新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)

api/app/controllers/memory_agent_controller.py

@@ -9,7 +9,7 @@ from app.db import get_db
 from app.dependencies import cur_workspace_access_guard, get_current_user
 from app.models import ModelApiKey
 from app.models.user_model import User
-from app.repositories import knowledge_repository
+from app.repositories import knowledge_repository, WorkspaceRepository
 from app.schemas.memory_agent_schema import UserInput, Write_UserInput
 from app.schemas.response_schema import ApiResponse
 from app.services import task_service, workspace_service
@@ -616,8 +616,10 @@ async def get_knowledge_type_stats_api(
 @router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse)
 async def get_hot_memory_tags_by_user_api(
     end_user_id: Optional[str] = Query(None, description="用户ID（可选）"),
+    language_type: Optional[str] ="zh",
     limit: int = Query(20, description="返回标签数量限制"),
-    current_user: User = Depends(get_current_user)
+    current_user: User = Depends(get_current_user),
+    db: Session=Depends(get_db),
 ):
     """
     获取指定用户的热门记忆标签
@@ -628,10 +630,22 @@ async def get_hot_memory_tags_by_user_api(
         ...
     ]
     """
+
+    workspace_id=current_user.current_workspace_id
+    workspace_repo = WorkspaceRepository(db)
+    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
+
+    if workspace_models:
+        model_id = workspace_models.get("llm", None)
+    else:
+        model_id = None
+
     api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}")
     try:
         result = await memory_agent_service.get_hot_memory_tags_by_user(
             end_user_id=end_user_id,
+            language_type=language_type,
+            model_id=model_id,
             limit=limit
         )
         return success(data=result, msg="获取热门记忆标签成功")

api/app/controllers/user_memory_controllers.py

@@ -12,6 +12,7 @@ from app.core.logging_config import get_api_logger
 from app.core.response_utils import success, fail
 from app.core.error_codes import BizCode
 from app.core.api_key_utils import timestamp_to_datetime
+from app.services.memory_base_service import Translation_English
 from app.services.user_memory_service import (
     UserMemoryService,
     analytics_memory_types,
@@ -20,7 +21,7 @@ from app.services.user_memory_service import (
 from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction
 from app.schemas.response_schema import ApiResponse
 from app.schemas.memory_storage_schema import GenerateCacheRequest
-
+from app.repositories.workspace_repository import WorkspaceRepository
 from app.schemas.end_user_schema import (
     EndUserProfileResponse,
     EndUserProfileUpdate,
@@ -44,6 +45,7 @@ router = APIRouter(
 @router.get("/analytics/memory_insight/report", response_model=ApiResponse)
 async def get_memory_insight_report_api(
     end_user_id: str,
+    language_type: str = "zh",
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
@@ -53,10 +55,18 @@ async def get_memory_insight_report_api(
     此接口仅查询数据库中已缓存的记忆洞察数据，不执行生成操作。
     如需生成新的洞察报告，请使用专门的生成接口。
     """
+    workspace_id = current_user.current_workspace_id
+    workspace_repo = WorkspaceRepository(db)
+    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
+
+    if workspace_models:
+        model_id = workspace_models.get("llm", None)
+    else:
+        model_id = None
     api_logger.info(f"记忆洞察报告查询请求: end_user_id={end_user_id}, user={current_user.username}")
     try:
         # 调用服务层获取缓存数据
-        result = await user_memory_service.get_cached_memory_insight(db, end_user_id)
+        result = await user_memory_service.get_cached_memory_insight(db, end_user_id,model_id,language_type)
 
