AI Integration Spring Boot Backend

A powerful Spring Boot backend for AI-powered applications that integrates various language models (LLMs), vector databases, and natural language processing capabilities.

🚀 Features

• Basic Chat Interface: Direct communication with AI models like GPT-4 and locally hosted models via Ollama
• RAG (Retrieval Augmented Generation): Enhanced responses with context from your knowledge base
• Function Calling: Execute predefined Java functions from natural language prompts
• Recommendation Engine: Book and author recommendations based on chat history analysis
• Image Generation: AI-powered image creation from text descriptions
• Speech-to-Text: Audio transcription services

🛠️ Tech Stack

• Framework: Spring Boot 3.2+
• AI SDKs:
  • Spring AI (for OpenAI, Ollama integration)
  • LangChain4j (for RAG, embeddings, function calling)
• Vector Database: Chroma DB
• LLMs:
  • OpenAI GPT models (3.5/4)
  • Ollama (for local model deployment)
• Database: PostgreSQL (user data, chat history)
• Authentication: JWT-based auth
• Documentation: OpenAPI/Swagger

📋 Prerequisites

• Java 17+
• Maven 3.8+
• Docker & Docker Compose
• Chroma DB instance
• API keys for OpenAI (if using their services)
• Ollama setup (for local models)

🚦 Getting Started

1. Clone the repository

   git clone https://github.com/your-username/ai-integration-spring.git
   cd ai-integration-spring

2. Configure environment variables Create a .env file in the root directory with the following variables:

   OPENAI_API_KEY=your_openai_key
   CHROMA_DB_HOST=localhost
   CHROMA_DB_PORT=8000
   OLLAMA_API_URL=http://localhost:11434
   DB_USERNAME=postgres
   DB_PASSWORD=yourpassword
   

3. Start the services

   docker-compose up -d

4. Build and run the application

   mvn clean install
   mvn spring-boot:run

5. Verify the setup Open your browser and navigate to http://localhost:8080/swagger-ui.html

🔌 API Endpoints

Chat Controllers

• POST /api/chat/basic - Basic chat with LLM
• POST /api/chat/rag - Enhanced chat with RAG

Function Calling Controller

• POST /api/function/call - Execute functions via natural language

Embedding Controller

• POST /api/embedding/generate - Generate embeddings
• POST /api/embedding/search - Search similar documents

Recommendation Controller

• GET /api/recommendations/books - Get book recommendations
• GET /api/recommendations/authors - Get author recommendations

Image Controller

• POST /api/images/generate - Generate images from text prompts

Transcript Controller

• POST /api/transcript/audio - Convert audio to text

📁 Project Structure

ai-integration-spring/
├── src/
│   ├── main/
│   │   ├── java/com/example/aiintegration/
│   │   │   ├── config/          # Configuration classes
│   │   │   ├── controller/      # REST controllers
│   │   │   ├── model/           # Entity classes
│   │   │   ├── repository/      # Data access
│   │   │   ├── service/         # Business logic
│   │   │   │   ├── ai/          # AI service implementations
│   │   │   │   ├── embedding/   # Embedding services
│   │   │   │   └── llm/         # LLM providers
│   │   │   ├── dto/             # Data transfer objects
│   │   │   └── util/            # Utility classes
│   │   └── resources/
│   │       ├── application.yml  # Application config
│   │       └── static/          # Static resources
│   └── test/                    # Test classes
├── .mvn/wrapper/                # Maven wrapper
├── docker-compose.yml           # Docker services
├── Dockerfile                   # Application container
├── pom.xml                      # Maven dependencies
└── README.md                    # This file

🔧 Configuration

Vector Database Setup

The application uses Chroma DB as the vector database. Configure connection in application.yml:

chroma:
  url: ${CHROMA_DB_HOST}:${CHROMA_DB_PORT}
  collection: ai-documents

LLM Configuration

Configure multiple LLM providers:

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4
          temperature: 0.7
    ollama:
      base-url: ${OLLAMA_API_URL}
      chat:
        options:
          model: llama3

🧩 Extending the Application

Adding New Functions for Function Calling

1. Create a new service with the function implementation
2. Register the function in FunctionRegistry
3. Add function metadata to the available tools

Adding New RAG Sources

1. Create a document loader in the repository package
2. Implement the chunking strategy
3. Register the new source in the EmbeddingService

📊 Performance Considerations

• Caching: Implement Redis caching for frequently accessed embeddings
• Async Processing: Use Spring's async capabilities for non-blocking operations
• Connection Pooling: Configure proper database and HTTP client pooling

🛡️ Security

• All API endpoints are secured with JWT authentication
• LLM API keys are stored securely using environment variables
• Input validation and sanitization for all user inputs
• Rate limiting on sensitive endpoints

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.