learn_anything

This codebase is designed to help a user learn about a topic by first learning the necessary foundational knowledge.

Backend

Setup Instructions for Python

Prerequisites

• Python 3.10 or higher
• pip (Python package installer)
• OpenAI api key

Installation

1. Clone the repository:

git clone https://github.com/yourusername/learn_anything.git
cd learn_anything

2. Save OpenAI api key to an env file

echo "OPENAI_API_KEY={key_here}" > .env

3. Create and activate a virtual environment:

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
# .\venv\Scripts\activate

# To deactivate the virtual environment when you're done:
(venv) deactivate

4. Install required packages:

(venv) pip install -r requirements.txt

5. Run the python scripts (example: graph creation script):

(venv) ./backend/src/graph_creation.py [--optional-arguments]

Code Quality and Development

Development Scripts

The project includes several helpful scripts in backend/scripts/:

# Setup development environment
./backend/scripts/setup.sh

# Format Python files
./backend/scripts/format_python.sh

# Run all code quality checks
./backend/scripts/lint.sh

Code Style and Quality Checks

The project uses several tools to maintain code quality:

• YAPF: Code formatting with custom style rules
• isort: Import sorting
• Flake8: Style guide enforcement
• MyPy: Static type checking
• Pylint: Code quality metrics (minimum score: 8.0)

All these checks can be run using the lint script:

./backend/scripts/lint.sh

Or run individual checks:

# Format code with YAPF
yapf -i -r backend

# Sort imports
isort backend

# Run style checks
flake8 backend

# Run type checks
mypy backend

# Check code quality score
pylint backend

GitHub Actions Workflow

The repository includes a GitHub Actions workflow that runs on Ubuntu and macOS. The workflow:

• Creates a Python virtual environment
• Installs all dependencies
• Runs all code quality checks with detailed output
• Only fails if critical checks fail (e.g., pylint score < 8.0)
• Provides helpful messages about fixing issues

The workflow runs on:

• Pull requests to main branch
• Pushes to main branch

To test GitHub Actions locally, you can use act:

# Install act (on macOS)
brew install act

# Run the workflow locally
act -j code-quality

Documentation

Doxygen Documentation

Documentation is automatically generated using Doxygen for both Python and JavaScript/TypeScript code.

Viewing Documentation

1. Generate documentation locally:

   doxygen Doxyfile

2. Open in your browser:

   # On macOS
   open docs/html/index.html
   # On Linux
   xdg-open docs/html/index.html