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)
• 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. 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 uses Black for consistent Python code formatting with a line length of 88 characters.

Check formatting:

./format.sh check

Apply formatting:

./format.sh format

Show formatting diff:

./format.sh diff

Pre-commit hooks are configured to automatically format code and run checks before each commit:

Install hooks (one-time setup):

uv run pre-commit install

Once installed, the hooks will automatically run on git commit and:

• Format Python code with Black
• Remove trailing whitespace
• Fix end-of-file newlines
• Validate YAML files
• Check for large files
• Detect merge conflicts

Run comprehensive quality checks (formatting + tests):

./quality-check.sh

This script:

1. Checks code formatting with Black
2. Runs the full test suite with pytest

Use this before pushing code or in CI/CD pipelines.