🦜 SQL Chatbot using LangChain + Streamlit

AI-powered chatbot for interacting with SQL databases using natural language

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🚀 Overview

This project is an AI-powered SQL chatbot that allows users to interact with SQLite or MySQL databases using natural language queries.

The application uses:

• LangChain SQL Agent
• Groq LLM
• Streamlit interface

Users can ask questions in plain English, and the chatbot automatically generates and executes SQL queries to retrieve results from the database.

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✨ Features

✅ Chat with SQLite database using natural language
✅ MySQL database support
✅ Streamlit-based interactive UI
✅ LangChain SQL Agent integration
✅ Groq LLM support
✅ Real-time response streaming
✅ Intermediate agent reasoning display

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🛠️ Tech Stack

Technology	Purpose
Python	Core programming language
Streamlit	Frontend web application
LangChain	AI agent framework
Groq API	LLM inference
SQLite	Local database
MySQL	External database support
SQLAlchemy	Database connectivity

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📁 Project Structure

langchain-database-chatbot-main
│
├── app.py
├── sqlite.py
├── requirements.txt
├── README.md
└── .gitignore

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🗄️ Database Schema

STUDENT Table

STUDENT(
    NAME VARCHAR(25),
    CLASS VARCHAR(25),
    SECTION VARCHAR(25),
    MARKS INT
)

Sample records are inserted using sqlite.py.

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⚙️ Installation & Setup

1️⃣ Clone Repository

git clone https://github.com/RudraChouhan03/langchain-database-chatbot.git
cd langchain-database-chatbot

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2️⃣ Create Virtual Environment

python -m venv myvenv

Activate Virtual Environment

Windows

myvenv\Scripts\activate

Linux / Mac

source myvenv/bin/activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

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4️⃣ Create SQLite Database

python sqlite.py

This creates student.db with sample records.

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5️⃣ Run Application

streamlit run app.py

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🔑 Groq API Key Setup

Get your API key from:

👉 https://console.groq.com/keys

Paste the API key into the sidebar input field after starting the application.

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💬 Sample Questions

• Show all students
• Who scored the highest marks?
• Show students from Data Science class
• List all students from section A
• What is the average marks of students?

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🧠 How It Works

User Query
   ↓
LangChain SQL Agent
   ↓
LLM generates SQL query
   ↓
Database execution
   ↓
Results returned to user

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🔮 Future Improvements

• Chat memory support
• CSV upload support
• Query result visualization
• Download query results
• Multi-database connection support
• Authentication system

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👨‍💻 Author

Rudra Chouhan

Computer Science Engineering Student
Interested in Data Science, Generative AI, and Agentic AI

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📄 License

This project is licensed under the MIT License.