⭐ NEW: Practical Implementation Complete!

The complete DIVA-SQL framework has been implemented with all research requirements:

• ✅ 53 SQL Templates - Comprehensive template library covering basic to complex operations
• ✅ Three-Stage Verification - Syntax → Semantic → Execution with auto-fix
• ✅ Performance Monitoring - Latency tracking (2.3s simple, 5.8s complex targets)
• ✅ Feedback Loop - Diagnostic reporting with localized repair

📚 Quick Links:

• Implementation Complete - Visual overview with diagrams
• Quick Start Guide - Get started in 5 minutes
• Practical Guide - Complete documentation
• All Documentation - Complete documentation index
• Demo - Run: python3 demos/demo_simple.py

🎯 Overview

DIVA-SQL introduces a three-agent architecture that transforms natural language queries into SQL through interpretable semantic decomposition:

• 🧩 Semantic Decomposer Agent: Breaks down complex NL queries into semantic Directed Acyclic Graphs (DAGs)
• ⚙️ Clause Generator Agent: Generates SQL clauses from individual semantic components
• ✅ Verification & Alignment Agent: Provides in-line verification and error correction

This approach enables:

• Higher accuracy on complex queries through structured decomposition
• Full interpretability via semantic DAGs and verification traces
• Error prevention through comprehensive in-line verification

🏗️ Architecture

Natural Language Query
          ↓
    [Decomposer Agent]
          ↓
    Semantic DAG
          ↓
    [Generator Agent] ←→ [Verifier Agent]
          ↓
    Final SQL Query

Key Components

1. Semantic DAG Representation: Captures query semantics as interpretable graph structures
2. Multi-Agent Pipeline: Specialized agents for decomposition, generation, and verification
3. Error Taxonomy: Comprehensive classification of SQL generation errors
4. Evaluation Framework: Tools for benchmarking against state-of-the-art baselines