Fix/memory bug fix (#162)

* 图谱数据量限制数量去掉

* 图谱数据量限制数量去掉

* 图谱数据量限制数量去掉

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 读取的接口，去掉全局锁

* 输出数组

* 反思优化1.0（优化隐私输出、时间检索）

* 反思优化1.0（优化隐私输出、时间检索）

* 反思优化1.0（优化隐私输出、时间检索）

* 反思优化测试接口

* 反思优化测试接口

* 读取接口内层嵌套BUG修复

* 读取接口内层嵌套BUG修复

* 读取接口内层嵌套BUG修复

* 读取接口内层嵌套BUG修复

* 读取接口内层嵌套BUG修复

* 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)

* 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)-接口添加翻译字段

* 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)-接口添加翻译字段

* 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)-接口添加翻译字段

* 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)-接口添加翻译字段

* 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)-接口添加翻译字段

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/memory_short_term_controller.py

@@ -20,6 +20,7 @@ router = APIRouter(
 @router.get("/short_term")
 async def short_term_configs(
         end_user_id: str,
+        language_type:Optional[str] = "zh",
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ):

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')}")
@@ -278,7 +296,13 @@ async def get_end_user_profile(
     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")
@@ -296,7 +320,6 @@ async def get_end_user_profile(
         if not end_user:
             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}")
-
         # 构建响应数据
         profile_data = EndUserProfileResponse(
             id=end_user.id,
@@ -396,12 +419,21 @@ 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="zh",
                                       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/emotion_schema.py

@@ -11,6 +11,7 @@ class EmotionTagsRequest(BaseModel):
     start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）")
     end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）")
     limit: int = Field(10, ge=1, le=100, description="返回数量限制")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
 
 
 class EmotionWordcloudRequest(BaseModel):
@@ -18,20 +19,24 @@ class EmotionWordcloudRequest(BaseModel):
     group_id: str = Field(..., description="组ID")
     emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）")
     limit: int = Field(50, ge=1, le=200, description="返回词语数量")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
 
 
 class EmotionHealthRequest(BaseModel):
     """获取情绪健康指数请求"""
     group_id: str = Field(..., description="组ID")
     time_range: str = Field("30d", description="时间范围（7d/30d/90d）")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
 
 
 class EmotionSuggestionsRequest(BaseModel):
     """获取个性化情绪建议请求"""
     group_id: str = Field(..., description="组ID")
     config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
 
 
 class EmotionGenerateSuggestionsRequest(BaseModel):
     """生成个性化情绪建议请求"""
     end_user_id: str = Field(..., description="终端用户ID")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")

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/schemas/memory_episodic_schema.py

@@ -51,6 +51,7 @@ class EpisodicMemoryOverviewRequest(BaseModel):
     """情景记忆总览查询请求"""
     
     end_user_id: str = Field(..., description="终端用户ID")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
     time_range: str = Field(
         default="all",
         description="时间范围筛选，可选值：all, today, this_week, this_month"
@@ -70,3 +71,4 @@ class EpisodicMemoryDetailsRequest(BaseModel):
     
     end_user_id: str = Field(..., description="终端用户ID")
     summary_id: str = Field(..., description="情景记忆摘要ID")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")

api/app/schemas/memory_explicit_schema.py

@@ -1,15 +1,19 @@
 """
 显性记忆的请求和响应模型
 """
+from typing import Optional
+
 from pydantic import BaseModel, Field
 
 class ExplicitMemoryOverviewRequest(BaseModel):
     """显性记忆总览查询请求"""
     
     end_user_id: str = Field(..., description="终端用户ID")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")
 
 class ExplicitMemoryDetailsRequest(BaseModel):
     """显性记忆详情查询请求"""
     
     end_user_id: str = Field(..., description="终端用户ID")
     memory_id: str = Field(..., description="记忆ID（情景记忆或语义记忆的ID）")
+    language_type: Optional[str] = Field("zh", description="语言类型（zh/en）")

