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)
• Node.js and npm (for frontend development tools)
• An Anthropic API key (for Claude AI)
• For Windows: Use Git Bash to run the application commands - Download Git for Windows

1. Install uv (if not already installed)

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

2. Install Python dependencies

   uv sync

3. Install Node.js dependencies (for development tools)

   npm install

4. 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

This project includes automated code quality tools for both Python backend and frontend code.

Quick quality check (format + lint):

./quality-check.sh

Format code only:

# Python backend
./format.sh

# Frontend (HTML/CSS/JS)
npm run format

Lint code only (no modifications):

# Python backend
./lint.sh

# Frontend
npm run format:check

Backend (Python):

• Black: Code formatter
• isort: Import statement organizer
• flake8: Style guide enforcement

Frontend (JavaScript/CSS/HTML):

• Prettier: Code formatter

For detailed information about code quality tools and configuration, see frontend-changes.md.