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Goutham Rayaprolu
Goutham Rayaprolu

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Building AI-Powered Apps with Spring AI and Spring Boot

#ai #llm #rag #java

We've seen how quickly AI is changing the game. Just a couple of years ago, integrating large language models (LLMs) into Java apps felt clunky—relying on Python wrappers or direct REST calls to external APIs. But in 2026, Spring AI (integrated seamlessly with Spring Boot 4) makes it feel natural, almost like adding any other Spring dependency.

If you're building microservices, chatbots, recommendation engines, or even intelligent data processors (common in India's fintech and e-commerce boom), Spring AI is the tool that's making AI accessible without leaving the Java ecosystem.

Why Spring AI Matters Now

Spring AI abstracts away the complexity of working with AI providers like OpenAI, Anthropic, Hugging Face, or even local models via Ollama. It follows the familiar Spring pattern: portable APIs across providers, just like Spring Data or Spring Security.

Key wins in 2026:

  • Provider Agnostic: Switch from GPT-4o to Claude or a local Llama model with minimal code changes.

  • RAG Made Easy: Retrieval-Augmented Generation for grounding responses in your data (e.g., company docs or databases).

  • Integration with Spring Ecosystem: Works beautifully with Spring Boot's observability, security, and cloud-native features.

  • Production-Ready: Built-in support for metrics, tracing, and rate limiting.

Combined with Spring Boot 4's modularization and Java 25 support, it's lighter and faster than ever.

Quick Start: A Simple Chatbot Endpoint

Add dependencies (Spring Boot 4 starter):

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
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Configure your API key in application.yml:

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4o-mini
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Create a service:

@Service
public class AiService {

    private final ChatClient chatClient;

    public AiService(ChatClient.Builder chatClientBuilder) {
        this.chatClient = chatClientBuilder.build();
    }

    public String generateResponse(String userInput) {
        return chatClient.prompt()
                .user(userInput)
                .call()
                .content();
    }
}
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Expose via a controller:

@RestController
@RequestMapping("/ai")
public class AiController {

    private final AiService aiService;

    public AiController(AiService aiService) {
        this.aiService = aiService;
    }

    @PostMapping("/chat")
    public String chat(@RequestBody String message) {
        return aiService.generateResponse(message);
    }
}
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That's it—a fully functional AI endpoint in under 50 lines!

Going Further: RAG with Your Data

For real-world apps, use vector stores (e.g., PGVector, Pinecone) to add context:

chatClient.prompt()
    .user(userInput)
    .documents(retrievedDocs)  // From your vector DB
    .call()
    .content();
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Spring AI handles embedding models automatically.

Why This Excites a Java Dev

No more context-switching to Python for AI prototypes. We can now build end-to-end AI features in pure Java/Spring, leveraging our existing skills in dependency injection, validation, and testing.

In fast-growing AI startup scene, this levels the playing field—faster iteration, lower costs with local models, and enterprise-grade reliability.

Get Started Today

Check out the official Spring AI docs or start a new project on start.spring.io with the AI starters. Experiment with local LLMs via Ollama for zero-cost testing.

Have you tried Spring AI yet? What's your favorite use case—chat interfaces, code generation, or data summarization? Share in the comments!

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