hnsw

**HNSW (Hierarchical Navigable Small World)** is an **approximate nearest neighbor algorithm optimized for high-dimensional vector search** — providing sub-millisecond query times on millions of vectors through a multi-layer graph structure, making it the foundation of modern vector databases. **What Is HNSW?** - **Type**: Approximate nearest neighbor (ANN) search algorithm. - **Structure**: Multi-layer graph with skip-list-like hierarchy. - **Speed**: Sub-millisecond queries on millions of vectors. - **Accuracy**: 95-99% recall with proper tuning. - **Usage**: Core algorithm in Qdrant, Milvus, Pinecone, FAISS. **Why HNSW Matters** - **Speed**: 100-1000× faster than brute-force search. - **Scalability**: Handles billions of vectors efficiently. - **Accuracy**: High recall rates for production use. - **Memory-Efficient**: Optimized graph structure. - **Industry Standard**: Used by all major vector databases. **How It Works** 1. **Build Phase**: Insert vectors into multi-layer graph. 2. **Layers**: Top layers have few nodes (long jumps), bottom layers dense (fine search). 3. **Search**: Start at top layer, greedily descend to find nearest neighbors. 4. **Result**: Fast approximate nearest neighbors with tunable accuracy. **Key Parameters** - **M**: Number of connections per node (higher = more accurate, slower). - **ef_construction**: Build-time search depth. - **ef_search**: Query-time search depth. HNSW is the **backbone of semantic search** — enabling real-time similarity search at scale.