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This article will discuss how to use ChatGPT API. Let’s get started!

Overview of ChatGPT API

ChatGPT is an upcoming and quite popular chatbot utilizing cutting-edge technologies. It is now an important criterion to use ChatGPT API and integrate it with the existing applications in a company for better usage. 

In this article, we will learn about the steps of using OpenAI ChatGPT API and the documentation references of ChatGPT Official API.

How to use ChatGPT API for text completion – step-by-step integration guide

In the first section, we will discover the steps for how to use ChatGPT API for text completion. You need to use the completion endpoint in ChatGPT API to accomplish many tasks, which serves as a powerful and simple interface for integrating applications. 

Step 1: Design the Prompts

There are a few guidelines for designing and creating the prompts:

1. Guide the ChatGPT model API on whether you would want the prompts in the form of instructions, examples, or a mix of both. Also if you want to use ChatGPT API for ranking a list or classify the items displayed, then you need to state that.
2. Provide appropriate ChatGPT API examples to help build the pattern. Ensure the examples are audited and proof-read multiple items
3. Next is to control the top_p and temperature in the settings for ChatGPT API integration. Adjust it to a lower value for one response and higher for diverse responses.

Refer document https://platform.openai.com/docs/guides/completion/prompt-design

Step 2: Text Classification

Here are some of the best practices for classifying text in ChatGPT Public API:

1. Decision on the implication and sentiment of the description or text – whether it is neutral, positive, or negative
2. Suggested to use of plain, and simple language to easily understand and interpret
3. The description must be elaborated as much as possible, but removing additional words to ensure consistent performance
4. There must be the presence of sentiment labels in each of the instructions for easy judgment by the users 
5. Lastly, you must run several tests to check that the probability of your settings is calibrated correctly

Step 3: New idea generation

1. Design the ChatGPT API to generate new ideas or different versions of the input. It can be slogans, some character descriptions, business plans, or even story ideas. 
2. To ensure a quality response, include some examples in the prompt section and add them to the ChatGPT API server

Step 4: Devising ways to converse

1. The ChatGPT API must be instructed on how to behave and respond to the instructions by providing the text explicitly
2. The ChatGPT API key needs to be provided with an identity through which it responds like an AI assistant or a chatbot
3. Create a questions and answers sequence and then allow the Chatbot API token to pick up the patterns and use it for the return responses

Step 5: Introducing transformations

1. Develop the ChatGPT API to translate from one language to another, especially the languages with which the API is not well-conversed or familiar. You need to provide multiple examples to make the translation work
2. Devise the adaptability of the ChatGPT API to convert texts into characters or emojis by entering the appropriate patterns. This method is called conversion

Step 6: Creating a summary

1. If you want the ChatGPT API to share a summary about a topic then it is recommended to provide a paragraph with details on that topic. The API will then traverse through the topic and answer queries as per the questions being asked. 
2. For example, if a child asks about the planet Saturn. You can write “Saturn is the sixth planet in the Solar system. After Jupiter, it is the second-largest planet. The unique feature of the planet is its ring system prominently visible and made of ice particles mainly. Saturn has 83 moons of which Titan is the largest and second-largest in the Solar system….”

Step 7: Setting the completion tips

1. The ChatGPT API limits need to be adjusted to check the completion of a response or statement 
2. The temperature setting must be controlled to increase focus on a topic and end the response or keep it neutral to extend the boundary on the topic before the completion. 

A sample code component for “React” statements

import React from ‘react’;

const HeaderComponent = () => (

Step 8: Preparing factual responses

Train the ChatGPT API to provide realistic responses

1. Do not train the API to read responses from entries like Wikipedia
2. Prepare responses after researching various topics, websites, factual materials, etc., and feed them to the API for generating quality responses
3. For queries that are tough to answer or ready answer not available, train the API to respond as “Dont know”

How to use ChatGPT API for chat completion – step by step integration guide

The next important topic related to ChatGPT API is how to use ChatGPT API for chat completion. Let’s now run through the steps, the examples, and the best practices and recommendations for using ChatGPT API for chats.

