Proposal: AI-Integrated Spring Boot Accelerator with Chatbot Functionality

1. Define the Accelerator Scope

• Target Audience: Developers building AI-driven applications.
• Features: Preconfigured setup for AI libraries (e.g., TensorFlow, OpenAI, Hugging Face). Easy-to-use services for AI model interaction. Sample REST APIs for text, image, or data processing. Model hosting or API integration.
• Customization: Configurable API keys. Plug-and-play model support.

2. Structure the Accelerator

Organize it as a Maven or Gradle project template:

ai-spring-accelerator/
├── src/main/java/com/example/ai
│   ├── config
│   │   └── AIConfig.java
│   ├── service
│   │   └── AIService.java
│   ├── controller
│   │   └── AIController.java
│   └── model
│       └── CompletionRequest.java
├── src/main/resources
│   └── application.yml
├── pom.xml
└── README.md
        

3. Build Components

a. Configuration Class

Set up configuration for API keys or libraries:

@Configuration
public class AIConfig {
    @Value("${ai.api.key}")
    private String apiKey;

    public String getApiKey() {
        return apiKey;
    }
}
        

b. Service Layer

Abstract AI model or API interactions:

@Service
public class AIService {
    private final AIConfig aiConfig;
    private final OpenAiService openAiService;

    public AIService(AIConfig aiConfig) {
        this.aiConfig = aiConfig;
        this.openAiService = new OpenAiService(aiConfig.getApiKey());
    }

    public String generateText(String prompt) {
        CompletionRequest request = CompletionRequest.builder()
                .prompt(prompt)
                .maxTokens(100)
                .build();
        return openAiService.createCompletion(request).getChoices().get(0).getText();
    }
}
        

c. REST Controller

Provide endpoints for developers:

@RestController
@RequestMapping("/api/ai")
public class AIController {
    private final AIService aiService;

    public AIController(AIService aiService) {
        this.aiService = aiService;
    }

    @PostMapping("/generate")
    public ResponseEntity<String> generateText(@RequestBody String prompt) {
        return ResponseEntity.ok(aiService.generateText(prompt));
    }
}
        

4. Preconfigure Dependencies

Predefine dependencies for AI libraries in pom.xml:

<dependency>
    <groupId>com.theokanning.openai-gpt3-java</groupId>
    <artifactId>client</artifactId>
    <version>0.14.0</version>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>
        

5. Add Example Endpoints

Provide ready-to-test APIs:

• Text Generation: /api/ai/generate
• Custom Model Inference: /api/ai/infer
• Configuration Test: /api/ai/test

6. Documentation

Include a README.md to guide developers:

• Setup: Environment variables, API keys.
• Usage: How to invoke endpoints.
• Customization: Adding new AI models or APIs.

7. Distribute the Accelerator

Options:

1. GitHub Template: Host on GitHub as a template repository.
2. Maven Archetype: Publish as a Maven archetype for easy generation.
3. Spring Initializr: Extend Spring Initializr with AI-specific configurations.

8. Enhancements

• Dockerization: Provide a Dockerfile for containerized deployment.
• Monitoring: Add metrics with Actuator and Prometheus.
• Examples: Preload with AI use cases like sentiment analysis or image classification.

This accelerator will enable developers to rapidly build AI-enhanced Spring Boot applications!