Imported from pur3v4d3r/pur3-pkb-codebase (999-v4d3r/__prompt-engineering-guidance/_generative-ai-for-beginners-main/AGENTS.md). Install upstream with npx skills add pur3v4d3r/pur3-pkb-codebase --skill _generative-ai-for-beginners-main. Copyright stays with the author.

AGENTS.md

Project Overview

This repository contains a comprehensive 21-lesson curriculum teaching Generative AI fundamentals and application development. The course is designed for beginners and covers everything from basic concepts to building production-ready applications.

Key Technologies:

• Python 3.9+ with libraries: openai, python-dotenv, tiktoken, azure-ai-inference, pandas, numpy, matplotlib
• TypeScript/JavaScript with Node.js and libraries: @azure/openai, @azure-rest/ai-inference, openai
• Azure OpenAI Service, OpenAI API, and GitHub Models
• Jupyter Notebooks for interactive learning
• Dev Containers for consistent development environment

Repository Structure:

• 21 numbered lesson directories (00-21) containing READMEs, code examples, and assignments
• Multiple implementations: Python, TypeScript, and sometimes .NET examples
• Translations directory with 40+ language versions
• Centralized configuration via .env file (use .env.copy as template)

Setup Commands

Initial Repository Setup

# Clone the repository
git clone https://github.com/microsoft/generative-ai-for-beginners.git
cd generative-ai-for-beginners

# Copy environment template
cp .env.copy .env
# Edit .env with your API keys and endpoints

Python Environment Setup

# Create virtual environment
python3 -m venv venv

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

# Install dependencies
pip install -r requirements.txt

Node.js/TypeScript Setup

# Install root-level dependencies (for documentation tooling)
npm install

# For individual lesson TypeScript examples, navigate to the specific lesson:
cd 06-text-generation-apps/typescript/recipe-app
npm install

Dev Container Setup (Recommended)

The repository includes a .devcontainer configuration for GitHub Codespaces or VS Code Dev Containers:

1. Open repository in GitHub Codespaces or VS Code with Dev Containers extension
2. Dev Container will automatically:
   • Install Python dependencies from requirements.txt
   • Run post-create script (.devcontainer/post-create.sh)
   • Set up Jupyter kernel

Development Workflow

Environment Variables

All lessons requiring API access use environment variables defined in .env:

• OPENAI_API_KEY - For OpenAI API
• AZURE_OPENAI_API_KEY - For Azure OpenAI Service
• AZURE_OPENAI_ENDPOINT - Azure OpenAI endpoint URL
• AZURE_OPENAI_DEPLOYMENT - Chat completion model deployment name
• AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT - Embeddings model deployment name
• AZURE_OPENAI_API_VERSION - API version (default: 2024-02-01)
• HUGGING_FACE_API_KEY - For Hugging Face models
• GITHUB_TOKEN - For GitHub Models

Running Python Examples

# Navigate to lesson directory
cd 06-text-generation-apps/python

# Run a Python script
python aoai-app.py

Running TypeScript Examples

# Navigate to TypeScript app directory
cd 06-text-generation-apps/typescript/recipe-app

# Build the TypeScript code
npm run build

# Run the application
npm start

Running Jupyter Notebooks

# Start Jupyter in the repository root
jupyter notebook

# Or use VS Code with Jupyter extension

Working with Different Lesson Types

• "Learn" lessons: Focus on README.md documentation and concepts
• "Build" lessons: Include working code examples in Python and TypeScript
• Each lesson has a README.md with theory, code walkthroughs, and links to video content

Code Style Guidelines

Python

• Use python-dotenv for environment variable management
• Import openai library for API interactions
• Use pylint for linting (some examples include # pylint: disable=all for simplicity)
• Follow PEP 8 naming conventions
• Store API credentials in .env file, never in code

TypeScript

• Use dotenv package for environment variables
• TypeScript configuration in tsconfig.json for each app
• Use @azure/openai or @azure-rest/ai-inference for Azure services
• Use nodemon for development with auto-reload
• Build before running: npm run build then npm start

General Conventions

• Keep code examples simple and educational
• Include comments explaining key concepts
• Each lesson's code should be self-contained and runnable
• Use consistent naming: aoai- prefix for Azure OpenAI, oai- for OpenAI API, githubmodels- for GitHub Models

Documentation Guidelines

Markdown Style

• All URLs must be wrapped in [text](url) format with no extra spaces
• Relative links must start with ./ or ../
• All links to Microsoft domains must include tracking ID: ?WT.mc_id=academic-105485-koreyst
• No country-specific locales in URLs (avoid /en-us/)
• Images stored in ./images folder with descriptive names
• Use English characters, numbers, and dashes in file names

Translation Support

• Repository supports 40+ languages via automated GitHub Actions
• Translations stored in translations/ directory
• Do not submit partial translations
• Machine translations are not accepted
• Translated images stored in translated_images/ directory

Testing and Validation

Pre-submission Checks

This repository uses GitHub Actions for validation. Before submitting PRs:

1. Check Markdown Links:

   # The validate-markdown.yml workflow checks:
   # - Broken relative paths
   # - Missing tracking IDs on paths
   # - Missing tracking IDs on URLs
   # - URLs with country locale
   # - Broken external URLs

2. Manual Testing:

   • Test Python examples: Activate venv and run scripts
   • Test TypeScript examples: npm install, npm run build, npm start
   • Verify environment variables are configured correctly
   • Check that API keys work with the code examples

3. Code Examples:

   • Ensure all code runs without errors
   • Test with both Azure OpenAI and OpenAI API when applicable
   • Verify examples work with GitHub Models where supported

No Automated Tests

This is an educational repository focused on tutorials and examples. There are no unit tests or integration tests to run. Validation is primarily:

• Manual testing of code examples
• GitHub Actions for Markdown validation
• Community review of educational content

Pull Request Guidelines

Before Submitting

1. Test code changes in both Python and TypeScript when applicable
2. Run Markdown validation (triggered automatically on PR)
3. Ensure tracking IDs are present on all Microsoft URLs
4. Check that relative links are valid
5. Verify images are properly referenced

PR Title Format

• Use descriptive titles: [Lesson 06] Fix Python example typo or Update README for lesson 08
• Reference issue numbers when applicable: Fixes #123

PR Description

• Explain what was changed and why
• Link to related issues
• For code changes, specify which examples were tested
• For translation PRs, include all files for a complete translation

Contribution Requirements

• Sign Microsoft CLA (automatic on first PR)
• Fork repository to your account before making changes
• One PR per logical change (don't combine unrelated fixes)
• Keep PRs focused and small when possible

Common Workflows

Adding a New Code Example

1. Navigate to the appropriate lesson directory
2. Create example in python/ or typescript/ subdirectory
3. Follow naming convention: {provider}-{example-name}.{py|ts|js}
4. Test with actual API credentials
5. Document any new environment variables in lesson README

Updating Documentation

1. Edit README.md in the lesson directory
2. Follow Markdown guidelines (tracking IDs, relative links)
3. Update translations are handled by GitHub Actions (don't edit manually)
4. Test all links are valid

Working with Dev Containers

1. Repository includes .devcontainer/devcontainer.json
2. Post-create script installs Python dependencies automatically
3. Extensions for Python and Jupyter are pre-configured
4. Environment is based on mcr.microsoft.com/devcontainers/universal:2.11.2

Deployment and Publishing

This is a learning repository - there is no deployment process. The curriculum is consumed by:

1. GitHub Repository: Direct access to code and documentation
2. GitHub Codespaces: Instant dev environment with pre-configured setup
3. Microsoft Learn: Content may be syndicated to official learning platform
4. docsify: Documentation site built from Markdown (see docsifytopdf.js and package.json)

Building Documentation Site

# Generate PDF from documentation (if needed)
npm run convert

Troubleshooting

Common Issues

Python Import Errors:

• Ensure virtual environment is activated
• Run pip install -r requirements.txt
• Check Python version is 3.9+

TypeScript Build Errors:

• Run npm install in the specific app directory
• Check Node.js version is compatible
• Clear node_modules and reinstall if needed

API Authentication Errors:

• Verify .env file exists and has correct values
• Check API keys are valid and not expired
• Ensure endpoint URLs are correct for your region

Missing Environment Variables:

• Copy .env.copy to .env
• Fill in all required values for the lesson you're working on
• Restart your application after updating .env

Additional Resources

• Course Setup Guide
• Contributing Guidelines
• Code of Conduct
• Security Policy
• Azure AI Discord
• Collection of Advanced Code Samples

Project-Specific Notes

• This is an educational repository focused on learning, not production code
• Examples are intentionally simple and focused on teaching concepts
• Code quality is balanced with educational clarity
• Each lesson is self-contained and can be completed independently
• The repository supports multiple API providers: Azure OpenAI, OpenAI, and GitHub Models
• Content is multilingual with automated translation workflows
• Active community on Discord for questions and support