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PostgreSQL 17 and pgvector in Action: Building Hybrid AI Retrieval Systems
PostgreSQL 17 is the latest evolution of the world-renowned open-source relational database system, delivering advanced performance, reliability, and extensibility. With the introduction of pgvector, PostgreSQL can now handle high-dimensional vector data, enabling semantic search, AI-driven retrieval, and hybrid systems that combine traditional structured queries with modern AI embeddings. This combination allows developers to integrate LLM-powered features directly into robust, transactional databases without needing separate vector-only solutions.
This book provides a comprehensive, hands-on guide to building hybrid AI retrieval systems using PostgreSQL 17 and pgvector. You will learn how to design efficient data schemas, store and query vector embeddings, implement similarity and hybrid searches, and integrate AI models for retrieval-augmented generation (RAG). Real-world examples such as semantic search engines, chatbots, and recommendation systems illustrate practical applications, giving you the skills to implement scalable, production-ready systems.
What you'll learn
-Detailed explanations of vector data types, distance metrics, and indexing strategies like IVFFlat and HNSW.
-Step-by-step guidance on generating embeddings from text, code, and images using Python and OpenAI APIs.
-Techniques for combining full-text and vector search for hybrid retrieval scenarios.
-Methods for storing metadata and context to optimize search accuracy and performance.
-Best practices for scaling, monitoring, replication, and deployment in cloud environments.
-Advanced topics including hardware acceleration, multimodal retrieval, security, and cost optimization.
Target Reader
This book is designed for developers, data engineers, AI practitioners, and system architects with a basic understanding of databases and programming. It is suitable for those who want to bridge the gap between relational database management and modern AI retrieval systems. No prior experience with pgvector is required, but familiarity with SQL, Python, and AI embeddings will enhance your learning experience.
Transform your data systems into intelligent retrieval engines. Master PostgreSQL 17 and pgvector to implement hybrid AI solutions that combine the best of structured querying and semantic understanding. Whether you’re building chatbots, recommendation engines, or knowledge search systems, this book equips you with the tools and expertise to innovate confidently and deploy production-ready AI solutions today.
This book provides a comprehensive, hands-on guide to building hybrid AI retrieval systems using PostgreSQL 17 and pgvector. You will learn how to design efficient data schemas, store and query vector embeddings, implement similarity and hybrid searches, and integrate AI models for retrieval-augmented generation (RAG). Real-world examples such as semantic search engines, chatbots, and recommendation systems illustrate practical applications, giving you the skills to implement scalable, production-ready systems.
What you'll learn
-Detailed explanations of vector data types, distance metrics, and indexing strategies like IVFFlat and HNSW.
-Step-by-step guidance on generating embeddings from text, code, and images using Python and OpenAI APIs.
-Techniques for combining full-text and vector search for hybrid retrieval scenarios.
-Methods for storing metadata and context to optimize search accuracy and performance.
-Best practices for scaling, monitoring, replication, and deployment in cloud environments.
-Advanced topics including hardware acceleration, multimodal retrieval, security, and cost optimization.
Target Reader
This book is designed for developers, data engineers, AI practitioners, and system architects with a basic understanding of databases and programming. It is suitable for those who want to bridge the gap between relational database management and modern AI retrieval systems. No prior experience with pgvector is required, but familiarity with SQL, Python, and AI embeddings will enhance your learning experience.
Transform your data systems into intelligent retrieval engines. Master PostgreSQL 17 and pgvector to implement hybrid AI solutions that combine the best of structured querying and semantic understanding. Whether you’re building chatbots, recommendation engines, or knowledge search systems, this book equips you with the tools and expertise to innovate confidently and deploy production-ready AI solutions today.
227 pages, Kindle Edition
Published October 19, 2025
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