         if result["is_cached"]:
             api_logger.info(f"成功返回缓存的记忆洞察报告: end_user_id={end_user_id}")
@@ -72,6 +82,7 @@ async def get_memory_insight_report_api(
 @router.get("/analytics/user_summary", response_model=ApiResponse)
 async def get_user_summary_api(
     end_user_id: str,
+    language_type: str="zh",
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
@@ -81,10 +92,18 @@ async def get_user_summary_api(
     此接口仅查询数据库中已缓存的用户摘要数据，不执行生成操作。
     如需生成新的用户摘要，请使用专门的生成接口。
     """
+    workspace_id = current_user.current_workspace_id
+    workspace_repo = WorkspaceRepository(db)
+    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
+
+    if workspace_models:
+        model_id = workspace_models.get("llm", None)
+    else:
+        model_id = None
     api_logger.info(f"用户摘要查询请求: end_user_id={end_user_id}, user={current_user.username}")
     try:
         # 调用服务层获取缓存数据
-        result = await user_memory_service.get_cached_user_summary(db, end_user_id)
+        result = await user_memory_service.get_cached_user_summary(db, end_user_id,model_id,language_type)
 
         if result["is_cached"]:
             api_logger.info(f"成功返回缓存的用户摘要: end_user_id={end_user_id}")
@@ -253,7 +272,6 @@ async def get_graph_data_api(
             depth=depth,
             center_node_id=center_node_id
         )
-
         # 检查是否有错误消息
         if "message" in result and result["statistics"]["total_nodes"] == 0:
             api_logger.warning(f"图数据查询返回空结果: {result.get('message')}")
@@ -274,11 +292,18 @@ async def get_graph_data_api(
 @router.get("/read_end_user/profile", response_model=ApiResponse)
 async def get_end_user_profile(
     end_user_id: str,
+    language_type: str = "zh",
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
     workspace_id = current_user.current_workspace_id
+    workspace_repo = WorkspaceRepository(db)
+    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
 
+    if workspace_models:
+        model_id = workspace_models.get("llm", None)
+    else:
+        model_id = None
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试查询用户信息但未选择工作空间")
@@ -297,12 +322,19 @@ async def get_end_user_profile(
             api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}")
             return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}")
 
+        other_name=end_user.other_name
+        position=end_user.position
+        department=end_user.department
+        if language_type!="zh":
+            other_name=await Translation_English(model_id,other_name)
+            position = await Translation_English(model_id, position)
+            department = await Translation_English(model_id, department)
         # 构建响应数据
         profile_data = EndUserProfileResponse(
             id=end_user.id,
-            other_name=end_user.other_name,
+            other_name=other_name,
-            position=end_user.position,
+            position=position,
-            department=end_user.department,
+            department=department,
             contact=end_user.contact,
             phone=end_user.phone,
             hire_date=end_user.hire_date,
@@ -396,12 +428,21 @@ async def update_end_user_profile(
         return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", str(e))
 
 @router.get("/memory_space/timeline_memories", response_model=ApiResponse)
-async def memory_space_timeline_of_shared_memories(id: str, label: str,
+async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str,
                                       current_user: User = Depends(get_current_user),
                                       db: Session = Depends(get_db),
                                       ):
+    workspace_id=current_user.current_workspace_id
+    workspace_repo = WorkspaceRepository(db)
+    workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
+
+    if workspace_models:
+        model_id = workspace_models.get("llm", None)
+    else:
+        model_id = None
     MemoryEntity = MemoryEntityService(id, label)
-    timeline_memories_result = await MemoryEntity.get_timeline_memories_server()
+    timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language_type)
+
     return success(data=timeline_memories_result, msg="共同记忆时间线")
 @router.get("/memory_space/relationship_evolution", response_model=ApiResponse)
 async def memory_space_relationship_evolution(id: str, label: str,

api/app/schemas/end_user_schema.py

@@ -44,6 +44,7 @@ class EndUserProfileResponse(BaseModel):
     updatetime_profile: Optional[datetime.datetime] = Field(description="核心档案信息最后更新时间", default=None)
 
