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 chat completions endpoint can also be used to complete unattended (incomplete) sentences. For example, if we leave a sentence half-written, the chat completions endpoint can complete the sentence provided that it has the appropriate context. In this case, the value of `temperature` is usually kept low so that the completion does not disregard the previous context.

Although the tiger is a solitary beast and\n

## 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:"Complete the sentence."},
  {role:"user",content:"Although the tiger is a solitary beast and\n"}],
  temperature: 0.20,
  max_tokens: 64,
  top_p: 1
});

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

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

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

// Calling function to make API call
completeContent();

If we want to generate a focused completion of the unattended sentence, we use a lower `temperature` value. Otherwise, if we want a more creative and abstract completion, we use a higher `temperature` value. In the code above, we use a low value of the `temperature` parameter. The value of `top_p` indicates that we include all the tokens in the results. The default values of `presence_penalty` and `frequency_penalty` are used. We can experiment with these parameters by changing these values in multiple runs and trying different `engines` to see their capabilities.

# Factual responses

The API has a good grasp of the data on which it is trained and can generate answers to hypothetical questions. If we want the API to reply with less vague answers, we should tell it how to respond when it does not know the answer.

Here is an example of the prompt:
```text
Q: Who is Batman?
A: Batman is a fictional comic book character.

Q: What is torsalplexity?
A: ?

Q: What is Devz9?
A: ?

Q: Who is George Lucas?
A: George Lucas is an American film director and producer famous for creating Star Wars.
...
<more questions-answers, if needed>
...
Q: What is the meaning of xyzz?
A:
```

In the example above, we have set `?` as an answer if it is unknown. The model will follow the same pattern for unknown answers.

## Example
Let's run the above example in the code widget below:

Learn about completing incomplete content and factual responses using the chat completions endpoint and its practical use cases.

Introduction

The Completions Endpoint

Miscellaneous Endpoints

APIs Integration in React

Conclusion

Appendix

Completing Incomplete Content and Factual Responses

Overview