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Using Large Language Models APIs with Python: A Comprehensive Guide | by Ganesh Lokare | Medium

📅 2026-07-14 ⏱ 1 min lettura logistar.it
Using Large Language Models APIs with Python: A Comprehensive Guide | by Ganesh Lokare | Medium
Prerequisites First, install the required packages: pip install openai anthropic google-generativeai requests python-dotenv Import Required Libraries # imports import os from dotenv import load_dotenv from openai import OpenAI import google.generativeai import anthropic from IPython.display import Markdown, display, update_display # Load environment variables in a file called .env load_dotenv() os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env') os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env') os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env') Connect to OpenAI, Anthropic and Google All 3 APIs are similar Having problems with API files? You can use openai = OpenAI(api_key=”your-key-here”) and same for claude openai = OpenAI() claude = anthropic.Anthropic() google.generativeai.configure() What information is included in the API Typically we’ll pass to the API: The name of the model that should be used. Learn about Medium’s values System Message: The system message sets the context for the assistant. This message informs the model about its role, tone, and any specific guidelines it should follow while responding. User Message: The user message represents the input from the user, such as questions or instructions, which the model responds to. It contains the primary prompt or query you want the model to answer. There are other parameters that can be used, including temperature which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic.