Text-to-SQL Query System with Multi-Source Data

Project Demo Video: Youtube video link

Project Overview

This project implements a Text-to-SQL Query System that can convert natural language queries into SQL queries to retrieve data from multiple sources, including a MySQL database and CSV files. The system uses Langchain, Flask, and Python to create an intelligent agent that can understand and process queries for different data sources.

Project Design

Features

• Convert natural language queries to SQL queries
• Query data from MySQL database
• Query data from CSV files
• RESTful API for submitting queries
• Simple web interface for interacting with the system

APP Images

Prerequisites

• Python 3.7+
• pip (Python package manager)
• MySQL database (for SQL queries)
• OpenAI API key

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/text-to-sql-system.git
   cd text-to-sql-system
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Set up your environment variables: Create a .env file in the root directory and add the following:

   OPENAI_API_KEY=your_openai_api_key
   SQL_DATABASE_URI=mysql://username:password@host:port/database
   

Project Structure

text_to_sql_system/
├── app.py
├── agents/
│   ├── __init__.py
│   ├── sql_agent.py
│   ├── csv_agent.py
│   └── orchestrator.py
├── data/
│   └── sample.csv
├── templates/
│   └── index.html
├── .env
└── README.md

Usage

1. Start the Flask application:

   python app.py
   

2. Open a web browser and navigate to http://localhost:5000 to use the web interface.

3. To use the API directly, send a POST request to http://localhost:5000/query with a JSON payload:

   curl -X POST http://localhost:5000/query \
        -H "Content-Type: application/json" \
        -d '{"query": "Show me all customers in the USA"}'
   

API Endpoints

• POST /query: Submit a natural language query
  • Request body: {"query": "Your natural language query here"}
  • Response: JSON object with the query result or error message