Using the chat option, the ChatGPT API can perform a wide variety of tasks like drafting an email, writing codes, displaying document links or references while responding to questions, providing tutorials, language translation, simulating characters for video games, conversations, and many more. 

Let’s now focus on certain key points to remember on how to use ChatGPT API for chat completion

1. The design should be to ease the multi-turn conversations as well as single-turn without any type of conversations
2. The message parameter is the key input, containing an array of objects. The objects have content and role
3. The system message is used to determine the behavior, the user message is used to provide instructions, and the assistant message is used to record the previous responses
4. All related and relevant information must be shared in the form of conversations as the ChatGPT Get API key does not possess the memory of storing past requests. 
5. The conversations need to be shortened and adjusted as per the ChatGPT API endpoint and token limits

Now let’s look at how to create the format of the response.

1. A ChatGPT API example of extracting the reply to the assistant message is 

           Response[‘choices’][0][‘message’][‘content’].

1. Next is to provide the different and probable values for the finish_reason. Every response must contain this particular parameter. The values which can be normally found in finish_reason are:

1. stop: ChatGPT API call returns the output of the entire model
2. length: limit, as set in the parameter, max_tokens for returning the model output, and due to the settings it returns the incomplete result
3. content_filter: a flag is set in the content_filter for omitting content
4. null: ChatGPT API response is incomplete or in progress

The next important part is to manage tokens. Tokens are an answer to the question of how to call ChatGPT API.

1. The models of the ChatGPT API can read chats in groups which are referred to as tokens
2. The token can be a string of characters or one word
3. The use of the number of tokens for ChatGPT API access to a particular sentence determines the cost of the API as the payment is done per token
4. The more you write the tokens, the more it takes time to call the API
5. It is important to remember that the total number of tokens must be less than the maximum limit of the model
6. While counting the tokens, you must consider both the input and output ones
7. You must check the total number of tokens used in the ChatGPT login screen and call through the usage field: response[‘usage’][‘total_tokens’]
8. If you do not want to go for an API call for counting tokens, then use the tiktoken Python library of OpenAI. 
9. In any message to the ChatGPT API, the tokens are consumed by the content, the role of the user, and some additional fields which may be used for formatting the messages. 
10. If the conversation cannot fit in the threshold limit of the tokens, then it is recommended to reduce the text length. But while reducing length, ensure the knowledge part is not compromised

Last but not least is how to provide instructions to the ChatGPT API model, which may vary from version to version.

Some models understand system messages while others understand the user messages better. It is advised to experiment with various approaches and modifications like using a specific format, leveraging explicit instructions, and providing logical guidance to the model to think before answering a response.

When you access ChatGPT through API, then remember the significance of the values set for the temperature and the max tokens. If the temperature is set at a higher value, then the response will appear randomly. And if the temperature is set at a lower value, then the output is more relevant and focused. 

In the case of max tokens, you need to set the length judiciously. Sometimes the response returned may be shrunk and not provide the complete message as desired by the user.

Refer documentation https://platform.openai.com/docs/guides/chat

ChatGPT API documentation references

If you want to know the detailed description of the models, libraries, coding, and lot more, please check out the following ChatGPT API documentation references:

1. For ChatGPT API overview, and key concepts like ChatGPT API model, prompts, and tokens,
refer: https://platform.openai.com/docs/introduction/overview
2. For quickstart, ChatGPT API tutorial, refer https://platform.openai.com/docs/quickstart
3. For the list of libraries available for installation, please refer https://platform.openai.com/docs/libraries
4. To learn details about the models, their capabilities and the price points, the ChatGPT API doc reference is https://platform.openai.com/docs/models
5. The tutorial for building real ChatGPI API apps is available here https://platform.openai.com/docs/tutorials
6. The different legal policies of ChatGPT API can be referred to here https://openai.com/policies
7. The text completion guide is present in https://platform.openai.com/docs/guides/completion
8. The chat completion guide is present at https://platform.openai.com/docs/guides/chat
9. The image generation guide is present in https://platform.openai.com/docs/guides/images
10. The overview of the plugins in ChatGPT API is mentioned in https://platform.openai.com/docs/plugins/introduction, the tutorial for the plugins is at
https://platform.openai.com/docs/plugins/getting-started, and the examples of plugins can be referred from https://platform.openai.com/docs/plugins/examples

How to use ChatGPT Python API?

