Added sparse search example [skip ci]

Author: ankane

README.md

@@ -16,6 +16,7 @@ Or check out some examples:
 - [Embeddings](examples/openai/example.php) with OpenAI
 - [Binary embeddings](examples/cohere/example.php) with Cohere
 - [Hybrid search](examples/hybrid/example.php) with Ollama (Reciprocal Rank Fusion)
+- [Sparse search](examples/sparse/example.php) with Text Embeddings Inference
 - [Morgan fingerprints](examples/rdkit/example.php) with RDKit
 - [Recommendations](examples/disco/example.php) with Disco
 - [Bulk loading](examples/loading/example.php) with `COPY`

examples/sparse/composer.json

@@ -0,0 +1,11 @@
+{
+    "repositories": [
+        {
+            "type": "path",
+            "url": "../.."
+        }
+    ],
+    "require": {
+        "pgvector/pgvector": "dev-master"
+    }
+}

examples/sparse/example.php

@@ -0,0 +1,65 @@
+<?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 fetchEmbeddings($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 $row) {
+        $embedding = [];
+        foreach ($row as $v) {
+            $embedding[$v['index']] = $v['value'];
+        }
+        $embeddings[] = $embedding;
+    }
+    return $embeddings;
+}
+
+$input = [
+  'The dog is barking',
+  'The cat is purring',
+  'The bear is growling'
+];
+$embeddings = fetchEmbeddings($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 = fetchEmbeddings([$query])[0];
+$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);