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Consolidate SQL for distance_function into one single source of truth #21

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Merged
ecairol merged 3 commits into from
Aug 18, 2026
Merged

Consolidate SQL for distance_function into one single source of truth #21

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33387a0
Unify SELECT distance_function AS distance into one single source of …
ecairol Aug 17, 2026
10757e6
Fixed error from previous refactor.
ecairol Aug 17, 2026
8cc1e45
Composer feedback: post_per_page normalization to take into account -1
ecairol Aug 17, 2026
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210 changes: 28 additions & 182 deletions admin/views/embeddings.php
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Original file line number Diff line number Diff line change
Expand Up @@ -84,198 +84,44 @@ class="regular-text">
<?php
// If we have a search query, use the semantic search.
$search_results = array();
$embeddings = array();
if ( ! empty( $search_query ) ) {
$table_name = $wpdb->prefix . 'wpvdb_embeddings';

// Initialize timing for search performance tracking.
$search_start_time = microtime( true );
$search_time_result = 0;
$total_vectors_searched = 0;

// Create a database instance instead of using static methods.
$database = new \WPVDB\Database();

// Get plugin settings.
$model = \WPVDB\Settings::get_default_model();
$api_base = \WPVDB\Settings::get_api_base();
$db_type = $database->get_db_type();
$has_vector_support = $database->has_native_vector_support() ? 'Yes' : 'No';
$model = \WPVDB\Settings::get_default_model();
$api_base = \WPVDB\Settings::get_api_base();
$api_key = \WPVDB\Settings::get_api_key();

\WPVDB\Logger::debug( 'Performing semantic search for query: ' . $search_query );
\WPVDB\Logger::debug( 'API key exists: ' . ( ! empty( $api_key ) ? 'Yes' : 'No' ) );
\WPVDB\Logger::debug( 'Model: ' . $model );
\WPVDB\Logger::debug( 'API base: ' . $api_base );

if ( $api_key && $model ) {
try {
$embedding_result = \WPVDB\Core::get_embedding( $search_query, $model, $api_base, $api_key );

if ( is_wp_error( $embedding_result ) ) {
\WPVDB\Logger::error( 'Error getting embedding: ' . $embedding_result->get_error_message() );
} else {
\WPVDB\Logger::debug( 'Successfully generated embedding with dimensions: ' . count( $embedding_result ) );

$embedding = $embedding_result;
$has_vector = $database->has_native_vector_support();
\WPVDB\Logger::debug( 'Database has native vector support: ' . ( $has_vector ? 'Yes' : 'No' ) );

if ( $has_vector ) {
// Convert the embedding array to JSON.
$embedding_json = wp_json_encode( $embedding );

// Use Database class to get the appropriate vector function.
$vector_function = $database->get_vector_from_string_function( $embedding_json );
\WPVDB\Logger::debug( 'Using vector function: ' . $vector_function );

// Get total count of vectors.
$total_vectors_searched = $wpdb->get_var( "SELECT COUNT(*) FROM {$wpdb->prefix}wpvdb_embeddings" ); // phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery, WordPress.DB.DirectDatabaseQuery.NoCaching
\WPVDB\Logger::debug( 'Total vectors searched: ' . $total_vectors_searched );

// Use Database class to get the appropriate distance function with both vectors.
$db_type = $database->get_db_type();
if ( 'mariadb' === $db_type ) {
$distance_function = "VEC_DISTANCE_COSINE(e.embedding, $vector_function)";
} else {
$distance_function = "DISTANCE(e.embedding, $vector_function, 'COSINE')";
}
\WPVDB\Logger::debug( 'Using distance function: ' . $distance_function );

// Optimized query that will use the vector index.
// The ORDER BY + LIMIT pattern is what triggers the vector index usage.
// phpcs:disable WordPress.DB.PreparedSQL.InterpolatedNotPrepared
$sql = $wpdb->prepare(
"SELECT e.*,
$distance_function AS distance
FROM $table_name e
WHERE e.model = %s
ORDER BY distance
LIMIT %d",
$model,
20 // Show top 20 matches.
);
// phpcs:enable WordPress.DB.PreparedSQL.InterpolatedNotPrepared

\WPVDB\Logger::debug( 'Executing SQL query: ' . $sql );

$search_results = $wpdb->get_results( $sql ); // phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery, WordPress.DB.DirectDatabaseQuery.NoCaching, WordPress.DB.PreparedSQL.NotPrepared

if ( $wpdb->last_error ) {
\WPVDB\Logger::error( 'SQL error: ' . $wpdb->last_error );

// Try executing a simpler query to test database connection.
$test_result = $wpdb->get_var( "SELECT COUNT(*) FROM {$wpdb->prefix}wpvdb_embeddings" ); // phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery, WordPress.DB.DirectDatabaseQuery.NoCaching

if ( $wpdb->last_error ) {
\WPVDB\Logger::error( 'Even simple query failed: ' . $wpdb->last_error );
} else {
\WPVDB\Logger::debug( 'Simple query succeeded, embedding count: ' . $test_result );

// Try a direct query without the vector function to see if that's the issue.
$basic_results = $wpdb->get_results( "SELECT e.* FROM {$wpdb->prefix}wpvdb_embeddings e LIMIT 20" ); // phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery, WordPress.DB.DirectDatabaseQuery.NoCaching

if ( $wpdb->last_error ) {
\WPVDB\Logger::error( 'Basic query failed: ' . $wpdb->last_error );
} else {
\WPVDB\Logger::debug( 'Basic query succeeded, returned ' . count( $basic_results ) . ' results' );
\WPVDB\Logger::debug( 'Issue is likely with the vector function: ' . $distance_function );

// Fall back to PHP-based distance calculation.
\WPVDB\Logger::debug( 'Falling back to PHP-based distance calculation' );
// phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery, WordPress.DB.DirectDatabaseQuery.NoCaching
$all_rows = $wpdb->get_results(
$wpdb->prepare( "SELECT * FROM {$wpdb->prefix}wpvdb_embeddings WHERE model = %s", $model ),
ARRAY_A
);
$distances = array();

foreach ( $all_rows as $r ) {
$stored_emb = json_decode( $r['embedding'], true );
if ( ! is_array( $stored_emb ) ) {
continue;
}
$similarity_score = \WPVDB\REST::cosine_distance( $embedding, $stored_emb );
$r['distance'] = $similarity_score;
$distances[] = $r;
}

usort(
$distances,
function ( $a, $b ) {
return $a['distance'] <=> $b['distance'];
}
);

$search_results = array_slice( $distances, 0, 20 );
$search_results = json_decode( wp_json_encode( $search_results ) ); // Convert to objects.

\WPVDB\Logger::debug( 'PHP fallback found ' . count( $search_results ) . ' results' );
}
}
} else {
\WPVDB\Logger::debug( 'Found ' . count( $search_results ) . ' results' );
if ( count( $search_results ) > 0 ) {
\WPVDB\Logger::debug(
'First result distance: ' .
( isset( $search_results[0]->distance ) ?
$search_results[0]->distance : 'Not set' )
);
}
}
} else {
// Fallback: do in PHP.
\WPVDB\Logger::debug( 'Using PHP fallback search' );
// phpcs:ignore WordPress.DB.DirectDatabaseQuery.DirectQuery, WordPress.DB.DirectDatabaseQuery.NoCaching
$all_rows = $wpdb->get_results(
$wpdb->prepare( "SELECT * FROM {$wpdb->prefix}wpvdb_embeddings WHERE model = %s", $model ),
ARRAY_A
);
$total_vectors_searched = count( $all_rows );
\WPVDB\Logger::debug( 'Total vectors searched: ' . $total_vectors_searched );

$distances = array();

foreach ( $all_rows as $r ) {
$stored_emb = json_decode( $r['embedding'], true );
if ( ! is_array( $stored_emb ) ) {
\WPVDB\Logger::debug( 'Invalid embedding in row: ' . $r['id'] );
continue;
}
$similarity_score = \WPVDB\REST::cosine_distance( $embedding, $stored_emb );
$r['distance'] = $similarity_score;
$distances[] = $r;
}

usort(
$distances,
function ( $a, $b ) {
return $a['distance'] <=> $b['distance'];
}
);

$search_results = array_slice( $distances, 0, 20 );
$search_results = json_decode( wp_json_encode( $search_results ) ); // Convert to objects.

\WPVDB\Logger::debug( 'PHP fallback found ' . count( $search_results ) . ' results' );
if ( count( $search_results ) > 0 ) {
\WPVDB\Logger::debug(
'First result similarity score: ' .
( isset( $search_results[0]->distance ) ?
$search_results[0]->distance : 'Not set' )
);
}
}

// Use search results instead of regular embeddings.
$embeddings = $search_results;

// Calculate and record the search time.
$search_time_result = microtime( true ) - $search_start_time;
}
} catch ( \Exception $e ) {
// Handle errors.
\WPVDB\Logger::error( 'Exception: ' . $e->getMessage() );
echo '<div class="notice notice-error"><p>' . esc_html__( 'Error performing semantic search: ', 'wpvdb' ) . esc_html( $e->getMessage() ) . '</p></div>';
$search_response = \WPVDB\Search::query(
array(
'text' => $search_query,
'model' => $model,
'limit' => 20,
// This screen manages the index, so drafts and protected
// posts stay visible here even though the API hides them.
'respect_visibility' => false,
'api_base' => $api_base,
'api_key' => $api_key,
'output' => OBJECT,
'explain' => true,
)
);

if ( is_wp_error( $search_response ) ) {
\WPVDB\Logger::error( 'Semantic search failed: ' . $search_response->get_error_message() );
echo '<div class="notice notice-error"><p>' . esc_html__( 'Error performing semantic search: ', 'wpvdb' ) . esc_html( $search_response->get_error_message() ) . '</p></div>';
} else {
$search_results = $search_response['results'];
$embeddings = $search_results;
$total_vectors_searched = (int) $search_response['plan']['total_rows'];
}

$search_time_result = microtime( true ) - $search_start_time;
} else {
\WPVDB\Logger::error( 'API key or model not configured' );
echo '<div class="notice notice-warning"><p>' . esc_html__( 'API key or model not configured. Please check your settings.', 'wpvdb' ) . '</p></div>';
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