🧠 Student Database RAG Assistant

This project is a Retrieval-Augmented Generation (RAG) chatbot built using LangGraph, LangChain, and Google Gemini. It allows users to interactively query and analyze student data stored in a local SQLite database.

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🚀 Features

• Conversational interface to query student information
• Uses a tool-augmented language model (LLM + SQL)
• Executes real-time queries on a SQLite database
• Built with LangGraph for dialogue flow management
• Includes sample student data for quick testing

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🛠️ Technologies Used

• Python
• SQLite
• LangGraph
• LangChain
• Google Generative AI (Gemini)
• Google Colab

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📁 Project Structure

File / Section	Description
DatabaseManager	Handles SQLite connection and queries
ask_students_db	LangChain tool for querying student database
chatbot_node	Generates assistant replies using Gemini
human_node	Handles user input
build_graph()	Creates LangGraph flow with nodes and edges
setup_database()	Initializes and populates sample data
main()	Runs the full application

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▶️ How to Run

1. Open the notebook in Google Colab.
2. Set your GOOGLE_API_KEY using userdata.get() (already in code).
3. Run all cells.
4. The assistant will greet you with: Welcome to the Student Database Assistant...
5. Ask questions like:

• How many students are studying Computer Engineering?
• Who has the highest GPA?
• Show me all married students in PhD.

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💡 Example Questions

• List all students over the age of 25.
• What is the average GPA for Physics majors?
• How many single students are there?

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⚠️ Notes

• Important: Use check_same_thread=False when connecting to SQLite to avoid thread-related errors.
• A Google Gemini API key is required to use the assistant.

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📌 License

MIT License (feel free to modify or extend)

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