Enhancing ChatGPT with Custom Extensions: Building Add-ons with TypeScript

ChatGPT is a powerful language model developed by OpenAI, built on the GPT-3.5 architecture. One of the capabilities offered by ChatGPT is the ability to extend it through add-ons. In this blog post, I will explain how you can extend ChatGPT using TypeScript and create add-ons to provide a more engaging chat experience.

• Step 1: The Basic Structure of ChatGPT Extensions

  • To add an extension to ChatGPT, you can follow the steps below:
  • Create a TypeScript project for your extension.
  • Add the necessary dependencies (e.g., the OpenAI API client) to your project.
  • Create a class or module to define the basic structure of your extension.

• Step 2: Interacting with ChatGPT using the API Client To send requests to ChatGPT and receive responses, you will need to use the OpenAI API. In TypeScript, you can use the openai library, which is the OpenAI API client. You can use this library to handle requests and analyze the responses.

Example usage:

ts
import * as openai from 'openai';

// Set your API key and other necessary information
const apiKey = 'YOUR_API_KEY';

// Configure the OpenAI API client
const openaiClient = new openai.DefaultApi();
openaiClient.getApiClient().authentications['Bearer'].apiKey = apiKey;

// Create a function to send a message to ChatGPT
async function sendMessage(message: string): Promise<string> {
  const response = await openaiClient.sendMessage({ message });
  return response.generatedText;
}

// Example message sending
sendMessage('Hello, how are you?').then((response) => {
  console.log('ChatGPT response:', response);
});

• Step 3: Creating Custom Functions for Extensions You can create custom functions to extend ChatGPT's capabilities. These functions can analyze the user's input, generate custom responses, and manage the interaction with ChatGPT. For example, you can perform analysis on the previous message and return a custom response based on it.

Example function:

ts
// Create a custom function
async function processMessage(message: string): Promise<string> {
  // Apply the necessary logic here to analyze the message and generate a custom response
  if (message.toLowerCase().includes('weather')) {
    return 'The weather is sunny at the moment.';
  }

  // Send the message to the OpenAI API
  const response = await sendMessage(message);
  return response;
}

// Example usage
processMessage('Hello, how are you?').then((response) => {
  console.log('Custom function response:', response);
});

• Step 4: Integrating with a User Interface To integrate your extension into a user interface, you can develop a web application or a mobile app. You can provide input fields and output areas for users to interact with ChatGPT. If you are developing a web application with TypeScript, you can use popular frameworks like React, Angular, or Vue.js.

Example React component:

ts
import React, { useState } from 'react';

function ChatComponent() {
  const [inputMessage, setInputMessage] = useState('');
  const [outputMessage, setOutputMessage] = useState('');

  async function sendMessage() {
    const response = await processMessage(inputMessage);
    setOutputMessage(response);
  }

  return (
    <div>
      <input
        type="text"
        value={inputMessage}
        onChange={(e) => setInputMessage(e.target.value)}
      />
      <button onClick={sendMessage}>Send</button>
      <p>{outputMessage}</p>
    </div>
  );
}

export default ChatComponent;

• Conclusion: In this blog post, I've explained how you can extend ChatGPT using TypeScript to create add-ons. By using the OpenAI API and the capabilities provided by TypeScript, you can interact with ChatGPT and create custom functions to analyze input and generate custom responses. Additionally, I've shown an example of integrating the extension into a user interface using a React component. By following these steps, you can provide a more engaging chat experience by extending ChatGPT.