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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
Connecting to Qdrant
include __DIR__ . "/../vendor/autoload.php";
include_once 'config.php';
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);
Creating a Collection
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);
Inserting Points Into Collection
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);
Wait for Acknowledges
While upsert data, if you want to wait for upsert to actually happen, you can use query parameters:
$client->collections('contents')->points()->upsert($points, ['wait' => 'true']);
You can check for more parameters : https://qdrant.github.io/qdrant/redoc/index.html#tag/points/operation/upsert_points
Search on 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 = $client->collections('contents')->points()->search($searchRequest);
Search on Points with OpenAI Embeddings
$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;
}