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)

Related MCP server: Langflow Document Q&A Server

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

git clone <your-repo-url> cd mcp-chat-3

2. Install Python dependencies

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

uvicorn main:app --reload --port 8080

With Docker

docker build -t mcp-chat-backend . docker run -p 8080:8080 --env-file .env mcp-chat-backend

Deploying to AWS Lambda (SAM)

Install AWS SAM CLI
Build and deploy:

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.

{ "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)

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Resources

GitHub Repository

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MCP Chatbot