Spring AI (Java)¶

Spring AI brings LLM integration to the Java/Spring Boot ecosystem. Provides Spring-native abstractions for AI providers, RAG, function calling, and vector stores with familiar dependency injection and auto-configuration.

Key Facts¶

Unified API for OpenAI, Anthropic, Ollama, Google, and other providers
Spring Boot auto-configuration for minimal setup
RAG support with vector store advisors
Function calling support
Best choice when building on existing Java/Spring infrastructure

Basic Usage¶

@RestController
public class ChatController {

    private final ChatClient chatClient;

    public ChatController(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }

    @GetMapping("/chat")
    public String chat(@RequestParam String message) {
        return chatClient.prompt()
            .user(message)
            .call()
            .content();
    }
}

Configuration¶

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4
          temperature: 0.7

RAG Integration¶

@Bean
public ChatClient chatClient(ChatClient.Builder builder, VectorStore vectorStore) {
    return builder
        .defaultAdvisors(new QuestionAnswerAdvisor(vectorStore))
        .build();
}

When to Use¶

Existing Java/Spring ecosystem
Enterprise applications already on Spring Boot
Team expertise in Java rather than Python
Integration with Spring Security, Spring Data, etc.
Organizations with Java-centric infrastructure

Gotchas¶

Smaller community and fewer examples compared to Python LangChain ecosystem
Provider support may lag behind Python SDKs for new features
Most AI/ML tutorials and examples are Python-first - need to adapt patterns
Spring AI is relatively new - API surface may change between versions