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HNSW Algorithm

Hierarchical Navigable Small World: the leading approximate-nearest-neighbor index used inside almost every modern vector database for fast similarity search at scale.

What Is HNSW Algorithm?

Pinecone, Qdrant, Weaviate, pgvector (with hnsw extension), and Milvus all default to HNSW. The index is a multi-layer graph: top layers are sparse for coarse search, bottom layers are dense for refinement. Trade-off: faster + more memory than IVFFlat, slower index build than flat. For most RAG workloads above 100k vectors, HNSW is the right default.

How Groovy Web Uses This

We default to HNSW for client RAG systems with 100k-50M vectors. Tune ef and m parameters per recall target during benchmarking.

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