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Vector Search in PostgreSQL 18 : A Practical Guide to Building Semantic Search and AI Applications in PostgreSQL
Bring Artificial Intelligence directly into your database.
Unlock the power of PostgreSQL 18 and transform it into a next-generation AI engine for semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG).
What This Book Allows You to
This hands-on guide shows you how to build intelligent applications that understand meaning, not just keywords. With step-by-step examples, you’ll learn how to store and search embeddings using the pgvector extension, create powerful HNSW and IVFFlat indexes, and integrate AI models directly into your PostgreSQL workflow. Whether you want to build a “Chat with Your Docs” bot, a semantic search engine, or enhance your analytics pipeline, this book equips you to do it, all inside PostgreSQL.
About the
PostgreSQL 18 introduces new possibilities for AI integration. The pgvector extension brings vector embeddings, semantic similarity search, and machine learning–ready storage into one unified system. Instead of maintaining separate databases for your app and your AI data, pgvector lets you keep everything in a single, ACID-compliant environment, simplifying architecture and improving performance. It’s the future of database-driven AI.
Book
Vector Search in PostgreSQL 18 bridges the gap between traditional relational databases and modern AI-driven applications. You’ll begin with foundational concepts, how embeddings work, what vector search means, and why pgvector changes everything. Then, you’ll dive into real-world projects, from building a semantic search engine to creating a full RAG chatbot that connects PostgreSQL with cutting-edge LLMs.
Every chapter is structured around practical implementation. You’ll install, configure, and optimize PostgreSQL for high-speed vector operations, explore distance metrics (L2, Cosine, and Inner Product), and learn performance-tuning techniques to achieve millisecond-level search speeds, even on massive datasets.
What’s Inside This
Step-by-step setup for PostgreSQL 18 and pgvector Hands-on Semantic Search Engine & RAG Bot Deep dives into HNSW and IVFFlat indexing Practical SQL, Python, and FastAPI examples Query optimization and benchmarking tips Hybrid keyword + vector search methods Strategies for scaling vector workloads in production About the
This book is written for developers, data scientists, and engineers who want to bring AI capabilities into their existing PostgreSQL environment. You don’t need to be an AI expert, just familiar with SQL and Python. Whether you’re a backend engineer exploring embeddings or a data professional deploying RAG systems, you’ll gain skills you can apply immediately.
Step into the future of intelligent data engineering.
Master pgvector and PostgreSQL 18 to build smarter, faster, and AI-powered applications today.
Grab your copy of Vector Search in PostgreSQL 18 and start building the next generation of intelligent software.
Unlock the power of PostgreSQL 18 and transform it into a next-generation AI engine for semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG).
What This Book Allows You to
This hands-on guide shows you how to build intelligent applications that understand meaning, not just keywords. With step-by-step examples, you’ll learn how to store and search embeddings using the pgvector extension, create powerful HNSW and IVFFlat indexes, and integrate AI models directly into your PostgreSQL workflow. Whether you want to build a “Chat with Your Docs” bot, a semantic search engine, or enhance your analytics pipeline, this book equips you to do it, all inside PostgreSQL.
About the
PostgreSQL 18 introduces new possibilities for AI integration. The pgvector extension brings vector embeddings, semantic similarity search, and machine learning–ready storage into one unified system. Instead of maintaining separate databases for your app and your AI data, pgvector lets you keep everything in a single, ACID-compliant environment, simplifying architecture and improving performance. It’s the future of database-driven AI.
Book
Vector Search in PostgreSQL 18 bridges the gap between traditional relational databases and modern AI-driven applications. You’ll begin with foundational concepts, how embeddings work, what vector search means, and why pgvector changes everything. Then, you’ll dive into real-world projects, from building a semantic search engine to creating a full RAG chatbot that connects PostgreSQL with cutting-edge LLMs.
Every chapter is structured around practical implementation. You’ll install, configure, and optimize PostgreSQL for high-speed vector operations, explore distance metrics (L2, Cosine, and Inner Product), and learn performance-tuning techniques to achieve millisecond-level search speeds, even on massive datasets.
What’s Inside This
Step-by-step setup for PostgreSQL 18 and pgvector Hands-on Semantic Search Engine & RAG Bot Deep dives into HNSW and IVFFlat indexing Practical SQL, Python, and FastAPI examples Query optimization and benchmarking tips Hybrid keyword + vector search methods Strategies for scaling vector workloads in production About the
This book is written for developers, data scientists, and engineers who want to bring AI capabilities into their existing PostgreSQL environment. You don’t need to be an AI expert, just familiar with SQL and Python. Whether you’re a backend engineer exploring embeddings or a data professional deploying RAG systems, you’ll gain skills you can apply immediately.
Step into the future of intelligent data engineering.
Master pgvector and PostgreSQL 18 to build smarter, faster, and AI-powered applications today.
Grab your copy of Vector Search in PostgreSQL 18 and start building the next generation of intelligent software.
220 pages, Kindle Edition
Published November 6, 2025
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