For more information, you can read laravel news blog post.

orhanerday/open-ai added to community libraries php section.

You can install the package via composer:

composer require orhanerday/open-ai

To get started with this package, you'll first want to be familiar with the OpenAI API documentation and examples.

According to the following code $open_ai is the base variable for all open-ai operations.

use Orhanerday\OpenAi\OpenAi;

$open_ai = new OpenAi(env('OPEN_AI_API_KEY'));

Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.

$complete = $open_ai->complete([
   'engine' => 'davinci',
   'prompt' => 'Hello',
   'temperature' => 0.9,
   'max_tokens' => 150,
   'frequency_penalty' => 0,
   'presence_penalty' => 0.6,
]);

Given a query and a set of documents or labels, the model ranks each document based on its semantic similarity to the provided query.

$search = $open_ai->search([
    'engine' => 'ada',
    'documents' => ['White House', 'hospital', 'school'],
    'query' => 'the president',
]);

Given a question, a set of documents, and some examples, the API generates an answer to the question based on the information in the set of documents. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base.

$answer = $open_ai->answer([
  'documents' => ['Puppy A is happy.', 'Puppy B is sad.'],
  'question' => 'which puppy is happy?',
  'search_model' => 'ada',
  'model' => 'curie',
  'examples_context' => 'In 2017, U.S. life expectancy was 78.6 years.',
  'examples' => [['What is human life expectancy in the United States?', '78 years.']],
  'max_tokens' => 5,
  'stop' => ["\n", '<|endoftext|>'],
]);

Given a query and a set of labeled examples, the model will predict the most likely label for the query. Useful as a drop-in replacement for any ML classification or text-to-label task.

$classification = $open_ai->classification([
   'examples' => [
       ['A happy moment', 'Positive'],
       ['I am sad.', 'Negative'],
       ['I am feeling awesome', 'Positive'],
   ],
   'labels' => ['Positive', 'Negative', 'Neutral'],
   'query' => 'It is a raining day =>(',
   'search_model' => 'ada',
   'model' => 'curie',
]);

Lists the currently available engines, and provides basic information about each one such as the owner and availability.

$engines = $open_ai->engines();

Retrieves an engine instance, providing basic information about the engine such as the owner and availability.

$engine = $open_ai->engine('davinci');

echo $search;

composer test

Please see CHANGELOG for more information on what has changed recently.

Please see CONTRIBUTING for details.

Please report security vulnerabilities to [email protected]

• Orhan Erday
• All Contributors

The MIT License (MIT). Please see License File for more information.