PGVector Guide

PGVector Guide is a comprehensive learning application for pgvector, the PostgreSQL extension that enables vector similarity search directly within your relational database. This app covers everything from basic vector embedding concepts and installation, through advanced indexing strategies (HNSW and IVFFlat), similarity search operations, and building production-ready Retrieval-Augmented Generation (RAG) pipelines with PostgreSQL. Key topics include: - Vector embedding fundamentals and choosing the right embedding models - Installing and configuring pgvector on all platforms - Understanding and optimizing HNSW and IVFFlat vector indexes - Distance metrics: cosine, L2, and inner product search - Building RAG pipelines with pgvector as the retrieval backbone - Performance tuning: memory configuration, query optimization, and hardware sizing - Production deployment: high availability, security, scaling, and monitoring - Advanced topics: hybrid search, multi-tenant architectures, and cost optimization Whether you are a database administrator, backend developer, or AI engineer, this app provides the knowledge you need to build efficient, scalable vector search systems using the power of PostgreSQL.