django-ai-lens 0.1.5

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
• Human-friendly summaries: Optional second LLM pass to render queryset results as plain-language answers
• 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-2.5-flash)
GEMINI_MODEL = "gemini-2.5-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"],  # Optional: omit to use all apps from INSTALLED_APPS
    force_regenerate_schema=False,   # Set True when models have changed
    human_friendly_result=False,     # Set True for human-friendly LLM summary (see below)
)

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"
    }
}

Human-friendly result (human_friendly_result=True)

When human_friendly_result=True, the pipeline runs a second LLM call with the queryset result and the original question to produce a plain-language answer. Useful for chatbots or when you want to show users a readable summary instead of raw data:

result = run_ai_query(
    question="How many users signed up last month?",
    human_friendly_result=True,
)
print(result["human_friendly_result"])  # e.g. "42 users signed up last month."
# result["data"] is still available with the raw rows

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"])

generate_schema(app_labels=None)

Generates the Django models schema only (no AI/LLM call) and prints it to screen. Use for debugging to inspect the schema that would be sent to the LLM:

# python manage.py shell
from django_ai_lens import generate_schema

generate_schema()                    # Schema for all installed apps
generate_schema(app_labels=["myapp"])  # Schema for specific apps

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=None, max_retries=2, force_regenerate_schema=False, human_friendly_result=False)

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, optional	Django app labels to query (e.g. ["myapp", "orders"]). If omitted, uses all apps from INSTALLED_APPS (excluding Django built-ins).
max_retries	int	Number of retries if the AI returns invalid JSON or queryset fails
force_regenerate_schema	bool	If True, regenerates and saves the schema JSON file before running the query. Use when models have changed.
human_friendly_result	bool	If True, runs a second LLM call to add human_friendly_result (plain-language summary). If False (default), returns raw queryset data only.

Returns: dict with success, question, query_schema, data, row_count, chart_type, chart_data. When human_friendly_result=True, also includes human_friendly_result.

Raises: ValueError if no app labels are available (empty app_labels and no apps in INSTALLED_APPS); RuntimeError if GEMINI_API_KEY is not set or all retries fail.

generate_schema(app_labels=None)

Generates the Django models schema only (no AI/LLM call) and prints it to screen. Use for debugging to inspect the schema that would be sent to the LLM.

Argument	Type	Description
app_labels	list, optional	App labels to include. If omitted, uses all installed apps.

Returns: str — the schema string (plain-text model/field description)

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.