SECRET_KEY

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

nodi.tar.gz

openai js

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

# Overview

The OpenAI models are also great at generating code and text as they have been trained on a large amount of data from different resources.

# Generate text

Now that we have learned about how OpenAI API's model work, we can utilize the following prompt to generate new ideas about deep learning and images:

```text
Brainstorm some ideas combining deep learning and images:
```

## Example

Let's try the above example in the code widget below:

// Define endpoint URL here
const endpointUrl = "https://api.openai.com/v1/chat/completions";

// Define Header Parameters here
const headerParameters = {
  "Authorization": "Bearer {{SECRET_KEY}}",
  "Content-Type": "application/json"
};

// Body Parameters
const bodyParameters = JSON.stringify({
    model: "gpt-3.5-turbo",
    messages: [{ role: "system", content: "You are a helpful assistant." }],
    messages: [{ role: "user", content: "Brainstorm some ideas combining deep learning and images." }],
    temperature: 0.75,
    max_tokens: 200,
    top_p: 1.0,
    frequency_penalty: 1,
    presence_penalty: 1
});

// Set API call options
const options = {
  method: "POST",
  headers: headerParameters,
  body: bodyParameters
};

// Function to make API call
async function generateCompletion() {
  try {
    const response = await fetch(`${endpointUrl}`, options);

    // Print the  response
    printResponse(response);
  } catch (error) {
    // Print the  error message
    printError(error);
  }
}

// Call the function to make the API call.
generateCompletion();

In the code above, we use `temperature` with a value of `0.75` to include 75% randomness for text generation, `max_tokens` as `200`, which is the output text’s maximum length, and `top_p` with a value of `1.0` to include all the tokens in new text generation. The `frequency_penalty` and `presence_penalty` are two ways to ensure that the new text has diverse words, and both are used with the values of `1`. The `frequency_penalty` decreases the chance of repeating words, while the `presence_penalty` increases the chance of including new words.


Try out all the engines mentioned in [this lesson](https://www.educative.io/courses/using-openai-api-for-natural-language-processing-in-javascript/some-useful-engines) and examine the different effects on the output text.

>**Note:** We can increase the quality of the results, if necessary, by giving some examples in the prompt.

Learn how to use chat completions for generating new text and its practical use cases.

Introduction

The Completions Endpoint

Miscellaneous Endpoints

APIs Integration in React

Conclusion

Appendix

Generation

Overview

Generate text