| --- | |
| description: | |
| globs: | |
| alwaysApply: true | |
| --- | |
| # Visualization Guidelines | |
| This document outlines the visualization capabilities and best practices for the AI-powered database interface. | |
| ## Visualization Components | |
| ### PandasAI Integration | |
| - Implemented in [postgre_mcp_server.py](mdc:postgre_mcp_server.py) | |
| - Uses OpenAI/Gemini for visualization generation | |
| - Supports multiple chart types: | |
| - Bar charts | |
| - Line charts | |
| - Pie charts | |
| - Scatter plots | |
| - Box plots | |
| ### Data Processing | |
| - Data formatting in [app.py](mdc:app.py) | |
| - JSON to DataFrame conversion | |
| - Column type handling | |
| - Data cleaning and preparation | |
| - Long text truncation | |
| ## Visualization Workflow | |
| ### 1. Request Processing | |
| - Natural language visualization request | |
| - Data extraction from query results | |
| - JSON data formatting | |
| - Visualization prompt generation | |
| ### 2. Chart Generation | |
| - PandasAI initialization | |
| - LLM-based chart type selection | |
| - Customization parameters: | |
| - Colors | |
| - Labels | |
| - Legends | |
| - Axis formatting | |
| - Title and description | |
| ### 3. Output Handling | |
| - Image file generation | |
| - Base64 encoding for web display | |
| - Temporary file management | |
| - Cleanup procedures | |
| ## Best Practices | |
| ### Data Preparation | |
| - Appropriate data types | |
| - Missing value handling | |
| - Outlier management | |
| - Data aggregation | |
| - Column selection | |
| ### Visualization Design | |
| - Clear labels and titles | |
| - Appropriate chart types | |
| - Color scheme consistency | |
| - Legend placement | |
| - Axis formatting | |
| ### Performance | |
| - Efficient data processing | |
| - Memory management | |
| - File cleanup | |
| - Caching strategies | |
| - Resource optimization | |
| ## Common Use Cases | |
| ### Business Analytics | |
| - Sales trends | |
| - Customer distribution | |
| - Product performance | |
| - Time series analysis | |
| - Comparative analysis | |
| ### Data Exploration | |
| - Distribution analysis | |
| - Correlation visualization | |
| - Pattern identification | |
| - Anomaly detection | |
| - Trend analysis | |
| ## Error Handling | |
| ### Common Issues | |
| - Data format errors | |
| - Visualization generation failures | |
| - Memory constraints | |
| - File system issues | |
| - API limitations | |
| ### Recovery Strategies | |
| - Fallback visualizations | |
| - Error messages | |
| - Data validation | |
| - Resource management | |
| - User feedback | |