nnethercott/hannoy
Production-ready KV-backed HNSW implementation in Rust using LMDB
This helps developers efficiently search through very large datasets of vectors, like those used in recommendation systems or semantic search. It takes your high-dimensional vectors and assigns unique identifiers, then lets you quickly find the most similar vectors to any given query. Software engineers working with large-scale data systems will find this useful for building fast similarity search capabilities.
71 stars and 47,446 monthly downloads. Available on PyPI.
Use this if you need to perform fast similarity searches on massive datasets of high-dimensional vectors that are too large to fit into your computer's memory.
Not ideal if your application requires GPU-accelerated indexing for even faster data processing.
Stars
71
Forks
9
Language
Rust
License
MIT
Category
Last pushed
Mar 05, 2026
Monthly downloads
47,446
Commits (30d)
0
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