pgvector-php/examples/sparse/example.php at master · pgvector/pgvector-php · GitHub

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<?php

// good resources

// https://opensearch.org/blog/improving-document-retrieval-with-sparse-semantic-encoders/

// https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1

// run with

// text-embeddings-router --model-id opensearch-project/opensearch-neural-sparse-encoding-v1 --pooling splade

require_once __DIR__ . '/vendor/autoload.php';

use Pgvector\SparseVector;

$db = pg_connect('postgres://localhost/pgvector_example');

pg_query($db, 'CREATE EXTENSION IF NOT EXISTS vector');

pg_query($db, 'DROP TABLE IF EXISTS documents');

pg_query($db, 'CREATE TABLE documents (id bigserial PRIMARY KEY, content text, embedding sparsevec(30522))');

function embed($inputs)

{

$url = 'http://localhost:3000/embed_sparse';

$data = [

'inputs' => $inputs

];

$opts = [

'http' => [

'method' => 'POST',

'header' => "Content-Type: application/json\r\n",

'content' => json_encode($data)

]

];

$context = stream_context_create($opts);

$response = file_get_contents($url, false, $context);

$embeddings = [];

foreach (json_decode($response, true) as $item) {

$embedding = [];

foreach ($item as $e) {

$embedding[$e['index']] = $e['value'];

}

$embeddings[] = $embedding;

}

return $embeddings;

}

$input = [

'The dog is barking',

'The cat is purring',

'The bear is growling'

];

$embeddings = embed($input);

foreach ($input as $i => $content) {

pg_query_params($db, 'INSERT INTO documents (content, embedding) VALUES ($1, $2)', [$content, new SparseVector($embeddings[$i], 30522)]);

}

$query = 'forest';

$queryEmbedding = embed([$query])[0];

$result = pg_query_params($db, 'SELECT content FROM documents ORDER BY embedding <#> $1 LIMIT 5', [new SparseVector($queryEmbedding, 30522)]);

while ($row = pg_fetch_array($result)) {

echo $row['content'] . "\n";

}

pg_free_result($result);

pg_close($db);

pgvector-php/examples/sparse/example.php at master · pgvector/pgvector-php · GitHub

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