​
Setup Your Development Environment

Set up LLM provider API keys - create .env file in the root of your project and add the following:

OPENAI_API_KEY=your-openai-api-key

NOTE: You can get your LLM provider API key from the provider’s dashboard, e.g.: OpenAI

You can also use API key directly in your code - see example.

​
Install Instructor with Composer

Installing Instructor is simple. Run following command in your terminal, and you’re on your way to a smoother data handling experience!

composer install cognesy/instructor-php

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Basic example

This is a simple example demonstrating how Instructor retrieves structured information from provided text (or chat message sequence).

Response model class is a plain PHP class with typehints specifying the types of fields of the object.

Create file instructor-test.php with the following content:

<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');

use Cognesy\Instructor\Instructor;

// Step 1: Define target data structure(s)
class Person {
    public string $name;
    public int $age;
}

// Step 2: Provide content to process
$text = "His name is Jason and he is 28 years old.";

// Step 3: Use Instructor to run LLM inference
$person = (new Instructor)->respond(
    messages: $text,
    responseModel: Person::class,
);

// Step 4: Work with structured response data
assert($person instanceof Person); // true
assert($person->name === 'Jason'); // true
assert($person->age === 28); // true

echo $person->name; // Jason
echo $person->age; // 28

var_dump($person);
// Person {
//     name: "Jason",
//     age: 28
// }

Now, you can run you example:

php instructor-test.php

NOTE: Instructor supports classes / objects as response models. In case you want to extract simple types like strings, integers, float, booleans or enums, you need to wrap them in Scalar adapter. See section: Extracting Scalar Values.

IntroductionLLM API Providers