This is a full-stack web application that provides a Retrieval-Augmented Generation (RAG) system for answering questions about course materials.

Main Technologies:

• Backend: Python, FastAPI, Uvicorn
• Frontend: HTML, CSS, JavaScript
• AI: Anthropic's Claude
• Vector Store: ChromaDB
• Embeddings: Sentence Transformers
• Package Manager: uv

Architecture:

The application is composed of a frontend and a backend.

• The backend is a FastAPI application that exposes a REST API for querying the RAG system. It handles document processing, vector storage, and AI-powered response generation.
• The frontend is a single-page application that provides a chat interface for users to interact with the RAG system.

Prerequisites:

• Python 3.13 or higher
• uv (Python package manager)
• An Anthropic API key

Installation:

1. Install uv:

   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

Running the Application:

• Quick Start:

  chmod +x run.sh
  ./run.sh

• Manual Start:

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

The application will be available at:

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

Testing:

To run the tests, use the following command:

pytest

• Code Style: The project uses black for code formatting, isort for import sorting, and flake8 for linting.
• Type Checking: The project uses mypy for static type checking.
• Testing: The project uses pytest for testing. Tests are located in the backend/tests directory.