Name: BUILDING AI APPLICATIONS WITH SPRING BOOT AND SPRING AI: Create Document Assistants with RAG, PostgreSQL, Tool Calling, Testing, and Deployment (en Inglés)
SKU: 69130715
Price: 34.64 USD
Availability: InStock
Author: Frost, Riley
ISBN: 9798177738802

portada BUILDING AI APPLICATIONS WITH SPRING BOOT AND SPRING AI: Create Document Assistants with RAG, PostgreSQL, Tool Calling, Testing, and Deployment (en Inglés)

BUILDING AI APPLICATIONS WITH SPRING BOOT AND SPRING AI: Create Document Assistants with RAG, PostgreSQL, Tool Calling, Testing, and Deployment (en Inglés)

Formato

Libro Físico

Editorial

Independently published

Autor

Frost, Riley

Idioma

Inglés

N° páginas

307

Encuadernación

Tapa Blanda

ISBN13

9798177738802

BUILDING AI APPLICATIONS WITH SPRING BOOT AND SPRING AI: Create Document Assistants with RAG, PostgreSQL, Tool Calling, Testing, and Deployment (en Inglés)

Frost, Riley (Autor) · Independently published · Tapa Blanda

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Reseña del libro "BUILDING AI APPLICATIONS WITH SPRING BOOT AND SPRING AI: Create Document Assistants with RAG, PostgreSQL, Tool Calling, Testing, and Deployment (en Inglés)"

BUILD PRODUCTION-READY AI APPLICATIONS WITH SPRING BOOT AND SPRING AI Building an AI demo is easy. Building a secure, reliable, and maintainable AI application for production is much harder. Building AI Applications with Spring Boot and Spring AI shows Java and Spring developers how to build real-world AI systems using Spring AI, PostgreSQL, pgvector, RAG, tool calling, testing, security, and deployment. Through the continuous KnowledgeDesk project, you will build a production-oriented document assistant that can ingest documents, generate embeddings, retrieve relevant information, return grounded answers with citations, execute controlled business tools, manage conversation context, and operate safely under real-world conditions. You will learn how to: Integrate Spring AI with Spring Boot Build document ingestion and embedding pipelines Store and search vectors with PostgreSQL and pgvector Implement production-quality Retrieval-Augmented Generation (RAG) Improve retrieval with hybrid search, reranking, and evaluation Build secure tool-calling workflows Manage conversation memory and context Handle retries, failures, and background processing Protect against prompt injection and unauthorized data access Test AI workflows with unit, integration, contract, and RAG evaluation tests Add observability for latency, failures, models, and retrieval Containerize, deploy, scale, and operate Spring AI applications The focus is not simply on connecting an LLM to Java. It is on the engineering practices required to make AI applications secure, testable, observable, maintainable, and production-ready. Written for Java and Spring developers, this practical guide requires no previous machine-learning experience.