YouTube AI Assistant Chrome Extension

A Chrome extension that uses AI to answer questions about YouTube video content using RAG (Retrieval Augmented Generation) with Groq's Llama AI.

Features

• 🤖 AI-powered Q&A about YouTube videos
• 📺 Automatic transcript extraction
• 💬 Chat-like interface
• 🎯 Context-aware responses using RAG
• ⚡ Lightning-fast responses with Groq API
• 🔒 Secure API key management

Tech Stack

• Frontend: HTML, CSS, JavaScript (Chrome Extension)
• Backend: FastAPI (Python)
• AI Model: Llama 3.1 8B Instant (via Groq API)
• Libraries:
  • youtube-transcript-api
  • groq
  • tiktoken
  • python-dotenv
  • uvicorn
  • fastapi

Installation

Prerequisites

1. Get a free Groq API key from console.groq.com
2. Python 3.8+ installed

Backend Setup

1. Clone the repository:

   git clone https://github.com/KUNDAN1334/YT_SCAN.git
   cd YT_SCAN

2. Install Python dependencies:

   pip install fastapi uvicorn youtube-transcript-api groq tiktoken python-dotenv

3. Create a .env file in the project root:

   GROQ_API_KEY=your-groq-api-key-here

4. Start the backend server:

   cd backend
   uvicorn main:app --reload

Chrome Extension Setup

1. Open Chrome and go to chrome://extensions/
2. Enable "Developer mode"
3. Click "Load unpacked"
4. Select the project root directory (YT_SCAN)

Usage

1. Make sure your .env file contains your Groq API key
2. Start the FastAPI backend server
3. Open the Chrome extension
4. Paste a YouTube URL
5. Click "Load Video"
6. Ask questions about the video content

Project Structure

YT_SCAN/
├── backend/
│   ├── main.py              # FastAPI server
│   ├── transcript_handler.py # YouTube transcript extraction
│   └── llama_chat.py        # Groq API integration
├── popup.html               # Extension popup UI
├── popup.js                 # Frontend JavaScript
├── style.css               # Styling
├── manifest.json           # Extension manifest
├── .env                    # Environment variables (API keys)
├── .gitignore              # Git ignore file
└── README.md

Configuration

The AI model is configured in backend/llama_chat.py using Groq API:

from groq import Groq
import os
from dotenv import load_dotenv

load_dotenv()
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

def generate_answer(context, question):
    response = client.chat.completions.create(
        model="llama-3.1-8b-instant",  # Fast Llama model
        messages=[{"role": "user", "content": prompt}],
        max_tokens=800,
        temperature=0.1,
        stream=False
    )
    return response.choices[0].message.content.strip()

API Endpoints

• POST /ask - Submit question about YouTube video
• GET / - Health check

Model Performance

Groq's Llama 3.1 8B Instant offers:

• ⚡ Ultra-fast response times (1-3 seconds)
• 🎯 High-quality text generation
• 🧠 Excellent context understanding
• 💰 Free tier with generous limits
• 🔄 No local setup required

Token Management

The extension automatically handles large video transcripts by:

• 📊 Counting tokens using tiktoken
• ✂️ Smart context truncation (keeps recent content)
• 🛡️ Preventing API limit errors
• 🎯 Maintaining answer quality

Environment Variables

Create a .env file with:

GROQ_API_KEY=your-actual-groq-api-key-here

Important: Never commit your .env file to version control!

Troubleshooting

1. API Key Error:

   • Check your .env file exists and contains valid Groq API key
   • Verify API key at console.groq.com

2. Token Limit Error:

   • The extension automatically truncates long transcripts
   • If issues persist, try shorter video segments

3. Connection Error:

   • Ensure FastAPI server is running on http://localhost:8000
   • Check your internet connection for Groq API access

4. Transcript Unavailable:

   • Some videos may not have transcripts available
   • Try videos with auto-generated captions

Rate Limits

Groq Free Tier:

• 6,000 tokens per minute
• Automatic context truncation handles this
• Upgrade to Dev Tier for higher limits

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

Security

• ✅ API keys stored in .env file
• ✅ .env file excluded from git
• ✅ No sensitive data in code
• ✅ Secure API communication

License

MIT License

Changelog

v2.0.0

• ⚡ Switched from local Mistral to Groq API
• 🚀 10x faster response times
• 🔧 Added automatic token management
• 🔒 Improved security with environment variables
• 📊 Added response time monitoring

v1.0.0

• 🎉 Initial release with local Mistral model

Acknowledgments

• Groq for lightning-fast AI inference
• Meta for the Llama model
• YouTube Transcript API for transcript extraction
• FastAPI for the backend framework

Now commit these changes:

```bash
git add .

git commit -m "Update README: Switch from Mistral to Groq API with Llama 3.1"

git push