This is a Retrieval-Augmented Generation (RAG) system for querying course materials using semantic search and AI-powered responses. The application uses ChromaDB for vector storage, Anthropic's Claude for AI generation, and provides a web interface for interaction.

Note: This repository is forked from https-deeplearning-ai/starting-ragchatbot-codebase. I'm using this repository as a sandbox for experimenting with Claude Code.

• 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 with your Anthropic API key:

   # Copy the template file
   cp .env.example .env
   
   # Edit .env and add your API key
   # Get your key from: https://console.anthropic.com/

   Your .env file should look like:

   ANTHROPIC_API_KEY=sk-ant-your-actual-key-here
   

   Important:

   • The .env file is gitignored and will NOT be committed
   • Never commit your API key to version control
   • If you see "query failed" errors, check that your API key is set correctly

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

If you see "query failed" when asking questions:

1. Check your API key is set

   # In project root, verify .env file exists
   ls -la .env
   
   # Check if key is configured (won't show the actual key)
   grep ANTHROPIC_API_KEY .env

2. Verify the key format

   • Should start with sk-ant-
   • No quotes around the value
   • No spaces before or after the =

3. Restart the application

   • Stop the server (Ctrl+C)
   • Start it again - you should see a warning if the key is missing

4. Check startup logs

   • Look for warnings about API key configuration
   • The application will display clear messages if the key is missing or invalid

Run the comprehensive test suite:

cd backend
uv run pytest tests/ -v

This project includes automated code quality tools to maintain consistent code formatting and catch common issues.

• Format code: ./format.sh - Automatically formats all Python code
• Check code style: ./lint.sh - Checks formatting without making changes
• Run all quality checks: ./quality-check.sh - Comprehensive check (formatting, linting, type checking, tests)

Before committing code, run:

./quality-check.sh

This will:

1. Check code formatting (Black)
2. Verify import organization (isort)
3. Run linting checks (flake8)
4. Perform type checking (mypy)
5. Run the test suite

If formatting issues are found, fix them with:

./format.sh

• Black: Automatic code formatter (88 character line length)
• isort: Organizes imports alphabetically and by type
• flake8: Linting for style and common errors
• mypy: Static type checking
• pytest: Testing framework

Quality tool settings are defined in:

• pyproject.toml - Black, isort, and mypy configuration
• .flake8 - flake8 configuration