QueryTheory is a web-based educational app that helps users understand how SQL queries translate into relational algebra and set theory. It provides real-time query execution, mathematical explanations, and an AI-powered chat interface for deeper learning.

• 🔎 Query Templates: Practice with 50+ categorized SQL queries.
• 🧮 Math Explainer: View relational algebra equivalents in LaTeX.
• 💬 Chat Support: Ask questions about the query’s mathematical structure.
• 🧪 Run SQL Queries: Execute SQL directly on a built-in dataset.
• 📊 Live Results: See output tables immediately after query execution.
• 📁 Preloaded Dataset: Work with Fruit-Prices-2022.csv by default.
• 🗃️ Theory-Driven: Designed around core relational concepts.

• Frontend: Streamlit
• Backend: Python (OpenAI API, DuckDB)
• Data Storage: CSV (local), JSON state
• Configuration: Python scripts, Streamlit secrets

QueryTheory/
│
├── app.py               # Main Streamlit application
├── app_state.json       # Stores query templates & chat history
├── Fruit-Prices-2022.csv # Demo dataset for SQL queries
├── venv/                # Python virtual environment
└── README.md            # Project documentation (this file)

Make sure your virtual environment is activated and required dependencies are installed.

source venv/bin/activate
streamlit run app.py

Store your OpenAI API key securely in .streamlit/secrets.toml:

[openai]
api_key = "your_openai_key"

• CS students learning database theory
• Bootcamp participants practicing SQL
• Instructors creating theory-backed demos
• Anyone exploring how SQL maps to mathematical logic

To contribute or test features:

git clone https://github.com/ThirDecade2020/QueryTheory.git
cd QueryTheory
source venv/bin/activate
streamlit run app.py

MIT License © 2025 Jason Ricciardi