🧠 Intelligent SQL Assistant: Natural Language to Database Query System

An AI-powered web application that allows users to convert natural language queries into SQL queries and retrieve results from a connected database. This tool bridges the gap between human language and database interaction, enabling non-technical users to access structured data efficiently.

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🚀 Demo

👉 Live App: Intelligent SQL Assistant

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🧩 Features

• ✅ Convert natural language to SQL queries using LLMs
• ✅ View both the generated SQL and query results
• ✅ Simple and clean Streamlit UI
• ✅ Supports dynamic database schema
• ✅ Includes sample users and products tables for testing
• ✅ Error handling for invalid queries

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🏗️ How It Works

1. User Input: User enters a query in plain English (e.g., “Show all products with price greater than 500”).
2. LLM Translation: The app uses an LLM to convert the input to a valid SQL query.
3. SQL Execution: The SQL query is executed on the connected SQLite database.
4. Display: Results and the SQL query are shown to the user in a readable format.

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📊 Sample Tables

users

CREATE TABLE users (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT NOT NULL,
    age INTEGER,
    email TEXT
);

INSERT INTO users (name, age, email)
VALUES 
('Alice', 25, 'alice@example.com'),
('Bob', 30, 'bob@example.com');

products

CREATE TABLE products (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT NOT NULL,
    price REAL,
    stock INTEGER
);

INSERT INTO products (name, price, stock)
VALUES 
('Laptop', 799.99, 10),
('Headphones', 199.99, 50),
('Mouse', 25.50, 100);

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❓ Example Questions You Can Ask

• "Show all users older than 25"
• "List all products with stock less than 20"
• "What is the average price of products?"
• "Count the number of users"
• "Get the names of products priced above 200"

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🛠️ Tech Stack

• Frontend: Streamlit
• Backend: Python + SQLite
• AI Engine: Large Language Model (LLM) via OpenAI or compatible provider
• Database: SQLite (local and in-memory)

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📁 Project Structure

├── main.py                   # Streamlit app entry point
├── database.db               # SQLite database file
├── models/                   # Folder for prompt and LLM logic
│   └── llm_utils.py
├── utils/                    # Utility functions for SQL execution and display
│   └── sql_executor.py
├── requirements.txt
└── README.md                 # This file

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⚙️ Setup & Run Locally

1. Clone the Repository

git clone https://github.com/yourusername/intelligent-sql-assistant.git
cd intelligent-sql-assistant

2. Create Virtual Environment

python -m venv venv
# Activate environment
# On Windows:
venv\Scripts\activate
# On Mac/Linux:
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the App

streamlit run main.py

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🔐 Environment Variables (Optional for OpenAI Integration)

If using OpenAI or another LLM API:

OPENAI_API_KEY=your_openai_api_key

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📌 Future Improvements

• 🔄 Add support for other databases (PostgreSQL, MySQL)
• 🧠 Model fine-tuning with user feedback
• 🧾 Natural language INSERT, UPDATE, DELETE support
• 📊 Visualize SQL results (charts and graphs)

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🙋‍♂️ Author

Hemavathi
Data Scientist & AI Developer
🌐 LinkedIn | ✨ Portfolio

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🪪 License

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