Spring Ai In Action Pdf Github ((hot)) Jun 2026

: The book uses a hands-on approach, starting with a basic "Hello AI World" and building up to a sophisticated application called "Board Game Buddy" that can answer complex questions about tabletop games. Advanced AI Techniques : Beyond simple chat, it covers: AI Agents and Tool Use

Master Spring AI: From Framework Fundamentals to Production-Ready GitHub Projects

<dependencies> <!-- The Core Spring AI BOM --> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-bom</artifactId> <version>0.8.1</version> <!-- Check for latest snapshot/release --> <type>pom</type> <scope>import</scope> </dependency> <!-- The OpenAI Starter (Example) --> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-openai-spring-boot-starter</artifactId> </dependency> spring ai in action pdf github

Moving from a local prototype using Ollama to a cloud-based enterprise application requires adherence to several strict engineering principles:

Write code once and switch between providers (OpenAI, Ollama, Anthropic, Azure, Amazon Bedrock) via simple configuration changes. : The book uses a hands-on approach, starting

The following service demonstrates how to parse local PDF files, vectorize them, and query them using Spring AI.

@Component public class DocumentIngestionService private final VectorStore vectorStore; private final EmbeddingModel embeddingModel; @Value("classpath:docs/enterprise-policy.pdf") private Resource pdfResource; public DocumentIngestionService(VectorStore vectorStore, EmbeddingModel embeddingModel) this.vectorStore = vectorStore; this.embeddingModel = embeddingModel; public void ingestPdfDocuments() // 1. Extract text from PDF PagePdfDocumentReader pdfReader = new PagePdfDocumentReader(pdfResource); // 2. Transform: Split text into manageable chunks (Tokens) TokenTextSplitter tokenSplitter = new TokenTextSplitter(); List splitDocuments = tokenSplitter.apply(pdfReader.get()); // 3. Load: Generate embeddings and save to Vector Database this.vectorStore.accept(splitDocuments); Use code with caution. Step 2: Querying the Vector Store (The RAG Controller) Load: Generate embeddings and save to Vector Database this

Let’s implement a practical REST controller that handles user prompts and streams back structured or unstructured information.

Leverage classic Spring Boot properties ( application.yml ) to manage model parameters, API keys, and temperature settings. Bootstrapping Your First Spring AI Application