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 +713,13 @@ 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:
+                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_base_service.py

@@ -3,17 +3,268 @@ Memory Base Service
 
 提供记忆服务的基础功能和共享辅助方法。
 """
-
+import asyncio
+import re
 from datetime import datetime
 from typing import Optional
-
+from pydantic import BaseModel
 from app.core.logging_config import get_logger
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.services.emotion_analytics_service import EmotionAnalyticsService
-
+from app.core.memory.llm_tools.openai_client import OpenAIClient
+from app.core.models.base import RedBearModelConfig
+from app.services.memory_config_service import MemoryConfigService
+from app.db import get_db_context
 logger = get_logger(__name__)
+class TranslationResponse(BaseModel):
+    """翻译响应模型"""
+    data: str
+
+class MemoryTransService:
+    """记忆翻译服务，提供中英文翻译功能"""
+    
+    def __init__(self, llm_client=None, model_id: Optional[str] = None):
+        """
+        初始化翻译服务
+        
+        Args:
+            llm_client: LLM客户端实例或模型ID字符串（可选）
+            model_id: 模型ID，用于初始化LLM客户端（可选）
+        
+        Note:
+            - 如果llm_client是字符串，会被当作model_id使用
+            - 如果同时提供llm_client和model_id，优先使用llm_client
+        """
+        # 处理llm_client参数：如果是字符串，当作model_id
+        if isinstance(llm_client, str):
+            self.model_id = llm_client
+            self.llm_client = None
+        else:
+            self.llm_client = llm_client
+            self.model_id = model_id
+        
+        self._initialized = False
+    
+    def _ensure_llm_client(self):
+        """确保LLM客户端已初始化"""
+        if self._initialized:
+            return
+        
+        if self.llm_client is None:
+            if self.model_id:
+                with get_db_context() as db:
+                    config_service = MemoryConfigService(db)
+                    model_config = config_service.get_model_config(self.model_id)
+                
+                extra_params = {
+                    "temperature": 0.2,
+                    "max_tokens": 400,
+                    "top_p": 0.8,
+                    "stream": False,
+                }
+                
+                self.llm_client = OpenAIClient(
+                    RedBearModelConfig(
+                        model_name=model_config.get("model_name"),
+                        provider=model_config.get("provider"),
+                        api_key=model_config.get("api_key"),
+                        base_url=model_config.get("base_url"),
+                        timeout=model_config.get("timeout", 30),
+                        max_retries=model_config.get("max_retries", 3),
+                        extra_params=extra_params
+                    ),
+                    type_=model_config.get("type")
+                )
+            else:
+                raise ValueError("必须提供 llm_client 或 model_id 之一")
+        
+        self._initialized = True
+    
+    async def translate_to_english(self, text: str) -> str:
+        """
+        将中文翻译为英文
+        
+        Args:
+            text: 要翻译的中文文本
+            
+        Returns:
+            翻译后的英文文本
+        """
+        self._ensure_llm_client()
+
+        translation_messages = [
+            {
+                "role": "user",
+                "content": f"{text}\n\n中文翻译为英文，输出格式为{{\"data\":\"翻译后的内容\"}}"
+            }
+        ]
+
+        try:
+            response = await self.llm_client.response_structured(
+                messages=translation_messages,
+                response_model=TranslationResponse
+            )
+            return response.data
+        except Exception as e:
+            logger.error(f"翻译失败: {str(e)}")
+            return text  # 翻译失败时返回原文
+
+    async def is_english(self, text: str) -> bool:
+        """
+        检查文本是否为英文
+        
+        Args:
+            text: 要检查的文本（必须是字符串）
+            
+        Returns:
+            True 如果文本主要是英文，False 否则
+            
+        Note:
+            - 只接受字符串类型
+            - 检查是否主要由英文字母和常见标点组成
+            - 允许数字、空格和常见标点符号
+        """
+        if not isinstance(text, str):
+            raise TypeError(f"is_english 只接受字符串类型，收到: {type(text).__name__}")
+        
+        if not text.strip():
+            return True  # 空字符串视为英文
+        
+        # 更宽松的英文检查：允许字母、数字、空格和常见标点
+        # 如果文本中英文字符占比超过 80%，认为是英文
+        english_chars = sum(1 for c in text if c.isascii() and (c.isalnum() or c.isspace() or c in '.,!?;:\'"()-'))
+        total_chars = len(text)
+        
+        if total_chars == 0:
+            return True
+        
+        return (english_chars / total_chars) >= 0.8
+    async def Translate(self, text: str, target_language: str = "en") -> str:
+        """
+        通用翻译方法（保持向后兼容）
+        
+        Args:
+            text: 要翻译的文本
+            target_language: 目标语言，"en"表示英文，"zh"表示中文
+            
+        Returns:
+            翻译后的文本
+        """
+        if target_language == "en":
+            return await self.translate_to_english(text)
+        else:
+            logger.warning(f"不支持的目标语言: {target_language}，返回原文")
+            return text
     
