A simple Python application that demonstrates how to interact with OpenAI's GPT-4 API to create a basic chatbot.

• Simple chat completion using OpenAI's GPT-4 model
• Environment variable configuration for secure API key management
• Configurable temperature settings for controlling response creativity
• Clean output extraction from API responses

• Python 3.7 or higher
• OpenAI API key

1. Clone this repository:

git clone <repository-url>
cd <repository-directory>

2. Install required dependencies:

pip install openai python-dotenv

3. Create a .env file in the project root and add your OpenAI API key:

API_KEY=your_openai_api_key_here

Run the script:

python main.py

The application will send a predefined question to GPT-4 and display the response.

The temperature parameter controls the randomness/creativity of the AI responses:

• 0.0: Deterministic responses (same input → same output)
  • Best for: factual questions, math problems, answers requiring consistency
• 0.1-0.9: Balanced randomness
  • Best for: marketing copy, creative writing, storytelling
• 1.0+: Highly creative/random responses

Currently configured to use gpt-4.1-2025-04-14. You can modify this in the code to use other available models.

# Load environment variables
load_dotenv()

# Initialize OpenAI client
client = OpenAI(api_key=os.getenv("API_KEY"))

# Create chat completion
response = client.chat.completions.create(
    model="gpt-4.1-2025-04-14",
    messages=[{"role": "user", "content": "Your question here"}],
    temperature=0.0
)

# Extract and display response
print(response.choices[0].message.content)

• Never commit your .env file to version control
• Keep your API key secure and don't share it publicly
• Consider using API key rotation for production applications

• openai: Official OpenAI Python client library
• python-dotenv: Load environment variables from .env file

The current example asks "왜 강남은 강남이라고 할까요?" (Why is Gangnam called Gangnam?) and displays the AI's response in Korean.

To modify the chatbot:

1. Change the question in the messages array
2. Adjust the temperature value for different response styles
3. Switch to a different OpenAI model if needed
4. Add conversation history by expanding the messages array

This project is open source and available under the MIT License.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

For issues related to:

• OpenAI API: Check the OpenAI documentation
• This code: Open an issue in this repository