Getting Started with HNSWlib

This post introduces the Hierarchical Navigable Small Worlds (HNSW) algorithm and its implementation in the HNSWlib library. It explains the core concepts of HNSW, including its hierarchical layers and navigable small worlds structure, and details how HNSWlib provides efficient Approximate Nearest Neighbor (ANN) search capabilities. The post includes a step-by-step guide on setting up HNSWlib, preparing data, building an index with key parameters (`space`, `dim`, `max_elements`, `ef_construction`, `M`, `ef`), and performing nearest neighbor searches using `knn_query`. Finally, it compares HNSWlib with purpose-built vector databases like Milvus, noting HNSWlib's suitability for prototyping and medium-scale datasets versus Milvus's advantages for larger, enterprise-level requirements.