 
+
 class EndUserProfileUpdate(BaseModel):
     """终端用户基本信息更新请求模型"""
     end_user_id: str = Field(description="终端用户ID")

api/app/services/memory_agent_service.py

@@ -26,6 +26,7 @@ from app.db import get_db_context
 from app.models.knowledge_model import Knowledge, KnowledgeType
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import ConfigurationError
+from app.services.memory_base_service import Translation_English
 from app.services.memory_config_service import MemoryConfigService
 from app.services.memory_konwledges_server import (
     write_rag,
@@ -692,7 +693,9 @@ class MemoryAgentService:
     async def get_hot_memory_tags_by_user(
         self,
         end_user_id: Optional[str] = None,
-        limit: int = 20
+        limit: int = 20,
+        model_id: Optional[str] = None,
+        language_type: Optional[str] = "zh"
     ) -> List[Dict[str, Any]]:
         """
         获取指定用户的热门记忆标签
@@ -710,7 +713,14 @@ class MemoryAgentService:
         try:
             # by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度）
             tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False)
-            payload = [{"name": t, "frequency": f} for t, f in tags]
+            payload=[]
+            for tag, freq in tags:
+                print(tag, freq)
+                if language_type!="zh":
+                    tag=await Translation_English(model_id, tag)
+                    payload.append({"name": tag, "frequency": freq})
+                else:
+                    payload.append({"name": tag, "frequency": freq})
             return payload
         except Exception as e:
             logger.error(f"热门记忆标签查询失败: {e}")

api/app/services/memory_entity_relationship_service.py

@@ -16,6 +16,7 @@ import json
 from datetime import datetime
 
 from app.schemas.memory_episodic_schema import EmotionType
+from app.services.memory_base_service import Translation_English
 
 logger = logging.getLogger(__name__)
 
@@ -24,7 +25,7 @@ class MemoryEntityService:
         self.id = id
         self.table = table
         self.connector = Neo4jConnector()
-    async def get_timeline_memories_server(self):
+    async def get_timeline_memories_server(self,model_id, language_type):
         """
         获取时间线记忆数据
 
@@ -48,10 +49,10 @@ class MemoryEntityService:
             logger.info(f"获取时间线记忆数据 - ID: {self.id}, Table: {self.table}")
 
             # 根据表类型选择查询
-            if self.table == 'Statement':
+            if   self.table == 'Statement':
                 # Statement只需要输入ID，使用简化查询
                 results = await self.connector.execute_query(Memory_Timeline_Statement, id=self.id)
-            elif self.table == 'ExtractedEntity':
+            elif  self.table == 'ExtractedEntity':
                 # ExtractedEntity类型查询
                 results = await self.connector.execute_query(Memory_Timeline_ExtractedEntity, id=self.id)
             else:
@@ -62,7 +63,7 @@ class MemoryEntityService:
             logger.info(f"时间线查询结果类型: {type(results)}, 长度: {len(results) if isinstance(results, list) else 'N/A'}")
             
             # 处理查询结果
-            timeline_data = self._process_timeline_results(results)
+            timeline_data =await self._process_timeline_results(results, model_id, language_type)
 
             logger.info(f"成功获取时间线记忆数据: 总计 {len(timeline_data.get('timelines_memory', []))} 条")
 
@@ -71,12 +72,14 @@ class MemoryEntityService:
         except Exception as e:
             logger.error(f"获取时间线记忆数据失败: {str(e)}", exc_info=True)
             return  str(e)
-    def _process_timeline_results(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
+    async def _process_timeline_results(self, results: List[Dict[str, Any]], model_id: str, language_type: str) -> Dict[str, Any]:
         """
         处理时间线查询结果
         
         Args:
             results: Neo4j查询结果
+            model_id: 模型ID用于翻译
+            language_type: 语言类型 ('zh' 或其他)
             
         Returns:
             处理后的时间线数据字典
@@ -104,19 +107,19 @@ class MemoryEntityService:
             # 处理MemorySummary
             summary = data.get('MemorySummary')
             if summary is not None:
-                processed_summary = self._process_field_value(summary, "MemorySummary")
+                processed_summary = await self._process_field_value(summary, "MemorySummary")
                 memory_summary_list.extend(processed_summary)
             
             # 处理Statement
             statement = data.get('statement')
             if statement is not None:
-                processed_statement = self._process_field_value(statement, "Statement")
+                processed_statement = await self._process_field_value(statement, "Statement")
                 statement_list.extend(processed_statement)
             
