A Comprehensive Tutorial on GPT4 API and ChatGPT 3.5 Turbo: A Beginner's Guide
Welcome to our comprehensive tutorial on the GPT4 API and ChatGPT 3.5 Turbo! In this crash course, we'll show you how to integrate GPT4 and GPT 3.5 Turbo, along with their latest chat API, into your next website application or software. This allows you to customize and perform your own chat directly with OpenAI and its models. We'll guide you through building and deploying an application from scratch using serverless technology on Microsoft Azure, which will interface directly with OpenAI and its APIs. Whether you're a beginner or an expert, we'll cover everything you need to know. Let's get started!
Setting Up OpenAI
To begin, head to the OpenAI website and create an account. Once you're signed up, you'll be taken to the OpenAI dashboard. Here, you'll find documentation to understand how OpenAI and its models work, the API reference to set up this video, and the playground to interact with GPT and test out how it works.
Setting Up Your Development Environment
Before we dive into the coding, let's set up your development environment. Download Node.js, a JavaScript runtime, to create a simple REST server. We recommend using version 18.15.0 LTS. Additionally, download VS Code, a code editor from Microsoft, which comes with great plugins to enhance your coding experience.
Installing Dependencies
Once you have Node.js and VS Code installed, open VS Code and create a new folder for your project. In the terminal, initialize the project by running the command "npm init". Next, install the following packages by running the command "npm install express openai body-parser cors". These packages will help us set up our server and interact with OpenAI.
Initializing OpenAI
In your main file, import the necessary modules: OpenAI, configuration, and API. Set up the configuration by providing your organization ID and API key. Initialize the configuration and OpenAI by calling the respective classes with the configuration you've set up.
Querying OpenAI
Now it's time to query OpenAI. Set the model you want to use for the chat completion, such as GPT 3.5 Turbo. Set up an array of messages with the role and content, and initialize the completion using the "createChatCompletion" method from OpenAI. Capture the response and print it out.
Creating an Interactive Web Server
Let's create a web server so that we can access the chatbot from a browser. Import Express, bodyParser, and cors. Initialize Express, specify the port, use bodyParser.json(), use cors(), and create a GET route. In this GET route, implement the same query to OpenAI as before, and return the response as a JSON object. Finally, make the app listen on the specified port and print a message when it's listening.
Building the Front-End
Now, let's create the front-end interface for our chatbot. Create an index.html file and include a basic HTML structure. Modify the title and add an H1 element for the chatbot's title. Create a form with an input field for the user's message and a submit button. Add a div element for the chat log. We'll use JavaScript to interact with these elements.
Handling User Input
In your JavaScript file, get references to the chat log, message input, and form. Add an event listener to the form for the "submit" event. Prevent the default form behavior. Get the value of the message input and reset the input field. Create a new message element, set its content to the user's message, append it to the chat log, and scroll to the top of the chat log.
Sending Messages to the Server
Send a POST request to the server when the user submits a message. Fetch the server URL, set the method to "POST", headers to "application/json", and the body to the user's message. Parse the JSON response and create a new message element with the response content. Append it to the chat log.
Enabling Message History
To enable message history, update the server-side code to handle arrays of messages instead of a single message. Update the query to OpenAI to use the array of messages. Modify the front-end JavaScript to store and send an array of messages to the server. Update the response handling to add the assistant's message to the message array.
Deploying to Microsoft Azure
Now, let's deploy our chatbot to the cloud using Microsoft Azure. Sign up for a free Azure account. Install the Azure Functions extension in VS Code. Navigate to the Azure portal and create a new functions app. Inside the functions app, create a new JavaScript project with the required dependencies. Implement the chatbot function by copying the OpenAI configuration and query code. Test the function locally and then deploy it to Azure.
Conclusion
And that's it! You've learned how to integrate GPT4 and GPT 3.5 Turbo into your website or software using OpenAI's chat API. You've set up your development environment, created a web server, built the front-end interface, and deployed your chatbot to Microsoft Azure. Now you can chat with the AI-powered chatbot on any device. Have fun experimenting and customizing your own chatbot!
If you have any further questions, please refer to the FAQs below:
FAQs
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1. Can I use GPT4 or GPT 3.5 Turbo on mobile devices?
Yes, you can use GPT4 and GPT 3.5 Turbo on both mobile and desktop devices. Simply integrate the chatbot API into your mobile application or website.
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2. How much does it cost to use GPT4 or GPT 3.5 Turbo?
Pricing for GPT4 and GPT 3.5 Turbo varies. Please refer to the OpenAI website or contact their sales team for detailed pricing information.
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3. Can I customize the behavior of the chatbot?
Yes, you can customize the behavior of the chatbot by providing different prompts and tweaking the parameters in your code.
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4. Is there a limit to the number of messages in a conversation?
Yes, there is a limit to the number of messages in a conversation. The current limit is 4096 tokens for GPT4 and 2048 tokens for GPT 3.5 Turbo.
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5. Can I use GPT4 and GPT 3.5 Turbo for other applications besides chatbots?
Yes, you can use GPT4 and GPT 3.5 Turbo for various applications, such as content generation, language translation, and more. The possibilities are endless!
