SQL Practice Questions Platform

A comprehensive platform for practicing SQL queries with automated validation, user progress tracking, and AI-powered assistance.

🚀 Overview

This platform helps users learn and practice SQL at multiple difficulty levels with intelligent feedback, hints, and explanations powered by local Llama AI models.

✨ Features

Current Features

• ✅ Automated SQL query validation
• ✅ User progress tracking
• ✅ Flexible question configuration
• ✅ Logging and error tracking
• ✅ YAML-based configuration

Roadmap Features

• 🔄 Multi-Level Difficulty System (Beginner → Expert)
• 🔄 Local Llama AI Integration for intelligent hints
• 🔄 RESTful API for platform integration
• 🔄 Expanded Question Library (40+ questions)
• 🔄 Advanced SQL Topics (Window Functions, CTEs, Recursive Queries)

📋 Quick Start

Prerequisites

• Python 3.10+
• SQLite (included with Python)
• (Optional) Docker for containerized usage
• (Optional) Ollama for AI features

Installation

# Clone the repository
git clone https://github.com/your-org/Practic_Questions_Platform.git
cd Practic_Questions_Platform

# Install dependencies
pip install -r requirements.txt

# Generate test data (uses infra/config/config.yml)
python -m infra.DataGenerator

# Override at runtime if needed
python -m infra.DataGenerator --num-records 2000 --seed 123 --table attempts_run

# Solve a question
python SQl_answer.py sql_basic_select

🐳 Docker Usage

The platform can be run in Docker containers for easy setup and consistent environments.

Quick Start with Docker

# Build and run the platform
docker compose up --build

# Or run with Docker directly
docker build -t sql-practice-platform .
docker run --rm sql-practice-platform

Docker Commands

# Generate test data only
docker compose run --rm data-generator

# Run interactively (for development)
docker compose run --rm interactive

# Run with persistent data
docker compose up -d

# View logs
docker compose logs -f

# Clean up
docker compose down -v

Volume Mounts

The Docker setup supports persistent data through volume mounts:

• ./output - Generated databases and CSV files
• ./users - User progress tracking
• ./logs - Application logs
• ./Questions - Custom practice questions

📁 Project Structure

Practic_Questions_Platform/
│
├── Questions/                    # SQL practice questions
│   └── sql_basic_select/        # Question folder
│       ├── question             # Problem description
│       ├── example_solution.sql # Reference solution
│       └── Sloutions/           # Note: typo to be fixed (see TASK-011)
│           └── sloution_df.csv  # Expected output
│
├── infra/                        # Core infrastructure
│   ├── AnswerValidator.py       # SQL validation logic
│   ├── DataGenerator.py         # Test data generation
│   ├── user.py                  # User management
│   ├── logging_config.py        # Logging setup
│   └── config/
│       ├── config.yml           # Data generation config
│       └── questions_config.yml # Questions config
│
├── docs/                         # Documentation
│   ├── CODING_STANDARDS.md      # Coding guidelines
│   ├── AI_INTEGRATION.md        # AI setup guide
│   └── DIFFICULTY_LEVELS.md     # Level specifications
│
├── .github/
│   └── ISSUE_TEMPLATE/          # Issue templates for AI agents
│
├── output/                       # Generated data
├── logs/                         # Application logs
├── users/                        # User progress files
│
├── Dockerfile                   # Docker image definition
├── docker-compose.yml           # Docker services orchestration
├── .dockerignore                # Docker build exclusions
├── PRODUCT_ROADMAP.md           # Development roadmap
├── requirements.txt             # Python dependencies
├── SQl_answer.py               # Main entry point
└── README.md                    # This file

📚 Documentation

Document	Description
PRODUCT_ROADMAP.md	Complete development roadmap with AI agent tasks
docs/CODING_STANDARDS.md	Coding standards and quality guidelines
docs/AI_INTEGRATION.md	Local Llama AI integration guide
docs/DIFFICULTY_LEVELS.md	SQL difficulty level specifications

🎯 Difficulty Levels

Level	Topics	Time Limit	Hints
BEGINNER	SELECT, WHERE, ORDER BY, LIMIT	5 min	5
INTERMEDIATE	JOINs, GROUP BY, Aggregations, Subqueries	10 min	3
ADVANCED	Window Functions, CTEs	15 min	2
EXPERT	Recursive CTEs, Query Optimization	20 min	1

🤖 AI Integration

The platform supports local Llama AI for:

• Progressive Hints: Context-aware hints that adapt to difficulty
• Query Explanations: Natural language explanations of SQL
• Error Analysis: AI-powered debugging assistance

Quick AI Setup (Ollama)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull recommended model
ollama pull llama3.2:3b

# Verify installation
curl http://localhost:11434/api/tags

See docs/AI_INTEGRATION.md for complete setup guide.

🛠️ Development

For Contributors

1. Review PRODUCT_ROADMAP.md for available tasks
2. Follow docs/CODING_STANDARDS.md
3. Use provided issue templates for task tracking

For AI Agents

This repository includes structured task delegation for AI agents:

1. Task Definitions: See PRODUCT_ROADMAP.md for detailed task specifications
2. Issue Templates: Use .github/ISSUE_TEMPLATE/ templates for creating tasks
3. Coding Standards: Follow guidelines in docs/CODING_STANDARDS.md
4. Quality Requirements: 80%+ test coverage, type hints, docstrings

🧪 Testing

# Run tests (when implemented)
pytest tests/ -v --cov=infra

# Lint code
flake8 infra/

# Type check
mypy infra/

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests following the coding standards.

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For detailed development tasks and AI agent instructions, see PRODUCT_ROADMAP.md