Architecture

This repository is reviewed through a modular architecture lens: clear boundaries between UI, domain logic, and external integrations, with emphasis on maintainability and scaling paths.

README

Course Materials RAG System

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

Overview

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.

Prerequisites

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

Installation

Install uv (if not already installed)

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

Install Python dependencies
```
uv sync
```
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

Use the provided shell script:

chmod +x run.sh
./run.sh

Manual Start

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

System Diagram

Replace this reference diagram with project-specific architecture assets when available.