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Spring Boot AI Integration

PublishedApril 23, 2025
UpdatedApril 24, 2025
Reading time3 min read
JavaKotlinSpring BootSpring AIArtificial Intelligence
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Spring Boot AI Integration

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Spring AI is a framework for AI engineering that applies Spring’s portability and modular design principles to AI workloads. It lets you build AI-driven applications using familiar Spring idioms and POJOs.

Last updatedApril 24, 2025

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Şuayb Şimşek

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🌟 Why Use Spring AI?

In this section, we clarify Why Use Spring AI? and summarize the key points you will apply in implementation.

  • Provider-agnostic portability: Write code once and switch between AI providers (OpenAI, Anthropic, Azure, etc.) without changes.
  • POJO-based design: Model AI inputs and outputs as plain Java/Kotlin objects for type safety and easy integration.
  • Structured outputs: Automatically map model responses to your domain objects.
  • Vector store integration: Seamless support for major vector databases (Chroma, Pinecone, Redis, etc.) via a unified API.
  • Tool/function calling: Enable LLMs to invoke custom functions or services for real-time data.
  • Observability & evaluation: Built-in metrics and evaluation utilities to monitor AI operations and detect hallucinations.
  • Chat abstractions: Fluent ChatClient API, similar to Spring’s WebClient, for building conversational agents.
  • Retrieval-augmented generation (RAG): Simplify document-based QA and memory-backed chat with advisors and memory APIs.

📋 Prerequisites

Ensure you have the following:

  • ☕ Java Development Kit (JDK) 17+
  • 📦 Maven or Gradle installed
  • 🐳 Spring Boot 3+
  • 🔑 OpenAI API Key (set as environment variable OPENAI_API_KEY)

🛠️ Step 1: Add Dependencies

Include Spring AI starter for OpenAI, Spring Web, and Lombok.

🛠️ Step 2: Configuration

In application.yml, configure your OpenAI key and set the ChatClient model:

YAMLapplication.yml
spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4.1-mini

🛠️ Step 3: Implement the Service Layer

Create an AIService to wrap your ChatClient. Inject ChatClient.Builder and build the client.

🛠️ Step 4: Expose a REST Controller

Create a ChatController to expose your AIService over HTTP.

▶️ Running the Application

Start your Spring Boot app:

BASH
./mvnw spring-boot:run
# or
gradle bootRun

🧪 Testing the Integration

Call your service via HTTP:

BASH
curl -X GET "http://localhost:8080/joke?topic=dogs"
# Returns a dog joke generated by the AI model.

🏁 Conclusion

You now have a practical Spring Boot AI Integration implementation with a clear, production-friendly Spring Boot structure. As a next step, adapt configuration and tests to your own domain, then validate behavior under realistic traffic and failure scenarios.

XMLpom.xml
<dependencies>
  <dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
  </dependency>
  <dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-openai</artifactId>
  </dependency>
  <dependency>
    <groupId>org.projectlombok</groupId>
    <artifactId>lombok</artifactId>
    <scope>provided</scope>
  </dependency>
</dependencies>
GROOVYbuild.gradle
plugins {
  id 'org.springframework.boot' version '3.2.0'
}

dependencies {
  implementation 'org.springframework.boot:spring-boot-starter-web'
  implementation 'org.springframework.ai:spring-ai-starter-model-openai'
  compileOnly 'org.projectlombok:lombok'
  annotationProcessor 'org.projectlombok:lombok'
}
JAVAAIService.java
package com.example.ai.service;

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.stereotype.Service;
import lombok.RequiredArgsConstructor;

@Service
public class AIService {
    private final ChatClient chatClient;

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

    public String getJoke(String topic) {
        return chatClient.prompt()
                .user(u -> u.text("Tell me a joke about {topic}").param("topic", topic))
                .call()
                .content();
    }
}
KOTLINAIService.kt
package com.example.ai.service

import org.springframework.ai.chat.client.ChatClient
import org.springframework.stereotype.Service

@Service
class AIService(builder: ChatClient.Builder) {
    private val chatClient: ChatClient = builder.build()

    fun getJoke(topic: String): String {
        return chatClient.prompt()
            .user { it.text("Tell me a joke about {topic}").param("topic", topic) }
            .call()
            .content()
    }
}
JAVAChatController.java
package com.example.ai.controller;

import com.example.ai.service.AIService;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import lombok.RequiredArgsConstructor;

@RestController
@RequiredArgsConstructor
public class ChatController {

    private final AIService aiService;

    @GetMapping("/joke")
    public String getJoke(@RequestParam(defaultValue = "dogs") String topic) {
        return aiService.getJoke(topic);
    }
}
KOTLINChatController.kt
package com.example.ai.controller

import com.example.ai.service.AIService
import org.springframework.web.bind.annotation.GetMapping
import org.springframework.web.bind.annotation.RequestParam
import org.springframework.web.bind.annotation.RestController

@RestController
class ChatController(private val aiService: AIService) {

    @GetMapping("/joke")
    fun getJoke(@RequestParam(defaultValue = "dogs") topic: String): String {
        return aiService.getJoke(topic)
    }
}