shopbuilder / app / AI / DB / AIDB.php

AIDB.php in ShopBuilder – WooCommerce Builder For Elementor 3.2.6, at app/AI/DB/AIDB.php

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1	<?php
2	/**
3	* AI Database Management Class.
4	*
5	* Handles database operations for AI-related embeddings,
6	* including fetching and upserting (insert or update) data in the embeddings table.
7	*
8	* @package RadiusTheme\SB
9	*/
10
11	namespace RadiusTheme\SB\AI\DB;
12
13	use RadiusTheme\SB\AI\AIFns;
14	use RadiusTheme\SB\Helpers\Fns;
15	use RadiusTheme\SB\Traits\SingletonTrait;
16
17	defined( 'ABSPATH' ) \|\| exit();
18
19	/**
20	* Class AIDB
21	*
22	* Provides methods to interact with the AI embeddings database table.
23	*/
24	class AIDB {
25	/**
26	* Use Singleton trait to ensure a single instance of this class.
27	*/
28	use SingletonTrait;
29
30	/**
31	* Retrieve all AI embeddings from the database.
32	*
33	* @since 1.0.0
34	*
35	* @return array List of all embedding records with properly unserialized data.
36	*/
37	public static function get_all() {
38	$results = Fns::DB()::select( '*' )
39	->from( AIFns::$ai_embeddings_table )
40	->get();
41	if ( empty( $results ) ) {
42	return [];
43	}
44	// Process results to ensure proper data types.
45	$processed = [];
46	foreach ( $results as $row ) {
47	$row_array = (array) $row;
48	// Unserialize embedding and convert to floats.
49	if ( isset( $row_array['embedding'] ) ) {
50	$embedding = maybe_unserialize( $row_array['embedding'] );
51	if ( is_array( $embedding ) ) {
52	// Critical: Ensure all values are floats for cosine similarity.
53	$row_array['embedding'] = array_map( 'floatval', $embedding );
54	} else {
55	continue; // Skip invalid embeddings.
56	}
57	}
58	// Unserialize info data.
59	if ( isset( $row_array['info'] ) ) {
60	$row_array['info'] = maybe_unserialize( $row_array['info'] );
61	}
62	$processed[] = $row_array;
63	}
64	return $processed;
65	}
66
67	/**
68	* Insert or update embedding data for a specific product.
69	*
70	* Checks if a record already exists for the given product ID.
71	* If it exists, the record is updated; otherwise, a new record is inserted.
72	*
73	* @since 1.0.0
74	*
75	* @param int $product_id Product ID to associate with the embedding.
76	* @param string $title Product title or reference title for the embedding.
77	* @param array $embedding Embedding vector data (array of floats).
78	* @param array $info Additional metadata or information related to the embedding.
79	*
80	* @return bool True on successful insert or update, false on failure.
81	*/
82	public static function upsert_embeding( $product_id, $title, $embedding, $info ) {
83	// Validate inputs.
84	if ( ! is_array( $embedding ) \|\| empty( $embedding ) ) {
85	return false;
86	}
87	// Critical: Ensure all embedding values are floats before serialization.
88	$embedding = array_map( 'floatval', $embedding );
89	$exists = Fns::DB()::select( 'id' )
90	->from( AIFns::$ai_embeddings_table )
91	->where( 'product_id', '=', absint( $product_id ) )
92	->get();
93	$data = [
94	'title' => sanitize_text_field( $title ),
95	'embedding' => maybe_serialize( $embedding ),
96	'info' => maybe_serialize( $info ),
97	];
98	if ( ! empty( $exists[0] ) ) {
99	$data['updated_at'] = current_time( 'mysql' );
100	// Update existing record.
101	Fns::DB()::update( AIFns::$ai_embeddings_table, $data )
102	->where( 'product_id', '=', absint( $product_id ) )
103	->execute();
104	return true;
105	} else {
106	// Insert new record.
107	$data['product_id'] = absint( $product_id );
108	$data['created_at'] = current_time( 'mysql' );
109	Fns::DB()::insert( AIFns::$ai_embeddings_table, [ $data ] )->execute();
110	return true;
111	}
112	}
113	}
114