A FastAPI-based backend service that powers an AI programming assistant with chat capabilities, code analysis, error detection, and multi-modal content processing.

• Real-time Chat: Streaming responses with session management
• Multi-Session Support: Create and manage multiple conversation sessions
• Chat History: Persistent conversation storage with MongoDB

• Comment-to-Code Generation: Convert comments into functional code
• Alternative Code Suggestions: Generate multiple implementation approaches
• Error Detection: AI-powered code error analysis and suggestions
• Context-Aware Responses: Leverages conversation history for better suggestions

• Repository Analysis: Git repository cloning and analysis
• PDF Processing: Extract and analyze PDF documents
• Web Content: URL content extraction and processing
• File Upload Support: Handle various file types for analysis

• JWT Authentication: Secure token-based authentication
• User Management: Registration, login, and session handling
• Protected Routes: Role-based access control

• Framework: FastAPI with async/await support
• Database: MongoDB for chat history and user data
• Vector Storage: FAISS for semantic search and embeddings
• AI Integration: Ollama LLM (perlbot3:latest model)
• Authentication: JWT tokens with bcrypt password hashing
• File Processing: PDF loaders, Git integration, web scraping

1. Clone the repository

   git clone <repository-url>
   cd git_check/backend

2. Install dependencies

   pip install -r requirements.txt

3. Environment Setup

   # Copy and configure environment variables
   cp .env.example .env
   # Edit .env with your configuration

4. Start the server

   uvicorn main:app --reload --port 8000

Create a .env file with the following variables:

# Database
MONGODB_URL=mongodb://localhost:27017
DATABASE_NAME=archelon_ai

# JWT Configuration
JWT_SECRET_KEY=your-secret-key
JWT_ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# AI Model Configuration
OLLAMA_MODEL=perlbot3:latest
OLLAMA_BASE_URL=http://localhost:11434

# Google Cloud (if using)
GOOGLE_APPLICATION_CREDENTIALS=Config/googlecloud.json

backend/
├── main.py                    # FastAPI application entry point
├── config.py                  # Configuration management
├── requirements.txt           # Python dependencies
├── routes/                    # API route definitions
│   ├── auth_routes.py        # Authentication endpoints
│   ├── chatHistoryRoutes.py  # Chat session management
│   ├── chatRoutes.py         # Basic chat functionality
│   ├── chatRoutesTharundi.py # Advanced chat features
│   ├── commentSuggestionRoutes.py # Code suggestion endpoints
│   ├── altCodeRoutes.py      # Alternative code generation
│   ├── errorRoutes.py        # Error detection endpoints
│   └── validateContentRoutes.py # Content validation
├── services/                  # Business logic layer
│   ├── auth/                 # Authentication services
│   ├── chatHistory/          # Chat and LLM integration
│   └── loaders/              # Content processing services
├── models/                    # Database models
├── schemas/                   # Pydantic schemas
├── utils/                     # Utility functions
├── database/                  # Database configuration
├── Controllers/               # Request controllers
└── vectordb/                  # Vector database integration

• POST /auth/register - User registration
• POST /auth/login - User login
• GET /auth/me - Get current user info

• GET /chatHistory/sessions - List user sessions
• POST /chatHistory/sessions - Create new session
• DELETE /chatHistory/sessions/{id} - Delete session
• GET /chatHistory/{session_id}/messages - Get session messages

• POST /chats/chat - Send chat message
• POST /commentCode/suggest - Generate code from comments
• POST /validate/error - Detect code errors
• POST /alt-code - Generate alternative implementations

• POST /upload - File upload and analysis
• POST /process-url - Web content analysis
• POST /analyze-repo - Git repository analysis

uvicorn main:app --reload --host 0.0.0.0 --port 8000

uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4

# Build image
docker build -t archelon-backend .

# Run container
docker run -p 8000:8000 archelon-backend

• Python: 3.9 or higher
• MongoDB: 4.4 or higher
• Ollama: With perlbot3:latest model installed
• Memory: 8GB+ recommended for AI model operations
• Storage: 10GB+ for model storage and vector databases

Once the server is running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

# Run tests
python -m pytest tests/

# Run specific test
python test.py

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

This project is part of the Programming Assistant AI Bot system. Please refer to the main project license for usage terms.

• Frontend Interface: ../frontend - React-based web interface
• VS Code Extension: ../VsCodeExtension - IDE integration
• Documentation: Full project documentation

Built with ❤️ using FastAPI and modern AI technologies.