Here are the steps on how to use ChatGPT Python API:

1. You need to create an account in OpenAI and sign in
2. You will find “Personal” on the right-side top part where you have to click on “View API Keys”
3. Click on the button “Create new secret key” and once the key is generated copy it
4. Install Open AI library in Python 
5. Import the library and store the key as generated for Python ChatGPT API integration
6. Next using the JSON file, you need to instruct the API the activities it needs to perform and set the role too.

Sample code:

while True:

    message = input(“User : “)

    if message:

        messages.append(

            {“role”: “user”, “content”: message},

        )

        chat = openai.ChatCompletion.create(

            model=“gpt-3.5-turbo”, messages=messages

        )    

    reply = chat.choices[0].message.content

    print(f”ChatGPT: {reply}”)

    messages.append({“role”: “assistant”, “content”: reply})

Also Read: How to use Spotify API – a step-by-step integration guide

How to use ChatGPT API nodejs?

The steps on how to use ChatGPT API nodejs are elaborated below

1. First is to create a new project in nodejs ChatGPT API
2. Second is to generate a ChatGPT API key after logging into OpenAI
3. The API key needs to be assigned to an environment variable of the nodejs application
4. Then implement the node.js application
5. The code needs to perform the following functions:
   1. Import the dependencies
   2. Perform asynchronous function
   3. Set up and configure the ChatGPT API npm
   4. Provision to store the chat history
   5. Chat loop for continuous prompting user for input and generating the response
   6. AI Model for response generation
   7. Error handling

Sample Code:

const { Configuration, OpenAIApi } = require(“openai”);

const readlineSync = require(“readline-sync”);

require(“dotenv”).config();

(async () => {

  const configuration = new Configuration({

    apiKey: process.env.OPENAI_API_KEY,

  });

  const openai = new OpenAIApi(configuration);

  const history = [];

  while (true) {

    const user_input = readlineSync.question(“Your input: “);

    const messages = [];

    for (const [input_text, completion_text] of history) {

      messages.push({ role: “user”, content: input_text });

      messages.push({ role: “assistant”, content: completion_text });

    }

    messages.push({ role: “user”, content: user_input });

    try {

      const completion = await openai.createChatCompletion({

        model: “gpt-3.5-turbo”,

        messages: messages,

      });

      const completion_text = completion.data.choices[0].message.content;

      console.log(completion_text);

      history.push([user_input, completion_text]);

      const user_input_again = readlineSync.question(

        “\nWould you like to continue the conversation? (Y/N)”

      );

      if (user_input_again.toUpperCase() === “N”) {

        return;

      } else if (user_input_again.toUpperCase() !== “Y”) {

        console.log(“Invalid input. Please enter ‘Y’ or ‘N’.”);

        return;

      }

    } catch (error) {

      if (error.response) {

        console.log(error.response.status);

        console.log(error.response.data);

      } else {

        console.log(error.message);

      }

    }

  }

})();

How to use ChatGPT API c#?

Follow the below steps to learn how to use ChatGPT API c# for integration:

1. First, you need to install the OpenAI library
2. Second, you need to create an account and log in to OpenAI
3. Third, create a class OpenAIProxy for encapsulating the logic which will help to access ChatGPT c# APIs
4. Fourth, create an implementation class called OpenAIProxy.cs
5. Fifth, set up the API key in the application which will be used to access the ChatGPT API Model
6. Finally, the ChatGPT model is used in the application that creates an infinite loop of user prompt and response generation.

Also Read: What is a dev shop and how to find the best dev shop?

How to use Azure ChatGPT API?

