Kriar-Learning Assistant

A context-aware AI learning assistant that extracts relevant content from YouTube videos based on timestamps and provides intelligent Q&A capabilities. 👉 Try the hosted app here: Kriar-Learning-Assistant (Streamlit)

Features

• 🎥 YouTube video integration with transcript extraction
• 🎯 Timestamp-based context extraction
• 🤖 AI-powered Q&A using video context
• 💻 Code assistance and review
• 📊 Session tracking and statistics
• 🔧 Multiple AI model provider support (OpenAI, Groq, Google)

How to get Groq API Key for Free: https://www.youtube.com/watch?v=TTG7Uo8lS1M

How to Use:

Setup Instructions

1. Install Dependencies

pip install -r requirements.txt

2. Environment Setup

1. Copy .env.template to .env
2. Add your API keys for the model providers you want to use:
   • For OpenAI: Add your API key to API_KEY
   • For Groq: Add your API key to GROQ_API_KEY
   • For Google AI: Add your API key to GOOGLE_API_KEY

3. Run the Application

streamlit run app.py

The application will open in your browser at http://localhost:8501

Usage Guide

1. Load a Video

• Enter a YouTube URL in the video section
• Click "Load Video" to extract the transcript
• Wait for the success message confirming transcript extraction

2. Ask Questions

• Go to the Q&A Chat tab
• Ask questions about the video content
• The AI will use the context from timestamp to provide accurate answers
• Check "Use current timestamp" to automatically include timestamp context

3. Get Code Help

• Use the Code Assistant tab for programming questions
• Enter code in the text area
• Use "Review Code" or "Get Help" buttons for AI assistance

Key Components

• app.py: Main Streamlit application with UI and session management
• model.py: AI model wrapper supporting OpenAI, Groq, and Google AI
• context_extractor.py: YouTube transcript extraction and timestamp-based context analysis
• agent.py: LangGraph-based AI agent for intelligent, context-aware responses
• tools.py: Additional tools for Wikipedia search

How It Works

1. Video Loading: The system extracts the full transcript from YouTube videos
2. Timestamp Context: When you set a timestamp, it finds relevant transcript segments around that time
3. Context-Aware Responses: The AI agent uses this specific context to answer your questions
4. Smart Integration: All components work together through LangGraph workflows

Architecture

The application uses LangGraph to create a stateful AI agent that:

1. Extracts relevant context from video transcripts at specific timestamps
2. Optimizes user queries for better understanding
3. Provides context-aware responses using the video content
4. Integrates with external tools when needed

Troubleshooting

Common Issues

1. "No transcript available": Some YouTube videos don't have transcripts
2. API Key Errors: Make sure your .env file has valid API keys
3. Import Errors: Run pip install -r requirements.txt to install all dependencies
4. Video Loading Issues: Check that the YouTube URL is valid and the video is public

Quick Fixes

• Refresh the Streamlit page if you encounter session state issues
• Check your internet connection for API calls
• Verify that your API keys have sufficient credits/quota
• Try a different YouTube video if transcript extraction fails

Model Providers

OpenAI

• Requires: OpenAI API key
• Models: GPT-3.5-turbo, GPT-4, etc.
• Set API_KEY in your .env file

Groq

• Requires: Groq API key
• Fast inference with various open-source models
• Set GROQ_API_KEY in your .env file

Google AI

• Requires: Google AI API key
• Models: Gemini and other Google AI models
• Set GOOGLE_API_KEY in your .env file

Contributing

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

License

This project is open source and available under the MIT License.

Support

If you encounter any issues or have questions, please check the troubleshooting section above or create an issue in the repository.