AutoConfig - AI-Powered Configuration Assistant

AutoConfig is a sophisticated AI-powered system that combines multiple components to provide intelligent configuration assistance. The system integrates a Next.js frontend, NeMo Guardrails for content moderation, and a Llama-based language model for natural language processing.

🚀 Features

• Modern Web Interface: Built with Next.js 14, providing a responsive and user-friendly experience
• Content Moderation: Powered by NeMo Guardrails to ensure appropriate and safe responses
• Language Model Integration: Utilizes Llama 3.2 1B model for natural language understanding
• Dockerized Deployment: Easy setup and deployment using Docker Compose
• Health Monitoring: Built-in health checks for all services
• Scalable Architecture: Modular design allowing for easy extension and maintenance

🏗️ Architecture

The system consists of several interconnected services:

1. UI Service (UI/)

   • Next.js 14 frontend application
   • Handles user interactions and displays responses
   • Communicates with NeMo Guardrails service

2. NeMo Guardrails Service (nemo_guardrails/)

   • Content moderation and safety layer
   • Custom LLM integration
   • Configurable guardrails for response filtering

3. Llama API Service (llama-api-docker/)

   • Hosts the Llama 3.2 1B language model
   • Provides text generation capabilities
   • REST API interface for model access

4. RAG Service (RAG/)

   • Retrieval-Augmented Generation capabilities
   • Document processing and embedding
   • Enhanced response generation

🛠️ Prerequisites

• Docker and Docker Compose
• Node.js 18+ (for local development)
• Python 3.10+ (for local development)
• Git
• Llama model files (see Quick Start section)

🚀 Quick Start

1. Clone the repository:

   git clone https://github.com/yourusername/AutoConfig.git
   cd AutoConfig

2. Prepare Model Files

   • Place your Llama model files in the following structure:

     llama-api-docker/
     └── models_new/
         └── Llama-3.2-1B_new/
             ├── model.safetensors
             ├── tokenizer.json
             ├── config.json
             └── tokenizer_config.json
     

   • The model files should be in the same format as the example files in the repository
   • Ensure all required model files are present before starting the services

3. Start the services:

   docker-compose up -d

4. Access the application:

   • UI: http://localhost:3001
   • NeMo Guardrails API: http://localhost:8080
   • Llama API: http://localhost:8000

🔧 Configuration

Environment Variables

• NEMO_API_URL: URL for the NeMo Guardrails service (default: http://nemo-guardrails:8080)
• LLAMA_API_URL: URL for the Llama API service (default: http://llama-api:8000)
• MONGODB_URI: MongoDB connection string (default: mongodb://mongodb:27017/autoconfig)

Service Configuration

Each service can be configured through its respective configuration files:

• NeMo Guardrails: nemo_guardrails/config/
• Llama API: llama-api-docker/config/
• RAG Service: RAG/config/

🧪 Development

Local Development Setup

1. Install dependencies:

   # UI
   cd UI
   npm install
   
   # NeMo Guardrails
   cd ../nemo_guardrails
   pip install -r requirements.txt
   
   # Llama API
   cd ../llama-api-docker
   pip install -r requirements.txt

2. Start services individually:

   # UI
   cd UI
   npm run dev
   
   # NeMo Guardrails
   cd ../nemo_guardrails
   python api.py
   
   # Llama API
   cd ../llama-api-docker
   python api_server.py

📚 API Documentation

NeMo Guardrails API

• Endpoint: /chat
• Method: POST
• Request Body:

  {
    "message": "Your message here",
    "max_tokens": 512,
    "temperature": 0.7
  }

• Response:

  {
    "success": true,
    "response": "Generated response"
  }

Llama API

• Endpoint: /generate
• Method: POST
• Request Body:

  {
    "prompt": "Your prompt here",
    "max_tokens": 1024,
    "temperature": 0.7
  }

🤝 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

🙏 Acknowledgments

• NeMo Guardrails
• Next.js