CUNY Course - RAG and LLM Core Concepts

Course materials and code examples for learning about RAG (Retrieval Augmented Generation) and LLM fundamentals.

Setup

Prerequisites

• Python 3.9 or higher
• Poetry for dependency management
• Azure OpenAI API access (or OpenAI API)

Installation

1. Clone this repository:

git clone https://github.com/drdk/CUNY_course.git
cd CUNY_course

2. Install dependencies using Poetry:

poetry install

3. Set up your environment variables in a .env file:

# For Azure OpenAI
AZURE_OPENAI_ENDPOINT=your_endpoint_here
AZURE_OPENAI_API_KEY=your_key_here
AZURE_OPENAI_DEPLOYMENT=your_deployment_name

# Or for OpenAI
OPENAI_API_KEY=your_key_here

Running Code Examples

Using Poetry

Run Python scripts directly:

poetry run python CUNY_course/example_code/structured_output.py
poetry run python CUNY_course/example_code/format_errror.py

Or activate the virtual environment first:

poetry shell
python CUNY_course/example_code/structured_output.py

Using Jupyter

Start Jupyter:

poetry run jupyter notebook

Project Structure

CUNY_course/
├── CUNY_course/
│   ├── example_code/       # Python code examples
│   │   ├── structured_output.py
│   │   └── format_errror.py
│   ├── example_data/       # Sample data files
│   │   ├── 10languages.txt
│   │   ├── usconstitution.txt
│   │   └── ...
│   ├── data_types/         # Pydantic models and schemas
│   │   └── person.py
│   ├── outline.md          # Course outline
│   └── links.md            # Useful links and resources
├── pyproject.toml          # Poetry dependencies
└── README.md               # This file

Key Files

• structured_output.py - Demonstrates how to extract structured data from text using LLMs with Pydantic models
• format_errror.py - Simple example showing Python data structures
• person.py - Pydantic models for representing people and relationships
• outline.md - Full course outline with lecture topics
• example_data/ - Various text files for experimentation

Development

Code Formatting

poetry run black .

Running Tests

poetry run pytest

Resources

• See outline.md for the full course outline and lecture structure
• See links.md for useful tools and resources

License

See the LICENSE file for details.