fastembed-rs and hypembed
The two Rust libraries are competitors, as both aim to provide local generation of text embeddings without external ML runtimes, with `hypembed` specifically emphasizing BERT compatibility and `fastembed-rs` focusing on broader vector embedding and reranking capabilities.
About fastembed-rs
Anush008/fastembed-rs
Rust library for vector embeddings and reranking.
Performs inference using ONNX Runtime via the `ort` crate and HuggingFace tokenizers for fast token encoding, enabling synchronous (non-async) embedding generation without external dependencies. Supports 25+ pre-trained text embedding models from BAAI, Sentence Transformers, and others, plus sparse embeddings, image embeddings, and reranking—with quantized variants and optional Candle backend support for advanced models like Qwen3.
About hypembed
minniwoodsy325/hypembed
Generate BERT-compatible text embeddings locally in Rust without Python or external ML runtimes using a simple, efficient library.
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