​
Changing LLM model and options

You can specify model and other options that will be passed to LLM endpoint. Commonly used option supported by many providers is temperature, which controls randomness of the output. Lower values make the output more deterministic, while higher values make it more random. 
<?php
$person = (new StructuredOutput)->with(
    messages: [['role' => 'user', 'content' => $text]],
    responseModel: Person::class,
    model: 'gpt-3.5-turbo',
    options: [
        // custom temperature setting
        'temperature' => 0.0
        // ... other options - e.g. provider or model specific
    ],
)->get();
NOTE: Please note that many options might be specific to the provider or even some model that you are using.

​
Customizing configuration

You can pass a custom LLM configuration to the Instructor. This allows you to specify your own API key, base URI or, which might be helpful in the case you are using OpenAI - organization. 
<?php
use Cognesy\Instructor\StructuredOutput;use Cognesy\Polyglot\Inference\Config\LLMConfig;

// Create instance of OpenAI client initialized with custom parameters
$config = new LLMConfig(
    apiUrl: 'https://api.openai.com/v1',
    apiKey: $yourApiKey,
    endpoint: '/chat/completions',
    metadata: ['organization' => ''],
    model: 'gpt-4o-mini',
    maxTokens: 128,
    httpClientPreset: 'guzzle',
    driver: 'openai',
));

/// Get Instructor with the default configuration overridden with your own
$structuredOutput = (new StructuredOutput)->withLLMConfig($driver);

$person = $structuredOutput->with(
    messages: [['role' => 'user', 'content' => $text]],
    responseModel: Person::class,
    options: ['temperature' => 0.0],
)->get();