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---
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