The book Spring AI in Action by Craig Walls is a guide to implementing these features. It takes developers from basic examples to more complex enterprise patterns. Key Feature Practical Application Building chatbots that use vector databases. Tool Calling Allowing models to execute local Java code. MCP Integration Providing context to LLMs. Multimodality Generating images from text and processing audio in Java. Navigating the GitHub Repositories
@RestController @RequestMapping("/api/ai") public class ChatController private final ChatClient chatClient; public ChatController(ChatClient.Builder builder) this.chatClient = builder .defaultSystem("You are a helpful and concise software architecture assistant.") .build(); @GetMapping("/generation") public String generateAnswer(@RequestParam(value = "message") String message) return this.chatClient.prompt() .user(message) .call() .content(); Use code with caution. Achieving Structured Output
Seamless integration with databases like PostgreSQL (PgVector), Pinecone, and Chroma for Retrieval-Augmented Generation (RAG). spring ai in action pdf github
(OpenAI, Ollama, etc.) do you plan to use?
Here's a sample code snippet that demonstrates the chatbot's implementation: The book Spring AI in Action by Craig
Detailed walkthroughs of ChatClient , ImageClient , and EmbeddingClient .
: Reviewers on Amazon and LinkedIn highlight Craig Walls' "relentless emphasis on getting stuff done," noting the book's clear-cut explanations and wonderful demos. Tool Calling Allowing models to execute local Java code
Start with the basics, using the chat client abstraction to generate text.
"Spring AI in Action: How a Team of Developers Built a Smart Chatbot"
Spring AI is built to eliminate boilerplate code. Historically, if you wanted to switch your application from OpenAI to Ollama or Anthropic, you had to rewrite your entire client layer. Spring AI solves this through a unified abstraction layer. Key Architectural Pillars