django-ai-lens 0.1.1

Natural language queries for Django models, powered by AI

Django AI Lens

Natural language queries for Django models, powered by AI. Ask questions in plain English and get structured data back—with optional Chart.js-ready output for visualizations.

Features

• Natural language → Django ORM: Converts questions like "Total revenue per customer country in 2024" into validated Django querysets
• Schema extraction: Automatically crawls your Django project for models, fields, and relationships
• AI-powered: Uses Google Gemini to interpret questions and produce structured query JSON
• Chart-ready output: Returns data shaped for bar, line, pie, doughnut, radar, and scatter charts
• Safe & validated: Pydantic schemas and field validation prevent SQL injection and unsafe operations

Requirements

• Python 3.10+
• Django 4.x or 5.x
• Google Gemini API key

Installation

pip install django-ai-lens

Configuration

Add the following to your Django project's settings.py:

# Required
GEMINI_API_KEY = "your_gemini_api_key_here"

# Optional (defaults to gemini-1.5-flash)
GEMINI_MODEL = "gemini-1.5-flash"  # or gemini-1.5-pro, gemini-2.0-flash, etc.

Get your API key at Google AI Studio.

How to Use

Use Django AI Lens from within your Django project (views, management commands, shell). Django must be configured before calling run_ai_query.

from django_ai_lens import run_ai_query

result = run_ai_query(
    question="Total revenue per customer country in 2024, as a bar chart",
    app_labels=["myapp", "orders"],  # Your Django app labels
)

print(result["data"])        # List of dicts (rows)
print(result["chart_data"])  # Chart.js-ready labels + datasets
print(result["query_schema"])  # The AI-generated query structure

Output structure:

{
    "success": True,
    "question": "Total revenue per customer country in 2024, as a bar chart",
    "query_schema": { ... },   # Raw JSON from the AI
    "data": [{"country": "US", "total_revenue": 12500.50}, ...],
    "row_count": 5,
    "chart_type": "bar",
    "chart_data": {
        "labels": ["US", "UK", "DE", ...],
        "datasets": [{"label": "Total Revenue", "data": [12500.5, 8900.0, ...]}],
        "label_field": "country",
        "chart_type": "bar"
    }
}

Example: Django view

# views.py
from django.http import JsonResponse
from django_ai_lens import run_ai_query

def ai_query_view(request):
    question = request.GET.get("q", "Count all users")
    result = run_ai_query(
        question=question,
        app_labels=["myapp", "auth"],
    )
    return JsonResponse(result)

Example: Django management command

# management/commands/query.py
from django.core.management.base import BaseCommand
from django_ai_lens import run_ai_query

class Command(BaseCommand):
    def add_arguments(self, parser):
        parser.add_argument("question", type=str)
        parser.add_argument("--apps", nargs="+", default=["myapp"])

    def handle(self, *args, **options):
        result = run_ai_query(
            question=options["question"],
            app_labels=options["apps"],
        )
        self.stdout.write(str(result["data"]))

Example: Django shell

# python manage.py shell
from django_ai_lens import run_ai_query

result = run_ai_query(
    question="Count of orders per month in 2024",
    app_labels=["orders", "myapp"],
)

Schema extraction (optional)

Extract and save the schema to a JSON file for debugging or documentation:

# python manage.py shell
from django_ai_lens import extract_and_save

# Saves to .django_ai_lens_schema.json in current directory
result = extract_and_save()
print(result["output_path"])
print(result["app_labels"])

Example questions

The AI understands a wide range of questions, such as:

• "Total revenue per customer country in 2024, as a bar chart"
• "Average order value per product category for orders with at least 2 items"
• "Top 10 customers by order count"
• "Count of orders per month in 2024"
• "Average price by product category"

Project structure

django-ai-lens/
├── django_ai_lens/
│   ├── __init__.py
│   ├── ai_query.py          # Main entry: run_ai_query()
│   ├── schema_extrator.py   # Schema extraction & loading
│   ├── prompt_builder.py    # LLM prompt construction
│   ├── query_schema.py      # Pydantic schemas for validation
│   └── queryset_builder.py  # Translates schema → Django ORM
├── pyproject.toml
└── README.md

API reference

run_ai_query(question, app_labels, max_retries=2)

Runs the full pipeline: build schema from Django models → ask LLM → validate → build queryset → return data.

Argument	Type	Description
question	str	Natural language query
app_labels	list	Django app labels to query (e.g. ["myapp", "orders"])
max_retries	int	Number of retries if the AI returns invalid JSON or queryset fails

Returns: dict with success, question, query_schema, data, row_count, chart_type, chart_data

Raises: ValueError if app_labels is empty; RuntimeError if GEMINI_API_KEY is not set or all retries fail.

extract_and_save(output_file=None)

Extracts model schemas from the currently loaded Django and saves to JSON. Requires Django to be configured.

Returns: {"schema": str, "app_labels": list, "output_path": str}

load_schema(schema_file=None, project_path=None)

Loads schema and app_labels from a cached JSON file (e.g. produced by extract_and_save).

Returns: (schema_str, app_labels)

License

MIT License. See LICENSE for details.