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/core/memory/models/metadata_models.py

@@ -11,16 +11,22 @@ from pydantic import BaseModel, ConfigDict, Field
 
 class UserMetadataProfile(BaseModel):
     """用户画像信息"""
-    model_config = ConfigDict(extra='ignore')
+
+    model_config = ConfigDict(extra="ignore")
     role: str = Field(default="", description="用户职业或角色")
     domain: str = Field(default="", description="用户所在领域")
-    expertise: List[str] = Field(default_factory=list, description="用户擅长的技能或工具")
+    expertise: List[str] = Field(
-    interests: List[str] = Field(default_factory=list, description="用户关注的话题或领域标签")
+        default_factory=list, description="用户擅长的技能或工具"
+    )
+    interests: List[str] = Field(
+        default_factory=list, description="用户关注的话题或领域标签"
+    )
 
 
 class UserMetadataBehavioralHints(BaseModel):
     """行为偏好"""
-    model_config = ConfigDict(extra='ignore')
+
+    model_config = ConfigDict(extra="ignore")
     learning_stage: str = Field(default="", description="学习阶段")
     preferred_depth: str = Field(default="", description="偏好深度")
     tone_preference: str = Field(default="", description="语气偏好")
@@ -28,21 +34,24 @@ class UserMetadataBehavioralHints(BaseModel):
 
 class UserMetadata(BaseModel):
     """用户元数据顶层结构"""
-    model_config = ConfigDict(extra='ignore')
+
+    model_config = ConfigDict(extra="ignore")
     profile: UserMetadataProfile = Field(default_factory=UserMetadataProfile)
-    behavioral_hints: UserMetadataBehavioralHints = Field(default_factory=UserMetadataBehavioralHints)
+    behavioral_hints: UserMetadataBehavioralHints = Field(
+        default_factory=UserMetadataBehavioralHints
+    )
     knowledge_tags: List[str] = Field(default_factory=list, description="知识标签")
 
 
 class MetadataExtractionResponse(BaseModel):
     """元数据提取 LLM 响应结构"""
-    model_config = ConfigDict(extra='ignore')
+
+    model_config = ConfigDict(extra="ignore")
     user_metadata: UserMetadata = Field(default_factory=UserMetadata)
     aliases_to_add: List[str] = Field(
         default_factory=list,
-        description="本次新发现的用户别名（用户自我介绍或他人对用户的称呼）"
+        description="本次新发现的用户别名（用户自我介绍或他人对用户的称呼）",
     )
     aliases_to_remove: List[str] = Field(
-        default_factory=list,
+        default_factory=list, description="用户明确否认的别名（如'我不叫XX了'）"
-        description="用户明确否认的别名（如'我不叫XX了'）"
     )

api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py

@@ -314,28 +314,48 @@ class ExtractionOrchestrator:
             # 步骤 7: 触发异步元数据和别名提取（仅正式模式）
             if not is_pilot_run:
                 try:
-                    from app.core.memory.storage_services.extraction_engine.knowledge_extraction.metadata_extractor import MetadataExtractor
+                    from app.core.memory.storage_services.extraction_engine.knowledge_extraction.metadata_extractor import (
-                    metadata_extractor = MetadataExtractor(llm_client=self.llm_client, language=self.language)
+                        MetadataExtractor,
-                    user_statements = metadata_extractor.collect_user_related_statements(
+                    )
-                        entity_nodes, statement_nodes,
+
-                        statement_entity_edges
+                    metadata_extractor = MetadataExtractor(
+                        llm_client=self.llm_client, language=self.language
+                    )
+                    user_statements = (
+                        metadata_extractor.collect_user_related_statements(
+                            entity_nodes, statement_nodes, statement_entity_edges
+                        )
                     )
                     if user_statements:
-                        end_user_id = dialog_data_list[0].end_user_id if dialog_data_list else None
+                        end_user_id = (
-                        config_id = dialog_data_list[0].config_id if dialog_data_list and hasattr(dialog_data_list[0], 'config_id') else None
+                            dialog_data_list[0].end_user_id
+                            if dialog_data_list
+                            else None
+                        )
+                        config_id = (
+                            dialog_data_list[0].config_id
+                            if dialog_data_list
+                            and hasattr(dialog_data_list[0], "config_id")
+                            else None
+                        )
                         if end_user_id:
                             from app.tasks import extract_user_metadata_task
+
                             extract_user_metadata_task.delay(
                                 end_user_id=str(end_user_id),
                                 statements=user_statements,
                                 config_id=str(config_id) if config_id else None,
                                 language=self.language,
                             )
-                            logger.info(f"已触发异步元数据提取任务，共 {len(user_statements)} 条用户相关 statement")
+                            logger.info(
+                                f"已触发异步元数据提取任务，共 {len(user_statements)} 条用户相关 statement"
+                            )
                     else:
                         logger.info("未找到用户相关 statement，跳过元数据提取")
                 except Exception as e:
-                    logger.error(f"触发元数据提取任务失败（不影响主流程）: {e}", exc_info=True)
+                    logger.error(
+                        f"触发元数据提取任务失败（不影响主流程）: {e}", exc_info=True
+                    )
 
