"You must create a ChatClient bean that leverages the Builder pattern to define default system prompts."

If you have been searching for the phrase , you are likely looking for two things: a comprehensive offline reference guide (PDF) and the living, breathing source code (GitHub) to make that theory work. You want to move from "Hello World" to production-ready AI agents.

This article is your definitive roadmap. We will explore what Spring AI offers, why "Spring AI in Action" is becoming the must-read resource, and, crucially, where to find the legitimate PDF and GitHub links to accelerate your learning. Before we dive into the PDF and GitHub specifics, let's align on the technology. Spring AI is an extension of the Spring ecosystem that provides an abstraction layer for AI models. Think of it as Spring Data, but for AI.

@Configuration public class AiConfig {

Without the GitHub link, you would wonder where VectorStore comes from. The repo contains the exact pom.xml dependency:

Enter .

@Bean public ChatClient chatClient(ChatClient.Builder builder, VectorStore vectorStore) { return builder .defaultSystem("You are a Java expert. Answer only based on the context provided.") .defaultAdvisors(new QuestionAnswerAdvisor(vectorStore)) // RAG pattern .build(); } }