This library is a PHP Client for Qdrant.
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
You can install the client in your PHP project using composer:
composer require hkulekci/qdrantAn example to create a collection :
use Qdrant\Endpoints\Collections;
use Qdrant\Http\GuzzleClient;
use Qdrant\Models\Request\CreateCollection;
use Qdrant\Models\Request\VectorParams;
include __DIR__ . "/../vendor/autoload.php";
include_once 'config.php';
$config = new \Qdrant\Config(QDRANT_HOST);
$config->setApiKey(QDRANT_API_KEY);
$client = new Qdrant(new GuzzleClient($config));
$createCollection = new CreateCollection();
$createCollection->addVector(new VectorParams(1024, VectorParams::DISTANCE_COSINE), 'image');
$response = $client->collections('images')->create($createCollection);So now, we can insert a point :
use Qdrant\Models\PointsStruct;
use Qdrant\Models\PointStruct;
use Qdrant\Models\VectorStruct;
$points = new PointsStruct();
$points->addPoint(
new PointStruct(
(int) $imageId,
new VectorStruct($data['embeddings'][0], 'image'),
[
'id' => 1,
'meta' => 'Meta data'
]
)
);
$client->collections('images')->points()->upsert($points);While upsert data, if you want to wait for upsert to actually happen, you can use query paramaters:
$client->collections('images')->points()->upsert($points, ['wait' => 'true']);
You can check for more parameters : https://qdrant.github.io/qdrant/redoc/index.html#tag/points/operation/upsert_points
Search with a filter :
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 = $client->collections('images')->points()->search($searchRequest);
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