                 # 别名同步已迁移到 Celery 元数据提取任务中，不再在此处执行
 
@@ -1501,9 +1521,6 @@ class ExtractionOrchestrator:
 
         except Exception as e:
             logger.error(f"更新 end_user other_name 失败: {e}", exc_info=True)
-
-
-    
     # 用户实体占位名称，不允许作为 other_name 或出现在 aliases 中
     # 复用 deduped_and_disamb 模块级常量，避免重复维护
     USER_PLACEHOLDER_NAMES = _USER_PLACEHOLDER_NAMES
@@ -1610,7 +1627,6 @@ class ExtractionOrchestrator:
             if candidate and candidate.lower() in self.USER_PLACEHOLDER_NAMES:
                 return None
             return candidate
-        
         return None
 
     async def _run_dedup_and_write_summary(

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/metadata_extractor.py

@@ -13,10 +13,6 @@ from app.core.memory.models.graph_models import (
     StatementEntityEdge,
     StatementNode,
 )
-from app.core.memory.models.metadata_models import (
-    UserMetadata,
-)
-from app.core.memory.models.message_models import DialogData
 
 logger = logging.getLogger(__name__)
 
@@ -45,40 +41,12 @@ class MetadataExtractor:
         如果文本中包含中文字符则返回 "zh"，否则返回 "en"。
         """
         import re
+
         combined = " ".join(statements)
-        if re.search(r'[\u4e00-\u9fff]', combined):
+        if re.search(r"[\u4e00-\u9fff]", combined):
             return "zh"
         return "en"
 
-    # def collect_user_raw_messages(
-    #     self,
-    #     dialog_data_list: List[DialogData],
-    # ) -> List[str]:
-    #     """
-    #     从原始对话数据中提取 speaker="user" 的消息原文。
-
-    #     直接使用用户的原始输入，不经过陈述句提取阶段的 LLM 改写，
-    #     避免第一人称被替换为第三人称导致元数据/别名提取错误。
-
-    #     Returns:
-    #         用户原始消息文本列表
-    #     """
-    #     result = []
-    #     for dialog in dialog_data_list:
-    #         if not dialog.context or not dialog.context.msgs:
-    #             continue
-    #         for msg in dialog.context.msgs:
-    #             if getattr(msg, 'role', '') == 'user':
-    #                 text = (getattr(msg, 'msg', '') or '').strip()
-    #                 if text:
-    #                     result.append(text)
-
-    #     logger.info(f"收集到 {len(result)} 条用户原始消息")
-    #     if result:
-    #         for i, text in enumerate(result):
-    #             logger.info(f"  [user message {i+1}] {text[:200]}")
-    #     return result
-
     def collect_user_related_statements(
         self,
         entity_nodes: List[ExtractedEntityNode],
@@ -119,7 +87,7 @@ class MetadataExtractor:
         for stmt_node in statement_nodes:
             if stmt_node.id in target_stmt_ids and stmt_node.id not in seen:
                 total_associated += 1
-                speaker = getattr(stmt_node, 'speaker', None) or 'unknown'
+                speaker = getattr(stmt_node, "speaker", None) or "unknown"
                 if speaker == "user":
                     text = (stmt_node.statement or "").strip()
                     if text:
@@ -135,7 +103,7 @@ class MetadataExtractor:
         )
         if result:
             for i, text in enumerate(result):
-                logger.info(f"  [user statement {i+1}] {text}")
+                logger.info(f"  [user statement {i + 1}] {text}")
         if total_associated > 0 and len(result) == 0:
             logger.warning(
                 f"有 {total_associated} 条直接关联 statement 但全部被 speaker 过滤，"
@@ -178,7 +146,10 @@ class MetadataExtractor:
                 json_schema="",
             )
 
