Chatgpt api typescript

Learn how to use the ChatGPT API with TypeScript. Discover step-by-step instructions and code examples to integrate ChatGPT into your TypeScript projects.

Chatgpt api typescript

ChatGPT API in TypeScript: A Comprehensive Guide

ChatGPT is an advanced language model developed by OpenAI that has the ability to generate human-like text responses given a prompt. It has been trained on a vast amount of data from the internet and is capable of conversing on a wide range of topics. With the ChatGPT API, developers can now integrate ChatGPT into their own applications and services to add natural language processing capabilities.

In this comprehensive guide, we will explore how to use the ChatGPT API with TypeScript, a popular programming language that adds static typing to JavaScript. TypeScript provides a great development experience with its strong type checking, autocompletion, and static analysis, making it an ideal choice for building applications that interact with the ChatGPT API.

We will cover everything you need to know to get started, from setting up your development environment and making API requests, to handling responses and incorporating additional features like user context and system messages. By the end of this guide, you will have a solid understanding of how to leverage the power of ChatGPT API in your TypeScript projects.

Whether you are a seasoned TypeScript developer looking to enhance your applications with natural language processing capabilities or a beginner interested in exploring the potential of ChatGPT, this guide will provide you with the knowledge and tools to get started. Let’s dive in and unlock the potential of ChatGPT API in TypeScript!

What is ChatGPT API?

The ChatGPT API is a powerful tool that allows developers to integrate OpenAI’s ChatGPT model into their own applications, products, or services. With the API, developers can send a series of messages to the model and receive a model-generated message as a response. This enables interactive and dynamic conversations with the model, making it ideal for a wide range of conversational applications.

Key Features

The ChatGPT API offers several key features that make it a valuable resource for developers:

  • Interactive Conversations: Developers can engage in back-and-forth conversations with the model by sending a series of messages instead of a single prompt. This allows for more dynamic and interactive conversations.
  • System Level Instructions: Developers can provide high-level instructions to guide the model’s behavior throughout the conversation. These instructions can help set the context or specify the desired outcome.
  • Flexible Formats: Messages can be sent in a simple string format or as an object with both a role (either “system”, “user”, or “assistant”) and content (the message text).
  • Stateful Conversations: Developers can maintain state across multiple API calls by including a unique conversation ID. This allows for context preservation and continuity in longer conversations.

Use Cases

The ChatGPT API can be used in a variety of applications and services, including but not limited to:

  • Chatbots: Developers can create intelligent chatbots that can hold dynamic and context-aware conversations with users.
  • Customer Support: The API can be used to build customer support systems that can provide automated responses and assist users in finding solutions.
  • Content Generation: Developers can leverage the model to generate content for various purposes, such as drafting emails, writing code snippets, or creating conversational narratives.
  • Virtual Assistants: The ChatGPT API can be used to develop virtual assistants that can understand user queries and provide helpful responses or perform tasks on their behalf.

Getting Started

To get started with the ChatGPT API, developers need to authenticate themselves using an API key. OpenAI provides client libraries and SDKs in multiple programming languages, including TypeScript, which facilitate the integration process. By following the API documentation and guidelines, developers can quickly start sending messages and receiving model-generated responses.

API Pricing

It’s important to note that the ChatGPT API is not available for free and usage of the API is subject to OpenAI’s pricing. Developers are billed based on the number of tokens used for both input and output. The API documentation provides detailed information on pricing, including the cost per token and example calculations to estimate usage costs.


The ChatGPT API opens up a world of possibilities for developers looking to build conversational applications. With its interactive conversations, system-level instructions, and flexible formats, the API allows for the creation of dynamic and context-aware chatbots, customer support systems, content generators, and virtual assistants. By getting started with the API and exploring its various use cases, developers can unlock the full potential of ChatGPT in their own applications.

Why use TypeScript with ChatGPT API?

TypeScript is a statically typed superset of JavaScript that brings several benefits to the development process. When working with the ChatGPT API, using TypeScript can enhance your codebase and improve the overall development experience. Here are some reasons why you should consider using TypeScript with the ChatGPT API:

1. Type Safety

TypeScript introduces static typing, allowing you to define the types of variables, function parameters, and return values. This helps catch potential bugs and provides better tooling support, as it can detect type-related issues during development. With the ChatGPT API, which involves sending and receiving JSON payloads, TypeScript’s type safety can ensure that you are correctly handling the API responses and avoid common mistakes.

2. Enhanced IDE Support

IDEs like Visual Studio Code have excellent support for TypeScript. They can provide intelligent code completion, automatic imports, and quick access to documentation. With TypeScript, you can take advantage of these features while working with the ChatGPT API, making development faster and more efficient.

