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: ```bash git clone <repository-url> cd streamlit_chatbot ``` 2. Install dependencies: ```bash 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: ```bash 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