Spring AI Starter Project: A Complete Guide with Examples

Introduction to Spring AI

Artificial Intelligence (AI) has become a core component of modern software applications, and integrating AI into Java-based applications is now easier with Spring AI. The Spring AI Starter Project simplifies AI model interactions, offering built-in support for LLMs (Large Language Models), embeddings, vector databases, and image generation models.

In this guide, we will explore how to set up a Spring AI Starter project, run queries, and leverage AI-powered functionalities. Whether you’re new to AI or an experienced developer, this guide will provide step-by-step instructions, sample code, and best practices for building AI applications using Spring AI.

What is Spring AI?

Spring AI is a project developed by Spring.io that provides integrations for various AI models and frameworks, making it easier to work with AI in Java applications. It allows developers to:

Access LLMs (Large Language Models)
Perform text generation and embeddings
Utilize vector databases
Generate images with AI models

Spring AI follows a modular design, allowing developers to choose and configure AI components as needed.

Key Features of Spring AI

Support for multiple AI providers (e.g., OpenAI, Hugging Face, Local LLMs)
Easy integration with Spring Boot applications
Simplified API for interacting with LLMs
Embeddings and vector database support
Built-in image generation capabilities
Configurable and extendable

Setting Up a Spring AI Starter Project

1. Prerequisites

Before starting, ensure you have:

Java 17+ installed
Spring Boot 3+
Maven or Gradle
An API key for an AI provider (e.g., OpenAI)

2. Creating a Spring Boot Project

To create a Spring Boot project, use Spring Initializr:

Using Spring Initializr (Manual Setup)

Go to Spring Initializr
Select Spring Boot 3.x
Add dependencies:
- spring-ai-openai-starter
- spring-boot-starter-web
Generate the project and extract it.

Using Spring Boot CLI

spring init --dependencies=web,ai-openai my-spring-ai-app

3. Adding Spring AI Dependencies

Add the following dependency to pom.xml (if using Maven):

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
    <version>0.5.0</version>
</dependency>

For Gradle users, add this to build.gradle:

dependencies {
    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter:0.5.0'
}

4. Configuring Spring AI

Add your OpenAI API Key in application.properties:

spring.ai.openai.api-key=your-api-key-here

Implementing AI Queries

Now that we have set up the project, let’s implement AI-powered queries using Spring AI.

1. Creating a Simple AI Service

Create a ChatService class to interact with OpenAI’s LLM:

import org.springframework.ai.chat.ChatClient;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class ChatService {
    @Autowired
    private ChatClient chatClient;
    
    public String getResponse(String prompt) {
        return chatClient.call(prompt);
    }
}

2. Creating a REST API Controller

Now, let’s expose the AI service through a REST API:

import org.springframework.web.bind.annotation.*;
import org.springframework.beans.factory.annotation.Autowired;

@RestController
@RequestMapping("/chat")
public class ChatController {
    @Autowired
    private ChatService chatService;
    
    @GetMapping("/ask")
    public String ask(@RequestParam String prompt) {
        return chatService.getResponse(prompt);
    }
}

3. Running the Application

Run your Spring Boot application:

mvn spring-boot:run

Now, test it by sending a GET request:

curl "http://localhost:8080/chat/ask?prompt=Hello! How are you?"

Advanced Features

1. Using Embeddings and Vector Databases

Spring AI supports embeddings that help store and retrieve AI-generated vectors efficiently.

Add the dependency:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-embedding-spring-boot-starter</artifactId>
    <version>0.5.0</version>
</dependency>

2. Implementing Image Generation

Use OpenAI’s DALL·E to generate images. Add this to your service:

import org.springframework.ai.image.ImageClient;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class ImageService {
    @Autowired
    private ImageClient imageClient;
    
    public String generateImage(String prompt) {
        return imageClient.call(prompt);
    }
}

Expose it via a REST API:

@RestController
@RequestMapping("/image")
public class ImageController {
    @Autowired
    private ImageService imageService;
    
    @GetMapping("/generate")
    public String generate(@RequestParam String prompt) {
        return imageService.generateImage(prompt);
    }
}

Conclusion

Spring AI provides a seamless way to integrate AI functionalities into Java applications. By following this guide, you can set up a Spring AI Starter Project, interact with LLMs, implement embeddings, and generate images. With the growing adoption of AI, mastering Spring AI will give developers a competitive edge in building smart applications.