title	SQL Query Agent Environment
emoji	🤖
colorFrom	blue
colorTo	green
sdk	python
sdk_version
python_version	3.10
app_file	app.py
pinned	false

SQL Query Agent Environment

A minimal but fully working SQL Query Agent Environment for hackathon validation with OpenEnv-compatible endpoints.

Project Structure

.
├── Dockerfile              # Docker container configuration
├── README.md              # This file
├── requirements.txt       # Python dependencies
├── openenv.yaml           # OpenEnv configuration
├── app.py                 # Gradio UI application
├── inference.py           # Main inference module
├── baseline_inference.py  # Baseline inference module
├── models.py              # Pydantic models
├── ecommerce.db           # SQLite database (auto-created)
├── __init__.py            # Package init
├── server/
│   ├── __init__.py
│   └── app.py             # FastAPI application
└── tests/
    └── test_smoke.py      # Smoke tests

Features

• SQLite-backed ecommerce database with sample data
• OpenEnv-compatible reset/step/state API
• FastAPI application with health checks
• Read-only SQL query execution
• Automatic database initialization

How to Run Locally

1. Create and Activate Virtual Environment

python3 -m venv .venv
source .venv/bin/activate

2. Install Dependencies

pip install -r requirements.txt

3. Run the Server

uvicorn server.app:app --host 0.0.0.0 --port 7860

Or run directly with Python:

python -c "from server.app import app; import uvicorn; uvicorn.run(app, host='0.0.0.0', port=7860)"

4. Test Endpoints with curl

# Health check
curl -X GET http://localhost:7860/health

# Get state
curl -X GET http://localhost:7860/state

# Reset environment
curl -X POST http://localhost:7860/reset \
  -H 'Content-Type: application/json' \
  -d '{"task_id":"task_1"}'

# Execute SQL query
curl -X POST http://localhost:7860/step \
  -H 'Content-Type: application/json' \
  -d '{"query":"SELECT COUNT(*) FROM customers;","task_id":"task_1"}'

5. Run openenv validate

source .venv/bin/activate
openenv validate

API Endpoints

Endpoint	Method	Description
/	GET	Root response
/health	GET	Health check
/state	GET	Get current environment state
/reset	POST	Reset environment and get task info
/step	POST	Execute SQL query and get observation
/docs	GET	FastAPI auto documentation

Deploy to Hugging Face Spaces

Option 1: Using Hugging Face CLI

# Install huggingface_hub
pip install huggingface_hub

# Login
huggingface-cli login

# Create space
huggingface-cli create-repo sql-query-agent --type space

# Clone and push
git clone https://huggingface.co/spaces/YOUR_USERNAME/sql-query-agent
cd sql-query-agent
# Copy all files from this repo
git add .
git commit -m "Initial commit"
git push

Option 2: Using Docker on HF Spaces

1. Create a new Space on Hugging Face
2. Select "Docker" as the SDK
3. Add your Dockerfile and requirements.txt
4. HF will automatically build and deploy

Database Schema

The environment includes an ecommerce database with these tables:

• customers: Customer information
• categories: Product categories
• products: Product catalog
• orders: Customer orders
• order_items: Individual order items

Task Examples

Task ID	Description
task_1	Count total customers
task_2	Find products with price > 50
task_3	Calculate revenue from completed orders
task_4	List top 3 customers by order count

Environment Variables

Variable	Default	Description
DB_PATH	ecommerce.db	Path to SQLite database

License

MIT