PHP code example of mbvb1223 / pinecone-php-client

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

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

    

mbvb1223 / pinecone-php-client example snippets

Mbvb1223\Pinecone\Pinecone;

// Initialize client
$pinecone = new Pinecone('your-api-key');

// Create an index
$pinecone->createIndex('my-index', [
    'dimension' => 1536,
    'metric' => 'cosine',
    'spec' => [
        'serverless' => [
            'cloud' => 'aws',
            'region' => 'us-east-1'
        ]
    ]
]);

// Get index reference
$index = $pinecone->index('my-index');

// Upsert vectors directly on the index (default namespace)
$index->upsert([
    [
        'id' => 'vec1',
        'values' => [0.1, 0.2, 0.3, /* ... more dimensions */],
        'metadata' => ['genre' => 'comedy', 'year' => 2020]
    ]
]);

// Or use a specific namespace
$namespace = $index->namespace('test-namespace');
$namespace->upsert([
    [
        'id' => 'vec2',
        'values' => [0.4, 0.5, 0.6, /* ... */],
        'metadata' => ['genre' => 'drama', 'year' => 2021]
    ]
]);

// Query vectors
$results = $index->query(
    vector: [0.1, 0.2, 0.3, /* ... query vector */],
    topK: 10,
    

$pinecone = new Pinecone();

$pinecone = new Pinecone('your-api-key', [
    'controllerHost' => 'https://api.pinecone.io',
    'timeout' => 30,
    'additionalHeaders' => [
        'Custom-Header' => 'value'
    ]
]);

$pinecone = new Pinecone('your-api-key');

// List all indexes
$indexes = $pinecone->listIndexes();

// Check if an index exists
if ($pinecone->hasIndex('my-index')) {
    echo "Index exists!\n";
}

// Create an index
$pinecone->createIndex('my-index', [
    'dimension' => 1536,
    'metric' => 'cosine',                    // optional, defaults to 'cosine'
    'spec' => [
        'serverless' => [
            'cloud' => 'aws',
            'region' => 'us-east-1'
        ]
    ],
    'deletion_protection' => 'enabled',       // optional
    'tags' => ['env' => 'production'],        // optional
]);

// Create an index for a specific embedding model
$pinecone->createForModel('my-model-index', [
    'cloud' => 'aws',
    'region' => 'us-east-1',
    'embed' => [
        'model' => 'multilingual-e5-large',
        'field_map' => ['text' => 'chunk_text']
    ]
]);

// Describe an index
$indexInfo = $pinecone->describeIndex('my-index');
echo "Host: " . $indexInfo['host'] . "\n";

// Configure an index
$pinecone->configureIndex('my-index', [
    'deletion_protection' => 'disabled',
]);

// Delete an index
$pinecone->deleteIndex('my-index');

$index = $pinecone->index('my-index');

// --- Direct operations on index (default namespace) ---

// Upsert vectors
$index->upsert([
    [
        'id' => 'vec1',
        'values' => [0.1, 0.2, 0.3],
        'metadata' => ['genre' => 'comedy']
    ],
    [
        'id' => 'vec2',
        'values' => [0.4, 0.5, 0.6],
        'metadata' => ['genre' => 'drama']
    ]
]);

// Query by vector
$results = $index->query(
    vector: [0.1, 0.2, 0.3],
    topK: 10,
    filter: ['genre' => ['$eq' => 'comedy']],
    namespace: 'old-data', deleteAll: true);

// --- Operations through a namespace ---

$namespace = $index->namespace('my-namespace');
$namespace->upsert([/* vectors */]);
$namespace->query(vector: [0.1, 0.2], topK: 5);
$namespace->fetch(['vec1']);
$namespace->update('vec1', values: [0.9, 0.8]);
$namespace->delete(ids: ['vec1']);

$index = $pinecone->index('my-index');

// List all vector IDs
$result = $index->listVectorIds();

// List with prefix filter and pagination
$result = $index->listVectorIds(
    prefix: 'doc1#',
    limit: 100,
    namespace: 'my-namespace'
);

foreach ($result['vectors'] as $vector) {
    echo $vector['id'] . "\n";
}

// Paginate through all results
$paginationToken = $result['pagination']['next'] ?? null;
while ($paginationToken) {
    $result = $index->listVectorIds(paginationToken: $paginationToken);
    // process results...
    $paginationToken = $result['pagination']['next'] ?? null;
}

// Also available on namespaces
$namespace = $index->namespace('my-namespace');
$result = $namespace->listVectorIds(prefix: 'doc1#', limit: 50);

$index = $pinecone->index('my-index');

// Query with both dense and sparse vectors for hybrid search
$results = $index->query(
    vector: [0.1, 0.2, 0.3],
    topK: 10,
    sparseVector: [
        'indices' => [0, 3, 5],
        'values' => [0.5, 0.3, 0.8]
    ],
    

$index = $pinecone->index('my-index');

// Get index statistics
$stats = $index->describeIndexStats();
echo "Total vectors: " . $stats['totalVectorCount'] . "\n";
echo "Dimension: " . $stats['dimension'] . "\n";

