MCP Chatbot

# MCP Chat Backend This project is a serverless FastAPI backend for a chatbot that generates and executes SQL queries on a Postgres database using OpenAI's GPT models, then returns structured, UI-friendly responses. It is designed to run on AWS Lambda via AWS SAM, but can also be run locally or in Docker. ## Features - FastAPI REST API with a single `/ask` endpoint - Uses OpenAI GPT models to generate and summarize SQL queries - Connects to a Postgres (Supabase) database - Returns structured JSON responses for easy frontend rendering - CORS enabled for frontend integration - Deployable to AWS Lambda (SAM), or run locally/Docker - Verbose logging for debugging (CloudWatch) ## Project Structure ``` ├── main.py # Main FastAPI app and Lambda handler ├── requirements.txt # Python dependencies ├── template.yaml # AWS SAM template for Lambda deployment ├── samconfig.toml # AWS SAM deployment config ├── Dockerfile # For local/Docker deployment ├── .gitignore # Files to ignore in git └── .env # (Not committed) Environment variables ``` ## Setup ### 1. Clone the repository ```sh git clone <your-repo-url> cd mcp-chat-3 ``` ### 2. Install Python dependencies ```sh python -m venv .venv source .venv/bin/activate # or .venv\Scripts\activate on Windows pip install -r requirements.txt ``` ### 3. Set up environment variables Create a `.env` file (not committed to git): ``` OPENAI_API_KEY=your-openai-key SUPABASE_DB_NAME=your-db SUPABASE_DB_USER=your-user SUPABASE_DB_PASSWORD=your-password SUPABASE_DB_HOST=your-host SUPABASE_DB_PORT=your-port ``` ## Running Locally ### With Uvicorn ```sh uvicorn main:app --reload --port 8080 ``` ### With Docker ```sh docker build -t mcp-chat-backend . docker run -p 8080:8080 --env-file .env mcp-chat-backend ``` ## Deploying to AWS Lambda (SAM) 1. Install [AWS SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html) 2. Build and deploy: ```sh sam build sam deploy --guided ``` - Configure environment variables in `template.yaml` or via the AWS Console. - The API will be available at the endpoint shown after deployment (e.g. `https://xxxxxx.execute-api.region.amazonaws.com/Prod/ask`). ## API Usage ### POST /ask - **Body:** `{ "question": "your question here" }` - **Response:** Structured JSON for chatbot UI, e.g. ```json { "messages": [ { "type": "text", "content": "Sample 588 has a resistance of 1.2 ohms.", "entity": { "entity_type": "sample", "id": "588" } }, { "type": "list", "items": ["Item 1", "Item 2"] } ] } ``` - See `main.py` for the full schema and more details. ## Environment Variables - `OPENAI_API_KEY`: Your OpenAI API key - `SUPABASE_DB_NAME`, `SUPABASE_DB_USER`, `SUPABASE_DB_PASSWORD`, `SUPABASE_DB_HOST`, `SUPABASE_DB_PORT`: Your Postgres database credentials ## Development Notes - All logs are sent to stdout (and CloudWatch on Lambda) - CORS is enabled for all origins by default - The backend expects the frontend to handle the structured response format ## License MIT (or your license here)