HNSW index for vector embeddings in approx 500 LOC

In plain words

This is a lightweight implementation of the HNSW (Hierarchical Navigable Small World) algorithm for indexing vector embeddings, written in approximately 500 lines of code. It is designed for developers who need fast approximate nearest neighbor search without heavy dependencies. The compact codebase makes it suitable for projects where minimal footprint and straightforward implementation are priorities.

written from the facts on this page · September 2026

From the sources

// Level 2 * * * * // Level 1 * * * * * * ** * * // Level 0 * ** * * * * * ** * * * * ** ** ** ** * * * * * * * HNSW is a graph structure that consists of levels that are more sparsely populated at the top and more densely populated at the bottom. Nodes within a layer have connections to other nodes that are near them on the same level. When a node is inserted a random level is picked and the node is inserted there. It is also inserted into all levels beneath that level down to 0. When searches arrive they start at the top and search that level (following connections) until they find the closest node in the top level. The search then descends and keeps searching nearby nodes. As the search…from github.com

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