Java AI Tutorial: Using Google Gemini AI in Java Application

Java Gen AI tutorial interface

Table Of Contents
Introduction
Prerequisites
Step 1: Create and Configure Spring Boot Project
Step 2: Obtain Google Gemini API Credentials
Step 3: Add Dependencies
Step 4: Store Your Gemini API Key
Step 5: Create a Gemini Client Service
Step 6: Create a REST Controller
Bonus Tips
Step 7: Test the API
Conclusion
Build Your Dream App and Go Live Today.
Introduction
Google Gemini is a powerful family of multimodal AI models developed by Google DeepMind, capable of understanding and generating text, code, images, and more. Built to rival and often surpass the capabilities of other leading large language models, Gemini offers state of the art performance in reasoning, summarization, question answering, content creation, and programming assistance.

Whether you're developing chatbots, intelligent virtual assistants, content summarization tools, or AI powered automation, integrating Gemini into your backend can significantly elevate the intelligence and responsiveness of your application.

In this blog, we’ll walk through how to integrate the Google Gemini API into a Java Spring Boot application using RESTful APIs. By the end, you'll have a working backend setup that can send prompts to Gemini, receive rich AI-generated responses, and be extended for a variety of use cases from real-time customer support to educational applications and beyond.

Java AI architecture diagram integrating Google Gemini API
Prerequisites
Java 17+ (recommended) — perfect for Java AI projects
Spring Boot 3.x
Maven or Gradle
A Google Cloud account
Google Cloud Project with Vertex AI API & Gemini API enabled
Step 1: Create and Configure Spring Boot Project
Generate a new Spring Boot app using Spring Initializr:

Dependencies:

Spring Web
Spring Configuration Processor
This setup aligns with typical AI Java application frameworks.
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Step 2: Obtain Google Gemini API Credentials
Create a Google Cloud Project:
Go to the Google Cloud Console.
Create a new project.
Enable the Gemini API:
In the Google Cloud Console, navigate to "APIs & Services" > "Library".
Search for "Gemini API Google" and enable it for your project.
Create Credentials:
Go to "APIs & Services" > "Credentials".
Click "Create Credentials" and select "API Key".
Copy the generated Google Gemini API key.
Steps to create a Google Cloud project for Java AI
Enabling Google Gemini API in Google Cloud Console
Generating API key for Google Gemini in Google Cloud Console
Refer to the official Gemini API docs for detailed info on API usage.

Step 3: Add Dependencies
pom.xml (Maven example):

org.springframework.boot spring-boot-starter-web com.fasterxml.jackson.core jackson-databind Or if you're using Gradle:

implementation 'org.springframework.boot:spring-boot-starter-web'

implementation 'com.fasterxml.jackson.core:jackson-databind'

This is a common setup for many Java gen AI and backend services.

Call to action for adopting Generative AI in Java
Step 4: Store Your Gemini API Key
Store it securely. For now, use application.properties:

gemini.api.key=YOUR_API_KEY_HERE
google.gemini.url=https://generativelanguage.googleapis.com/v1/models/gemini-1.5-flash:generateContent?key=
To check accessible Gemini models for your API key, you can use the following HTTP GET request:

curl -X GET \
"https://generativelanguage.googleapis.com/v1/models?key=YOUR_API_KEY"
Output will be as follows:

Gemini AI model list output in Java application
Access to gemini-1.5-flash is recommended for evaluation or trial purposes.

Step 5: Create a Gemini Client Service
Create a service to interact with the Gemini AI API.
GeminiService.java

@Service
public class GeminiService {
@Value("${gemini.api.key}")
private String apiKey;

@Value("${google.gemini.url}")
private String geminiUrl;


public String generateContent(String prompt) throws IOException {
    RestTemplate restTemplate = new RestTemplate();
    HttpHeaders headers = new HttpHeaders();
    headers.setContentType(MediaType.APPLICATION_JSON);
    String requestJson = """
    {
      "contents": [
        {
          "parts": [
            {
              "text": "%s"
            }
          ]
        }
      ]
    }
    """.formatted(prompt);
    HttpEntity<String> entity = new HttpEntity<>(requestJson, headers);
    ResponseEntity<String> response = restTemplate.postForEntity(geminiUrl + apiKey, entity, String.class);
    return response.getBody();
}

}
This service is a great example of using gen AI with Java in practical projects.

Step 6: Create a REST Controller
GeminiController.java

@RestController
@RequestMapping("/api/gemini")
public class GeminiController {
private final GeminiService geminiService;
public GeminiController(GeminiService geminiService) {
this.geminiService = geminiService;
}
@PostMapping("/generate")
public ResponseEntity generate(@RequestBody Map<String, String> body) {
try {
String prompt = body.get("prompt");
String response = geminiService.generateContent(prompt);
return ResponseEntity.ok(response);
} catch (Exception e) {
return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
.body("Error: " + e.getMessage());
}
}
}
This completes the backend setup to use the Gemini Google API effectively.
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