Text To SQL

Text-to-SQL Skill

Identity

Text-to-SQL - Converts natural language queries to SQL using database schema context and query patterns.

Capabilities

• Query Generation: Convert natural language to SQL
• Schema Awareness: Uses database schema for accurate queries
• Query Optimization: Generates optimized SQL queries
• Parameterized Queries: Creates safe, parameterized queries

Usage

Basic SQL Generation

When to Use:

• Database queries from natural language
• Data analysis requests
• Reporting queries
• Ad-hoc database queries

How to Invoke:

"Generate SQL to find all users who signed up in the last month"
"Create a query to calculate total revenue by product"
"Write SQL to find duplicate records"

What It Does:

• Analyzes natural language query
• References database schema
• Generates SQL query
• Validates query syntax
• Returns parameterized query

Advanced Features

Schema Integration:

• Loads database schema
• Understands table relationships
• Uses column types and constraints
• Handles joins and aggregations

Query Optimization:

• Generates efficient queries
• Uses appropriate indexes
• Optimizes joins
• Minimizes data transfer

Safety:

• Parameterized queries (prevents SQL injection)
• Validates query syntax
• Tests on sample data
• Error handling

Best Practices

1. Schema Context: Provide complete database schema
2. Query Validation: Validate SQL before execution
3. Parameterization: Always use parameterized queries
4. Testing: Test queries on sample data
5. Optimization: Review query performance

Integration

With Database Architect

Text-to-SQL uses schema from database-architect:

• Table definitions
• Relationships
• Constraints
• Indexes

With Developer

Text-to-SQL generates queries for developers:

• Query templates
• Parameterized queries
• Query optimization
• Error handling

Examples

Example 1: Simple Query

User: "Find all users who signed up in the last month"

Text-to-SQL:
1. Analyzes query
2. References users table schema
3. Generates SQL:
   SELECT * FROM users
   WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 MONTH)
4. Returns parameterized query

Example 2: Complex Query

User: "Calculate total revenue by product for Q4"

Text-to-SQL:
1. Analyzes query
2. References orders and products tables
3. Generates SQL:
   SELECT p.name, SUM(o.total) as revenue
   FROM orders o
   JOIN products p ON o.product_id = p.id
   WHERE o.created_at >= '2024-10-01'
     AND o.created_at < '2025-01-01'
   GROUP BY p.id, p.name
4. Returns optimized query

Evaluation

Evaluation Framework

Based on Claude Cookbooks patterns, text-to-SQL evaluation includes:

Syntax Validation:

• SQL syntax correctness
• Schema compliance
• Query structure validation

Functional Testing:

• Query execution on test database
• Result correctness
• Performance validation

Promptfoo Integration:

• Multiple prompt variants (basic, few-shot, chain-of-thought, RAG)
• Temperature sweeps
• Model comparisons (Haiku vs Sonnet)

Evaluation Configuration: See .claude/evaluation/promptfoo_configs/text_to_sql_config.yaml for complete evaluation setup.

Running Evaluations

# Run text-to-SQL evaluation
npx promptfoo@latest eval -c .claude/evaluation/promptfoo_configs/text_to_sql_config.yaml

Evaluation Metrics

• Syntax Accuracy: Percentage of queries with valid SQL syntax
• Functional Correctness: Percentage of queries returning correct results
• Schema Compliance: Percentage of queries using correct schema
• Performance: Query execution time and optimization

Best Practices from Cookbooks

1. Provide Schema Context

Always include complete database schema:

• Table definitions with column types
• Relationships and foreign keys
• Constraints and indexes
• Sample data patterns

2. Use Few-Shot Examples

Provide examples of similar queries:

• Simple queries
• Complex queries with joins
• Aggregation queries
• Subquery patterns

3. Chain-of-Thought for Complex Queries

For complex queries, use chain-of-thought reasoning:

• Break down query into steps
• Identify required tables
• Plan joins and aggregations
• Generate SQL step by step

4. RAG for Schema Understanding

Use RAG to retrieve relevant schema information:

• Find relevant tables for query
• Understand relationships
• Get column details
• Retrieve query patterns

Related Skills

• classifier: Classify database queries
• database-architect: Use for schema design
• developer: Generate query code

Related Documentation

• Classification Patterns - Classification guide
• Evaluation Guide - Comprehensive evaluation
• Claude Cookbooks - Text-to-SQL