🎬 YouTube Transcript Chatbot

An intelligent multilingual chatbot that enables natural conversations with YouTube videos in 50+ languages using RAG (Retrieval Augmented Generation).

📋 Table of Contents

• Overview
• Project Structure
• Features
• Demo
• Architecture
• Tech Stack
• Prerequisites
• Installation
• Configuration
• Usage
• Deployment
• Contributing
• License
• Acknowledgments

🌟 Overview

YouTube Transcript Chatbot is an AI-powered application that allows users to have natural language conversations with YouTube videos. The system extracts video transcripts, processes them using advanced NLP techniques, and enables semantic search and question-answering in multiple languages.

📁 Project Structure

youtube-chatbot/
├── app.py                      # Main application file
├── requirements.txt            # Python dependencies
├── .gitignore                 # Git ignore rules
│
├── docker/
│   ├── Dockerfile             # Docker container configuration
│   └── .dockerignore          # Docker ignore rules
└── README.md                  # Readme file

Key Highlights

• 🌍 Multilingual Support: Works seamlessly with videos in English, Hindi, Urdu, Arabic, Spanish, and 50+ other languages
• 🚀 No Translation Required: Uses original transcripts for maximum accuracy and preserves cultural context
• 💬 Natural Conversations: Powered by state-of-the-art LLMs from OpenRouter
• 🎯 Smart Retrieval: Employs RAG with vector embeddings for precise context retrieval
• ⚡ Fast & Efficient: Cloud-based embeddings with no local model downloads

✨ Features

Core Functionality

• 📺 YouTube Integration: Automatic transcript extraction from any YouTube video
• 🔍 Semantic Search: Intelligent context retrieval using vector similarity
• 💡 Conversational AI: Natural dialogue with multiple LLM options
• 🌐 Cross-lingual Queries: Ask questions in any language, get relevant answers

Technical Features

• 🎯 RAG Architecture: Retrieval Augmented Generation for accurate responses
• 🔄 Streaming Responses: Real-time token streaming for better UX
• 🔁 Automatic Retry Logic: Handles API rate limits and timeouts gracefully
• 📊 Vector Database: Persistent storage with Qdrant Cloud
• 🧠 Multiple LLM Support: Choose from Mistral & LLaMA

# Quick start
streamlit run app.py

🏗️ Architecture

┌─────────────────────┐
│  Youtube Video URL  │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│ Transcript Extractor│
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│   Text Splitter     │ (600 char chunks)
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  HuggingFace API    │ (Multilingual Embeddings)
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│   Qdrant Vector DB  │
└────────┬────────────┘
         │
         ▼
    User Query
         │
         ▼
┌─────────────────────┐
│  Semantic Search    │ (MMR Retrieval)
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  OpenRouter LLM     │ (Generate Response)
└────────┬────────────┘
         │
         ▼
    Streamed Answer

🛠️ Tech Stack

Frontend

• Streamlit - Interactive web application framework

Backend & AI

• LangChain - LLM orchestration framework
• OpenRouter - Unified LLM API gateway
• HuggingFace - Multilingual embeddings API
• Qdrant - Vector database for Maximal Marginal Relevance Retrieval (MMR)

Core Libraries

• youtube-transcript-api - YouTube transcript extraction
• sentence-transformers - Multilingual embeddings model
• langchain-openai - OpenRouter integration
• langchain-qdrant - Qdrant vector store integration

📦 Prerequisites

• Python 3.10 or higher
• pip or conda package manager
• API keys (see Configuration Options)

🚀 Installation

Method 1: Local Installation

# Clone the repository
git clone https://github.com/yourusername/youtube-chatbot.git
cd youtube-chatbot

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
cp .env.example .env
# Edit .env with your API keys

# Run the application
streamlit run app.py

Method 2: Docker

# Build the image
docker build -t youtube-chatbot .

# Run the container
docker run -d \
  --name yt-chatbot \
  -p 8501:8501 \
  --env-file .env \
  youtube-chatbot

⚙️ Configuration

Create a .env file in the root directory:

# OpenRouter API Key (for LLM generation)
OPENROUTER_API_KEY=sk-or-v1-xxxxx

# Qdrant Cloud Configuration
QDRANT_URL=https://xxxxx.qdrant.io
QDRANT_API_KEY=xxxxx

# HuggingFace API Key (for embeddings)
HUGGINGFACE_API_KEY=hf_xxxxx

Configuration Options

Variable	Description	Required
OPENROUTER_API_KEY	API key for LLM access	✅ Yes
QDRANT_URL	Qdrant cluster URL	✅ Yes
QDRANT_API_KEY	Qdrant authentication key	✅ Yes
HUGGINGFACE_API_KEY	HuggingFace API token	✅ Yes

📖 Usage

Basic Workflow

1. Start the Application

   streamlit run app.py

2. Ingest a Video

   • Paste a YouTube URL in the sidebar
   • Click "📥 Ingest Transcript"
   • Wait for processing (10-30 seconds)

3. Start Chatting

   • Type your question in the chat input
   • Receive AI-generated answers based on video content
   • Continue the conversation naturally

Supported Video Languages

The chatbot supports transcripts in:

• 🇬🇧 English
• 🇮🇳 Hindi
• 🇵🇰 Urdu
• 🇸🇦 Arabic
• 🇪🇸 Spanish
• 🇫🇷 French
• 🇩🇪 German
• 🇯🇵 Japanese
• 🇰🇷 Korean
• 🇨🇳 Chinese
• And 50+ more languages!

Available LLM Models

Choose from multiple AI models:

Model	Provider	Strengths
LLaMA 3.2 3B	Meta	Fast, efficient
Mistral Small	Mistral AI	Balanced performance

🤝 Contributing

Contributions are welcome! Here's how you can help:

Ways to Contribute

• 🐛 Report Bugs: Open an issue describing the problem
• 💡 Suggest Features: Share your ideas for improvements
• 📝 Improve Documentation: Help make docs clearer
• 🔧 Submit Pull Requests: Fix bugs or add features

Development Setup

# Fork and clone the repository
git clone https://github.com/yourusername/youtube-chatbot.git

# Create a feature branch
git checkout -b feature/amazing-feature

# Make your changes and commit
git commit -m "Add amazing feature"

# Push to your fork
git push origin feature/amazing-feature

# Open a Pull Request

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

Technologies

• Streamlit - For the amazing web framework
• LangChain - For LLM orchestration
• OpenRouter - For unified Multiple LLM access
• Qdrant - For vector database
• HuggingFace - For embeddings API

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: muhammadtalhasheikh50@gmail.com

Built with ❤️ by Muhammad Talha

⭐ Star this repo if you find it helpful!