An intelligent AI tutoring system using Socratic teaching methods to guide students through personalized learning experiences.
- Socratic Teaching: Guides learning through questions, not direct answers
 - Document Knowledge Base: Vector-based semantic search through uploaded materials
 - Web Search Integration: Real-time information retrieval for current topics
 - Interactive Web Interface: Drag-and-drop file upload with real-time tool visualization
 - Docker Ready: Complete containerization with development and production configurations
 
Two-container system with shared vector store:
├── frontend/          # Chainlit web app (Port 8001)
├── mcp-server/        # FastMCP server (Port 8000)
└── vector_store/      # Shared ChromaDB volume
- Docker and Docker Compose
 - Google Gemini API key
 
- 
Clone and configure:
git clone https://github.com/DevHammad0/openai-study-mode-clone.git cd openai-study-mode-clone # Create environment files echo "GEMINI_API_KEY=your_api_key_here" > mcp-server/.env echo "GEMINI_API_KEY=your_api_key_here" > frontend/.env echo "MCP_SERVER_URL=http://mcp-server:8000/mcp" >> frontend/.env
 - 
Start the application:
# Production docker-compose up -d # Development (with hot reload) docker-compose -f docker-compose.yml -f docker-compose.override.yml up
 - 
Access:
http://localhost:8001 
- Upload documents: Drag and drop 
.txtor.mdfiles - Start learning: AI tutor will introduce itself and assess your level
 - Ask questions: Receive guided responses with real-time tool visualization
 
- Document-only: 
"Explain X, answer only from document" - Web search: 
"What are the latest developments in AI?" - Mixed (default): 
"Help me understand machine learning" 
# Start/stop services
docker-compose up -d
docker-compose down
# View logs
docker-compose logs -f
# Rebuild containers
docker-compose build --no-cache
# Check status
docker-compose ps# Start with hot reload
docker-compose -f docker-compose.yml -f docker-compose.override.yml up
# View logs
docker-compose logs -f# MCP Server
cd mcp-server && uv sync && uv run server.py
# Frontend (new terminal)
cd frontend && uv sync && uv run chainlit_app.pyContainer fails to start
- Check Docker daemon: 
docker info - View logs: 
docker-compose logs [service-name] 
Port already in use
- Stop services: 
docker-compose down - Check ports: 
netstat -tulpn | grep :8000 
API key not working
- Verify key at Google AI Studio
 - Restart containers: 
docker-compose restart 
Document upload fails
- Check internet connection (needed for embeddings)
 - Verify API quota limits
 - Only 
.txtand.mdfiles supported 
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