Spring Ai In Action Pdf Github [work] Jun 2026

Create a simple endpoint that uses the ChatClient .

The book covers 12 core chapters focused on building production-ready AI services:

Older implementations used the raw ChatModel interface, but modern GitHub examples highlight the fluent ChatClient API.

The landscape of enterprise Java development is shifting. For years, Spring Framework has been the undisputed king of dependency injection, web MVC, and data access. But 2023 and 2024 brought a tidal wave of Generative AI—Large Language Models (LLMs) like GPT-4, Gemini, and Llama. The question on every Spring developer’s lips became: How do I integrate AI into my existing Spring Boot applications without rewriting everything from scratch? spring ai in action pdf github

<dependencyManagement> <dependencies> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-bom</artifactId> <version>1.0.0-M3</version> <!-- Check for latest version --> <type>pom</type> <scope>import</scope> </dependency> </dependencies> </dependencyManagement>

As of late 2024/early 2025, there is no official "Spring AI in Action" book from Manning (the "In Action" series publishers). However, there are rapidly evolving alternatives:

record Actor(String name, Integer age) {} Create a simple endpoint that uses the ChatClient

Works out of the box with Spring Data, Spring Security, and Spring Integration. 2. Core Architecture Components

LLMs communicate via raw text, but enterprise software requires structured data (like JSON or Java POJOs). Spring AI solves this with OutputParser implementations (e.g., BeanOutputConverter ). You can define a Java record, pass it to the parser, and Spring AI automatically instructs the LLM to format its response to match your object structure perfectly. Embeddings and Vector Databases

4. Practical Implementation: Building a Smart REST Controller For years, Spring Framework has been the undisputed

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

Since the technology is moving faster than traditional publishing, the best "books" right now are open-source repositories. If you are looking for the source code equivalent of a PDF, here are the definitive GitHub projects:

@Service public class RagService private final ChatModel chatModel; private final VectorStore vectorStore; public RagService(ChatModel chatModel, VectorStore vectorStore) this.chatModel = chatModel; this.vectorStore = vectorStore; public String queryPrivateData(String userQuery) // 1. Retrieve similar documents matching the user query from the vector database List similarDocuments = vectorStore.similaritySearch( SearchRequest.query(userQuery).withTopK(3) ); // 2. Extract content text from documents String context = similarDocuments.stream() .map(Document::getContent) .collect(Collectors.joining("\n")); // 3. Enrich the Prompt Template with context String systemPromptTemplate = """ You are a helpful enterprise assistant. Answer the question using only the provided context below. If you do not know the answer based on the context, say 'I cannot find the answer in internal records'. Context: context """; PromptTemplate promptTemplate = new PromptTemplate(systemPromptTemplate); Message systemMessage = promptTemplate.createMessage(Map.of("context", context)); UserMessage userMessage = new UserMessage(userQuery); // 4. Fire the combined prompt to the LLM ChatResponse response = chatModel.call(new Prompt(List.of(systemMessage, userMessage))); return response.getResult().getOutput().getContent(); Use code with caution. 6. Curated GitHub Repositories and Resources

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