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  5. HNSW
Retrieval & Searchaka Hierarchical Navigable Small World

HNSW

Hierarchical Navigable Small World — a graph-based approximate nearest neighbor algorithm that balances recall and speed, the default index type in most vector databases.

Ask Melo about this← all terms

HNSW builds a multi-layer graph where each node is a vector and edges connect nearby points. The top layers are sparse for fast long-range navigation; lower layers are dense for precise local search. At query time, the algorithm greedily traverses from top to bottom, narrowing in on the nearest neighbors. HNSW offers excellent recall-speed trade-offs and is the default ANN index in Pinecone, Weaviate, Qdrant, pgvector, and most other vector databases.

Related terms

Approximate Nearest NeighborNearest Neighbor SearchVector DatabaseIndexingParent Document RetrievalColBERT

Where HNSW comes up

  • Google TurboVec: How TurboQuant Compresses 10M Vectors from 31GB to 4GB (2026)
  • What Are Embeddings? Vector Search and Semantic AI Explained (2026 Guide)
  • Zvec: Alibaba's Open-Source In-Process Vector Database (2026)