Transform natural language questions into SQL queries with the power of AI! 🚀

SQL Natural Language Interface is a revolutionary tool that bridges the gap between human language and database queries. Simply ask questions in plain English, and watch as our AI-powered system generates precise SQL queries and delivers beautiful, actionable results.

• Democratize Data Access: No SQL knowledge required - anyone can query databases
• Boost Productivity: Get insights in seconds, not hours
• Reduce Errors: AI-generated queries minimize human mistakes
• Universal Compatibility: Works with PostgreSQL and MySQL databases

• Dual AI Support: Choose between OpenAI GPT-4 or Google Gemini
• Context-Aware: Understands your database schema for accurate queries
• Natural Language: Ask questions like "Show me customers who bought products last month"

• Read-Only Queries: Only SELECT statements allowed - your data stays safe
• SQL Injection Protection: Advanced validation prevents malicious queries
• Input Sanitization: Multiple layers of security checks

• Interactive Web Interface: Beautiful, responsive React-based UI
• Real-Time Results: Instant query execution and results display
• Export Capabilities: Download results as CSV files
• Smart Summaries: AI-generated natural language summaries of your data

• RESTful API: Easy integration with existing applications
• Comprehensive Logging: Full audit trail of all operations
• Error Handling: Graceful error management with detailed feedback
• Database Agnostic: Seamless switching between PostgreSQL and MySQL

• Python 3.8 or higher
• PostgreSQL or MySQL database
• OpenAI API key or Google Gemini API key

1. Clone the repository

   git clone https://github.com/yourusername/sql-nlp-interface.git cd sql-nlp-interface

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment variables Create a .env file in the project root:

   # API KeysOPENAI_API_KEY=your_openai_api_key_hereGEMINI_API_KEY=your_gemini_api_key_here# Database ConfigurationDATABASE_URL=postgresql://username:password@localhost:5432/database_nameDATABASE_TYPE=postgresql# or mysqlMYSQLDB_URL=mysql+pymysql://username:password@localhost:3306/database_name# AI Model ProviderMODEL_PROVIDER=openai# or gemini

4. Launch the application

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

5. Open your browser Navigate to http://localhost:8000 and start querying!

Natural Language: "Show me all customers from New York" Generated SQL: SELECT * FROM customers WHERE city = 'New York'; 

Natural Language: "What are the top 5 products by sales in the last quarter?" Generated SQL: SELECT product_name, SUM(quantity * price) as total_sales FROM orders o JOIN order_items oi ON o.id = oi.order_id JOIN products p ON oi.product_id = p.id WHERE o.created_at >= DATE_SUB(NOW(), INTERVAL 3 MONTH) GROUP BY product_name ORDER BY total_sales DESC LIMIT 5; 

importrequestsresponse=requests.post('http://localhost:8000/generate-query', json={'user_query': 'Show me monthly revenue trends', 'db_schema': 'your_schema_here' }) data=response.json() print(f"SQL Query: {data['sql_query']}") print(f"Results: {data['results']}")

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Frontend │ │ FastAPI │ │ Database │ │ (React) │◄──►│ Backend │◄──►│ (PostgreSQL/ │ │ │ │ │ │ MySQL) │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ ▼ ┌─────────────────┐ │ AI Models │ │ (OpenAI/ │ │ Gemini) │ └─────────────────┘ 

Generate Query

POST /generate-query{"user_query": "Show me all users who registered this month", "db_schema": "Table: users\nColumns: id (integer), name (varchar), email (varchar), created_at (timestamp)" }

Response

{"sql_query": "SELECT * FROM users WHERE created_at >= DATE_TRUNC('month', CURRENT_DATE);", "results": [...], "csv_base64": "...", "csv_filename": "result_abc123.csv", "summary": "Found 42 users who registered this month." }

*At least one API key is required **Required if DATABASE_TYPE is mysql

• PostgreSQL: Full support with advanced features
• MySQL: Complete compatibility with MySQL-specific syntax

We welcome contributions from the community! Here's how you can help:

• Use the issue tracker
• Include detailed steps to reproduce
• Provide system information and error logs

• Open a feature request
• Describe the use case and expected behavior
• Include mockups or examples if possible

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

• Follow PEP 8 style guidelines
• Add tests for new features
• Update documentation as needed
• Ensure all tests pass before submitting

FROM python:3.9-slim WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . EXPOSE 8000 CMD ["uvicorn", "db_NLP:app", "--host", "0.0.0.0", "--port", "8000"]

• Use environment-specific configuration
• Implement proper logging and monitoring
• Set up database connection pooling
• Configure CORS for your domain
• Use HTTPS in production

• Query Generation: < 2 seconds average response time
• Database Execution: Depends on query complexity and data size
• Concurrent Users: Supports 100+ concurrent requests
• Memory Usage: ~200MB base memory footprint

• Input Validation: All user inputs are sanitized
• SQL Injection Protection: Multiple layers of protection
• Read-Only Access: Only SELECT queries are allowed
• API Key Security: Secure handling of API credentials
• CORS Configuration: Configurable cross-origin policies

Connection Errors

# Check database connectivity python -c "from db_NLP import engine; print(engine.connect())"

API Key Issues

# Verify environment variables python -c "import os; print(os.getenv('OPENAI_API_KEY'))"

Port Already in Use

# Use different port uvicorn db_NLP:app --port 8001

• OpenAI for providing powerful language models
• Google for Gemini AI capabilities
• FastAPI for the excellent web framework
• SQLAlchemy for robust database interactions
• React for the beautiful frontend interface

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

If this project helped you, please consider:

• ⭐ Starring the repository
• 🍴 Forking for your own use
• 📢 Sharing with your network

• GitHub: @yourusername
• Email: [email protected]
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