PHP code example of nahid-hasan / ai-notes

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

nahid-hasan / ai-notes example snippets

// Store a voice note
AINote::fromAudio($request->file('audio'));

// Store a text note
AINote::fromText("Met with client Sarah about renewing the contract.");

// Search semantically — finds related notes even with different wording
AINote::search("contract renewal discussion");

// Ask a question across all your notes
AINote::ask("What did I promise the client?");

use AINote;

// In a controller
public function store(Request $request)
{
    $request->validate([
        'audio' => '� omit for single-user apps
    );

    // Note is queued for processing. Returns immediately.
    return response()->json([
        'note_id' => $note->id,
        'status'  => $note->status,  // "pending"
    ], 202);
}

$note = AINote::fromText(
    "Had a call with John. He wants the proposal by Friday.",
    userId: auth()->id(),
    title: "John call"  // optional
);

use Nahid\AINotesPackage\Models\AINote;

$note = AINote::find($noteId);

echo $note->status;
// "pending"    → queued, not started
// "processing" → AI is working on it
// "done"       → transcription, summary, and embedding ready
// "failed"     → something went wrong, check logs

// Basic search
$results = AINote::search("payment discussion");

// Limit results
$results = AINote::search("contract renewal", 3);

// Scoped to a user
$results = AINote::search("invoice delay", 5, auth()->id());

// Each result has a `distance` score — lower = more relevant (0.0 to 1.0)
foreach ($results as $note) {
    echo $note->summary;
    echo $note->distance;  // e.g. 0.31 = very relevant
}

// Ask a question — searches relevant notes and generates an answer
$answer = AINote::ask("What did I say about the Q4 budget?");
echo $answer;

// Scoped to a user
$answer = AINote::ask("What promises did I make to clients?", userId: auth()->id());

// Control how many notes are used as context (default: 3)
$answer = AINote::ask("Summarize my week", contextLimit: 5, userId: auth()->id());

use Nahid\AINotesPackage\Models\AINote;

// All done notes
AINote::done()->get();

// All pending notes
AINote::pending()->get();

// Notes for a specific user
AINote::where('user_id', $userId)->done()->latest()->get();

// Retry a failed note
$note = AINote::find($id);
$note->update(['status' => 'pending']);
\Nahid\AINotesPackage\Jobs\ProcessNote::dispatch($note);

return [
    // 'ollama' (free/local) or 'openai' (paid)
    'driver' => env('AI_DRIVER', 'ollama'),

    // Set false to process synchronously (useful in tests)
    'queue' => env('AI_NOTES_QUEUE', true),

    'ollama' => [
        'base_url'    => env('OLLAMA_BASE_URL', 'http://localhost:11434'),
        'chat_model'  => env('OLLAMA_CHAT_MODEL', 'llama3.2'),
        'embed_model' => env('OLLAMA_EMBED_MODEL', 'nomic-embed-text'),
    ],

    'whisper' => [
        'base_url' => env('WHISPER_BASE_URL', 'http://localhost:9000'),
    ],

    'openai' => [
        'api_key'     => env('OPENAI_API_KEY'),
        'chat_model'  => env('OPENAI_CHAT_MODEL', 'gpt-4o-mini'),
        'embed_model' => env('OPENAI_EMBED_MODEL', 'text-embedding-3-small'),
    ],

    'database' => [
        // 768 for Ollama nomic-embed-text
        // 1536 for OpenAI text-embedding-3-small
        'vector_dimension' => env('AI_NOTES_VECTOR_DIM', 768),
    ],
];

use Nahid\AINotesPackage\Contracts\AIProvider;
use Nahid\AINotesPackage\Tests\Fakes\FakeAIProvider;

// In your TestCase setUp or individual tests:
app()->bind(AIProvider::class, fn() => FakeAIProvider::make());
config(['ai-notes.queue' => false]); // process synchronously

// Now AINote works without Ollama or Whisper running
$note = AINote::fromText("Test note content");
expect($note->status)->toBe('done');

$fake = FakeAIProvider::make()
    ->withTranscription("Custom transcription text")
    ->withSummary("Custom summary");

app()->bind(AIProvider::class, fn() => $fake);

use Nahid\AINotesPackage\Contracts\AIProvider;

class MistralProvider implements AIProvider
{
    public function transcribe(string $audioPath): string
    {
        // your transcription logic
    }

    public function summarize(string $text): string
    {
        // your summarization logic
    }

    public function embed(string $text): array
    {
        // your embedding logic — must return float[]
    }
}

use Nahid\AINotesPackage\Contracts\AIProvider;

$this->app->bind(AIProvider::class, fn() => new MistralProvider());

// Reset all notes to re-embed them
Nahid\AINotesPackage\Models\AINote::query()->update(['status' => 'pending', 'embedding' => null]);
// Then reprocess each one via the job

bash
php artisan ai-notes:install

bash
php artisan migrate

dotenv
AI_DRIVER=ollama

OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_CHAT_MODEL=llama3.2
OLLAMA_EMBED_MODEL=nomic-embed-text

WHISPER_BASE_URL=http://whisper:9000

DB_CONNECTION=pgsql
DB_HOST=postgres
DB_PORT=5432
DB_DATABASE=your_db
DB_USERNAME=your_user
DB_PASSWORD=your_password

QUEUE_CONNECTION=redis
REDIS_HOST=redis

bash
php artisan queue:work

bash
php artisan queue:work
# or in Docker:
docker exec -it your_queue_container php artisan queue:work