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"content": " return array_slice($suggestions, 0, 5);\n }\n\n \/** Performs semantic search in Qdrant. *\/\n private function semanticSearch(string $query, array $filters, int $limit): array\n {\n $embedding = $this->ollama->getEmbedding($query);\n\n $url = sprintf('%s\/collections\/%s\/points\/search', $this->qdrantHost, self::COLLECTION);\n\n $payload = [\n 'vector' => array_values($embedding),\n 'limit' => $limit,\n 'with_payload' => true,\n ];\n\n \/\/ Add Qdrant filter if taxonomy category specified\n if (isset($filters['taxonomy_category'])) {\n $payload['filter'] = [\n 'must' => [\n [\n 'key' => 'taxonomy_category',\n 'match' => ['value' => $filters['taxonomy_category']],\n ],\n ],\n ];\n }\n\n try {\n $response = $this->makeRequest($url, $payload, 'POST');\n\n if (!isset($response['result']) || !is_array($response['result'])) {\n return [];\n }\n\n return array_map(static function (array $item): array {\n return [\n 'id' => (string) $item['id'],\n 'score' => (float) ($item['score'] ?? 0),\n 'payload' => is_array($item['payload'] ?? null) ? $item['payload'] : [],\n ];\n }, $response['result']);\n } catch (RuntimeException) {\n return [];\n }\n }\n\n \/** Enriches vector results with SQL data and applies filters. *\/\n private function enrichAndFilter(array $vectorResults, array $filters): array\n {",
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