dJetLawyer ChatBot Backend

This is the backend for the dJetLawyer ChatBot, a sophisticated AI-powered legal assistant.

Setup

1. Clone the repository
2. Create a virtual environment: python3.10 -m venv ChatBotBackend
3. Activate the virtual environment:
   • On Windows: venv\Scripts\activate
   • On macOS and Linux: source ChatBotBackend/bin/activate
4. Install dependencies: pip install -r requirements.txt
5. Copy .env.example to .env and fill in the required environment variables
6. Set up the database:
   • Create a PostgreSQL database
   • Update the DATABASE_URL in .env with your database credentials
7. Run database migrations: alembic upgrade head
8. Start the server: uvicorn main:app --reload

Features

Chat Sharing

The system supports sharing chat conversations publicly:

• Authenticated users can share their chats with a public link
• Anonymous users can also share their chats, which will be saved from Redis to the permanent database
• Shared chats are accessible to anyone with the link without requiring authentication
• Shared chats maintain all original messages and source references

Interactive Shared Chats

• Users can continue the conversation from a shared chat
• The system creates a separate copy of the shared chat for each user who continues it
• For anonymous users, the conversation history is stored in Redis with the regular usage limits
• For authenticated users, a new chat is created in their account with the shared chat's history
• API supports passing previous messages as context when creating a new chat

To share a chat:

• API endpoint: POST /api/v1/chat/chats/{chat_id}/share (for authenticated users)
• API endpoint: POST /api/v1/chatbot/share-anonymous-chat (for anonymous users)

To view a shared chat:

• API endpoint: GET /api/v1/chat/shared/{chat_id}

To continue a shared chat:

• API endpoint: POST /api/v1/chatbot/chat with the previous_messages parameter

API Documentation

Once the server is running, you can access the API documentation at http://localhost:8000/docs.

Testing

To run tests, use the command: ./run_tests.sh

Deployment

For production deployment, consider the following:

1. Use a production WSGI server like Gunicorn
2. Set up HTTPS
3. Use environment variables for all sensitive information
4. Set up proper logging
5. Configure a production-ready database
6. Set up monitoring and error tracking