DSPy_MCP_Client_Chatbot

# MCP Versatile Assistant A conversational AI assistant built with DSPy and Streamlit that helps users interact with various data sources through natural language, with Airbnb data as one example use case. ## Project Overview  This project implements an AI-powered assistant that can answer questions, search for information, and provide insights across multiple domains through natural language conversations. The system's capabilities depend on the MCP tools configured - from querying Airbnb listings to performing web searches, accessing databases, and more. The system uses: - **DSPy**: A framework for programmatically controlling language models - **Streamlit**: For building the interactive web interface - **FastAPI**: For the backend API server - **MCP (Model Control Protocol)**: For managing the communication with various tool servers [DSPy Unofficial](https://github.com/ThanabordeeN/dspy-mcp-intregation.git) ## Architecture The project consists of two main components: ### 1. Backend (FastAPI Server) - Handles communication with DSPy and the language model - Manages MCP server connections to various tools - Implements the ReAct agent for reasoning and tool use - Maintains conversation history ### 2. Frontend (Streamlit App) - Provides a chat interface for users - Sends queries to the backend - Displays responses from the AI assistant - Manages chat history with persistence ## Setup Instructions ### Prerequisites - Python 3.9+ - API key for Google Gemini (or other compatible LLM) ### Installation 1. Clone this repository: ``` git clone <repository-url> cd <dir> ``` 2. Install dependencies: ``` pip install -r requirements.txt ``` ## Running the Application 0. Install DSPy (MCP Version - Unofficial): ```bash git clone https://github.com/ThanabordeeN/dspy-mcp-intregation.git && cd dspy-mcp-intregation && pip install . ``` 1. Start the backend server: ``` cd backend uvicorn main:app --host 0.0.0.0 --port 8001 ``` 2. In a separate terminal, start the Streamlit frontend: ``` streamlit run app.py ``` 3. Open a web browser and navigate to `http://localhost:8501` ## Usage 1. Enter your questions in the chat input 2. The assistant will process your query, using the appropriate tools configured in the MCP server 3. Review the response in the chat interface 4. You can save and manage multiple chat sessions through the sidebar ## Example Queries Depending on configured tools, you can ask questions like: - **Airbnb data:** "Find me Airbnb listings in New York under $150 per night" - **Web browsing:** "Search for the latest news about artificial intelligence" - **Data analysis:** "Analyze the trend of housing prices in San Francisco over the last decade" - **Image generation:** "Create an image of a futuristic city skyline" - **Math problems:** "What is the solution to this equation: 3x^2 + 2x - 5 = 0?" ## Project Structure ``` MCP_assistant/ ├── README.md # This documentation ├── app.py # Streamlit frontend ├── backend/ │ └── main.py # FastAPI backend server ├── data/ │ └── chat_sessions.json # Persisted chat history └── requirements.txt # Project dependencies ``` ## Features - Natural language querying with multiple tool capabilities - Configurable tools through MCP server settings - Persistent chat history management - Export/import of chat sessions - Customizable language model settings ## License MIT License ## Contributing Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.