OpenAI – Use cases and Automation

A Couple of days ago, the people at OpenAI released ChatGPT which is an AI-based system optimized for dialogue. The service is available for free preview here –> ChatGPT (openai.com) 

The service itself uses large language models trained by OpenAI and uses deep learning to generate natural language text based on the input. The service has support for a wide range of different languages such as:

English, Spanish, French, German, Italian, Portuguese, Dutch, Norwegian, Swedish, Danish, Finnish, Polish, Czech, Russian, Arabic, Chinese, Japanese, Korean, Hindi, Bengali, Urdu, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Oriya, Assamese, Mongolian, Armenian, Turkish, Greek, Hebrew, Persian, Latin, Icelandic, Basque, Catalan, Galician, Welsh, Irish, Manx, Scots, Cornish, Breton, Provencal, Corsican, Quechua, Aymara, Tupi, Greenlandic, Albanian, Serbian, Croatian

And the language database is already several hundred GB already. Now the service itself has several use cases. Think about instead of building APIs or integration you can use this service to interpret content and build what you need of it. 

For instance, we can send an API call containing a certain payload and as create a table out of it for us, as seen in the example below.

In addition, think about how Chatbots would work powered by these engines.

ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human Feedback (RLHF) – a method that uses human demonstrations to guide the model toward desired behavior.
You can read more about GPT 3-5 here –> Model index for researchers – OpenAI API

You however have different models that you can interact with and use from an automation perspective, depending on the use case.

Within GPT-3 you have different models, the most used and the most versatile is
text-davinci-003 which contains data points up to June 2021. 

Since ChatGPT is only available as a web service and not as an API, you cannot directly build applications around it, however, since ChatGPT is built upon OpenAI you can use the services there to interact with it.

To interact with the OpenAI API you need to have an account, which you can create here –> OpenAI API then you need to create an API key which is used to authenticate to the API under your account settings.

There are different ways to interact with the API, either using Python, Node, REST APIs which allows use to PowerShell and others. Below is an example of a Python request against the Completion API.

import os
import openai
openai.api_key = "apikey"

response = openai.Completion.create(
  model="text-davinci-003",
  prompt="What is the meaning of life?",
  temperature=0.9,
  max_tokens=1200,
  top_p=1,
  frequency_penalty=0,
  presence_penalty=0
)

print(response.choices[0].text)

import os
import openai
openai.api_key = "APIKEY"
response = openai.Image.create(
  prompt="cyberpunk 2077 city background",
  n=1,
  size="1024x1024"
)
image_url = response['data'][0]['url']
print(response)

This was one example showing how you can use the API calls to interact with the OpenAPI engine to build more automation.