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Akash Pratap Singh
Akash Pratap Singh

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Building a Chat REST API with Spring Boot and Google Generative AI

#springboot #java #tutorial #gemini

Generative AI is transforming how applications interact with users—enabling natural conversations, intelligent responses, and dynamic content generation. With Spring Boot as a backend framework and Google Generative AI (Gemini models) as the intelligence layer, you can build a simple chat API that lets clients send messages and get AI-powered responses.

In this article, we’ll walk through how to set up a REST API in Spring Boot that connects to Google’s Gemini API for conversational AI.

Why Spring Boot + Google GenAI?

  • Spring Boot makes it easy to set up REST APIs quickly with minimal configuration.
  • Google Generative AI (Gemini) provides state-of-the-art models for natural conversations, text generation, and reasoning.
  • Together, they give you a lightweight but powerful way to integrate AI into your apps.

Prerequisites

Before starting, make sure you have:

  • Java 17+ installed
  • Spring Boot 3.x
  • A Google Cloud project with the Generative AI API enabled
  • A Gemini API Key (exported as an environment variable) 
export GEMINI_API_KEY="your-google-genai-api-key"
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Step 1: Create a Spring Boot Project

Generate a project using Spring Initializr:

  • Project: Maven
  • Dependencies: Spring Web and Lombok (optional for less boilerplate)

Step 2: Add Dependency for Google GenAI Client

In your pom.xml, include:

<dependency>
    <groupId>com.google.genai</groupId>
    <artifactId>genai</artifactId>
    <version>0.1.0</version> <!-- check latest available -->
</dependency>
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This gives access to the Google Generative AI SDK.

Step 3: Application Properties

Configure your API key inside application.properties:

spring.application.name=chat-api
gemini.api.key=${GEMINI_API_KEY}
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Step 4: Create a Chat Request DTO 

package com.example.chatapi;

import lombok.Data;

@Data
public class ChatRequest {
    private String message;
}
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Step 5: Implement the Chat Service 

package com.example.chatapi;

import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

@Service
public class ChatService {

    private final Client client;

    public ChatService(@Value("${gemini.api.key}") String apiKey) {
        this.client = new Client(apiKey); // initialize with API key
    }

    public String askAI(String userMessage) {
        GenerateContentResponse response = client.models.generateContent(
                "gemini-2.5-flash", // choose Gemini model
                userMessage,
                null
        );
        return response.text();
    }
}
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Step 6: Expose REST API Endpoint 

package com.example.chatapi;

import lombok.RequiredArgsConstructor;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/api/chat")
@RequiredArgsConstructor
@CrossOrigin(origins = "*") // You can add your frontend url here
public class ChatController {

    private final ChatService chatService;

    @PostMapping("/ask")
    public ResponseEntity<String> ask(@RequestBody ChatRequest chatRequest) {
        String reply = chatService.askAI(chatRequest.getMessage());
        return ResponseEntity.ok(reply);
    }
}
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Step 7: Test the Chat API

Start your application:

mvn spring-boot:run
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Send a request with curl or Postman:

curl -X POST http://localhost:8080/api/chat/ask \
     -H "Content-Type: application/json" \
     -d '{"message": "Hello, can you explain what Spring Boot is?"}'
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✅ You should receive a response generated by the Gemini model.

Conclusion

By combining Spring Boot with Google’s Generative AI, you can quickly build a REST API that powers intelligent chat experiences. This foundation can be expanded into customer support bots, productivity assistants, or interactive educational tools—all backed by Gemini’s state-of-the-art AI capabilities.

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