An AI-powered data analysis tool that converts natural language to SQL queries and visualizes the results.

• Natural Language Queries: Automatically converts natural language questions to SQL without knowing complex SQL syntax
• Multiple LLM Support: Supports OpenAI GPT-4 and Anthropic Claude
• Automatic Visualization: Automatically analyzes query results and generates optimal charts
• Multiple Databases: Supports SQLite, PostgreSQL, and MySQL
• Interactive UI: User-friendly interface based on Streamlit
• Query History: Track and reuse previous query history
• Data Download: Download query results as CSV

easy-sql/
├── app.py                    # Main Streamlit application
├── config/
│   └── settings.py          # Configuration settings
├── src/
│   ├── llm/
│   │   └── nl_to_sql.py     # Natural language to SQL converter
│   ├── database/
│   │   └── db_manager.py    # Database manager
│   └── visualization/
│       └── chart_generator.py # Chart generation
├── data/
│   └── sample.db            # Sample SQLite database
├── tests/                    # Test files
├── requirements.txt          # Python dependencies
├── .env.example             # Environment variables template
└── create_sample_db.py      # Sample database creation script

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

python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

pip install -r requirements.txt

Copy the .env.example file to .env and configure your API keys:

cp .env.example .env

Edit the .env file to enter your API keys:

# For OpenAI
OPENAI_API_KEY=your_openai_api_key_here

# Or for Anthropic
ANTHROPIC_API_KEY=your_anthropic_api_key_here

DEFAULT_LLM_PROVIDER=openai  # or anthropic
DEFAULT_MODEL=gpt-4          # or claude-3-opus-20240229

python create_sample_db.py

streamlit run app.py

The application will automatically open in your browser at http://localhost:8501.

1. Select LLM provider and model from the sidebar
2. Enter your question in natural language
   • Example: "Show me total sales by category"
   • Example: "What are the top 5 customers by order amount?"
3. Click the "Execute" button
4. View the generated SQL query and visualized results

When using the sample database:

• "Show me total sales by category"
• "What are the top 5 customers by order amount?"
• "Show monthly revenue trend"
• "Which products have low stock?"
• "Show customer distribution by city"
• "What is the average order value?"
• "Show products with the highest sales"

In the .env file:

DATABASE_TYPE=sqlite
DATABASE_PATH=data/your_database.db

In the .env file:

DATABASE_TYPE=postgresql
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=your_database
POSTGRES_USER=your_username
POSTGRES_PASSWORD=your_password

In the .env file:

DATABASE_TYPE=mysql
MYSQL_HOST=localhost
MYSQL_PORT=3306
MYSQL_DB=your_database
MYSQL_USER=your_username
MYSQL_PASSWORD=your_password

• Uses GPT-4 or Claude to convert natural language to SQL
• Automatically analyzes database schema to generate accurate queries
• Maintains conversation history for context-based queries

Automatically selects various chart types:

• Bar Chart: Category comparisons
• Line Chart: Time series data and trends
• Pie Chart: Composition ratios
• Scatter Plot: Relationship between two variables
• Histogram: Distribution analysis

• Only SELECT queries allowed (INSERT, UPDATE, DELETE blocked)
• Query validation to prevent SQL injection
• Uses SQLAlchemy for safe query execution

pytest tests/

You can add new LLM providers in src/llm/nl_to_sql.py:

def _convert_with_new_provider(self, query: str, system_prompt: str, conversation_history: Optional[List[Dict]] = None) -> str:
    # Implementation
    pass

You can add new chart generation methods in src/visualization/chart_generator.py.

• Frontend & Backend: Streamlit
• LLM: OpenAI GPT-4, Anthropic Claude
• Database: SQLAlchemy (SQLite, PostgreSQL, MySQL)
• Visualization: Plotly
• Data Processing: Pandas

MIT License

Pull Requests and Issues are always welcome!

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

If you have any questions or suggestions about the project, please create an Issue.

• Query result caching
• Support for more chart types
• Query optimization suggestions
• Automatic multi-table join recommendations
• Natural language-based data filtering
• Dashboard save and share
• Excel file upload support
• Real-time data streaming