

It ships a wide set of text embedding models by default, including several BAAI bge sizes, sentence-transformers models, multilingual e5 models and Qwen3 embedding models, some with quantized variants, plus sparse text embedding models such as Splade. Equivalent libraries are maintained separately for Python, Go and JavaScript for developers not working in Rust.

More in RAG

See all 540
  • ai·rete·rag

    ai-rete-rag.com

    A platform that pairs a Rete rule engine with retrieval-augmented generation, so deterministic rules decide an outcome and grounded retrieval explains the decision in plain language.

  • Xtriever

    github.com

    A hybrid retrieval engine for retrieval-augmented generation, written in Rust to run on-device on a phone or a laptop, combining lexical BM25 search, dense embedding search, reciprocal-rank fusion and cross-encoder re-ranking with no server.

  • FileFlux

    github.com

    A .NET library that turns PDF, DOCX, HWP and other documents into optimized chunks for RAG systems, through a five-stage pipeline built on high-performance Rust FFI readers.