AI2SQL simplifies Django database queries by transforming natural language into Django ORM querysets. Ideal for developers seeking to boost productivity and streamline database operations.

Start building queries effortlessly with AI2SQL Django SQL AI.

How It Works

1. Visit AI2SQL.

2. Select Django as your framework.

3. Enter your query in plain English.

4. Instantly receive Django ORM code or raw SQL.

Example Queries with AI2SQL

1. Basic Queries

Natural Language: "Find all active users who joined in the last month."

from django.utils import timezone
from datetime import timedelta

User.objects.filter(
    is_active=True,
    date_joined__gte=timezone.now() - timedelta(days=30)
)

2. Complex Queries

Natural Language: "Get orders with their items and customer details, where total amount is over $100."

from django.db.models import Sum, F

Order.objects.annotate(
    total_amount=Sum(F('orderitem__quantity') * F('orderitem__price'))
).filter(
    total_amount__gt=100
).select_related(
    'customer'
).prefetch_related(
    'orderitem_set'
)

Key Features of Django SQL AI

1. ORM Optimization

• Prevents N+1 queries by recommending select_related and prefetch_related.

• Suggests optimized annotations and aggregations.

• Enhances database performance through efficient query design.

2. Flexible Query Types

• Filters and aggregations.

• Subqueries and complex joins.

• Annotations for custom calculations.

3. Performance Tools

• Index usage recommendations.

• Query impact analysis.

• Database caching suggestions.

Common Use Cases for Django Developers

1. Data Analysis

Natural Language: "Show monthly sales totals with previous month comparisons."

from django.db.models import Sum, F
from django.db.models.functions import TruncMonth

Order.objects.annotate(
    month=TruncMonth('created_at')
).values('month').annotate(
    total_sales=Sum('amount'),
    prev_month_sales=Window(
        expression=Sum('amount'),
        order_by=F('month').asc(),
        offset=-1
    )
).values('month', 'total_sales', 'prev_month_sales')

2. Advanced Filtering

Natural Language: "Find products with low inventory but high demand."

from django.db.models import Count, F, Q

Product.objects.annotate(
    order_count=Count('orderitem', filter=Q(
        orderitem__order__created_at__gte=timezone.now() - timedelta(days=30)
    ))
).filter(
    inventory_level__lte=F('reorder_point'),
    order_count__gte=10
)

3. Relationship-Based Queries

Natural Language: "Find users who commented on their own posts."

from django.db.models import Exists, OuterRef

User.objects.filter(
    Exists(
        Comment.objects.filter(
            post__author=OuterRef('pk'),
            author=OuterRef('pk')
        )
    )
).select_related('profile').prefetch_related(
    'post_set',
    'comment_set'
)

Best Practices for Django ORM Queries

1. Optimize Queries

• Use select_related for foreign keys.

• Apply prefetch_related for reverse relations.

• Avoid N+1 queries by leveraging ORM capabilities.

2. Boost Performance

• Utilize Django’s database functions for calculations.

• Implement caching for frequently accessed data.

• Use bulk operations for large data modifications.

3. Structure Queries Well

• Keep queries readable and modular.

• Follow Django naming conventions for clarity.

• Document complex query logic for maintainability.

Advanced Examples

Aggregation Example

Natural Language: "Show customer purchase statistics by category."

from django.db.models import Count, Sum, Avg
from django.db.models.functions import ExtractYear

Customer.objects.annotate(
    order_count=Count('order'),
    total_spent=Sum('order__amount'),
    avg_order_value=Avg('order__amount'),
    purchase_years=Count(
        ExtractYear('order__created_at'),
        distinct=True
    )
).prefetch_related(
    Prefetch(
        'order_set',
        queryset=Order.objects.annotate(
            category_totals=Sum('orderitem__amount')
        ).values('category')
    )
)

FAQs About Django SQL AI

Q: Can it generate raw SQL instead of ORM queries?

A: Yes, AI2SQL supports both Django ORM and raw SQL output formats.

Q: Does it work with custom model managers?

A: Absolutely! The generated queries can be integrated into custom model managers with ease.

Next Steps

1. Start Generating Queries: Try the Django SQL AI Tool.

2. Explore Advanced Examples: Test with real-world scenarios.

3. Optimize Your Workflow: Simplify database operations with AI2SQL.

For assistance, contact us at support@ai2sql.io.

Revolutionize your Django database management with AI2SQL!