Added hybrid search example [skip ci]

Author: ankane

README.md

@@ -15,6 +15,7 @@ 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)
 - [Morgan fingerprints](examples/rdkit/example.php) with RDKit
 - [Recommendations](examples/disco/example.php) with Disco
 - [Bulk loading](examples/loading/example.php) with `COPY`

examples/hybrid/composer.json

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

examples/hybrid/example.php

@@ -0,0 +1,76 @@
+<?php
+
+require_once __DIR__ . '/vendor/autoload.php';
+
+use Pgvector\Vector;
+
+$db = pg_connect('postgres://localhost/pgvector_example');
+
+pg_query($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 vector(768))');
+pg_query($db, "CREATE INDEX ON documents USING GIN (to_tsvector('english', content))");
+
+function fetchEmbeddings($input)
+{
+    $url = 'http://localhost:11434/api/embed';
+    $data = [
+        'input' => $input,
+        'model' => 'nomic-embed-text'
+    ];
+    $opts = [
+        'http' => [
+            'method' => 'POST',
+            'header' => "Content-Type: application/json\r\n",
+            'content' => json_encode($data)
+        ]
+    ];
+    $context = stream_context_create($opts);
+    $response = file_get_contents($url, false, $context);
+    return json_decode($response, true)['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 Vector($embeddings[$i])]);
+}
+
+$sql = <<<SQL
+WITH semantic_search AS (
+    SELECT id, RANK () OVER (ORDER BY embedding <=> $2) AS rank
+    FROM documents
+    ORDER BY embedding <=> $2
+    LIMIT 20
+),
+keyword_search AS (
+    SELECT id, RANK () OVER (ORDER BY ts_rank_cd(to_tsvector('english', content), query) DESC)
+    FROM documents, plainto_tsquery('english', $1) query
+    WHERE to_tsvector('english', content) @@ query
+    ORDER BY ts_rank_cd(to_tsvector('english', content), query) DESC
+    LIMIT 20
+)
+SELECT
+    COALESCE(semantic_search.id, keyword_search.id) AS id,
+    COALESCE(1.0 / ($3 + semantic_search.rank), 0.0) +
+    COALESCE(1.0 / ($3 + keyword_search.rank), 0.0) AS score
+FROM semantic_search
+FULL OUTER JOIN keyword_search ON semantic_search.id = keyword_search.id
+ORDER BY score DESC
+LIMIT 5
+SQL;
+$query = 'growling bear';
+$queryEmbedding = fetchEmbeddings($query)[0];
+$k = 60;
+$result = pg_query_params($db, $sql, [$query, new Vector($queryEmbedding), $k]);
+while ($row = pg_fetch_array($result)) {
+    echo 'document: ' . $row['id'] . ', RRF score: ' . $row['score'] . "\n";
+}
+
+pg_free_result($result);
+pg_close($db);