AI-Powered SQL Query Builder: Transform Natural Language to SQL

Natural Language:

"Show me monthly sales trends by product category for the last year, including year-over-year growth"

Generated SQL:

WITH monthly_sales AS (
    SELECT 
        DATE_TRUNC('month', order_date) as sale_month,
        pc.category_name,
        SUM(oi.quantity * oi.unit_price) as total_sales
    FROM order_items oi
    JOIN products p ON oi.product_id = p.product_id
    JOIN product_categories pc ON p.category_id = pc.category_id
    WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 year')
    GROUP BY 1, 2
)
SELECT 
    sale_month,
    category_name,
    total_sales,
    LAG(total_sales, 12) OVER (
        PARTITION BY category_name 
        ORDER BY sale_month
    ) as last_year_sales,
    ROUND(((total_sales / NULLIF(LAG(total_sales, 12) OVER (
        PARTITION BY category_name 
        ORDER BY sale_month
    ), 0) - 1) * 100), 2) as yoy_growth
FROM monthly_sales
ORDER BY sale_month DESC, total_sales DESC;

Natural Language:

"Segment customers into high, medium, and low value based on their lifetime purchase value and frequency of orders"

Generated SQL:

WITH customer_metrics AS (
    SELECT 
        customer_id,
        COUNT(order_id) as order_count,
        SUM(total_amount) as lifetime_value,
        AVG(total_amount) as avg_order_value
    FROM orders
    GROUP BY customer_id
),
customer_segments AS (
    SELECT 
        *,
        NTILE(3) OVER (ORDER BY lifetime_value) as value_segment,
        NTILE(3) OVER (ORDER BY order_count) as frequency_segment
    FROM customer_metrics
)
SELECT 
    customer_id,
    CASE 
        WHEN value_segment = 3 AND frequency_segment >= 2 THEN 'High Value'
        WHEN value_segment = 1 AND frequency_segment <= 2 THEN 'Low Value'
        ELSE 'Medium Value'
    END as customer_segment,
    lifetime_value,
    order_count,
    avg_order_value
FROM customer_segments;

1. Natural Language Understanding

Our system employs advanced NLP models to:

• Parse and understand natural language queries
• Extract key entities and relationships
• Identify query intent and requirements
• Handle complex business logic expressions

2. Query Optimization Engine

The optimization engine considers multiple factors:

• Available indexes and their selectivity
• Table statistics and cardinality estimates
• Join order optimization
• Subquery optimization and materialization