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Overview

Instructor can process LLM’s streamed responses to provide partial response model updates that you can use to update the model with new data as the response is being generated.

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Example

<?php
require 'examples/boot.php';

use Cognesy\Instructor\Instructor;
use Cognesy\Polyglot\LLM\Enums\Mode;
use Cognesy\Utils\Cli\Console;

class UserRole
{
    /** Monotonically increasing identifier */
    public int $id;
    public string $title = '';
}

class UserDetail
{
    public int $age = 0;
    public string $name = '';
    public string $location = '';
    /** @var UserRole[] */
    public array $roles = [];
    /** @var string[] */
    public array $hobbies = [];
}

// This function will be called every time a new token is received
function partialUpdate($partial) {
    // Clear the screen and move the cursor to the top
    Console::clearScreen();

    // Display the partial object
    dump($partial);

    // Wait a bit before clearing the screen to make partial changes slower.
    // Don't use this in your application :)
    // usleep(250000);
}
?>

Now we can use this data model to extract arbitrary properties from a text message. As the tokens are streamed from LLM API, the partialUpdate function will be called with partially updated object of type UserDetail that you can use, usually to update the UI.

<?php
$text = <<<TEXT
    Jason is 25 years old, he is an engineer and tech lead. He lives in
    San Francisco. He likes to play soccer and climb mountains.
    TEXT;

$stream = (new Instructor)->withConnection('openai')->request(
    messages: $text,
    responseModel: UserDetail::class,
    options: ['stream' => true],
    mode: Mode::Json,
)->stream();

foreach ($stream->partials() as $partial) {
    partialUpdate($partial);
}

$user = $stream->getLastUpdate();

assert($user->name === 'Jason');
assert($user->age === 25);
?>
Extracting sequences of objectsStructures