🚀 DBChat (text-to-sql) - Intelligent Database Query Assistant

A powerful, AI-driven database query assistant that leverages RAG (Retrieval-Augmented Generation) to provide intelligent SQL query generation and database interaction.

🌟 Features

• 🤖 AI-Powered Query Generation: Utilizes Qwen 1.8b LLM for natural language to SQL conversion
• 📊 Schema-Aware Responses: Automatically detects and uses database structure
• 🔗 Smart Table Relationships: Identifies and suggests proper table joins
• 📝 Query Validation: Ensures generated queries match existing schema
• 📈 Result Export: Automatic CSV export with metadata tracking
• 🛡️ Error Handling: Robust error management and recovery

🛠️ Installation

1. Clone the repository:

git clone https://github.com/98tarunkumar/text_to_sql.git
cd text_to_sql

2. Install dependencies:

pip install -r requirements.txt

3. Configure database connection in dbQuery.py:

self.db_connection = mysql.connector.connect(
    host="your_host",
    user="your_user",
    password="your_password",
    database="your_database"
)

📋 Prerequisites

• Python 3.8+
• MySQL 8.0+
• Ollama with Qwen 1.8b model
• Required Python packages:
  • mysql-connector-python
  • pandas
  • ollama

🚀 Usage

1. Start the application:

python dbQuery.py

2. Enter your database questions in natural language:

Enter your database question: Show me all orders from the last month

3. Get AI-generated responses with:

• Query explanation
• SQL query
• Results in both console and CSV format

💡 Example Queries

-- Natural Language: "Show active users and their orders"
SELECT 
    u.username,
    o.order_id,
    o.order_date
FROM users u
JOIN orders o ON u.user_id = o.user_id
WHERE u.status = 'active';

🏗️ Architecture

dbchat/
├── dbQuery.py          # Main application logic
├── requirements.txt    # Project dependencies
└── README.md          # Documentation

🔍 Key Components

• DatabaseQueryAssistant: Core class handling database interactions
• RAG Implementation: Schema-aware context generation
• Query Validation: Schema compliance checking
• Result Management: CSV export and metadata tracking

🛡️ Error Handling

• Database connection management
• Query validation
• Schema compliance checking
• JSON serialization handling
• Graceful error recovery

🤝 Contributing

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

📝 License

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

👏 Acknowledgments

• Qwen LLM team for the language model
• MySQL team for the database engine
• All contributors and users of this project

📞 Support

For support, please open an issue in the GitHub repository or contact the maintainers.

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Made with ❤️ by Tarun