SAT Tutor - AI-Powered SAT Preparation System

An intelligent SAT preparation system that combines knowledge graphs, semantic augmentation, and Large Language Models (LLMs) to provide personalized SAT tutoring with GPU acceleration support.

🚀 Features

• Knowledge Graph Integration: Builds and queries semantic knowledge graphs from educational content
• Semantic Augmentation: Enhances questions and explanations using AI models
• GPU Acceleration: Optimized for AMD GPUs using DirectML on Windows
• Multi-Model Support: Works with both local models and cloud APIs (Groq)
• Interactive Learning: Provides detailed explanations and concept extraction
• Cross-Platform: Supports Windows, macOS, and Linux

🏗️ Architecture

The system consists of several key components:

• Knowledge Graph Module (knowledge_graph.py): Manages semantic relationships and entity extraction
• Semantic Augmenter (semantic_augmenter.py): Enhances content using LLMs with GPU acceleration
• Graph RAG (graph_rag.py): Retrieval-Augmented Generation using knowledge graphs
• Main Application (sat_tutor.py): Orchestrates all components for SAT tutoring

🛠️ Installation

Prerequisites

• Python 3.10 or higher
• AMD GPU (for DirectML acceleration) or NVIDIA GPU (for CUDA)
• Windows 10/11 (for DirectML support)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/sat-tutor.git
   cd sat-tutor

2. Create a virtual environment:

   python -m venv sat-tutor-env

3. Activate the virtual environment:

   • Windows:

     sat-tutor-env\Scripts\activate

   • macOS/Linux:

     source sat-tutor-env/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

GPU Acceleration Setup

For AMD GPUs (DirectML - Windows)

1. Install DirectML:

   pip install torch-directml

2. Verify installation:

   python -c "import torch_directml; print('DirectML available:', torch_directml.is_available())"

For NVIDIA GPUs (CUDA)

1. Install PyTorch with CUDA:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

2. Verify installation:

   python -c "import torch; print('CUDA available:', torch.cuda.is_available())"

🚀 Usage

Basic Usage

from sat_tutor import SATTutor

# Initialize the tutor
tutor = SATTutor()

# Ask a question
question = "What is the quadratic formula?"
answer = tutor.ask_question(question)
print(answer)

Advanced Usage

from semantic_augmenter import SemanticAugmenter
from knowledge_graph import KnowledgeGraph

# Initialize components
augmenter = SemanticAugmenter(use_groq=False, model_name="gpt2")
knowledge_graph = KnowledgeGraph()

# Extract concepts from text
text = "The American Revolution was a political and military conflict..."
concepts = augmenter.extract_concepts(text)

# Build knowledge graph
knowledge_graph.add_concepts(concepts)

# Generate explanations
explanation = augmenter.generate_explanation(
    question="What caused the American Revolution?",
    correct_answer="Taxation without representation",
    wrong_answers=["British military weakness", "French intervention"]
)

📁 Project Structure

sat-tutor/
├── sat_tutor.py              # Main application
├── knowledge_graph.py        # Knowledge graph management
├── semantic_augmenter.py     # AI model integration
├── graph_rag.py             # Graph-based RAG system
├── requirements.txt          # Python dependencies
├── README.md                # This file
└── .gitignore              # Git ignore rules

🔧 Configuration

Environment Variables

Create a .env file in the project root:

# Groq API (optional)
GROQ_API_KEY=your_groq_api_key_here

# Model Configuration
DEFAULT_MODEL=gpt2
USE_GPU=true

Model Configuration

The system supports various models:

• Local Models: GPT-2, OPT, Llama-2 (requires sufficient RAM/VRAM)
• Cloud APIs: Groq (requires API key)

🧪 Testing

Run the GPU acceleration test:

python test_gpu.py

Test DirectML functionality:

python test_directml.py

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Hugging Face for transformer models
• Microsoft DirectML for AMD GPU support
• Groq for cloud inference API
• The SAT community for educational content

📞 Support

If you encounter any issues:

1. Check the Issues page
2. Create a new issue with detailed information
3. Include your system specifications and error messages

🔄 Updates

Stay updated with the latest features and improvements:

git pull origin main
pip install -r requirements.txt --upgrade

---