Overview of ChatGPT

ChatGPT is a powerful language model that can generate human-like text responses to a wide range of prompts through API integration. You can integrate this cutting-edge technology into your applications and unlock a whole new world of possibilities. By the end of this course, you’ll be equipped with the knowledge and skills to harness the power of the OpenAI API to revolutionize your work and open up a world of creativity and innovation.

Course Navigation

We’ve provided timestamps in this course for easy navigation between sections. We will be building our knowledge from previous sections, so be careful as we learn how to use the API in various real-life projects that you can apply to your own work. We’ll also learn how to build our own AI-driven, AI-powered, AI-generated queries. We have given timestamped sections of the course to help you navigate. You can skip to a section if you want to, and we’re happy to clarify any comments or questions you have.

JavaScript and React Knowledge Required

A basic understanding of JavaScript and React is necessary for this course.

API Key Security

The OpenAI API, like most APIs, uses API keys for authentication. In this section, I’m going to show you how to get your own API key for communicating with the API from the applications we’ll be building. Remember not to share your API key or publish it somewhere public. This is because if someone takes it, they could use up all your free credits or rack up loads of charges on your credit card.

If you have a credit card attached to it, this also includes exposing any client-side code, meaning building an app without a backend and deploying it onto the internet. The code in this case will be visible simply by inspecting it, and someone will then be able to go and take it.

To use this key safely, requests must be routed through a backend server where your API keys can be securely loaded from an environment variable or key management service. To make sure that this key is not shared, you need to create a demo key or a secret key. To create your demo key, copy the code somewhere safe and create a new key to use it again.

Remember not to copy the key and save it. If you don’t want it, don’t forget to share it. Please don’t use it or expose it to the public. It’s important to keep your API key secure for your own use.

Models Available on OpenAI API

The OpenAI API is powered by a diverse set of models with different capabilities and price points. At the time of writing, the newest model available to us is GPT4, which uses a set of models that improve on GPT3.5 and can understand as well as generate natural language or code. We can make requests to create numerous images of a certain size, and we will be using this in our upcoming projects. There is also Wispr, which is a general-purpose speech recognition model trained on a large data set of diverse audio and is also a multitask model that can perform multilingual speech recognition.

Let’s get to understanding some of these models that are currently at our disposal and write some requests for our upcoming projects. We’ll get to understand these models and create realistic images and art from a description in natural language. We’ll be using them to create our own creations and images, and we’ll be asking for your own pictures and descriptions to create good examples.

Please be careful to use the best examples and understand this section, as it is a great way to unlock the power of the OpenAI API.

The API is non-deterministic by default, which means it cannot be predicted what will happen. However, it can be made more deterministic by setting a temperature, which will be covered later. Here is the endpoint we will use for this demo; for those unfamiliar with POST requests, a tutorial on HTTP request methods may be useful to get the most out of the next steps.

The models understand the statistical relationship between text and completed text content, and we will use text of Vinci 003 for the demo. The next step is to use the API to generate or manipulate text, either from a single text or to complete or modify a completed text.

We will then use a different model for a new message and use the encode to create and manipulate content. The encode is then used to send a message to complete or modify, and the content is sent to a response, resulting in an image.

The API allows tokens to be used to represent pieces of text, with one token corresponding to roughly four characters of text for common English text. The token count of your prompt plus maximum tokens cannot exceed the model’s context length, which is generally 2048 tokens except for newer models that can support 4069 tokens.

A user can also be passed through the API to represent the end-user and help monitor and detect abuse. The API allows us to set how many completions to generate for each prompt by passing an integer value, or we can use top p as an alternative to nuclear sampling, with temperature or top p recommended but not both.

The model that we use is recommended to use either top p or temperature, and we will generate tokens and completions accordingly. To avoid errors, we need to specify a new user and use the token to send data to a new text or context. The API’s URL is included in the documentation.

To use the API, we will save the response to a const for authorization and include headers with an API key. The response will then be passed to the completions endpoint using cURL commands.

Here is the code:

const response = `API key`; const api_key = `${response}`; cURL command to be used to the completions endpoint with options and headers, including authorization with the API key.