This bot allows users to query lecture notes using a RAG (Retrieval-Augmented Generation) pipeline through Mattermost.

.
├── src/                    # Source code directory
│   ├── mattermost_bot.py   # Mattermost bot implementation
│   ├── app.py             # RAG pipeline server
│   └── ...                # Other source files
├── data/                  # Data directory for lecture notes
├── chroma_db/             # Vector database storage
├── requirements.txt       # Python dependencies
├── start_servers.sh      # Script to start all services
└── manage_ngrok_service.sh # Script to manage ngrok service

• Python 3.8 or higher
• Mattermost server with admin access
• Google API key for the RAG pipeline
• OpenAI API key for web search functionality

1. Clone the repository
2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Unix/macOS
   # or
   .\venv\Scripts\activate  # On Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Copy .env.example to .env and fill in the required values:
   • MATTERMOST_URL: Your Mattermost server URL
   • BOT_TOKEN: Your Mattermost bot token
   • GOOGLE_API_KEY: Your Google API key
   • OPENAI_API_KEY: Your OpenAI API key

1. Log in to your Mattermost server as an administrator
2. Go to System Console > Integrations > Bot Accounts
3. Create a new bot account:
   • Username: lecture-bot
   • Display Name: Lecture Notes Bot
   • Description: Bot for querying lecture notes
4. Copy the generated bot token and add it to your .env file

1. Start the RAG pipeline server:

   python src/app.py

2. Start the Mattermost bot server:

   python src/mattermost_bot.py

   Alternatively, you can use the provided script to start both services:

   ./start_servers.sh

3. The bot will be available in Mattermost using the /lecture command

In any Mattermost channel, users can interact with the bot using:

@knowledge-agent <your question>

For example:

@knowledge-agent What is the definition of a vector norm?

The bot will respond with:

1. The answer to your question
2. Sources used to generate the answer
3. Any relevant context from the lecture notes

• Natural language querying of lecture notes
• Source attribution for answers
• Web search fallback when local context is insufficient
• Markdown formatting for better readability
• Error handling and user feedback

If you encounter issues:

1. Check the logs in the logs/ directory for error messages
2. Verify your API keys are correct in the .env file
3. Ensure the Mattermost bot has proper permissions
4. Check that the lecture notes are properly indexed in the RAG pipeline

• Keep your API keys and bot tokens secure
• Do not commit the .env file to version control
• Regularly rotate your bot tokens
• Monitor bot usage and implement rate limiting if necessary