Master ChatGPT with JavaScript!

Table of Contents

1. Introduction
2. Setting Up the Project
3. Creating the API Endpoint
4. Handling the Request
5. Sending the Request to OpenAI
6. Parsing the Response
7. Testing the API Endpoint
8. Conclusion

Introduction

In this article, we will explore how to use OpenAI's API platform along with Node.js and Express to Create our own GPT3-powered REST API. The API endpoint we will create takes a piece of code as input and returns the Big O time complexity of that code. We will walk through the process step-by-step, from setting up the project to testing the API endpoint. By the end, You will have a thorough understanding of how to utilize OpenAI's API to build powerful applications.

Setting Up the Project

To get started, you will need to have Node.js installed on your system. If you don't already have it, you can download it from the official Node.js Website.

Additionally, you will need an account on beta.openai.com. When you create an account, you will receive 18 free credits to use over your first month. You won't need to add a credit card for this initial phase.

Finally, you will need an API client to Interact with your server. In this article, we will be using Postman, which is a free tool for testing REST APIs. However, you are free to use any API client you prefer.

Once you have everything set up, navigate to an empty folder where you want your project to reside. Open your terminal in this folder and run the command "npm init -y" to generate a blank Package.json file. Next, you will need to install the necessary dependencies by running the command "npm install express axios dotenv" . Finally, we will install nodemon as a dev dependency, which will automatically restart our server when changes are made to our code. Run the command "npm install nodemon --save-dev" to install nodemon.

With all the dependencies installed, you can open your project in your preferred code editor. You should see your package.json file with the installed dependencies.

Creating the API Endpoint

In this section, we will create the API endpoint that will handle our request for the code's time complexity.

First, we need to make sure that we have a server running. Create a new file called "index.js" in the root of your project. In this file, we will import our dependencies and configure our server.

To start, import Express by requiring it at the top of the file:

const express = require('express');

Next, we need to configure Express by calling the express() function and saving it to a variable called app:

const app = express();

We also need to configure the server to parse JSON data in the request body. Add the following line of code:

app.use(express.json());

For simplicity, we will define the API key as an environment variable. Create a file called .env in the root of your project and add the following line:

OPENAI_KEY=<your-openai-api-key>

Replace <your-openai-api-key> with your actual API key.

Next, we need to define the port on which our server will run. If a PORT environment variable is available, we will use that value. Otherwise, we will default to port 5000. Add the following lines of code:

const PORT = process.env.PORT || 5000;

Finally, start the server by calling the listen method and passing it the PORT variable, as shown below:

app.listen(PORT, () => {
  console.log(`Server started on port ${PORT}`);
});

With these steps completed, our server setup is complete. We can now move on to handling the request and sending it to OpenAI.

Handling the Request

In this section, we will define the functionality that will handle the incoming request to our API endpoint.

First, we need to define the endpoint URL and the HTTP method it will listen for. In this example, our endpoint will be /find-complexity and the method will be POST. We can define this by adding the following code:

app.post('/find-complexity', async (req, res) => {
  // Code to handle the request will go here
});

Inside the request handler function, we will write the code for interacting with the OpenAI API. We will start by extracting the code to be analyzed from the request body. Add the following line of code inside the request handler function:

const { code } = req.body;

Next, we will initialize the OpenAI API client using our API key from the environment variable. Add the following line of code:

const openai = require('openai')({ apiKey: process.env.OPENAI_KEY });

We Are now Ready to Send the request to OpenAI and retrieve the time complexity of the code.

Sending the Request to OpenAI

To send the request to OpenAI, we will use the openai.createCompletion function provided by the OpenAI API client. This function generates text Based on a prompt.

Update the request handler function as follows:

app.post('/find-complexity', async (req, res) => {
  const { code } = req.body;

  const response = await openai.createCompletion({
    model: 'text-davinci-003',
    prompt: code,
    maxTokens: 100,
  });

  // Code to handle the response will go here
});

In the above code, we specify the model as 'text-davinci-003', which is one of the available models provided by OpenAI. We set the prompt to the code we received from the request body. Finally, we limit the number of tokens to 100 to ensure a concise response.

We will handle the response in the next section.

Parsing the Response

In this section, we will parse the response from the OpenAI API and format it to send back to the client.

To start, add the following code after the response variable declaration:

const result = response.choices[0].text;

The choices property of the response object contains an array of potential responses. In our case, we expect only one choice, so we can access it using the index [0]. Finally, we extract the text property, which contains the actual response.

Next, we can add the code to send the response back to the client. Modify the response handler function as shown below:

app.post('/find-complexity', async (req, res) => {
  // Existing code

  res.status(200).json({ success: true, data: result });
});

In the above code, we send a response with HTTP status code 200, indicating success, and a JSON object containing the resulting time complexity.

Testing the API Endpoint

To test our API endpoint, we can use Postman or any other API client of your choice.

In Postman, select the "POST" method and enter the URL http://localhost:5000/find-complexity. In the body section, select "raw" and choose "JSON" as the format. Add the following JSON object:

{
  "code": "your code here"
}

Replace "your code here" with the actual code you want to analyze.

Send the request, and you should receive a response with the time complexity of the code.

Conclusion

In this article, we learned how to create a GPT3-powered API endpoint using OpenAI's API platform, Node.js, and Express. We walked through the process of setting up the project, handling the request, sending it to OpenAI, parsing the response, and testing the API endpoint.

By leveraging the power of OpenAI's models, we can build a wide range of applications that can analyze and generate code, text, and much more. The possibilities are endless, limited only by our imagination.

Thank you for reading, and happy coding!

Highlights

• Learn how to use OpenAI's API platform with Node.js and Express
• Create a GPT3-powered REST API for code time complexity analysis
• Handle API requests and send them to the OpenAI API for processing
• Parse and format the response to send back to the client
• Test the API endpoint using Postman or any other API client

FAQ

Q: What is GPT3? A: GPT3 is a state-of-the-art language model developed by OpenAI. It stands for "Generative Pre-trained Transformer 3" and is capable of generating human-like text.

Q: Can I use a different programming language with OpenAI's API? A: Yes, OpenAI's API is language-agnostic, meaning you can use it with any programming language that supports HTTP requests.

Q: Are there any limitations to the free credits provided by OpenAI? A: The free credits provided by OpenAI are limited to a certain number of tokens and requests per month. Once you exceed these limits, you will need to upgrade to a paid plan.

Q: Can I use the API for other applications besides code time complexity analysis? A: Absolutely! The API can be used for a wide range of applications including text generation, language translation, sentiment analysis, and much more. It's up to your creativity to explore the possibilities.

Q: What are the potential drawbacks of using GPT3 for code time complexity analysis? A: One potential drawback is that GPT3 may not always provide accurate time complexity analysis, especially for complex code snippets. It is recommended to validate the results from GPT3 with other methods or tools.

Q: Are there alternative tools or libraries for code time complexity analysis? A: Yes, there are several alternative tools and libraries available for code time complexity analysis, such as Big O notation calculators and profilers. It's important to consider multiple sources when analyzing time complexity.