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Learn to integrate the OpenAI API with JavaScript and React for NLP tasks like text generation, classification, moderation, and embeddings in dynamic apps.

Using OpenAI API for Natural Language Processing in JavaScript.png

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react-app

This course will teach you how to use the OpenAI API for natural language processing (NLP) in JavaScript, specifically focusing on the powerful GPT-3 models. You’ll begin by getting familiar with the registration process and OpenAI API overview, laying the foundation for deeper exploration into text generation and manipulation tasks.

Next, you’ll explore the core API functionality, including the completions endpoint, which performs various NLP tasks, such as text classification, transformation, and completing incomplete content. You’ll also gain hands-on experience using the moderation endpoint for content safety and embeddings for advanced text analysis.

Finally, you’ll develop your skills by integrating the OpenAI API into React, creating dynamic and interactive web applications. After completing this course, you’ll have the practical knowledge to build AI-driven applications to generate, analyze, and safely manage text-based data in real-world projects.

Using OpenAI API for Natural Language Processing in JavaScript

https://api.openai.com/v1/chat/completions

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Learn how to generate or manipulate text using the chat completions endpoint of OpenAI API.

Fields	Format	Type	Description
`messages`	Array	Required	This is a list of messages comprising the conversation as of yet.
`model`	String	Required	This is the ID of the model that the chat completions endpoint will use.
`max_tokens`	Integer	Optional	This is the maximum number of tokens to generate in the chat completion.
`temperature`	Float	Optional	Which sampling temperature should be employed, ranging from `0` to `2`? The default temperature is set at `1`. Opting for higher values, such as `0.8`, will increase randomness in the output, whereas lower values, like `0.2`, will enhance focus and determinism.
`top_p`	Float	Optional	Nucleus sampling is an alternative to temperature sampling in which the model evaluates the outcomes of tokens with top p probability mass. So, 0.1 indicates that only the top 10% probability mass tokens will be evaluated. Default value: `1`
`response_format`	object	Optional	This is an object that specifies the format of the output. This parameter is compatible with the newer models. Setting to `{ "type" : "json_object" }` will guarantee a valid JSON object.
`n`	Integer	Optional	It indicates the number of chat completions to generate. Default value: `1`
`logprobs`	Integer	Optional	This value decides whether to return the log probabilities of output tokens. This option is not available on the `gpt-4-vision-preview` model. Default value: `false`
`stop`	String/array	Optional	This allows you to provide four sequences where the API will stop generating further tokens. Default value: `null`
`presence_penalty`	Float	Optional	A value range between -2.0 to 2.0. The positive value penalizes the new tokens if they exist in the text, increasing the possibility of generating new things. Default value: `0`
`frequency_penalty`	Float	Optional	A value range between -2.0 to 2.0. The positive value penalizes the new tokens concerning their existing frequency in the text, decreasing the possibility of repeating the same words. Default value: `0`

Introduction

The Completions Endpoint

Miscellaneous Endpoints

APIs Integration in React

Conclusion

Appendix

Chat Completions

The chat completions endpoint

Understanding the chat completions endpoint

Request parameters