Mastering-LLM

In this Repo we will be mastering the LLM Calling

LLM Caller Application

A Python application demonstrating how to call different LLM APIs using UV package manager.

Features

• OpenAI GPT5-nano: Active implementation using the standard OpenAI library
• Google Gemini: Commented implementation using the Google Generative AI library
• Azure OpenAI GPT5-nano: Commented implementation using Azure AI Foundry

Prerequisites

• Python 3.12 or higher
• UV package manager

Installation

1. Clone the repository:

git clone https://github.com/Dev-AI-Bootcamp/Mastering-LLM.git
cd Mastering-LLM

2. The project uses UV for dependency management. Dependencies are already configured in pyproject.toml

3. Create a .env file based on .env.example:

cp .env.example .env

4. Add your API keys to the .env file:
   • For OpenAI: Add your OPENAI_API_KEY

Usage

Run the application using UV:

uv run Simple_call.py

Project Structure

.
├── Simple_call.py      # Main application file with LLM API calls
├── pyproject.toml      
# UV project configuration and dependencies
├── uv.lock             # UV lock file for reproducible builds
├── .env.example        # Example environment variables file
├── .env                # Your actual environment variables (git-ignored)
└── README.md           # This file

Dependencies

• openai>=2.7.2 - OpenAI API client (also used for Azure OpenAI)
• python-dotenv>=1.2.1 - Load environment variables from .env file

Getting API Keys

• Get your OpenAI API Keys here: https://platform.openai.com/api-keys