VectorTutor

A comprehensive AI-powered study assistant built with Streamlit and Groq API. This application uses 5 specialized agents to help you learn effectively from PDF documents. (Live now at : https://vectortutor.streamlit.app/ )

Features

5 Specialized AI Agents

1. Reader Agent: Extracts and structures content from PDF documents
2. Flashcard Agent: Generates Q&A flashcards for active recall practice
3. Quiz Agent: Creates adaptive multiple-choice questions (easy/medium/hard)
4. Planner Agent: Builds personalized revision schedules
5. Chat Agent: Answers questions and summarizes notes from PDF context

Key Capabilities

• PDF Processing: Upload and extract text from PDF documents
• Flashcard Generation: Create study flashcards with questions and answers
• Adaptive Quizzes: Generate quizzes with different difficulty levels
• Revision Planning: Create personalized study schedules
• Q&A Chat: Ask questions about your documents and get AI-powered answers
• Performance Tracking: Monitor your quiz performance and accuracy
• Note Summarization: Generate concise summaries of your notes

Architecture

Knowledge Memory (SQLite) ← All agents read/write here
    ↓
1. Reader Agent: Extract & structure PDF content
2. Flashcard Agent: Generate Q&A flashcards  
3. Quiz Agent: Create adaptive MCQs (easy/medium/hard)
4. Planner Agent: Build revision schedules
5. Chat Agent: Answer doubts from PDF context

Project Structure

vectortutor/
├── app.py                  # Streamlit UI (single-page modern interface)
├── agents/
│   ├── reader_agent.py     # PDF extraction & structuring
│   ├── flashcard_agent.py  # Flashcard generation
│   ├── quiz_agent.py       # Quiz question generation
│   ├── planner_agent.py    # Revision plan creation
│   └── chat_agent.py       # Q&A and summarization
├── utils/
│   ├── groq_client.py      # Groq API wrapper
│   ├── memory.py           # Knowledge Memory Module (SQLite)
│   └── pdf_utils.py        # PDF text extraction
├── .env                    # Environment variables (API key)
├── requirements.txt        # Python dependencies
└── README.md              # This file

Installation

Prerequisites

• Python 3.9 or higher
• Groq API key (Get one here)

Setup Steps

1. Clone or download this repository

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables

   • Copy env.example to .env
   • Add your Groq API key:

     GROQ_API_KEY=your_actual_api_key_here
     

4. Run the application

   streamlit run app.py

5. Open your browser

   • The app will automatically open at http://localhost:8501

Usage Guide

1. Upload & Read PDF

• Navigate to "📄 Upload & Read PDF" page
• Upload a PDF file
• Click "Process PDF" to extract and structure content
• The Reader Agent will analyze and organize the content

2. Generate Flashcards

• Go to "🎴 Flashcards" page
• Select a document
• Choose number of flashcards and optional topic filter
• Click "Generate Flashcards"
• Study with show/hide answer mode

3. Take Quizzes

• Visit "📝 Quizzes" page
• Select a document and difficulty level
• Generate quiz questions
• Answer the questions and submit to see your score
• Performance is automatically tracked

4. Create Revision Plans

• Open "📅 Revision Planner" page
• Select a document
• Set days until exam and hours per day
• Optionally specify focus topics
• Generate a personalized revision schedule

5. Chat & Ask Questions

• Go to "💬 Chat & Doubts" page
• Select a document
• Ask questions about the content
• Get AI-powered answers based on the document
• Generate summaries of your notes

6. Track Performance

• Check "📊 Performance & Summary" page
• View quiz accuracy and statistics
• See recommendations based on your performance
• Monitor your learning progress

🛠️ Tech Stack

• Python 3.9+: Core programming language
• Streamlit: Web UI framework
• Groq API: AI model (llama-3.3-70b-versatile)
• PyMuPDF (fitz): PDF text extraction
• SQLite: Knowledge Memory Module for data persistence
• JSON: Data serialization for plans and structured content

🔧 Configuration

Groq API Model

The application uses llama-3.3-70b-versatile model by default. You can modify this in utils/groq_client.py:

self.model = "llama-3.3-70b-versatile"

Database Location

The SQLite database is created as vectortutor.db in the project root. You can change this in utils/memory.py:

def __init__(self, db_path: str = "vectortutor.db"):

📝 Notes

• PDF Size: Very large PDFs may be truncated during processing. The system processes the first ~15,000 characters for structuring.
• API Limits: Be mindful of Groq API rate limits and usage quotas.
• Data Persistence: All data is stored locally in SQLite. The database persists between sessions.

🐛 Troubleshooting

"GROQ_API_KEY not found"

• Make sure you've created a .env file with your API key
• Verify the key is correct and has no extra spaces

"Error extracting PDF text"

• Ensure the PDF is not password-protected
• Check that PyMuPDF is properly installed
• Try with a different PDF file

Import errors

• Make sure all dependencies are installed: pip install -r requirements.txt
• Verify you're using Python 3.9 or higher

📄 License

This project is open source and available for educational purposes.

🤝 Contributing

Feel free to submit issues, fork the repository, and create pull requests for any improvements.

🙏 Acknowledgments

• Built with Streamlit
• Powered by Groq API
• PDF processing with PyMuPDF

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Happy Studying! 📚✨