 
+
+ # 测试翻译服务
+async def Translation_English(modid, text, fields=None):
+    """
+    将数据翻译为英文（支持字段级翻译）
+
+    Args:
+        modid: 模型ID
+        text: 要翻译的数据（可以是字符串、字典或列表）
+        fields: 需要翻译的字段列表（可选）
+                如果为None，默认翻译: ['content', 'summary', 'statement', 'description',
+                                      'name', 'aliases', 'caption', 'emotion_keywords']
+
+    Returns:
+        翻译后的数据，保持原有结构
+        
+    Note:
+        - 对于字符串：直接翻译
+        - 对于列表：递归处理每个元素，保持列表长度和索引不变
+        - 对于字典：只翻译指定字段（fields参数）
+        - 对于其他类型：原样返回
+    """
+    trans_service = MemoryTransService(modid)
+    
+    # 处理字符串类型
+    if isinstance(text, str):
+        # 空字符串直接返回
+        if not text.strip():
+            return text
+        
+        try:
+            is_eng = await trans_service.is_english(text)
+            if not is_eng:
+                english_result = await trans_service.Translate(text)
+                return english_result
+            return text
+        except Exception as e:
+            logger.warning(f"翻译字符串失败: {e}")
+            return text
+    
+    # 处理列表类型
+    elif isinstance(text, list):
+        english_result = []
+        for item in text:
+            # 递归处理列表中的每个元素
+            if isinstance(item, str):
+                # 字符串元素：检查是否需要翻译
+                if not item.strip():
+                    english_result.append(item)
+                    continue
+                
+                try:
+                    is_eng = await trans_service.is_english(item)
+                    if not is_eng:
+                        translated = await trans_service.Translate(item)
+                        english_result.append(translated)
+                    else:
+                        # 保留英文项，不改变列表长度
+                        english_result.append(item)
+                except Exception as e:
+                    logger.warning(f"翻译列表项失败: {e}")
+                    english_result.append(item)
+            
+            elif isinstance(item, dict):
+                # 字典元素：递归调用自己处理字典
+                translated_dict = await Translation_English(modid, item, fields)
+                english_result.append(translated_dict)
+            
+            elif isinstance(item, list):
+                # 嵌套列表：递归处理
+                translated_list = await Translation_English(modid, item, fields)
+                english_result.append(translated_list)
+            
+            else:
+                # 其他类型（数字、布尔值等）：原样保留
+                english_result.append(item)
+        
+        return english_result
+    
+    # 处理字典类型
+    elif isinstance(text, dict):
+        # 确定要翻译的字段
+        if fields is None:
+            # 默认翻译字段
+            fields = [
+                'content', 'summary', 'statement', 'description',
+                'name', 'aliases', 'caption', 'emotion_keywords',
+                'text', 'title', 'label', 'type'  # 添加常用字段
+            ]
+        
+        # 创建副本，避免修改原始数据
+        result = text.copy()
+        
+        for field in fields:
+            if field in result and result[field] is not None:
+                # 递归翻译字段值（可能是字符串、列表或嵌套字典）
+                try:
+                    result[field] = await Translation_English(modid, result[field], fields)
+                except Exception as e:
+                    logger.warning(f"翻译字段 {field} 失败: {e}")
+                    # 翻译失败时保留原值
+                    continue
+        
+        return result
+    
+    # 其他类型（数字、布尔值、None等）：原样返回
+    else:
+        return text
 class MemoryBaseService:
     """记忆服务基类，提供共享的辅助方法"""
     

