Use OpenAI Python Library With ChatGPT

This blog post will show you how to use the OpenAI Python library with ChatGPT and generate text completion based on input using a Python script.

To use the OpenAI Python library with ChatGPT, you will first need to install the library by running pip install openai in your command line. (see this post for more info) Once the library is installed, you will need to create an API key from the OpenAI website.

With your API key, you can then use the OpenAI library to interact with ChatGPT. For example, you can use the openai.Completion.create() method to generate text completions based on a prompt and the openai.Completion.create(engine="text-davinci-003") method to use the davinci version of the model.

Python Code

The code below will prompt you to submit input to ChatGPT, and the results will show in the terminal.

import os
import openai

from dotenv import load_dotenv
load_dotenv()

chat = input("Enter something? ")

openai.api_key = os.getenv("OPENAI_API_KEY")
result =openai.Completion.create(
  model="text-davinci-003",
  prompt= chat ,
  max_tokens=1000,
  temperature=0,  
)

print(result['choices'][0]['text'])

Note: To authenticate to OpenAI, save your API key in .env file with the key inside, as shown below.

OPENAI_API_KEY=yourapikeygoeshere

This will print the response of the model based on the provided prompt. You can also use the openai.Completion.create() method to generate text completions based on prompt and other parameters.

OpenAI’s GPT models parameters

There are several parameters that you can use to control the behaviour of OpenAI’s GPT models when generating text. Some of the most commonly used parameters include:

• prompt: This is the text that the model will use as the starting point for generating text completions.
• engine: This parameter controls which version of the GPT model you want to use. For example, you can use text-davinci-003 to use the latest version of the model.
• max_tokens: This parameter controls the maximum number of tokens (i.e. words or word pieces) that the model will generate in its response.
• n: This parameter controls the number of text completions that the model will generate for a given prompt.
• stop: This parameter controls when the model will stop generating text. You can specify a string, and the model will stop generating text as soon as it encounters that string.
• temperature: This parameter controls the “creativity” or “divergence” of the generated text.
• top_p: This parameter controls the proportion of the mass of the distribution that the model will sample from.
• frequency_penalty: This parameter controls the model’s preference for more frequent or less frequent words.
• presence_penalty: This parameter controls the model’s preference for words that appear more or less often in the context.

You can also use other parameters depending on the task and the version of the model you are using. You can find more details on the official OpenAI documentation.