-            from app.core.memory.models.metadata_models import MetadataExtractionResponse
+            from app.core.memory.models.metadata_models import (
+                MetadataExtractionResponse,
+            )
+
             response = await self.llm_client.response_structured(
                 messages=[{"role": "user", "content": prompt}],
                 response_model=MetadataExtractionResponse,
@@ -187,7 +158,9 @@ class MetadataExtractor:
             if response:
                 metadata = response.user_metadata if response.user_metadata else None
                 to_add = response.aliases_to_add if response.aliases_to_add else []
-                to_remove = response.aliases_to_remove if response.aliases_to_remove else []
+                to_remove = (
+                    response.aliases_to_remove if response.aliases_to_remove else []
+                )
                 return metadata, to_add, to_remove
 
             logger.warning("LLM 返回的响应为空")

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py

@@ -1,6 +1,5 @@
 import asyncio
 import logging
-import os
 from datetime import datetime
 from typing import Any, Dict, List, Optional
 
@@ -82,17 +81,6 @@ class StatementExtractor:
         logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty")
         return None
 
-    @staticmethod
-    def _replace_first_person_with_user(text: str) -> str:
-        """将用户消息中的第一人称代词"我"替换为"用户"。
-        
-        替换规则：
-        - 所有独立的"我"都替换为"用户"（包括"我的"→"用户的"、"叫我"→"叫用户"）
-        - 不替换"我们"中的"我"（"我们"是复数，不指代用户个人）
-        """
-        import re
-        result = re.sub(r'我(?!们)', '用户', text)
-        return result
 
     async def _extract_statements(self, chunk, end_user_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]:
         """Process a single chunk and return extracted statements
@@ -106,18 +94,12 @@ class StatementExtractor:
             List of ExtractedStatement objects extracted from the chunk
         """
         chunk_content = chunk.content
+        chunk_speaker = self._get_speaker_from_chunk(chunk)
 
         if not chunk_content or len(chunk_content.strip()) < 5:
             logger.warning(f"Chunk {chunk.id} content too short or empty, skipping")
             return []
 
-        # 对 speaker="user" 的 chunk，将第一人称"我"替换为"用户"，
-        # 避免 LLM 在陈述句提取时将"我"替换为具体名字（如"齐齐"），
-        # 导致下游元数据/别名提取无法识别第一人称语义。
-        chunk_speaker = self._get_speaker_from_chunk(chunk)
-        if chunk_speaker == "user":
-            chunk_content = self._replace_first_person_with_user(chunk_content)
-
         prompt_content = await render_statement_extraction_prompt(
             chunk_content=chunk_content,
             definitions=LABEL_DEFINITIONS,

api/app/core/memory/utils/metadata_utils.py

@@ -1,45 +0,0 @@
-"""
-Metadata utility functions for cleaning and validating user metadata.
-"""
-
-import logging
-from typing import Optional
-
-from app.core.memory.models.metadata_models import UserMetadata
-
-logger = logging.getLogger(__name__)
-
-
-def clean_metadata(raw: dict) -> dict:
-    """
-    Clean metadata by removing empty string values and empty array fields recursively.
-    Only keeps fields with actual content. If a nested dict becomes empty after cleaning,
-    it is removed too.
-    """
-    cleaned = {}
-    for key, value in raw.items():
-        if isinstance(value, dict):
-            nested = clean_metadata(value)
-            if nested:
-                cleaned[key] = nested
-        elif isinstance(value, list):
-            if len(value) > 0:
-                cleaned[key] = value
-        elif isinstance(value, str):
-            if value != "":
-                cleaned[key] = value
-        else:
-            cleaned[key] = value
-    return cleaned
-
-
-def validate_metadata(raw: dict) -> Optional[UserMetadata]:
-    """
-    Validate metadata structure using the Pydantic UserMetadata model.
-    Returns None and logs a WARNING on validation failure.
-    """
-    try:
-        return UserMetadata.model_validate(raw)
-    except Exception as e:
-        logger.warning("Metadata validation failed: %s", e)
-        return None

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)}")