Project Description

This project is a natural language interface for interacting with SQL databases. It allows users to ask questions in plain English and get answers directly from a database. The system uses LangChain to convert natural language queries into SQL commands, which are then executed against either a local SQLite database (student.db) or a remote MySQL database.

The interface is built using Streamlit, enabling an interactive chat-like experience where users can input their queries and receive responses in real time. This setup is particularly useful for non-technical users who want to explore or analyze data without writing SQL queries manually.

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Process Overview

1. User Input: The user selects the database type (SQLite or MySQL) and provides credentials or uses the local student.db.
2. API Key Entry: The user inputs their OpenAI API key to enable natural language processing.
3. Language Model Interaction: LangChain, in conjunction with OpenAI's GPT model, processes the user's query and converts it into a valid SQL command.
4. SQL Execution: The generated SQL query is executed on the selected database using SQLAlchemy.
5. Results Display: The results of the query are displayed in the chat interface for the user to review.

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Tools and Technologies Used

• Python: Core programming language used for building the application.
• Streamlit: Framework for creating the interactive web application.
• LangChain: Framework to interface with language models and generate SQL queries from natural language.
• OpenAI GPT (via LangChain): Language model used for interpreting and generating queries.
• SQLite3: Local database used for demo/testing (student.db).
• MySQL: External database support for real-world integration.
• SQLAlchemy: ORM and database toolkit for managing SQL connections and query execution.
• ChatOpenAI & create_sql_agent: LangChain modules used for creating the agent that handles query translation and execution.

Installation

1. Clone the repository:

   git clone https://github.com/DevaPriya5102/ChatSQL.git
   cd ChatSQL
   

2. (Optional) Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required Python packages:

   pip install -r requirements.txt

Usage

Run the Streamlit app:

streamlit run app.py

In the sidebar:

• Choose between SQLite (student.db) or connect to your own MySQL database
• Enter your OpenAI API key
• Start chatting with your database using natural language queries