Artificial Intelligence (AI) has moved from being a buzzword to becoming an integral part of modern applications. Spring Boot, a popular Java framework, makes integrating AI simpler than ever. In this blog, we’ll explore how to leverage AI features in Spring Boot with a practical use case, a complete Java project setup, and code implementation to bring these concepts to life.

Why Spring Boot for AI?

Spring Boot simplifies application development by providing:

• Out-of-the-box configurations to get you started quickly.
• Seamless integration with machine learning libraries like TensorFlow, OpenAI APIs, or Python services via REST.
• Scalability to handle complex AI workloads.

Real-World Use Case: Chatbot for Customer Support

Imagine a retail business wanting to enhance customer support with a chatbot. The chatbot should:

1. Understand customer queries.
2. Provide answers or route complex issues to a human agent.
3. Learn from interactions to improve over time.

Let’s build this using Spring Boot.

Step 1: Project Setup

1. Prerequisites
• Java 11 or later.
• Spring Boot 3.x.
• Maven or Gradle.
• Machine Learning model or API (e.g., OpenAI GPT or custom ML model served via REST).
2. Create a New Spring Boot Project

You can use Spring Initializr:

1. Project: Maven.
2. Dependencies: Spring Web, Spring Boot Starter JSON, RestTemplate, and Spring Data JPA (if persistence is required).

Maven pom.xml

<dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-json</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-jpa</artifactId> </dependency> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> </dependency> </dependencies> ---

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

<dependencies>   <dependency>   <groupId>org.springframework.boot</groupId>   <artifactId>spring-boot-starter-web</artifactId>   </dependency>   <dependency>   <groupId>org.springframework.boot</groupId>   <artifactId>spring-boot-starter-json</artifactId>   </dependency>   <dependency>   <groupId>org.springframework.boot</groupId>   <artifactId>spring-boot-starter-data-jpa</artifactId>   </dependency>   <dependency>   <groupId>mysql</groupId>   <artifactId>mysql-connector-java</artifactId>   </dependency>   </dependencies>   ---

Step 2: Implementation

1. Design the Chatbot Controller

Create a REST controller to handle user queries.

@RestController @RequestMapping("/api/chat") public class ChatController { private final ChatService chatService; public ChatController(ChatService chatService) { this.chatService = chatService; } @PostMapping("/query") public ResponseEntity<String> getResponse(@RequestBody ChatRequest chatRequest) { String response = chatService.getResponse(chatRequest.getMessage()); return ResponseEntity.ok(response); } }

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

@RestController   @RequestMapping("/api/chat")   public class ChatController {     private final ChatService chatService;     public ChatController(ChatService chatService) {   this.chatService = chatService;   }     @PostMapping("/query")   public ResponseEntity<String> getResponse(@RequestBody ChatRequest chatRequest) {   String response = chatService.getResponse(chatRequest.getMessage());   return ResponseEntity.ok(response);   }   }

2. Create a Service to Process Queries

The service integrates with an AI API (e.g., OpenAI GPT) or a local ML model.

@Service public class ChatService { private final RestTemplate restTemplate; private static final String AI_API_URL = "https://api.openai.com/v1/completions"; private static final String API_KEY = "your-api-key"; public ChatService(RestTemplate restTemplate) { this.restTemplate = restTemplate; } public String getResponse(String userInput) { HttpHeaders headers = new HttpHeaders(); headers.setContentType(MediaType.APPLICATION_JSON); headers.set("Authorization", "Bearer " + API_KEY); Map<String, Object> requestBody = new HashMap<>(); requestBody.put("model", "text-davinci-003"); requestBody.put("prompt", userInput); requestBody.put("max_tokens", 150); HttpEntity<Map<String, Object>> entity = new HttpEntity<>(requestBody, headers); ResponseEntity<Map> response = restTemplate.postForEntity(AI_API_URL, entity, Map.class); Map<String, Object> responseBody = response.getBody(); return responseBody != null ? responseBody.get("choices").toString() : "Sorry, I couldn’t process that."; } }

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

@Service   public class ChatService {     private final RestTemplate restTemplate;   private static final String AI_API_URL = "https://api.openai.com/v1/completions";   private static final String API_KEY = "your-api-key";     public ChatService(RestTemplate restTemplate) {   this.restTemplate = restTemplate;   }     public String getResponse(String userInput) {   HttpHeaders headers = new HttpHeaders();   headers.setContentType(MediaType.APPLICATION_JSON);   headers.set("Authorization", "Bearer " + API_KEY);     Map<String, Object> requestBody = new HashMap<>();   requestBody.put("model", "text-davinci-003");   requestBody.put("prompt", userInput);   requestBody.put("max_tokens", 150);     HttpEntity<Map<String, Object>> entity = new HttpEntity<>(requestBody, headers);     ResponseEntity<Map> response = restTemplate.postForEntity(AI_API_URL, entity, Map.class);   Map<String, Object> responseBody = response.getBody();     return responseBody != null ? responseBody.get("choices").toString() : "Sorry, I couldn’t process that.";   }   }

3. Define the Request Model

Create a class for the chatbot request.

public class ChatRequest { private String message; // Getters and Setters public String getMessage() { return message; } public void setMessage(String message) { this.message = message; } } ---

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

public class ChatRequest {   private String message;     // Getters and Setters   public String getMessage() {   return message;   }     public void setMessage(String message) {   this.message = message;   }   }   ---

Step 3: Test and Deploy

1. Test the Application

Run the application and send a POST request to /api/chat/query with a JSON payload:

{ "message": "What are your store hours?"

1 2	{ "message": "What are your store hours?"

Expected response:

“Our store is open from 9 AM to 9 PM, Monday to Saturday.”

2. Deploy the Application

You can deploy the application on platforms like AWS, Azure, or Google Cloud. For simplicity:

• Package the application with mvn package.
• Deploy the jar file on a cloud-hosted machine or container.

Key Features of This Implementation

• Scalability: Spring Boot’s ecosystem can handle high traffic.
• Extensibility: You can easily switch between AI APIs or integrate a custom ML model.
• Security: Leverage Spring Security to protect the chatbot API endpoints.

Conclusion

Integrating AI into applications is no longer a luxury but a necessity. With Spring Boot, you can seamlessly build intelligent systems like chatbots, recommendation engines, or predictive analytics tools. This blog demonstrated a real-world use case of building a customer-support chatbot, including project setup, implementation, and deployment. Now it’s your turn to explore the endless possibilities of AI with Spring Boot!