PHP code example of mcpuishor / qdrant-laravel

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

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

    

mcpuishor / qdrant-laravel example snippets

return [
    'default' => env('QDRANT_DEFAULT', 'main'),

    'connections' => [
        'main' => [
            'host' => env('QDRANT_HOST', 'http://localhost:6333'),
            'api_key' => env('QDRANT_API_KEY', null),
            'collection' => env('QDRANT_COLLECTION', 'default_collection'),
            'vector_size' => env('QDRANT_VECTOR_SIZE', 128),
        ],
    ],

    'default_distance_metric' => env('QDRANT_DEFAULT_DISTANCE_METRIC', 'Cosine'),
];

use \Mcpuishor\QdrantLaravel\Facades\Schema;
use \Mcpuishor\QdrantLaravel\Enums\DistanceMetric;
use \Mcpuishor\QdrantLaravel\DTOs\Vector;

$vector = Vector::fromArray([
            'size' => 128,
            'distance' => DistanceMetric::COSINE
       ]);

$response = Schema::create(
                   name: "new_collection",
                   vector: $vector,
                   options: []
                );

if ($response) {
    echo "Schema created successfully";
}

use \Mcpuishor\QdrantLaravel\Schema;
use \Mcpuishor\QdrantLaravel\Enums\DistanceMetric;
use \Mcpuishor\QdrantLaravel\DTOs\Vector;

$vector = Vector::fromArray([
            'size' => 128,
            'distance' => DistanceMetric::COSINE
       ]);

$response = Schema::connection('backup')
                ->create(
                   name: "new_collection",
                   vector: $vector,
                );

if ($response) {
    echo "Schema created successfully";
}

use \Mcpuishor\QdrantLaravel\Schema;
use \Mcpuishor\QdrantLaravel\QdrantTransport;
use \Mcpuishor\QdrantLaravel\Enums\DistanceMetric;
use \Mcpuishor\QdrantLaravel\DTOs\Vector;
use \Mcpuishor\QdrantLaravel\DTOs\HnswConfig;

$vector1 = Vector::fromArray([
            'size' => 128,
            'distance' => DistanceMetric::COSINE
            //optional parameters
            'on_disk' => true,
            ]);

$vector2 = Vector::fromArray([
            'size' => 1024,
            'distance' => DistanceMetric::COSINE,
            //optional parameters
            'hsnw_config' => Hnswconfig::fromArray([
                    'm' => 10,
                    'ef_construct' => 4,
                    'on_disk' => true,
                ]),
            ]);

$response = Schema::create(
               name: "new_collection",
               vector: array($vector1, $vector2),
            );

if ($response) {
    echo "Schema created successfully";
}

use \Mcpuishor\QdrantLaravel\Facades\Schema;

$result = Schema::delete('collection_name');

if ($result) {
    echo "Collection has been successfully deleted.";
}

use \Mcpuishor\QdrantLaravel\Facades\Schema;

if ( Schema::exists() ) {
    echo "Collection exists.";
}

use \Mcpuishor\QdrantLaravel\Facades\Schema;

if ( Schema::exists( 'another_collection' ) ) {
    echo "Collection 'another_collection' exists.";
}

use \Mcpuishor\QdrantLaravel\Facades\Schema;
use \Mcpuishor\QdrantLaravel\DTOs\HnswConfig;
use \Mcpuishor\QdrantLaravel\DTOs\Collection\Params;

Schema::update(
    vectors: [

    ], 
    options: [
       'hnsw_config' => HnswConfig::fromArray([
                'm' => 100,
                'ef_construct' => 5,
            ]),
       'params' => Params::fromArray([
                'replication_factor' => 4,
                'on_disk_payload' => true,
            ]),
    ]
);

use \Mcpuishor\QdrantLaravel\Facades\Qdrant;
use \Mcpuishor\QdrantLaravel\Enums\FieldType;

$result = Qdrant::indexes()->add('field_name', FieldType::KEYWORD);

    $result = Qdrant::indexes()->parameterized()->add('field_name', FieldType::INTEGER);

    use \Mcpuishor\QdrantLaravel\Enums\TokenizerType;
    use \Mcpuishor\QdrantLaravel\Facades\Qdrant;

    $result = Qdrant::indexes()->fulltext('text_field_name', TokenizerType::WORD);

    use \Mcpuishor\QdrantLaravel\Facades\Qdrant;

    $result = Qdrant::indexes()->delete('payload_field');

use Mcpuishor\QdrantLaravel\Facades\Qdrant;

// Search using a vector
$results = Qdrant::search()
    ->vector([0.2, 0.3, 0.4, ...]) // Your vector data
    ->limit(10)
    ->get();

use Mcpuishor\QdrantLaravel\Facades\Qdrant;
use Mcpuishor\QdrantLaravel\DTOs\Point;

$point = new Point(id: 123);
$results = Qdrant::search()
    ->point($point)
    ->limit(5)
    ->get();

// Include all payload data
$results = Qdrant::search()
    ->vector($vector)
    ->withPayload()
    ->get();

// Include only specific payload fields
$results = Qdrant::search()
    ->vector($vector)
    ->   ->vector($vector)
    ->withVectors()
    ->get();

// Limit results
$results = Qdrant::search()
    ->vector($vector)
    ->limit(20)
    ->get();

