Build OpenAI GPT Chatbot in Laravel
Table of Contents
- Introduction
- What is GPT-3 OpenAI Chatbot?
- What is Laravel?
- Integrating GPT-3 OpenAI Chatbot into Laravel
- Creating a Chat App with HTML and CSS
- Using jQuery Ajax for Chatbot Functionality
- Creating a Chatbot Controller in Laravel
- Using OpenAI API to Generate Text Completions
- Pros and Cons of Using GPT-3 OpenAI Chatbot in Laravel
- Conclusion
Integrating GPT-3 OpenAI Chatbot into Laravel
GPT-3 OpenAI Chatbot is a cutting-edge language model that can generate human-like responses to textual input, making it an ideal candidate for building conversational agents. Laravel, on the other HAND, is a popular PHP web framework that provides a robust and scalable platform for building web applications. By combining these two powerful technologies, we can Create a chatbot that can handle a wide range of queries and provide personalized responses.
To get started, we need to create a Laravel project and install the OpenAI Package for the project. We can do this by running the following command in the terminal:
Composer create-project --prefer-dist laravel/laravel chatbot-example
Once the project is created, we can open it in Visual Studio Code and install the OpenAI package using the following command:
composer require openai/openai
Next, we need to publish the config file for OpenAI by running the following command:
php artisan vendor:publish --provider="OpenAI\OpenAIServiceProvider"
Now, we can go to the OpenAI Website and create an API for our account. We can then configure the API code into Laravel by creating a chatbot controller to handle the request data.
Creating a Chat App with HTML and CSS
To create a chat app, we can use HTML and CSS to design the user interface. We can turn on the server with the command php artisan serve and embed Bootstrap and Font Awesome CDNs in the website blade.
We can also use jQuery Ajax to add functionality to the chatbot. This code sets up default options for all jQuery Ajax requests using the Ajax setup method. Specifically, it sets a custom HTTP header named X-CSRF-Token for each request using the value of a meta tag named csrf-token in the HTML document.
$.ajaxSetup({
headers: {
'X-CSRF-Token': $('meta[name="csrf-token"]').attr('content')
}
});
We can then add an input and a button for writing content. This code listens for a click event on an element with the ID of button-submit. When the button is clicked, it executes a function that performs several actions. First, it retrieves the value of an input field with the ID of input and assigns it to the variable value. Then, it appends a new div to an element with the ID of content-box. This new div has two child divs. The first child div is a floating box with a Blue background color and displays the value of $value. The Second child div is a spacer that clears the floating box.
Next, an Ajax request is made using the jQuery Ajax function. The request is a post request to a URL defined by the expression url: '/send-chat'. The request includes the value of $value as the data payload. If the request is successful, a new div is appended to the content-box element. This new div has two child divs. The first child div is an image of a male Avatar, and the second child div is a floating box with a dark blue background color and displays the data returned by the server. Finally, the value of the input field is cleared.
Creating a Chatbot Controller in Laravel
To create a chatbot controller in Laravel, we can use the following code:
<?php
namespace App\Http\Controllers;
use Illuminate\Http\Request;
use OpenAI\OpenAI;
class ChatbotController extends Controller
{
public function sendChat(Request $request)
{
$input = $request->input('input');
$openai = new OpenAI();
$openai->setApiKey(config('openai.api_key'));
$result = $openai->completions([
'model' => 'text-davinci-002',
'prompt' => $input,
'max_tokens' => 100,
]);
$response = array_reduce($result['choices'], function ($result, $choice) {
return $result . $choice['text'];
}, '');
return response()->json(['response' => $response]);
}
}
This code uses the OpenAI API to generate text completions Based on a given input prompt. This code uses the OpenAI class to call the completions method, which creates a new text completion request. The create method takes an array of parameters that configure the request:
model: The ID of the language model to use for the completion. In this case, the model is set to text-davinci-002, which is a powerful language model that can generate high-quality text.prompt: The input prompt for the completion. This is passed in as a parameter from a request object, which presumably contains user input that the model should complete.max_tokens: The maximum number of tokens (words or punctuation) to generate in the completion. In this case, the value is set to 100, which means that the completion will contain at most 100 tokens.
The result of the completion request is stored in the $result variable. This code processes the result of the completion request and stores it in the $response variable. It uses the array_reduce function to iterate over the choices array in the $result. Each choice represents a possible completion generated by the model. The array_reduce function takes three arguments:
- The array to iterate over
- A result to start with
- A callback function that is called for each element in the array
The function takes two arguments: the Current result of the reduction ($result) and the current element in the array ($choice). The function returns a new result, which is passed as the first argument to the next call of the function. The initial value for the reduction is an empty STRING.
The callback function concatenates the text property of each choice to the current result, effectively merging all the possible completions into a single string. Finally, the completed text is stored in the $response variable.
Pros and Cons of Using GPT-3 OpenAI Chatbot in Laravel
There are several pros and cons to using GPT-3 OpenAI Chatbot in Laravel. Some of the pros include:
- GPT-3 OpenAI Chatbot can generate human-like responses to textual input, making it an ideal candidate for building conversational agents.
- Laravel provides a robust and scalable platform for building web applications, making it an ideal framework for integrating GPT-3 OpenAI Chatbot.
- By combining these two powerful technologies, we can create a chatbot that can handle a wide range of queries and provide personalized responses.
Some of the cons include:
- GPT-3 OpenAI Chatbot is a relatively new technology, and there may be some limitations and bugs that have not yet been discovered.
- Integrating GPT-3 OpenAI Chatbot into Laravel may require some technical expertise and may not be suitable for beginners.
Conclusion
In conclusion, integrating GPT-3 OpenAI Chatbot into Laravel can provide a powerful and scalable platform for building conversational agents. By following the steps outlined in this tutorial, You can create a chatbot that can handle a wide range of queries and provide personalized responses. However, it is important to consider the pros and cons of using this technology before implementing it in your project.
