streamlit-chatbot

Streamlit Chatbot

A lightweight, modular chatbot interface built with Streamlit that connects to Claude for natural language interactions.

Features

• Clean, intuitive chat interface
• Real-time message streaming
• Session-based chat history
• Easy to extend with different LLM providers
• Responsive design with sidebar controls

Setup

1. Clone the repository:

git clone <repository-url>
cd streamlit_chatbot

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the project root with your API key:

ANTHROPIC_API_KEY=your-api-key-here

4. Run the application:

streamlit run app.py

The app will be available at http://localhost:8501

Project Structure

• app.py: Main Streamlit application with chat interface
• llm.py: LLM provider abstraction layer
• requirements.txt: Project dependencies

Architecture

The application uses a modular design with the following components:

1. Frontend: Built with Streamlit's native chat components for a responsive and intuitive interface
2. LLM Integration: Abstracted through the LLMProvider class, making it easy to switch between different LLM providers
3. Session Management: Uses Streamlit's session state to maintain chat history during user sessions

Extending

To add support for a different LLM provider:

1. Create a new class that inherits from LLMProvider in llm.py
2. Implement the required methods: _initialize_model() and get_response()
3. Update the get_llm_provider() factory function to return your new provider

Roadmap & Future Improvements

Persistent Chat History

• PostgreSQL/MongoDB integration for chat storage
• User session management and authentication
• Chat history search and filtering capabilities
• Export/import conversation functionality

MCP Tool Integration

• Slack integration for team collaboration
  • Send/receive messages
  • Channel monitoring
  • Thread management
• Email integration for notifications
  • Send email summaries
  • Process email inquiries
  • Attachment handling
• Google Docs integration
  • Document processing and analysis
  • Content generation
  • Collaborative editing
• Custom MCP servers for each integration
  • Modular server architecture
  • Secure credential management
  • Rate limiting and error handling

Vector Database Integration

• Pinecone setup for semantic search
  • Document embedding and retrieval
  • Similar conversation finding
  • Knowledge base integration
• Chat history vectorization
  • Contextual conversation search
  • Pattern recognition
  • User behavior analysis
• Enhanced context management
  • Dynamic context window
  • Relevance scoring
  • Memory optimization

Development Timeline

Phase 1 (Current): Basic Chatbot ✓

• Streamlit interface with native chat components
• Claude integration with abstraction layer
• Session-based chat history
• Basic error handling and user feedback

Phase 2: Persistence & Search

• Database integration for permanent storage
• Vector database setup for semantic search
• Enhanced chat history management
• User authentication system

Phase 3: MCP Integration

• Tool server implementation
• External service connections
• Enhanced context handling
• Advanced error recovery

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

MIT