The S2A (System 2 Attention) technique auto-refines a prompt by asking the model to
rewrite the prompt to include only relevant information.
<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__.'../../src/');
use Cognesy\Instructor\Extras\Scalar\Scalar;
use Cognesy\Instructor\Features\Schema\Attributes\Description;
use Cognesy\Instructor\Instructor;
class RewrittenTask {
#[Description("Relevant context")]
public string $relevantContext;
#[Description("The question from the user")]
public string $userQuery;
}
class RefineAndSolve {
private string $prompt = <<<PROMPT
Given the following text by a user, extract the part
that is actually relevant to their question. Please
include the actual question or query that the user
is asking.
Text by user:
{query}
PROMPT;
public function __invoke(string $problem) : int {
$rewrittenPrompt = $this->rewritePrompt($problem);
return (new Instructor)->respond(
messages: "{$rewrittenPrompt->relevantContext}\nQuestion: {$rewrittenPrompt->userQuery}",
responseModel: Scalar::integer('answer'),
);
}
private function rewritePrompt(string $query) : RewrittenTask {
return (new Instructor)->respond(
messages: str_replace('{query}', $query, $this->prompt),
responseModel: RewrittenTask::class,
model: 'gpt-4o',
);
}
}
$answer = (new RefineAndSolve)(problem: <<<PROBLEM
Mary has 3 times as much candy as Megan.
Mary then adds 10 more pieces of candy to her collection.
Max is 5 years older than Mary.
If Megan has 5 pieces of candy, how many does Mary have in total?
PROBLEM,
);
echo $answer . "\n";
?>
