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

Add 'ai_analyst' to your INSTALLED_APPS setting:

INSTALLED_APPS = [
    ...
    'ai_analyst',
]

Include the ai_analyst URLconf in your project urls.py:

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

Run migrations to create the AnalysisCache model:

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:

{
  "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:

{
  "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:

# 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:

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:

// 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
        }]
      }
    });
  }
});

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