# Django AI Analyst - README

This package provides a Django application that enables AI-powered analysis of Django models through natural language prompts. The AI agent can analyze model structures, relationships, and data to provide insights in JSON format.

## Features

- Natural language prompt processing for model analysis
- Support for various types of insights:
  - Count queries (e.g., "How many cars do we have?")
  - Relationship analysis between models
  - Performance optimization suggestions
  - Statistical analysis of model fields
  - General model structure analysis
- Dealer-specific data access controls
- Caching mechanism for improved performance
- Visualization data generation for frontend display
- Comprehensive test suite

## Installation

1. Add 'ai_analyst' to your INSTALLED_APPS setting:

```python
INSTALLED_APPS = [
    ...
    'ai_analyst',
]
```

2. Include the ai_analyst URLconf in your project urls.py:

```python
path('api/ai/', include('ai_analyst.urls')),
```

3. Run migrations to create the AnalysisCache model:

```bash
python manage.py makemigrations ai_analyst
python manage.py migrate
```

## Usage

Send POST requests to the `/api/ai/analyze/` endpoint with a JSON body containing:

```json
{
  "prompt": "How many cars do we have?",
  "dealer_id": 1  // Optional, for dealer-specific queries
}
```

The response will be a JSON object with insights based on the prompt:

```json
{
  "status": "success",
  "request_id": "a1b2c3d4",
  "timestamp": "2025-05-25T23:21:56Z",
  "prompt": "How many cars do we have?",
  "insights": [
    {
      "type": "count_analysis",
      "results": [
        {
          "model": "Car",
          "count": 42,
          "filters_applied": {}
        }
      ],
      "visualization_data": {
        "chart_type": "bar",
        "labels": ["Car"],
        "data": [42]
      }
    }
  ]
}
```

## Customization

### Cache Duration

You can customize the cache duration by setting the `CACHE_DURATION` class variable in the `ModelAnalystView` class:

```python
# In your settings.py
AI_ANALYST_CACHE_DURATION = 7200  # 2 hours in seconds

# Then in views.py
class ModelAnalystView(View):
    CACHE_DURATION = getattr(settings, 'AI_ANALYST_CACHE_DURATION', 3600)
    # ...
```

### Permission Logic

The `_check_permissions` method in `ModelAnalystView` can be customized to match your application's permission model:

```python
def _check_permissions(self, user, dealer_id):
    # Your custom permission logic here
    return user.has_perm('ai_analyst.can_analyze_models')
```

## Example Prompts

- "How many cars do we have?"
- "Show relationship between User and Order"
- "What is the average price of products?"
- "Count active users"
- "Identify performance issues in the Order model"
- "Show maximum age of customers"

## Frontend Integration

The JSON responses include visualization_data that can be used with charting libraries like Chart.js:

```javascript
// Example with Chart.js
fetch('/api/ai/analyze/', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    prompt: 'How many cars do we have?',
    dealer_id: 1
  }),
})
.then(response => response.json())
.then(data => {
  if (data.status === 'success' && data.insights.length > 0) {
    const insight = data.insights[0];
    const vizData = insight.visualization_data;
    
    const ctx = document.getElementById('insightChart').getContext('2d');
    new Chart(ctx, {
      type: vizData.chart_type,
      data: {
        labels: vizData.labels,
        datasets: [{
          label: insight.type,
          data: vizData.data,
          backgroundColor: [
            'rgba(255, 99, 132, 0.2)',
            'rgba(54, 162, 235, 0.2)',
            'rgba(255, 206, 86, 0.2)'
          ],
          borderColor: [
            'rgba(255, 99, 132, 1)',
            'rgba(54, 162, 235, 1)',
            'rgba(255, 206, 86, 1)'
          ],
          borderWidth: 1
        }]
      }
    });
  }
});
```