SQ3M - AI-Powered Database Query Assistant

A Python CLI tool that converts natural language queries into SQL using Large Language Models (LLM). Built with Clean Architecture principles.

🚀 Features

• 🤖 Natural language to SQL conversion using OpenAI completion models
• 🗄️ Multi-database support for MySQL and PostgreSQL
• 🧠 Automatic table purpose inference using LLM
• 🎨 Beautiful CLI interface with Rich
• ⚙️ Environment variable configuration
• 🏗️ Clean Architecture design

📦 Installation

Using pip

pip install sq3m

Using uv (recommended for development)

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository and setup
git clone https://github.com/leegyurak/sq3m.git
cd sq3m
uv sync

⚙️ Configuration

Set up your environment variables in a .env file or export them:

# OpenAI Configuration
export OPENAI_API_KEY=your_openai_api_key
export OPENAI_MODEL=gpt-3.5-turbo  # Optional, defaults to gpt-3.5-turbo

# Database Configuration (Optional - can be set interactively)
export DB_TYPE=mysql  # mysql or postgresql
export DB_HOST=localhost
export DB_PORT=3306
export DB_NAME=your_database
export DB_USERNAME=your_username
export DB_PASSWORD=your_password

🔧 How to Use

Quick Start

1. Install sq3m:

   pip install sq3m
   

2. Set up your OpenAI API key:

   export OPENAI_API_KEY=your_openai_api_key
   

3. Run the tool:

   sq3m
   

Step-by-Step Usage

When you run sq3m, the tool will guide you through an interactive setup:

1. 🤖 LLM Configuration

• If OPENAI_API_KEY is not set, you'll be prompted to enter it
• Optionally configure the model (defaults to gpt-3.5-turbo)

2. 🗄️ Database Connection

The tool will ask for your database details:

• Database Type: Choose between MySQL, PostgreSQL, or SQLite
• Host: Database server address (e.g., localhost)
• Port: Database port (e.g., 3306 for MySQL, 5432 for PostgreSQL)
• Database Name: Your database name
• Username & Password: Your database credentials

Pro Tip: Set these as environment variables to skip the interactive setup:

export DB_TYPE=mysql
export DB_HOST=localhost
export DB_PORT=3306
export DB_NAME=your_database
export DB_USERNAME=your_username
export DB_PASSWORD=your_password

3. 📊 Schema Analysis

• sq3m automatically analyzes all tables in your database
• Uses AI to infer the purpose of each table
• Creates a comprehensive understanding of your database structure

4. 💬 Interactive Query Mode

Now you can ask questions in natural language!

💡 Example Conversations

🤖 sq3m > Show me all users
Generated SQL:
SELECT * FROM users;

Results:
┌────┬──────────┬─────────────────────┬────────────────────┐
│ id │   name   │       email         │    created_at      │
├────┼──────────┼─────────────────────┼────────────────────┤
│ 1  │ John Doe │ john@example.com    │ 2024-01-15         │
│ 2  │ Jane Doe │ jane@example.com    │ 2024-01-16         │
└────┴──────────┴─────────────────────┴────────────────────┘

🤖 sq3m > How many orders were placed this month?
Generated SQL:
SELECT COUNT(*) as order_count
FROM orders
WHERE MONTH(created_at) = MONTH(CURRENT_DATE())
  AND YEAR(created_at) = YEAR(CURRENT_DATE());

Results:
┌─────────────┐
│ order_count │
├─────────────┤
│     47      │
└─────────────┘

🤖 sq3m > What are the top 3 selling products?
Generated SQL:
SELECT p.name, SUM(oi.quantity) as total_sold
FROM products p
JOIN order_items oi ON p.id = oi.product_id
GROUP BY p.id, p.name
ORDER BY total_sold DESC
LIMIT 3;

Results: [showing results...]

🎯 Available Commands

While in the interactive mode, you can use these special commands:

🔧 Advanced Configuration

Create a .env file in your working directory:

# .env file
OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=gpt-4  # Use GPT-4 for better results

DB_TYPE=postgresql
DB_HOST=localhost
DB_PORT=5432
DB_NAME=myapp_production
DB_USERNAME=myuser
DB_PASSWORD=mypassword

💡 Tips for Better Results

1. Be Specific: "Show users created this week" vs "Show users"
2. Use Table Names: If you know them, mention specific table names
3. Ask Follow-ups: "Can you also show their email addresses?"
4. Use Business Terms: "Show revenue by month" instead of "sum sales"

🏗️ Architecture

The project follows Clean Architecture principles:

sq3m/
├── domain/           # Business logic and entities
│   ├── entities/     # Core business objects
│   └── interfaces/   # Abstract interfaces
├── application/      # Use cases and business rules
│   ├── services/     # Application services
│   └── use_cases/    # Specific business use cases
├── infrastructure/   # External interfaces
│   ├── database/     # Database implementations
│   ├── llm/          # LLM service implementations
│   └── prompts/      # System prompts
├── interface/        # User interface
│   └── cli/          # CLI implementation
└── config/           # Configuration management

🛠️ Development

Prerequisites

• Python 3.10+
• uv package manager (recommended for fast dependency management)

UV Package Manager Setup

This project uses uv for fast Python package management.

Install uv:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or with pip
pip install uv

Setup Development Environment

# Clone the repository
git clone https://github.com/leegyurak/sq3m.git
cd sq3m

# Initialize Python environment and install dependencies
uv sync --all-extras --dev

# Install pre-commit hooks
uv run pre-commit install

Activate Virtual Environment (optional):

# Linux/macOS
source .venv/bin/activate

# Windows
.venv\Scripts\activate

Development Workflow

1. Make changes to the code
2. Run tests: uv run pytest
3. Run linting: uv run ruff check --fix .
4. Run formatting: uv run ruff format .
5. Run type checking: uv run mypy sq3m/
6. Commit changes (pre-commit hooks will run automatically)

Running Tests

# Run all tests
uv run pytest

# Run unit tests only
uv run pytest tests/unit

# Run integration tests
uv run pytest tests/integration

# Run with coverage
uv run pytest --cov=sq3m

# Run tests excluding slow ones
uv run pytest -m "not slow"

Code Quality

# Linting and formatting with ruff
uv run ruff check --fix .
uv run ruff format .

# Type checking
uv run mypy sq3m/

# Pre-commit hooks (run automatically on commit)
uv run pre-commit run --all-files

Running the Application

# Run directly with uv
uv run sq3m

# Or activate environment first
source .venv/bin/activate
sq3m

📚 Dependencies

Runtime Dependencies

• click: CLI framework
• rich: Beautiful terminal UI
• openai: OpenAI API client
• python-dotenv: Environment variable management
• psycopg2-binary: PostgreSQL driver
• pymysql: MySQL driver
• sqlparse: SQL parsing utilities
• pydantic: Data validation

Development Dependencies

• pytest: Testing framework
• pytest-cov: Coverage reporting
• pytest-asyncio: Async testing support
• ruff: Fast Python linter and formatter
• pre-commit: Git hooks framework
• mypy: Static type checker

📋 Requirements

• Python: 3.10 or higher
• uv: Package manager (recommended) or pip

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• Thanks to OpenAI for providing the completion models
• Built with modern Python tools: uv, ruff, pytest
• Inspired by Clean Architecture principles