             # 处理ExtractedEntity
             extracted_entity = data.get('ExtractedEntity')
             if extracted_entity is not None:
-                processed_entity = self._process_field_value(extracted_entity, "ExtractedEntity")
+                processed_entity = await self._process_field_value(extracted_entity, "ExtractedEntity")
                 extracted_entity_list.extend(processed_entity)
         
         # 去重 - 现在处理的是字典列表，需要更智能的去重
@@ -128,6 +131,21 @@ class MemoryEntityService:
         all_timeline_data = memory_summary_list + statement_list
         all_timeline_data = self._merge_same_text_items(all_timeline_data)
         
+        # 如果需要翻译（非中文），对整个结果进行翻译
+        if language_type != 'zh':
+            # 定义需要翻译的字段
+            fields_to_translate = ['text', 'type']
+            
+            # 翻译各个列表
+            if memory_summary_list:
+                memory_summary_list = await self._translate_list(memory_summary_list, model_id, fields_to_translate)
+            if statement_list:
+                statement_list = await self._translate_list(statement_list, model_id, fields_to_translate)
+            if extracted_entity_list:
+                extracted_entity_list = await self._translate_list(extracted_entity_list, model_id, fields_to_translate)
+            if all_timeline_data:
+                all_timeline_data = await self._translate_list(all_timeline_data, model_id, fields_to_translate)
+        
         result = {
             "MemorySummary": memory_summary_list,
             "Statement": statement_list,
@@ -233,7 +251,7 @@ class MemoryEntityService:
         except Exception:
             return False
 
-    def _process_field_value(self, value: Any, field_name: str) -> List[Dict[str, Any]]:
+    async def _process_field_value(self, value: Any, field_name: str) -> List[Dict[str, Any]]:
         """
         处理字段值，支持字符串、列表等类型
         
@@ -251,13 +269,13 @@ class MemoryEntityService:
                 # 如果是列表，处理每个元素
                 for item in value:
                     if self._is_valid_item(item):
-                        processed_item = self._process_single_item(item)
+                        processed_item = await self._process_single_item(item)
                         if processed_item:
                             processed_values.append(processed_item)
             elif isinstance(value, dict):
                 # 如果是字典，直接处理
                 if self._is_valid_item(value):
-                    processed_item = self._process_single_item(value)
+                    processed_item = await self._process_single_item(value)
                     if processed_item:
                         processed_values.append(processed_item)
             elif isinstance(value, str):
@@ -304,7 +322,7 @@ class MemoryEntityService:
         return (str(item).strip() != '' and 
                "MemorySummaryChunk" not in str(item))
 
-    def _process_single_item(self, item: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    async def _process_single_item(self, item: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         """
         处理单个项目
         
@@ -369,6 +387,42 @@ class MemoryEntityService:
             logger.warning(f"转换时间格式失败: {e}, 原始值: {dt}")
             return str(dt) if dt is not None else None
 
+    async def _translate_list(self, data_list: List[Dict[str, Any]], model_id: str, fields: List[str]) -> List[Dict[str, Any]]:
+        """
+        翻译列表中每个字典的指定字段
+        
+        Args:
+            data_list: 要翻译的字典列表
+            model_id: 模型ID
+            fields: 需要翻译的字段列表
+            
+        Returns:
+            翻译后的字典列表
+        """
+        if not data_list:
+            return data_list
+        
+        translated_list = []
+        for item in data_list:
+            if not isinstance(item, dict):
+                translated_list.append(item)
+                continue
+            
+            translated_item = item.copy()
+            for field in fields:
+                if field in translated_item and translated_item[field]:
+                    try:
+                        # 调用Translation_English翻译单个字段
+                        translated_value = await Translation_English(model_id, translated_item[field])
+                        if translated_value:
+                            translated_item[field] = translated_value
+                    except Exception as e:
+                        logger.warning(f"翻译字段 {field} 失败: {e}")
+            
+            translated_list.append(translated_item)
+        
+        return translated_list
+
 
 
 
