Course Materials RAG System

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, Ollama for local AI generation, and provides a web interface for interaction.

• Python 3.13 or higher
• Mamba (or Conda) package manager
• Ollama installed and running locally
• For Windows: Use Git Bash to run the application commands - Download Git for Windows

1. Install Mamba (if not already installed)

   # Install Miniforge which includes mamba
   # See: https://github.com/conda-forge/miniforge

2. Install Python dependencies

   mamba install --file requirements.txt

3. Install Ollama and pull a model

   curl -fsSL https://ollama.com/install.sh | sh
   ollama pull qwen2.5:7b

4. Set up environment variables (optional - defaults work out of the box)

   Create a .env file in the root directory:

   # Optional overrides (defaults shown)
   OLLAMA_BASE_URL=http://localhost:11434
   OLLAMA_MODEL=qwen2.5:7b

Use the provided shell script:

chmod +x run.sh
./run.sh

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

The application will be available at:

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