3. Improved Code Maintainability

TypeScript enables better code organization and maintainability by allowing you to define interfaces, classes, and modules. This makes your code more structured and readable, especially when dealing with complex interactions with the ChatGPT API. TypeScript’s strong typing also helps with code documentation and makes it easier for other developers to understand and work with your codebase.

4. Early Detection of Errors

TypeScript performs static type checking during the development phase, which helps catch errors early on. This can save you time by preventing runtime errors and reducing the need for extensive debugging. When integrating the ChatGPT API into your application, TypeScript can help identify potential issues with the API requests and responses before they cause problems in production.

5. Easier Refactoring

Refactoring code is a common part of the development process, and TypeScript makes it easier and safer. By providing type information, TypeScript can help you navigate through your codebase and identify all the places that need to be updated when you make changes to types or interfaces. This can be especially valuable when working with the ChatGPT API, as you may need to modify your code to accommodate new API features or updates.

Overall, TypeScript can enhance the development experience when using the ChatGPT API. It provides type safety, improved IDE support, better code maintainability, early error detection, and easier refactoring. If you want to write robust and reliable code while working with the ChatGPT API, TypeScript is a valuable tool to consider.

Getting started with ChatGPT API in TypeScript

The ChatGPT API allows you to integrate OpenAI’s powerful ChatGPT language model into your TypeScript applications. With the API, you can build chatbots, virtual assistants, and other conversational agents that can understand and generate human-like text responses.


Before you can get started with the ChatGPT API in TypeScript, you’ll need a few things:

  • An OpenAI account: To access the API, you’ll need to sign up for an OpenAI account and obtain an API key.
  • TypeScript environment: Make sure you have TypeScript installed on your development machine. You can install TypeScript globally using the Node Package Manager (npm).
  • An API client library: You’ll need an HTTP client library to make API requests. Axios is a popular choice, but you can use any library of your preference.

Setting up your project

Once you have the prerequisites in place, you can start setting up your TypeScript project:

  1. Create a new directory for your project and navigate to it in your terminal.
  2. Initialize a new TypeScript project by running the command: npm init -y. This will create a new package.json file.
  3. Install the required dependencies by running the command: npm install axios. This will install Axios as a dependency in your project.
  4. Create a new TypeScript file, for example, index.ts, where you’ll write your code.

Writing the code

With your project set up, you can start writing the code to interact with the ChatGPT API:

  1. Import the necessary modules, including the HTTP client library and any other required dependencies.
  2. Set up your API client by providing your OpenAI API key and the base URL for the API endpoints.
  3. Write a function to send a message to the ChatGPT API. This function should make a POST request to the appropriate endpoint, passing the message as input.
  4. Handle the response from the API and extract the generated reply.
  5. Call the function with a message to receive a response from the ChatGPT model.

Testing your code

After writing your code, you can test it by running the TypeScript file using the TypeScript compiler:

  1. Compile the TypeScript file to JavaScript by running the command: tsc index.ts. This will generate a JavaScript file with the same name.
  2. Run the JavaScript file using Node.js by running the command: node index.js. This will execute your code and display the response from the ChatGPT model.


By following these steps, you can easily get started with the ChatGPT API in TypeScript. With the power of ChatGPT at your fingertips, you can create intelligent conversational agents that can provide valuable responses to user input.

Creating a ChatGPT API client

In order to interact with the ChatGPT API, we need to create a client that can send requests to the API and handle the responses. Here’s an example of how to create a ChatGPT API client in TypeScript:

Step 1: Install Dependencies

First, we need to install the necessary dependencies. We’ll use the axios library to send HTTP requests and the dotenv library to load environment variables from a .env file.

Install the dependencies by running the following command:

npm install axios dotenv

Step 2: Create a ChatGPT API Client Class

Next, we’ll create a class for our ChatGPT API client. This class will have a method to send messages to the API.

import axios from ‘axios’;

import dotenv from ‘dotenv’;


class ChatGPTApiClient

private apiKey: string;

private apiUrl: string;


this.apiKey = process.env.CHATGPT_API_KEY

async sendMessage(message: string): Promise<string>


const response = await,

prompt: message,

max_tokens: 50



‘Content-Type’: ‘application/json’,

‘Authorization’: `Bearer $this.apiKey`



catch (error)

console.error(‘Failed to send message to ChatGPT API:’, error);

throw error;

export default ChatGPTApiClient;

In the constructor, we initialize the API key and API URL using environment variables. Make sure to create a .env file in the root of your project and add the following line:


Replace your-api-key with your actual ChatGPT API key.

The sendMessage method sends a message to the API and returns the generated response. We use the method to make a POST request to the API endpoint. The request payload includes the message prompt and the max_tokens parameter to limit the length of the response. We also add the API key to the request headers for authentication.

If the API request is successful, we extract the generated text from the response and return it. If there’s an error, we log the error and re-throw it.