// Pagination with offset
$results = Qdrant::search()
    ->vector($vector)
    ->limit(10)
    ->offset(20) // Skip first 20 results
    ->get();

// Simple equality filter
$results = Qdrant::search()
    ->vector($vector)
    ->where('category', '=', 'electronics')
    ->get();

// Range filter
$results = Qdrant::search()
    ->vector($vector)
    ->where('price', '>=', 100)
    ->where('price', '<=', 500)
    ->get();

// Multiple conditions
$results = Qdrant::search()
    ->vector($vector)
    ->where('category', '=', 'electronics')
    ->where('in_stock', '=', true)
    ->get();

// Nested conditions
$results = Qdrant::search()
    ->vector($vector)
    ->where(function($query) {
        $query->where('category', '=', 'electronics')
              ->orWhere('category', '=', 'gadgets');
    })
    ->where('price', '<', 1000)
    ->get();

// Group results by category
$results = Qdrant::search()
    ->vector($vector)
    ->groupBy('category', 5) // 5 results per group
    ->get();

$search1 = Qdrant::search()->vector($vector1)->limit(5);
$search2 = Qdrant::search()->vector($vector2)->limit(5);

$batchResults = Qdrant::search()->batch([$search1, $search2]);

$randomPoints = Qdrant::search()->random();

$results = Qdrant::search()
    ->vector($vector)
    ->using('image_embedding') // Use the named vector
    ->get();

use Mcpuishor\QdrantLaravel\Facades\Qdrant;

// Recommend based on point IDs
$recommendations = Qdrant::recommend()
    ->positive([123, 456]) // Points you like
    ->limit(10)
    ->get();

$recommendations = Qdrant::recommend()
    ->positive([123, 456]) // Points you like
    ->negative([789, 101]) // Points you don't like
    ->limit(10)
    ->get();

use Mcpuishor\QdrantLaravel\Enums\AverageVectorStrategy;

$recommendations = Qdrant::recommend()
    ->positive([123, 456])
    ->strategy(AverageVectorStrategy::WEIGHTED) // Use weighted average
    ->limit(10)
    ->get();

use Mcpuishor\QdrantLaravel\Facades\Qdrant;

// Get multiple points
$points = Qdrant::points()->get([123, 456, 789]);

// Find a single point
$point = Qdrant::points()->find(123);

// With payload (default)
$points = Qdrant::points()->withPayload()->get([123, 456]);

// Without payload
$points = Qdrant::points()->withoutPayload()->get([123, 456]);

// With vector data
$points = Qdrant::points()->withVector()->get([123, 456]);

// Without vector data (default)
$points = Qdrant::points()->withoutVector()->get([123, 456]);

use Mcpuishor\QdrantLaravel\DTOs\Point;

// Create a point
$point = new Point(
    id: 123,
    vector: [0.2, 0.3, 0.4, ...],
    payload: ['name' => 'Example', 'category' => 'test']
);

// Insert the point
$success = Qdrant::points()->insert($point);

use Mcpuishor\QdrantLaravel\PointsCollection;
use Mcpuishor\QdrantLaravel\DTOs\Point;

// Create points collection
$points = new PointsCollection([
    new Point(id: 123, vector: [0.2, 0.3, 0.4, ...], payload: ['name' => 'First']),
    new Point(id: 456, vector: [0.5, 0.6, 0.7, ...], payload: ['name' => 'Second'])
]);

// Upsert the points
$success = Qdrant::points()->upsert($points);

// Delete specific points
$success = Qdrant::points()->delete([123, 456]);

// Delete points matching a filter
$success = Qdrant::points()
    ->where('category', '=', 'test')
    ->delete([]);

// Create an autochunker with chunk size of 100
$chunker = Qdrant::points()->autochunk(100);

// Add points - they'll be automatically upserted when the chunk size is reached
foreach ($largeDataset as $data) {
    $point = new Point(
        id: $data['id'],
        vector: $data['embedding'],
        payload: $data['metadata']
    );
    $chunker->add($point);
}

// Manually flush any remaining points
$chunker->flush();

use Mcpuishor\QdrantLaravel\Facades\Qdrant;
use Mcpuishor\QdrantLaravel\PointsCollection;
use Mcpuishor\QdrantLaravel\DTOs\Point;

// Create a collection of points with updated vectors
$points = new PointsCollection([
    new Point(id: 123, vector: [0.2, 0.3, 0.4, ...]),
    new Point(id: 456, vector: [0.5, 0.6, 0.7, ...])
]);

// Update the vectors
$success = Qdrant::vectors()->update($points);

use Mcpuishor\QdrantLaravel\Facades\Qdrant;

// Delete vectors for specific points
$success = Qdrant::vectors()->delete([123, 456]);

use Mcpuishor\QdrantLaravel\QdrantClient;

QdrantClient::macro('byClimate', function ($climate) {
    return $this->where('climate', '=', $climate);
});

$results = Qdrant::collection('plants')->byClimate('tropical')->get();

sh
php artisan vendor:publish --tag=qdrant-laravel-config

sh
php artisan qdrant:migrate --collection=plants --vector-size=256 --distance-metric=euclidean --indexes='{"species":"text","age":"integer"}'

sh
php artisan qdrant:migrate --rollback --collection=plants