hkulekci / qdrant
PHP Client for Qdrant
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Requires
- php: ^8.1
- php-http/discovery: ^1.19
- psr/http-client: ^1.0
- psr/http-client-implementation: ^1.0
- psr/http-factory: ^1.0
- psr/http-factory-implementation: ^1.0
- psr/http-message: ^1.0|^2.0
- psr/log: ^1.0|^2.0|^3.0
- webmozart/assert: ^1.11
Requires (Dev)
- mockery/mockery: ^1.5
- nyholm/psr7: ^1.8@dev
- php-http/mock-client: 1.x-dev
- phpunit/php-code-coverage: ^10.1@dev
- phpunit/phpunit: ^10.0
- symfony/http-client: ^6.4.3|^7.1.0
README
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; }