Qdrant PHP Client

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!

Installation

You can install the client in your PHP project using composer:

composer require hkulekci/qdrant

An 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);