Step 3: Use the ChatGPT API Client

Now that we have our ChatGPT API client, we can use it to send messages to the API and get the generated responses. Here’s an example of how to use the client:

import ChatGPTApiClient from ‘./ChatGPTApiClient’;

const client = new ChatGPTApiClient();

async function main()


const response = await client.sendMessage(‘Hello, how are you?’);

console.log(‘Generated response:’, response);

catch (error)

console.error(‘Failed to get response:’, error);


In this example, we create an instance of the ChatGPT API client and call the sendMessage method with a message prompt. The generated response is then logged to the console.

That’s it! You’ve now created a ChatGPT API client in TypeScript. You can use this client to interact with the ChatGPT API and build chat-based applications.

Sending a message to the ChatGPT API

To interact with the ChatGPT API, you need to send a message using an HTTP POST request. The message contains the conversation history and the user’s input, and the API responds with the model’s generated message.

Request Format

The request to the ChatGPT API should include the following information:

  • model (required): The identifier of the model to use for generating the response. For example, “gpt-3.5-turbo”.
  • messages (required): An array of message objects representing the conversation history and the user’s input. Each message object has two properties: “role” (either “system”, “user”, or “assistant”) and “content” (the text of the message).
  • temperature (optional): A parameter that controls the randomness of the model’s output. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic.
  • max_tokens (optional): The maximum number of tokens in the model’s response. This can be used to limit the length of the generated message.

Example Request

Here’s an example of a request to the ChatGPT API:

POST /v1/chat/completions HTTP/1.1


Authorization: Bearer <YOUR_API_KEY>

Content-Type: application/json

“model”: “gpt-3.5-turbo”,

“messages”: [

“role”: “system”, “content”: “You are a helpful assistant.”,

“role”: “user”, “content”: “Who won the world series in 2020?”,

“role”: “assistant”, “content”: “The Los Angeles Dodgers won the World Series in 2020.”,

“role”: “user”, “content”: “Where was it played?”


Response Format

The response from the ChatGPT API will contain the generated message from the model. The response includes the assistant’s reply, its role, and the content of the message.

Example Response

“id”: “chatcmpl-6p9XYPYSTTRi0xEviKjjilqrWUWVe”,

“object”: “chat.completion”,

“created”: 1677649420,

“model”: “gpt-3.5-turbo”,

“usage”: “prompt_tokens”: 56, “completion_tokens”: 31, “total_tokens”: 87,

“choices”: [


“role”: “assistant”,

“content”: “The 2020 World Series was played in Arlington, Texas at the Globe Life Field, which was the new home stadium for the Texas Rangers.”


“finish_reason”: “stop”,

“index”: 0


Once you receive the response, you can extract the assistant’s reply using the choices[0].message.content property.

Remember to handle potential errors or failures when making API requests and ensure you have valid authentication and the necessary permissions to access the ChatGPT API.

Handling responses from the ChatGPT API

When using the ChatGPT API, you will receive a response object containing the generated message from the model. This response object contains several properties that you can use to extract the information you need.

Response Object Properties

Here are the main properties of the response object:

  • id: A unique identifier for the API call.
  • object: The string “chat.completion” indicating the type of object returned.
  • created: The timestamp of when the API call was made.
  • model: The model ID used for the API call.
  • usage: The usage details including the number of tokens used.
  • choices: An array of message objects representing the generated chat messages.

Message Object Properties

Each message object in the choices array contains the following properties:

  • message: The generated message text.
  • role: The role of the message, which can be “system”, “user”, or “assistant”.
  • id: A unique identifier for the message.

Extracting the Generated Response

To extract the generated message from the response object, you can access the response.choices array and retrieve the message property of the desired message object. For example, in JavaScript:

const response = await openai.chatCompletion.create(params);

const generatedMessage =[0].message;


Handling Multiple Messages

If your prompt involves a multi-turn conversation, the response object will contain multiple message objects in the choices array. You can iterate through these messages to access each generated message individually.

Error Handling

If an error occurs during the API call, the response object will contain an error property with details about the error. You should handle these errors gracefully in your code to provide a user-friendly experience.

Additional Handling and Post-processing

Depending on your use case, you may need to perform additional handling or post-processing on the generated response. This can include filtering or modifying the generated message, extracting specific information, or formatting the response for display purposes.


By understanding the structure of the response object and its properties, you can effectively handle the responses from the ChatGPT API and extract the generated messages for further processing or display.

Advanced usage of ChatGPT API in TypeScript

In this section, we will explore some advanced usage of the ChatGPT API in TypeScript. These techniques will help you unlock the full potential of the API and create more interactive and dynamic conversational experiences.