As the first step on how to use Azure ChatGPT API, you need to have an Azure subscription, access to Azure OpenAI, the latest version for Python and deployed Azure OpenAI service resource model

Next is to configure the Azure environment and the OpenAI variables. The variables can be checked from the ChatGPT Azure API environment under “Keys and Endpoint”. 

The third step is to define the input from the user and the logic that needs to be coded for the same. 

The fourth step is to process the message from the user and display the content of the message.

Sample code:

var msg = Console.ReadLine();

do

{

  var results = await chatOpenAI.SendChatMessage(msg);

  for each (var item in results)

  {

    Console.WriteLine($”{item.Role}: {item.Content}”);

  } 

  Console.WriteLine(“Next Prompt:”);

  msg = Console.ReadLine(); 

} while (msg != “bye”);

Also Read: What Developers Need To Know About Intellectual Property Rights?

How to use ChatGPT API js?

The steps on how to use ChatGPT API js are as follows:

1. Create a ChatGPT javascript API key after creating an account in OpenAI API
2. Set up the javascript environment by creating a new file with the libraries and dependencies
3. After adding the key to the ChatGPT API, you need to send a request and receive the JSON response, to receive the extracted information
4. Next, you need to display the response from the ChatGPT API to the user. You can have a timestamp, date, and emoticons added to the response as additional features.

How to use ChatGPT API php?

Displaying the steps on how to use ChatGPT API php:

1. Create an account in OpenAI and sign in to it
2. Get the ChatGPT php API keys by clicking on the menu “Personal” and clicking on “View API keys”
3. Develop now the application using the ChatGPT API
4. Select the library and deduce the codes 
5. Run the application
6. You need to define the input, process the message and receive the response

Sample code:

$curl = curl_init();

curl_setopt_array($curl, array(

  CURLOPT_URL => ‘https://api.openai.com/v1/completions’,

  CURLOPT_RETURNTRANSFER => true,

  CURLOPT_ENCODING => ”,

  CURLOPT_MAXREDIRS => 10,

  CURLOPT_TIMEOUT => 0,

  CURLOPT_FOLLOWLOCATION => true,

  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,

  CURLOPT_CUSTOMREQUEST => ‘POST’,

  CURLOPT_POSTFIELDS =>‘{

  “model”: “text-davinci-003”,

  “prompt”: “Translate this into 1. French, 2. Hindi, 3. Spanish, 4.Japanese:\n\n1 What rooms do you have available?\n\n.”,

  “temperature”: 0.3,

  “max_tokens”: 100,

  “top_p”: 1.0,

  “frequency_penalty”: 0.0,

  “presence_penalty”: 0.0

}’,

  CURLOPT_HTTPHEADER => array(

    ‘Authorization: Bearer sk-NRPZ9Ybx*****************L2mTVd8’,

    ‘Content-Type: application/json’

  ),

));

 $response = curl_exec($curl);

curl_close($curl);

 echo “<pre>”;

print_r(json_decode($response));

How to use ChatGPT API Java?

Just like the above sub-sections, the steps for how to use ChatGPT API java are quite simple.

1. Create an account in OpenAI and sign in to it
2. Generate a ChatGPT API key
3. Go to Settings under the Organization menu and request for “Organization ID” for ChatGPT Java API
4. Create the files – FileHelper.java, Request.json, and ChatGPTAPIExample.java as part of the Java ChatGPT API integration
5. Compile and run the application as per the run commands and check the response

Sample code

import java.io.BufferedReader;

import java.io.File;

import java.io.InputStreamReader;

import java.io.OutputStream;

import java.net.HttpURLConnection;

import java.net.URL;

import java.nio.charset.StandardCharsets;

public class ChatGPTAPIExample {

public static void listTokens() {

try {

//This API will fetch the models available.