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 +131,8 @@ 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
+
         result = {
             "MemorySummary": memory_summary_list,
             "Statement": statement_list,
@@ -233,7 +238,7 @@ 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 +256,13 @@ 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 +309,7 @@ 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 +374,117 @@ 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（并发有限度以降低整体延迟）
+        
+        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
+        """
+        # 空列表或无字段时直接返回
+        if not data_list or not fields:
+            return data_list
+        
+        import asyncio
+        
+        # 并发限制，避免一次性发起过多请求
+        # 可根据实际情况调整（建议 5-10）
+        concurrency_limit = 5
+        semaphore = asyncio.Semaphore(concurrency_limit)
+        
+        async def translate_single_field(
+            index: int,
+            field: str,
+            value: Any,
+        ) -> Optional[tuple]:
+            """
+            翻译单个字段并返回 (索引, 字段名, 翻译结果)
+            
+            Returns:
+                (index, field, translated_value) 或 None（如果跳过）
+            """
+            # 跳过空值
+            if value is None or value == "":
+                return None
+            
+            # 统一转成字符串再翻译，防止非字符串类型导致错误
+            text = str(value)
+            
+            try:
+                async with semaphore:
+                    # 调用 Translation_English 进行翻译
+                    # 注意：Translation_English 的参数顺序是 (model_id, text)
+                    translated = await Translation_English(model_id, text)
+                    
+                    # 如果翻译结果为空，保留原值
+                    if translated is None or translated == "":
+                        return None
+                    
+                    return index, field, translated
+            except Exception as e:
+                logger.warning(f"翻译字段 {field} (索引 {index}) 失败: {e}")
+                return None
+        
+        # 构造所有需要翻译的任务
+        tasks = []
+        for idx, item in enumerate(data_list):
+            # 防御性检查：确保 item 是字典
+            if not isinstance(item, dict):
+                continue
+            
+            for field in fields:
+                if field not in item:
+                    continue
+                
+                value = item.get(field)
+                
+                # 对于 None 或空字符串的值，直接跳过，不创建任务
+                if value is None or value == "":
+                    continue
+                
+                tasks.append(
+                    asyncio.create_task(
+                        translate_single_field(idx, field, value)
+                    )
+                )
+        
+        # 如果没有需要翻译的任务，直接返回原列表
+        if not tasks:
+            return data_list
+        
+        # 使用 gather 并发执行翻译任务（受 semaphore 限制）
+        # return_exceptions=True 可以防止单个任务失败导致整体失败
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        
+        # 创建深拷贝以避免修改原始数据
+        translated_list = [item.copy() if isinstance(item, dict) else item for item in data_list]
+        
+        # 将翻译结果回填到列表
+        for result in results:
+            # 跳过 None 结果和异常
+            if result is None or isinstance(result, Exception):
+                if isinstance(result, Exception):
+                    logger.warning(f"翻译任务异常: {result}")
+                continue
+            
+            idx, field, translated = result
+            
+            # 防御性检查索引范围
+            if 0 <= idx < len(translated_list) and isinstance(translated_list[idx], dict):
+                translated_list[idx][field] = translated
+        
+        return translated_list
+
 
 
 
@@ -426,15 +542,19 @@ 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 +570,10 @@ 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 +714,14 @@ 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,
@@ -1607,10 +1625,6 @@ 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