@@ -426,15 +480,19 @@ class MemoryEmotion:
             # 如果解析失败，返回原始字符串
             return iso_string
 
-    async def get_emotion(self) -> Dict[str, Any]:
+    async def get_emotion(self, model_id: str = None, language_type: str = 'zh') -> Dict[str, Any]:
         """
         获取情绪随时间变化数据
         
+        Args:
+            model_id: 模型ID用于翻译
+            language_type: 语言类型 ('zh' 或其他)
+        
         Returns:
             包含情绪数据的字典
         """
         try:
-            logger.info(f"获取情绪数据 - ID: {self.id}, Table: {self.table}")
+            logger.info(f"获取情绪数据 - ID: {self.id}, Table: {self.table}, language_type={language_type}")
 
             if self.table == 'Statement':
                 results = await self.connector.execute_query(Memory_Space_Emotion_Statement, id=self.id)
@@ -450,6 +508,10 @@ class MemoryEmotion:
             # 转换Neo4j类型
             final_data = self._convert_neo4j_types(emotion_data)
             
+            # 如果需要翻译（非中文）
+            if language_type != 'zh' and model_id and final_data:
+                final_data = await self._translate_emotion_data(final_data, model_id)
+            
             logger.info(f"成功获取 {len(final_data)} 条情绪数据")
             
             return final_data
@@ -590,16 +652,14 @@ class MemoryInteraction:
         """
         try:
             logger.info(f"获取交互数据 - ID: {self.id}, Table: {self.table}")
-
             ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id)
             if ori_data!=[]:
                 # name = ori_data[0]['name']
-                group_id = ori_data[0]['group_id']
+                group_id = [i['group_id'] for i in ori_data][0]
                 Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id)
                 if not Space_User:
                     return []
                 user_id=Space_User[0]['id']
-
                 results = await self.connector.execute_query(Memory_Space_Associative, id=self.id,user_id=user_id)
 
 

api/app/services/user_memory_service.py

@@ -18,7 +18,7 @@ from app.repositories.end_user_repository import EndUserRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_episodic_schema import EmotionSubject, EmotionType, type_mapping
 from app.services.implicit_memory_service import ImplicitMemoryService
-from app.services.memory_base_service import MemoryBaseService
+from app.services.memory_base_service import MemoryBaseService, MemoryTransService, Translation_English
 from app.services.memory_config_service import MemoryConfigService
 from app.services.memory_perceptual_service import MemoryPerceptualService
 from app.services.memory_short_service import ShortService
@@ -360,7 +360,9 @@ class UserMemoryService:
     async def get_cached_memory_insight(
         self, 
         db: Session, 
-        end_user_id: str
+        end_user_id: str,
+        model_id: str,
+        language_type: str
     ) -> Dict[str, Any]:
         """
         从数据库获取缓存的记忆洞察（四个维度）
@@ -419,11 +421,18 @@ class UserMemoryService:
                     key_findings_array = []
                 
                 logger.info(f"成功获取 end_user_id {end_user_id} 的缓存记忆洞察（四维度）")
+                memory_insight=end_user.memory_insight
+                behavior_pattern=end_user.behavior_pattern
+                growth_trajectory=end_user.growth_trajectory
+                if language_type!='zh':
+                    memory_insight=await Translation_English(model_id,memory_insight)
+                    behavior_pattern=await Translation_English(model_id,behavior_pattern)
+                    growth_trajectory=await Translation_English(model_id,growth_trajectory)
                 return {
-                    "memory_insight": end_user.memory_insight,  # 总体概述存储在 memory_insight
+                    "memory_insight":memory_insight,  # 总体概述存储在 memory_insight
-                    "behavior_pattern": end_user.behavior_pattern,
+                    "behavior_pattern":behavior_pattern,
                     "key_findings": key_findings_array,  # 返回数组
-                    "growth_trajectory": end_user.growth_trajectory,
+                    "growth_trajectory": growth_trajectory,
                     "updated_at": self._datetime_to_timestamp(end_user.memory_insight_updated_at),
                     "is_cached": True
                 }
@@ -457,7 +466,9 @@ class UserMemoryService:
     async def get_cached_user_summary(
         self, 
         db: Session, 
-        end_user_id: str
+        end_user_id: str,
+        model_id:str,
+        language_type:str="zh"
     ) -> Dict[str, Any]:
         """
         从数据库获取缓存的用户摘要（四个部分）
@@ -481,7 +492,6 @@ class UserMemoryService:
             user_uuid = uuid.UUID(end_user_id)
             repo = EndUserRepository(db)
             end_user = repo.get_by_id(user_uuid)
-            
             if not end_user:
                 logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户")
                 return {
@@ -495,20 +505,29 @@ class UserMemoryService:
                 }
             
