Understanding vector search and HNSW index with pgvector

Vector search and embeddings are crucial for generative AI applications, enabling models to find semantically similar texts. The pgvector extension for PostgreSQL facilitates vector similarity searches. The latest version introduces the Hierarchical Navigable Small Worlds (HNSW) index to perform approximate nearest neighbor searches, improving query speed for large datasets. This guide explains vectors, vector embeddings, various distance metrics, and how to use pgvector and the HNSW index for efficient and scalable AI applications.