ChatGPT needs no further introduction lately and on this post we would love to look deeper into how we will programmatically interact with the models and engines that power ChatGPT (e.g. GPT-4, GPT-3.5, DALL·E, etc.) through the official OpenAI API (OpenAI is the corporate behind ChatGPT). Machine learning scientists and engineers generally exhibit a preference for using APIs fairly than graphical user interfaces, reminiscent of ChatGPT, since APIs provide a much higher level of flexibility and customization, as we are going to see within the implementation examples, that are required in business settings.
With the intention to use OpenAI’s API, we are going to arrange and activate a Python virtual environment (this a really useful but optional step), install OpenAI Python library and begin implementing 11 practical examples. These examples are my personal favorite ones amongst many who I even have explored and can cover the next:
- Explain Code
- Image Generation
- Emoji Translation (i.e. we offer a text description and the model returns emojis that describe the provided text!)
- Grammatical Error Correction
- Airport Code Extractor
- Named-Entity Extractor
- Machine Translation
- Sentiment Evaluation
- Text Summarization
- Parse Unstructured Data
- Write SQL Queries
I’ll provide more details about each task as we undergo them but now that we all know the outline of what we are going to cover, let’s start!
This step is simply to create a virtual environment so that you would be able to isolate what’s created and utilized in this post out of your other Python bodies of labor. As I discussed earlier within the post, use of a virtual environment is optional but is mostly among the many really useful best practices for machine learning practitioners and programmers. There are a couple of methods to create this and below is one approach that I even have used. We’ll create the…