             # 检查是否有缓存数据（至少有一个字段不为空）
+            user_summary=end_user.user_summary
+            personality_traits=end_user.personality_traits
+            core_values=end_user.core_values
+            one_sentence_summary=end_user.one_sentence_summary
+            if language_type!='zh':
+                user_summary=await Translation_English(model_id, user_summary)
+                personality_traits = await Translation_English(model_id, personality_traits)
+                core_values = await Translation_English(model_id, core_values)
+                one_sentence_summary = await Translation_English(model_id, one_sentence_summary)
             has_cache = any([
-                end_user.user_summary,
+                user_summary,
-                end_user.personality_traits,
+                personality_traits,
-                end_user.core_values,
+                core_values,
-                end_user.one_sentence_summary
+                one_sentence_summary
             ])
             
             if has_cache:
                 logger.info(f"成功获取 end_user_id {end_user_id} 的缓存用户摘要")
                 return {
-                    "user_summary": end_user.user_summary,
+                    "user_summary": user_summary,
-                    "personality": end_user.personality_traits,
+                    "personality": personality_traits,
-                    "core_values": end_user.core_values,
+                    "core_values":core_values,
-                    "one_sentence": end_user.one_sentence_summary,
+                    "one_sentence": one_sentence_summary,
                     "updated_at": self._datetime_to_timestamp(end_user.user_summary_updated_at),
                     "is_cached": True
                 }
@@ -1367,7 +1386,6 @@ async def analytics_memory_types(
     
     return memory_types
 
-
 async def analytics_graph_data(
     db: Session,
     end_user_id: str,
@@ -1557,7 +1575,7 @@ async def analytics_graph_data(
             f"成功获取图数据: end_user_id={end_user_id}, "
             f"nodes={len(nodes)}, edges={len(edges)}"
         )
-        
+
         return {
             "nodes": nodes,
             "edges": edges,
@@ -1606,11 +1624,7 @@ async  def _extract_node_properties(label: str, properties: Dict[str, Any],node_
     
     # 获取该节点类型的白名单字段
     allowed_fields = field_whitelist.get(label, [])
-    
+
-    # 如果没有定义白名单，返回空字典（或者可以返回所有字段）
-    # if not allowed_fields:
-    #     # 对于未定义的节点类型，只返回基本字段
-    #     allowed_fields = ["name", "created_at", "caption"]
     count_neo4j=f"""MATCH (n)-[r]-(m) WHERE elementId(n) ="{node_id}" RETURN count(r) AS rel_count;"""
     node_results = await (_neo4j_connector.execute_query(count_neo4j))
     # 提取白名单中的字段
@@ -1618,13 +1632,12 @@ async  def _extract_node_properties(label: str, properties: Dict[str, Any],node_
     for field in allowed_fields:
         if field in properties:
             value = properties[field]
-            if str(field) == 'entity_type':
+            if  str(field) == 'entity_type':
                 value=type_mapping.get(value,'')
             if str(field)=="emotion_type":
                 value=EmotionType.EMOTION_MAPPING.get(value)
-            if str(field)=="emotion_subject":
+            if  str(field)=="emotion_subject":
                 value=EmotionSubject.SUBJECT_MAPPING.get(value)
-            # 清理 Neo4j 特殊类型
             filtered_props[field] = _clean_neo4j_value(value)
     filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0]
     return filtered_props