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Connecting to OpenAI GPT-4o Using Python

🚀 Connecting to OpenAI GPT-4o Using Python

This guide explains how to connect to OpenAI’s GPT-4o model using Python, pass user roles and questions, and retrieve responses. It also includes steps to check existing API tokens and generate a new one if required.

🔹 Prerequisites

1️⃣ Check If You Have an OpenAI API Key

Before making API requests, check if you already have an OpenAI API Key:

  1. Go to OpenAI Platform.
  2. Click on your profile (top right corner) → View API Keys.
  3. If you see an existing key, copy it.
  4. If no key exists, proceed to the next step.

2️⃣ Generate a New OpenAI API Key

If you don’t have an API key:

  1. Log in to OpenAI API Keys.
  2. Click “Create new secret key”.
  3. Copy the generated key (it won’t be visible again).
  4. Store it securely in a .env file, environment variable, or directly in your Python script (if necessary).

🔹 Step-by-Step: Connecting to OpenAI GPT-4o

1️⃣ Install Required Python Packages

Ensure you have the openai package installed:

Terminal window
pip install openai python-dotenv

2️⃣ Setting Up the API Key

You can store the API key in two different ways:

Set your API key in your terminal (recommended for security):

Terminal window
export OPENAI_API_KEY="your-api-key-here"  # Linux/macOS
set OPENAI_API_KEY="your-api-key-here"  # Windows (CMD)
$env:OPENAI_API_KEY="your-api-key-here"  # Windows (PowerShell)

Then, retrieve it in Python:

import os
api_key = os.getenv("OPENAI_API_KEY")

Option 2: Use a .env File

Create a .env file in your project folder and add:

OPENAI_API_KEY=your-api-key-here

Then load it in Python:

from dotenv import load_dotenv
import os


load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")

If you don’t want to use environment variables or a .env file, you can hardcode the API key in your script:

api_key = "your-api-key-here"  # Hardcoded API Key (Avoid for security reasons)

⚠ Warning: Hardcoding API keys in scripts is insecure and should be avoided in production.

3️⃣ Write Python Code to Connect to OpenAI API

Create a Python file (openai_chat.py) and add the following:

import openai
import os
from dotenv import load_dotenv


# Load API Key (Choose One of the Three Methods Above)
load_dotenv()  # Only needed if using .env file
api_key = os.getenv("OPENAI_API_KEY") or "your-api-key-here"  # Fallback to hardcoded key if needed


# Function to interact with GPT-4o
def chat_with_gpt(user_role, user_question):
    client = openai.OpenAI(api_key=api_key)


    response = client.chat.completions.create(
        model="gpt-4o",  # You can use 'gpt-4' or 'gpt-3.5-turbo'
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
            {"role": user_role, "content": user_question}
        ]
    )


    return response.choices[0].message.content


# Example Usage
user_role = "user"
user_question = "What is the capital of France?"
response = chat_with_gpt(user_role, user_question)
print("AI Response:", response)

4️⃣ Run the Script

Execute the Python script:

Terminal window
python openai_chat.py

✅ Expected Output:

AI Response: The capital of France is Paris.

🔹 Additional Configurations

Change the Model Version

Modify the model parameter in the chat_with_gpt() function to use different models:

model="gpt-4o"  # GPT-4 Omni
model="gpt-4"   # Standard GPT-4
model="gpt-3.5-turbo"  # Cheaper & faster option

Modify Temperature for Creativity

The temperature parameter controls randomness:

temperature=0.7  # Higher values (0.7-1) make responses more creative

Use System Messages for Custom Behavior

Modify the system message to adjust the assistant’s personality:

messages=[
    {"role": "system", "content": "You are a Python expert. Answer coding-related queries only."}
]

🚀 Conclusion

You have successfully: ✔ Verified and generated an OpenAI API key
✔ Installed necessary dependencies
✔ Written Python code to connect to OpenAI’s GPT-4o
✔ Sent user roles & questions and received responses
✔ Learned different ways to manage API keys (Environment Variable, .env file, Hardcoded - last one not recommended)

Now, you can build chatbots, AI-powered apps, and more using OpenAI’s powerful LLM models! 🚀

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