1. Managing conversation state

One important aspect of using the ChatGPT API is managing the conversation state. The conversation state includes the history of messages exchanged with the model and is crucial for maintaining context and coherence in the conversation.

To manage the conversation state, you can maintain an array of messages and update it with each API call. You can use this array to keep track of the conversation history and provide it as input to the API when making subsequent requests. This way, the model will have access to the previous messages and generate responses accordingly.

2. System-level instructions

System-level instructions can be used to guide the behavior of the model during the conversation. By providing high-level instructions, you can influence the style, tone, or specific actions of the model. For example, you can instruct the model to speak like Shakespeare, provide a detailed description, or ask it to think step-by-step before answering.

To use system-level instructions, you can include a special message in the conversation array with the role “system”. This message can provide instructions or guidance to the model, and subsequent messages from the user or assistant can refer to these instructions.

3. Multi-turn conversations

The ChatGPT API supports multi-turn conversations, allowing you to have back-and-forth interactions with the model. You can extend the conversation array with each turn and make API calls to generate responses at each step.

In a multi-turn conversation, you can ask follow-up questions, refer to previous answers, or provide additional context to help the model understand the conversation better. By maintaining the conversation state and updating it with each turn, you can create more engaging and interactive conversations.

4. Customizing the temperature and max tokens

The ChatGPT API allows you to customize the temperature and max tokens parameters to fine-tune the output of the model. The temperature parameter controls the randomness of the generated responses, with higher values producing more random outputs.

The max tokens parameter determines the maximum length of the response generated by the model. By adjusting these parameters, you can influence the creativity and length of the responses based on your specific requirements.

5. Error handling

When making API calls, it’s important to handle errors gracefully. The ChatGPT API can return various types of errors, such as rate limit errors or input validation errors. By catching and handling these errors in your TypeScript code, you can provide appropriate feedback to the user and handle exceptional cases more effectively.

It’s also important to handle potential issues like timeouts or network errors when making API calls. Implementing appropriate error handling mechanisms will ensure a smooth and reliable conversational experience for your users.


By using these advanced techniques in TypeScript, you can harness the full power of the ChatGPT API and create dynamic and engaging conversational applications. Managing conversation state, providing system-level instructions, using multi-turn conversations, customizing parameters, and handling errors will help you build more interactive and intelligent chatbots and virtual assistants.

ChatGPT API with TypeScript

ChatGPT API with TypeScript

What is the ChatGPT API?

The ChatGPT API is an interface provided by OpenAI that allows developers to integrate ChatGPT into their applications or services. It enables users to make dynamic and interactive conversations with the model by sending a series of messages as input and receiving model-generated message as output.

How can I use the ChatGPT API in TypeScript?

To use the ChatGPT API in TypeScript, you can make HTTP requests to the API endpoint using a library like Axios. You need to authenticate your requests with an API key and send a series of messages as input to the API. The API will respond with the model-generated message, which you can then process and display in your application.

What are the benefits of using the ChatGPT API?

Using the ChatGPT API offers several benefits. Firstly, it allows developers to leverage the power of ChatGPT within their own applications or services without having to build and train their own models. Secondly, it provides a simple and straightforward interface for making dynamic and interactive conversations with the model. Lastly, it enables developers to customize the behavior of the model by controlling the system and user messages sent to the API.

Can I use the ChatGPT API for commercial purposes?

Yes, you can use the ChatGPT API for commercial purposes. OpenAI offers both free and paid access to the API, with different rate limits and pricing tiers. You can refer to OpenAI’s pricing page for more details on the cost of using the ChatGPT API for commercial purposes.

Are there any limitations or restrictions when using the ChatGPT API?

Yes, there are limitations and restrictions when using the ChatGPT API. The API has rate limits depending on whether you are using the free or paid access. It also has a maximum message limit of 4096 tokens for a conversation. Additionally, there are usage restrictions on certain types of content, such as explicit or harmful content, and OpenAI provides guidelines on what kind of content is allowed.

Can I use the ChatGPT API with other programming languages?

Yes, you can use the ChatGPT API with other programming languages. The API is language-agnostic, so you can make HTTP requests to the API endpoint using any programming language that supports HTTP requests. You just need to ensure that you properly format the request payload and authenticate your requests with an API key.

Is there a limit on the number of messages I can send to the ChatGPT API?

Yes, there is a limit on the number of messages you can send to the ChatGPT API. The maximum number of messages in a conversation is 10, including both user and system messages. If you exceed this limit, you will need to truncate or omit some messages to fit within the limit.

Can I use the ChatGPT API to build a chatbot?

Yes, you can use the ChatGPT API to build a chatbot. By sending a series of user and system messages to the API, you can create dynamic and interactive conversations with the model. You can customize the behavior of the chatbot by controlling the messages sent to the API and process the model-generated responses to provide a seamless chatbot experience to users.

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