InfoJOE

InfoJOE is an AI-powered natural language interface for PostgreSQL databases. It allows users to explore and query structured data using plain English, powered by Qwen 2.5 running via LM Studio.

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Getting Started

Start the Application

Run the following command to build and start the containers:

docker compose up -d

This will:

1. Build the Docker images for the FastAPI and Streamlit services.
2. Start the containers and serve the application.

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Features

• Auto Table Discovery:
  Automatically detects all tables in your connected PostgreSQL database.

• Interactive Table Selection:
  Easily select tables using a click-to-select interface.

• LLM-Generated Data Dictionary:
  Generates a clear, human-readable data dictionary for any selected table using Qwen 2.5.

• Diverse Data Sampling:
  Previews a representative sample of rows to reflect different value distributions and data types, not just the top rows.

• Natural Language to SQL:
  Translate natural language queries into SQL commands using Qwen 2.5.

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Prerequisites

Before you start, ensure the following tools are installed on your system:

• Docker
• Docker Compose

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Access the Application

• FastAPI Backend:
  Visit http://localhost:8000 to access the API.
  Documentation is available at http://localhost:8000/docs

• Streamlit Frontend:
  Visit http://localhost:8080 to interact with the frontend.

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Development Workflow

Stopping the Application

To stop the application, press Ctrl+C or run the following command:

docker-compose down

This will stop and remove the containers, but the built images will remain.

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Directory Structure

The project structure is as follows:

.  
├── backend/               # FastAPI application  
│   ├── db.py              # Handles database connection and queries (e.g., to PostgreSQL or SQLite)
│   ├── main.py            # FastAPI entrypoint  
│   ├── requirements.txt   # Python dependencies for FastAPI  
│   └── Dockerfile         # Dockerfile for FastAPI  
├── frontend/              # Streamlit application  
│   ├── app.py             # Streamlit entrypoint  
│   ├── Dockerfile         # Dockerfile for Streamlit
│   └── requirements.txt   # Python dependencies for streamlit  
├── postgres/              # PostgreSQL setup folder
│   └── init.sql           # SQL script to initialize the database (e.g., table creation, seed data)
├── shared_utils/          # Shared Python utilities used by both frontend and backend
│   └── functions.py       # Streamlit entrypoint  
├── docker-compose.yml     # Docker Compose configuration
└── README.md              # Project documentation  

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Troubleshooting

• Ensure Docker and Docker Compose are installed and running on your system.
• Verify that the required ports (8000 and 8080) are not in use by other applications.

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License

This project is licensed under the MIT License. See the LICENSE file for details.