pgvector for Postgres

"pgvector for Postgres: Practical Vector Search Without a New Database"
Vector search no longer has to mean introducing a separate datastore, duplicating operational effort, and compromising transactional simplicity. This book is written for experienced PostgreSQL practitioners, backend engineers, data infrastructure teams, and technical leaders who want to add modern embedding retrieval directly inside Postgres with rigor rather than hype. It treats pgvector not as a novelty feature, but as a serious production capability that must be understood in terms of correctness, planner behavior, indexing tradeoffs, and lifecycle management.
Across the book, readers will learn how pgvector fits into PostgreSQL’s relational model, how to design embedding schemas and choose distance semantics, and how to build reliable SQL retrieval patterns that survive real-world filtering and joins. The book then goes deep on exact search, HNSW, and IVFFlat, showing how to choose between them, tune them against measurable recall baselines, and interpret execution plans with confidence. It also develops a disciplined benchmarking methodology so performance decisions are based on evidence, not default settings or vendor fashion.
A key differentiator is its PostgreSQL-first perspective: the book focuses on extension installation, version-sensitive guidance, operational maintenance, safe upgrades, and scaling with replicas or sharding while preserving the “no new database” promise. Readers should already be comfortable with SQL and core Postgres operations; in return, they will gain a practical, de