Project 7: Natural Language to SQL Query Generator

Build a LangChain tool that converts natural language questions into safe, validated SQL queries.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 12-18 hours
Language Python or JavaScript
Prerequisites SQL basics, schema design
Key Topics SQL generation, validation, safety

1. Learning Objectives

By completing this project, you will:

  1. Generate SQL from natural language queries.
  2. Validate SQL against schema and safety rules.
  3. Prevent dangerous queries (DROP, DELETE).
  4. Execute queries and return structured results.
  5. Evaluate accuracy on a query test set.

2. Theoretical Foundation

2.1 Safe SQL Generation

SQL generation must be constrained to the known schema and safe query patterns.

3. Project Specification

3.1 What You Will Build

A system that accepts a question, generates SQL, validates it, and executes against a test database.

3.2 Functional Requirements

  1. Schema-aware prompting with table/column context.
  2. SQL validator for safety and correctness.
  3. Execution layer for safe queries only.
  4. Result formatting as JSON/table.
  5. Evaluation against expected outputs.

3.3 Non-Functional Requirements

  • No destructive queries allowed.
  • Deterministic mode for testing.
  • Trace logs for prompts and SQL.

4. Solution Architecture

4.1 Components

Component Responsibility
Prompt Builder Provide schema context
SQL Generator Produce SQL from NL
Validator Check safety and schema
Executor Run safe SQL
Evaluator Score accuracy

5. Implementation Guide

5.1 Project Structure

LEARN_LANGCHAIN_PROJECTS/P07-nl-to-sql/
├── src/
│   ├── schema.py
│   ├── prompt.py
│   ├── generate.py
│   ├── validate.py
│   ├── execute.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Schema + prompt (3-4h)

  • Provide table/column context.
  • Checkpoint: generated SQL references valid tables.

Phase 2: Validation + execution (4-6h)

  • Block unsafe SQL.
  • Execute safe queries.
  • Checkpoint: valid query returns results.

Phase 3: Evaluation (4-6h)

  • Test against gold queries.
  • Checkpoint: accuracy metrics reported.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit validator block DROP TABLE
Integration execution query returns results
Regression eval accuracy stable

6.2 Critical Test Cases

  1. Unsafe SQL is rejected.
  2. Unknown column triggers validation error.
  3. Generated SQL matches expected output.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Hallucinated columns query fails include schema in prompt
Unsafe SQL data loss risk strict validator
Poor accuracy wrong queries add few-shot examples

8. Extensions & Challenges

Beginner

  • Add SQL explain output.
  • Add query templates.

Intermediate

  • Add query rewriting for optimization.
  • Add caching for repeated queries.

Advanced

  • Add multi-step reasoning for complex joins.
  • Add SQL linting and formatting.

9. Real-World Connections

  • BI assistants need safe SQL generation.
  • Analytics tools require schema-aware queries.

10. Resources

  • LangChain SQL toolkit docs
  • SQL validation best practices
  • “AI Engineering” (safe generation)

11. Self-Assessment Checklist

  • I can generate SQL from NL queries.
  • I can block unsafe SQL.
  • I can evaluate accuracy against a test set.

12. Submission / Completion Criteria

Minimum Completion:

  • Schema-aware SQL generator
  • Safe execution layer

Full Completion:

  • Evaluation metrics
  • Trace logs

Excellence:

  • Complex joins support
  • Query optimization

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LANGCHAIN_PROJECTS.md.