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Overview

Instructor supports Description and Instructions attributes to provide more context to the language model or to provide additional instructions to the model.

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Example

<?php

$loader = require 'vendor/autoload.php';

$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');



use Cognesy\Instructor\Features\Schema\Attributes\Description;

use Cognesy\Instructor\Features\Schema\Attributes\Instructions;

use Cognesy\Instructor\Instructor;



// Step 1: Define a class that represents the structure and semantics

// of the data you want to extract

#[Description("Information about user")]

class User {

    #[Description("User's age")]

    public int $age;

    #[Instructions("Make user name ALL CAPS")]

    public string $name;

    #[Description("User's job")]

    #[Instructions("Ignore hobbies, identify profession")]

    #[Instructions("Make the profession name lowercase")]

    public string $job;

}



// Step 2: Get the text (or chat messages) you want to extract data from

$text = "Jason is 25 years old, 10K runner, speaker and an engineer.";

print("Input text:\n");

print($text . "\n\n");



// Step 3: Extract structured data using default language model API (OpenAI)

print("Extracting structured data using LLM...\n\n");

$user = (new Instructor)->respond(

    messages: $text,

    responseModel: User::class,

);



// Step 4: Now you can use the extracted data in your application

print("Extracted data:\n");



dump($user);



assert(isset($user->name));

assert($user->name === "JASON");

assert(isset($user->age));

assert($user->age === 25);

assert(isset($user->job));

assert($user->job === "engineer");

?>
Automatic correction based on validation resultsUsing LLM API connections from config file