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portada Production-Ready Spring AI with Spring Boot: Build RAG Applications with PostgreSQL, pgvector, Tool Calling, Testing, Guardrails, and Deployment (The Practical Spring AI Development Series)
Type
Physical Book
Publisher
Author
Language
English
Pages
342
Format
Paperback
ISBN13
9798178763704

Production-Ready Spring AI with Spring Boot: Build RAG Applications with PostgreSQL, pgvector, Tool Calling, Testing, Guardrails, and Deployment (The Practical Spring AI Development Series)

Byteforge, Liora (Author) · Independently published · Paperback

Production-Ready Spring AI with Spring Boot: Build RAG Applications with PostgreSQL, pgvector, Tool Calling, Testing, Guardrails, and Deployment (The Practical Spring AI Development Series) - Byteforge, Liora

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Synopsis "Production-Ready Spring AI with Spring Boot: Build RAG Applications with PostgreSQL, pgvector, Tool Calling, Testing, Guardrails, and Deployment (The Practical Spring AI Development Series)"

Build production-ready AI applications with Spring Boot and Spring AI. Production-Ready Spring AI with Spring Boot shows Java developers how to move beyond chatbot demos and build reliable AI applications using RAG, PostgreSQL, pgvector, tool calling, testing, guardrails, and Docker. Throughout the book, you will build KnowledgeDesk, a practical document assistant that ingests business documents, creates embeddings, retrieves relevant evidence, generates grounded answers with citations, manages conversation context, and connects safely to business functionality through tool calling. You will learn how to: • Integrate Spring AI with Spring Boot • Build RAG workflows with PostgreSQL and pgvector • Ingest, chunk, embed, and retrieve business documents • Generate grounded answers with reliable citations • Design prompts and structured outputs • Manage memory and conversation context • Implement controlled tool calling with validation and authorization • Test AI applications and evaluate answer quality • Protect against prompt injection and data-boundary failures • Add Docker, metrics, tracing, retries, and production-readiness controls The examples use Java 21, Spring Boot 4.1.x, Spring AI 2.0.x, Maven, PostgreSQL, pgvector, and Docker. Designed for Java developers, Spring Boot developers, backend engineers, and software architects, this practical guide requires no machine-learning background. If you want to build AI applications that are grounded, testable, observable, and ready for production, this book provides a clear path from architecture to deployment.

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