A Retrieval-Augmented Generation (RAG) system designed to answer questions about course materials using semantic search and AI-powered responses.

This application is a full-stack web application that enables users to query course materials and receive intelligent, context-aware responses. It uses ChromaDB for vector storage, Anthropic's Claude for AI generation, and provides a web interface for interaction.

• Python 3.13 or higher
• uv (Python package manager)
• One of the following AI backends:
  • Anthropic API key (for Claude AI) - Paid, best quality
  • Groq API key (for Llama models) - Free, fast
  • Ollama (for local models) - Free, private

💡 New to this project? Check out:

• OLLAMA_STARTUP_GUIDE.md - Complete guide for using local Ollama models (recommended for beginners)
• MULTI_BACKEND_GUIDE.md - Comparison and setup for all AI backends
• QUICK_START.md - Quick start guide with Groq (free)

1. Install uv (if not already installed)

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install Python dependencies

   uv sync

3. Set up environment variables

   Create a .env file in the root directory:

   ANTHROPIC_API_KEY=your_anthropic_api_key_here

Use the provided shell script:

chmod +x run.sh
./run.sh

cd backend
uv run uvicorn app:app --reload --port 8000

The application will be available at:

• Web Interface: http://localhost:8000
• API Documentation: http://localhost:8000/docs