// List all namespaces
$namespaces = $index->listNamespaces();
foreach ($namespaces as $ns) {
    echo "Namespace: $ns\n";
}

// Get stats for a specific namespace
$nsStats = $index->describeNamespace('my-namespace');
echo "Vectors in namespace: " . $nsStats['vectorCount'] . "\n";

// Delete all vectors in a namespace
$index->deleteNamespace('old-namespace');

$index = $pinecone->index('my-index');

// Start a bulk import
$import = $index->startImport([
    'uri' => 's3://my-bucket/vectors/',
    'integration_id' => 'my-integration'
]);

// List imports
$imports = $index->listImports();

// Check import status
$status = $index->describeImport($import['id']);

// Cancel an import
$index->cancelImport($import['id']);

$inference = $pinecone->inference();

// Generate embeddings
$embeddings = $inference->embed('multilingual-e5-large', [
    ['text' => 'The quick brown fox jumps over the lazy dog'],
    ['text' => 'A journey of a thousand miles begins with a single step']
]);

echo "Generated " . count($embeddings['data']) . " embeddings\n";

// Rerank documents
$ranked = $inference->rerank(
    model: 'bge-reranker-v2-m3',
    query: 'What is machine learning?',
    documents: [
        ['text' => 'Machine learning is a subset of artificial intelligence'],
        ['text' => 'Python is a programming language'],
        ['text' => 'Deep learning uses neural networks']
    ],
    topN: 2,
    returnDocuments: true,
    rankFields: ['text']
);

foreach ($ranked['data'] as $result) {
    echo "Score: " . $result['score'] . "\n";
}

// List available models
$models = $inference->listModels();

// Create an assistant via the control plane
$pinecone->createAssistant([
    'name' => 'my-assistant',
    'instructions' => 'You are a helpful customer support bot.',
]);

// Get an assistant client (resolves host automatically)
$assistant = $pinecone->assistant('my-assistant');

// Chat with the assistant
$response = $assistant->chat([
    ['role' => 'user', 'content' => 'How do I return an item?']
]);

echo $response['message']['content'];

// List all assistants
$assistants = $pinecone->listAssistants();

// Update an assistant
$pinecone->updateAssistant('my-assistant', [
    'instructions' => 'Updated instructions here.',
]);

// Delete an assistant
$pinecone->deleteAssistant('my-assistant');

// Create a collection from an existing index
$pinecone->createCollection([
    'name' => 'my-collection',
    'source' => 'my-pod-index'
]);

// List collections
$collections = $pinecone->listCollections();

// Describe a collection
$info = $pinecone->describeCollection('my-collection');

// Delete a collection
$pinecone->deleteCollection('my-collection');

// Create a backup
$backup = $pinecone->createBackup([
    'source_index_name' => 'my-index',
    'name' => 'my-backup',
]);

// List backups
$backups = $pinecone->listBackups();

// Describe a backup
$info = $pinecone->describeBackup($backup['backup_id']);

// List restore jobs
$jobs = $pinecone->listRestoreJobs();

// Delete a backup
$pinecone->deleteBackup($backup['backup_id']);

$index = $pinecone->index('my-index');

// Equality
$results = $index->query(
    vector: [0.1, 0.2, 0.3],
    topK: 10,
    filter: ['genre' => ['$eq' => 'comedy']]
);

// Comparison operators: $eq, $ne, $gt, $gte, $lt, $lte
$results = $index->query(
    vector: [0.1, 0.2, 0.3],
    topK: 10,
    filter: ['year' => ['$gte' => 2020, '$lt' => 2024]]
);

// Set membership: $in, $nin
$results = $index->query(
    vector: [0.1, 0.2, 0.3],
    topK: 10,
    filter: ['genre' => ['$in' => ['comedy', 'drama']]]
);

// Logical operators: $and, $or
$results = $index->query(
    vector: [0.1, 0.2, 0.3],
    topK: 5,
    filter: [
        '$and' => [
            ['genre' => ['$eq' => 'comedy']],
            ['year' => ['$gte' => 2020]]
        ]
    ],
    

use Mbvb1223\Pinecone\Pinecone;
use Mbvb1223\Pinecone\Errors\PineconeException;
use Mbvb1223\Pinecone\Errors\PineconeApiException;

$pinecone = new Pinecone('your-api-key');

try {
    $pinecone->createIndex('my-index', [
        'dimension' => 1536,
        'spec' => [
            'serverless' => ['cloud' => 'aws', 'region' => 'us-east-1']
        ]
    ]);
} catch (PineconeApiException $e) {
    // HTTP error from the API
    echo "Status: " . $e->getCode() . "\n";
    echo "Message: " . $e->getMessage() . "\n";
    echo "Response: " . print_r($e->getResponseData(), true) . "\n";
} catch (PineconeException $e) {
    // General client error (network, JSON decode, etc.)
    echo "Error: " . $e->getMessage() . "\n";
}

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