URL url = new URL(“https://api.openai.com/v1/models”);

HttpURLConnection con = (HttpURLConnection) url.openConnection();

con.setRequestMethod(“GET”);

con.setRequestProperty(“Content-Type”, “application/json”);

con.setRequestProperty(“Accept”, “application/json”);

//Make sure you put the right Organization key saved earlier.

con.setRequestProperty(“OpenAI-Organization”, “your-org-key”);

con.setDoOutput(true);

//Make sure you put the right API Key saved earlier.

con.setRequestProperty(“Authorization”, “Bearer your-api-key”);

int responseCode = con.getResponseCode();

System.out.println(“Response Code : “ + responseCode);

BufferedReader in = new BufferedReader(

new InputStreamReader(con.getInputStream()));

String inputLine;

StringBuffer response = new StringBuffer();

while ((inputLine = in.readLine()) != null) {

response.append(inputLine);

}

in.close();

System.out.println(response);

} catch (Exception e) {

System.out.println(e.getMessage());

}

}

public static void prompts() {

try {

URL url = new URL(“https://api.openai.com/v1/completions”);

HttpURLConnection con = (HttpURLConnection) url.openConnection();

con.setRequestMethod(“POST”);

con.setRequestProperty(“Content-Type”, “application/json”);

con.setRequestProperty(“Accept”, “application/json”);

//Make sure you put the right Organization key saved earlier.

con.setRequestProperty(“OpenAI-Organization”, “your-org-key”);

con.setDoOutput(true);

//Make sure you put the right API Key saved earlier.

con.setRequestProperty(“Authorization”, “Bearer your-api-key”);

//Make sure to relace the path of the json file created earlier.

String jsonInputString = FileHelper.readLinesAsString(new File(“C:\\yourpath\\request.json”));

try (OutputStream os = con.getOutputStream()) {

byte[] input = jsonInputString.getBytes(StandardCharsets.UTF_8);

os.write(input, 0, input.length);

}

int responseCode = con.getResponseCode();

System.out.println(“Response Code : “ + responseCode);

BufferedReader in = new BufferedReader(

new InputStreamReader(con.getInputStream()));

String inputLine;

StringBuffer response = new StringBuffer();

while ((inputLine = in.readLine()) != null) {

response.append(inputLine);

}

in.close();

System.out.println(response);

} catch (Exception e) {

System.out.println(e.getMessage());

}

}

public static void main(String[] args) {

listTokens();

prompts();

}

}

Also read: 7 Ways to reduce the costs of software development outsourcing

How to use ChatGPT API Flutter?

The prerequisites for using ChatGPT API with Flutter are the latest versions of Android Studio, Flutter, and Dart installed, and a basic understanding of Flutter development.

Step 1: Open Android Studio and create a new Flutter project.

Step 2: Edit the project name, Android and iOS language, and platform.

Step 3: Generate the ChatGPT API key and add it to the environment.

Step 4: Implement ChatGPT along with the necessary access and dependencies.

Sample code:

dynamic saveChatHistory(

  dynamic chatHistory,

  dynamic newChat,

) {

  /// MODIFY CODE ONLY BELOW THIS LINE

  // If chatHistory isn’t a list, make it a list and then add newChat

  if (chatHistory is List) {

    chatHistory.add(newChat);

    return chatHistory;

  } else {

    return [newChat];

  }

  /// MODIFY CODE ONLY ABOVE THIS LINE

}

ChatGPT API pricing

The ChatGPT API pricing is said to be 10 times cheaper than the existing models. The ChatGPT API cost is now USD 0.003 for 1000 tokens. Tokens are the series of messages along with the metadata that the ChatGPT Model consumes. 

Along with the ChatGPT API price, one more thing that needs to be understood is that the enterprise data submitted via the ChatGPT API will not be leveraged for model training or any kind of improvements without the permission of the organization. Customers using Apps and services integrated with ChatGPT must be clear that they are interacting with a bot.

Does ChatGPT have an API?

If you want to know if is there an API for ChatGPT, the answer is Yes. The ChatGPT API release date initially was 30th November 2022 and the stable release was on 23rd March 2023. The API equips developers to generate responses that are personalized and human-like for any text input, even in the form of a chat.