A fast HNSW implementation in Rust

In plain words

A Rust-based implementation of the HNSW (Hierarchical Navigable Small World) algorithm for fast similarity search. Built for developers who need efficient vector search capabilities in their applications, it offers high-performance approximate nearest neighbor search optimized for speed. The implementation prioritizes Rust's performance characteristics to deliver faster indexing and query times compared to alternative approaches. Available as open source on GitHub.

written from the facts on this page · September 2026

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  HNSW index for vector embeddings in approx 500 LOC2025 · github.com · ▲73

  Heirarchical Navigable Small Worlds. Contribute to dicroce/hnsw development by creating an account on GitHub.

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  Tinyhnsw – The Littlest Vector Database2025 · github.com · ▲17

  In an effort to understand it, I put together a simple, pure python implementation of HNSW, an approximate nearest neighbor library. Learned a lot, and I think for anyone interested in vector search it's an exercise that's absolutely worth doing. The code is optimized (imo) for readability, and working (albeit, quite slowly) on putting together a tutorial that walks through the motivation and implementation of HNSW. There's also working code examples for using the library for text and image search with sentence transformers and CLIP!

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  I recently found myself computing the similarity between lots of very high dimensional vectors (i.e., sentence embedding vectors from LLMs), and I wanted to try some more powerful measures of similarity/dependency than just Cosine similarity, which seems to be the default for everything nowadays because of its computational efficiency. There are many other more involved measures that can detect more subtle relationships, but the problem is that some of them are quite slow to compute, especially if you're trying to do it in Python. For my favorite measure of statistical dependency,…

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