RAG Applications with Spring AI and PostgreSQL: Build Document Search and Question-Answering Systems with pgvector, Hybrid Retrieval, Reranking, Citat
Build Production-Ready RAG Applications with Spring AI, PostgreSQL, and pgvector
Go beyond basic AI chatbots and master the engineering of reliable, secure, and scalable retrieval-augmented generation (RAG) systems.
RAG Applications with Spring AI and PostgreSQL provides a practical approach to building intelligent document search and question-answering applications using Java 21, Spring Boot 4, Spring AI 2, PostgreSQL, and pgvector.
Through AtlasRAG, a realistic enterprise knowledge-search project, you'll explore how to design, implement, evaluate, and operate RAG applications.
What You'll LearnBuild RAG applications with Spring AI and Spring Boot.
Implement vector similarity search with PostgreSQL and pgvector.
Design reliable document ingestion, chunking, and embedding pipelines.
Combine semantic and full-text search using hybrid retrieval and reranking.
Generate evidence-grounded answers with trustworthy citations.
Evaluate retrieval accuracy, answer quality, and system performance.
Implement tenant isolation, access controls, and prompt-injection defenses.
Optimize caching, observability, scalability, and deployment.
Test production workflows using Docker, Testcontainers, and CI pipelines.
Who This Book Is ForIdeal for Java developers, Spring Boot engineers, AI application developers, backend architects, and technical professionals building enterprise AI solutions.
With practical Java examples, engineering exercises, and production-focused scenarios, this book takes you beyond RAG demonstrations toward dependable real-world applications.
Build smarter retrieval systems. Deliver trustworthy answers. Engineer RAG for production.
The Practical Spring AI Development Series - Book 2
Liora Byteforge
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