Transcription to tasks

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

use Cognesy\Instructor\StructuredOutput;
use Cognesy\Instructor\StructuredOutputRuntime;
use Cognesy\Instructor\Enums\OutputMode;
use Cognesy\Polyglot\Inference\LLMProvider;

// Step 1: Define a class that represents the structure and semantics
// of the data you want to extract
enum TaskStatus : string {
    case Pending = 'pending';
    case Completed = 'completed';
}

enum Role : string {
    case PM = 'pm';
    case Dev = 'dev';
}

class Task {
    public string $title;
    public string $description;
    public DateTimeImmutable $dueDate;
    public Role $owner;
    public TaskStatus $status;
}

class Tasks {
    public DateTime $meetingDate;
    /** @var Task[] */
    public array $tasks;
}

// Step 2: Get the text (or chat messages) you want to extract data from
$text = <<<TEXT
Transcription of meeting from 2024-01-15, 16:00
---
PM: Hey, how's progress on the video transcription engine?
Dev: I've got basic functionality working, but accuracy isn't great yet. Might need to switch to a different API.
PM: So the plan is to research alternatives and provide a comparison? Is it possible by Jan 20th?
Dev: Sure, I'll make it available before the meeting.
PM: The one at 12?
Dev: Yes, at 12. By the way, are we still planning to support real-time transcription?
PM: Yes, it's a key feature. Speaking of which, I need to update the product roadmap. I'll have that ready by Jan 18th.
Dev: Got it. I'll keep that in mind while evaluating APIs. Oh, and the UI for the summary view is ready for review.
PM: Great, I'll take a look tomorrow by 10.
TEXT;

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");
$tasks = new StructuredOutput(
        StructuredOutputRuntime::fromProvider(LLMProvider::using('openai'))
            ->withOutputMode(OutputMode::Json)
    )
    ->with(
        messages: $text,
        responseModel: Tasks::class,
        //model: 'gpt-4o-mini',
    )
    ->get();

// Step 4: Now you can use the extracted data in your application
print("Extracted data:\n");

dump($tasks);

assert($tasks->meetingDate->format('Y-m-d') === '2024-01-15');
assert(count($tasks->tasks) === 3);

// Index tasks by due date — LLM ordering is non-deterministic
$byDate = [];
foreach ($tasks->tasks as $task) {
    $byDate[$task->dueDate->format('Y-m-d')] = $task;
}

assert(isset($byDate['2024-01-20']));
assert($byDate['2024-01-20']->status === TaskStatus::Pending);

assert(isset($byDate['2024-01-18']));
assert($byDate['2024-01-18']->status === TaskStatus::Pending);
assert($byDate['2024-01-18']->owner === Role::PM);

assert(isset($byDate['2024-01-16']));
assert($byDate['2024-01-16']->status === TaskStatus::Pending);
?>