🚀 Gemini RAG Chatbot Team: Team-Kernel An intelligent chatbot using Google's Gemini API and ChromaDB for Retrieval-Augmented Generation.

👥 Team Details

Name	Role	Email
Sandeep	Team Lead	sandeepprajapati1202@gmail.com

🎯 Problem Statement

Users need quick access to knowledge bases (Operating Systems, History, etc.) without manually searching through documentation.

Context: Traditional chatbots lack context-aware responses and require frequent retraining with new information. Students and learners struggle to get accurate, sourced answers.

Impact: Students, professionals, and researchers who need reliable, context-aware information from specific domains.

💡 Solution

A Retrieval-Augmented Generation (RAG) chatbot that combines Google's Gemini API with ChromaDB vector database to deliver accurate, context-based responses.

• RAG Architecture: Retrieves relevant documents before generating responses for accuracy
• Gemini Integration: Leverages advanced language models for natural, coherent answers
• Multi-Domain Support: Easily extensible to different knowledge bases

🛠 Tech Stack

Category	Technologies Used
Frontend	HTML5, CSS, JavaScript (Vanilla)
Backend	Python, Flask
AI/ML	Google Gemini API, ChromaDB
Database	ChromaDB (Vector Database)
Tools/APIs	Google Generative AI

📊 MVP Features

• ✅ OS Bot: RAG-based chatbot for Operating Systems questions
• ✅ Web Interface: Flask-based UI with real-time chat
• 🔄 WW2 History Bot: Domain-specific knowledge base (In Progress)

🔗 Links & Demo

• 📂 GitHub Repo: https://github.com/GDGoC-GalgotiasUniversity/techsprint-2026-team-team-kernel

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Google Gemini API Key

Installation

1. Clone the repository:

   git clone https://github.com/simplysandeepp/try-gemini.git
   cd try-gemini

2. Create a virtual environment:

   # Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up API Key:

   • Get your Google Gemini API key from Google AI Studio
   • Open os_bot.py and replace GOOGLE_API_KEY with your actual key

Running the Application

Web Interface (Flask):

python app.py

Then open your browser and navigate to: http://127.0.0.1:5000

Console Mode - OS Bot:

python os_bot.py

Console Mode - WW2 History Bot:

python main.py

📁 Project Structure

Team-Kernel/
├── app.py              # Flask web application
├── os_bot.py           # Operating Systems RAG bot
├── main.py             # WW2 History bot
├── requirements.txt    # Python dependencies
├── templates/
│   └── index.html      # Web interface
└── README.md           # Project documentation

Testing Credentials

User: user@demo.com
Pass: hack2026

🏆 Acknowledgements

This project was developed during TechSprint Hackathon 2026, organized by GDG on Campus Galgotias University.