Moving past basic chat features requires leveraging Spring AI’s advanced processing capabilities. Structured Output Parsing
If you are looking for documentation or tutorial-style content in lieu of the full book PDF, these resources offer direct code examples: spring-projects/spring-ai : The official framework repository. Note that the Spring AI Reference Documentation is currently available only in HTML format. alexandreroman/spring-ai-101
Which are you planning to use? (OpenAI, Ollama, Anthropic, etc.)
For those searching for official code and documentation, the following resources are essential: spring ai in action pdf github link
At the center of Spring AI are functional interfaces like ChatModel and EmbeddingModel .
@GetMapping("/ai/generate") public String generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) return chatClient.prompt() .user(message) .call() .content();
To pull the required dependencies, use the to ensure version compatibility across starters. 1. Maven Dependency Configuration Moving past basic chat features requires leveraging Spring
Once you have the PDF and the GitHub link, follow this checklist to get running in under 10 minutes.
Spring AI mirrors the design philosophy of classic Spring modules like Spring Data or Spring Security. 1. Chat Models and Prompts
To get started, initialize a Spring Boot 3.x application and add the appropriate Spring AI BOM (Bill of Materials) and starter dependencies to your pom.xml : alexandreroman/spring-ai-101 Which are you planning to use
How to use vector databases to make AI context-aware.
When searching for standard learning materials, reference guides, or book source code on GitHub, it helps to know exactly what to look for. 1. Official Documentation and PDFs
"Spring AI in Action" is the definitive guide to one of the most important developments in the Java ecosystem in years. Craig Walls has delivered a pragmatic, example-driven book that takes Spring developers from zero to AI-ready in 12 chapters.
Fully functional sample applications covering Chat, Embeddings, Vector Databases (PGvector, Redis), and complete RAG implementations. Generating Your Own Local Reference Documentation (PDF)
It allows you to: