refactor(memory): remove first-person pronoun replacement and inline metadata utils

- Remove _replace_first_person_with_user from StatementExtractor to preserve
  original user text for downstream metadata/alias extraction
- Delete metadata_utils.py module, inline clean_metadata into Celery task
- Remove unused imports and commented-out collect_user_raw_messages method
- Apply formatting cleanup across metadata models and extraction orchestrator

api/app/tasks.py

@@ -2969,7 +2969,6 @@ def extract_user_metadata_task(
 
     async def _run() -> Dict[str, Any]:
         from app.core.memory.storage_services.extraction_engine.knowledge_extraction.metadata_extractor import MetadataExtractor
-        from app.core.memory.utils.metadata_utils import clean_metadata, validate_metadata
         from app.repositories.end_user_info_repository import EndUserInfoRepository
         from app.repositories.end_user_repository import EndUserRepository
         from app.services.memory_config_service import MemoryConfigService
@@ -3029,6 +3028,14 @@ def extract_user_metadata_task(
         logger.info(f"[CELERY METADATA] LLM 别名新增: {aliases_to_add}, 移除: {aliases_to_remove}")
 
         # 4. 清洗元数据、覆盖写入元数据和别名
+        def clean_metadata(raw: dict) -> dict:
+            """递归移除空字符串、空列表、空字典。"""
+            return {
+                k: (cleaned if isinstance(v, dict) and (cleaned := clean_metadata(v)) else v)
+                for k, v in raw.items()
+                if not (v == "" or v == [] or (isinstance(v, dict) and not clean_metadata(v)))
+            }
+
         raw_dict = user_metadata.model_dump(exclude_none=True) if user_metadata else {}
         logger.info(f"[CELERY METADATA] LLM 输出完整元数据: {json.dumps(raw_dict, ensure_ascii=False)}")