PHP code example of hkulekci / qdrant

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

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

    

hkulekci / qdrant example snippets

use Qdrant\Qdrant;
use Qdrant\Config;
use Qdrant\Http\Builder;

$config = new Config(QDRANT_HOST);
$config->setApiKey(QDRANT_API_KEY);

$transport = (new Builder())->build($config);
$client = new Qdrant($transport);

use Qdrant\Endpoints\Collections;
use Qdrant\Models\Request\CreateCollection;
use Qdrant\Models\Request\VectorParams;

$createCollection = new CreateCollection();
$createCollection->addVector(new VectorParams(1536, VectorParams::DISTANCE_COSINE), 'content');
$response = $client->collections('contents')->create($createCollection);

use Qdrant\Models\PointsStruct;
use Qdrant\Models\PointStruct;
use Qdrant\Models\VectorStruct;

$openai = OpenAI::client(OPENAI_API_KEY);

$query = 'sustainable agricultural startups';
$response = $openai->embeddings()->create([
    'model' => 'text-embedding-ada-002',
    'input' => $query,
]);
$embedding = array_values($response->embeddings[0]->embedding);

$points = new PointsStruct();
$points->addPoint(
    new PointStruct(
        (int) $imageId,
        new VectorStruct($embedding, 'content'),
        [
            'id' => 1,
            'meta' => 'Meta data'
        ]
    )
);
$client->collections('contents')->points()->upsert($points);

$client->collections('contents')->points()->upsert($points, ['wait' => 'true']);

use Qdrant\Models\Filter\Condition\MatchString;
use Qdrant\Models\Filter\Filter;
use Qdrant\Models\Request\SearchRequest;
use Qdrant\Models\VectorStruct;

$searchRequest = (new SearchRequest(new VectorStruct($embedding, 'elev_pitch')))
    ->setFilter(
        (new Filter())->addMust(
            new MatchString('name', 'Palm')
        )
    )
    ->setLimit(10)
    ->setParams([
        'hnsw_ef' => 128,
        'exact' => false,
    ])
    ->setWithPayload(true);

$response = $client->collections('contents')->points()->search($searchRequest);

$openai = OpenAI::client(OPENAI_API_KEY);

$query = 'lorem ipsum dolor sit amed';
$response = $openai->embeddings()->create([
    'model' => 'text-embedding-ada-002',
    'input' => $query,
]);
$embedding = array_values($response->embeddings[0]->embedding);

$searchRequest = (new SearchRequest(new VectorStruct($embedding, 'content')))
    ->setLimit(10)
    ->setParams([
        'hnsw_ef' => 128,
        'exact' => false,
    ])
    ->setWithPayload(true);

$response = $client->collections('contents')->points()->search($searchRequest);

foreach ($response['result'] as $item) {
    echo $item['score'] . ';' . $item['payload']['id'] . ';' . $item['payload']['meta_data'] . PHP_EOL;
}