Tinyhnsw – The Littlest Vector Database
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!
Tinyhnsw – The Littlest Vector Database launched on January 7, 2025 with 17 votes, #835 of 6,142 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Tinyhnsw is a pure Python implementation of HNSW (Hierarchical Navigable Small World), an approximate nearest neighbor search algorithm. Designed for developers wanting to understand vector search, it prioritizes readable code over performance. The project includes a tutorial explaining HNSW's motivation and implementation, plus working examples for text and image search using sentence transformers and CLIP. It's a learning-focused tool for anyone exploring vector databases and similarity search.
written from the facts on this page · September 2026
From the sources
build your own vector database -- the littlest hnsw - jbarrow/tinyhnsw
TinyHNSW is a tiny, simple vector database. It weighs in at a measly few hundred lines of code. It's built on a straightforward (but not fast) implementation of HNSW in Python with minimal dependencies. It has an associated set of tutorials that build up to understanding how HNSW works, and how you can build your own TinyHNSW. ⚠️ The vector database part of this works, but the tutorial is not yet complete. I will update the README checklist below as chapters and code are completed. ⚠️ tinyhnsw is NOT production-quality. Compared to literally any standard implementation, it's slow and probably buggy. If you want to use an Approximate Nearest Neighbor library in a real application, consider…from github.com
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