Merge branch 'develop' of github.com:SuanmoSuanyangTechnology/MemoryBear into develop

api/app/controllers/__init__.py

@@ -24,6 +24,7 @@ from . import (
     memory_storage_controller,
     memory_dashboard_controller,
     memory_reflection_controller,
+    memory_short_term_controller,
     api_key_controller,
     release_share_controller,
     public_share_controller,
@@ -71,6 +72,7 @@ manager_router.include_router(emotion_controller.router)
 manager_router.include_router(emotion_config_controller.router)
 manager_router.include_router(prompt_optimizer_controller.router)
 manager_router.include_router(memory_reflection_controller.router)
+manager_router.include_router(memory_short_term_controller.router)
 manager_router.include_router(tool_controller.router)
 manager_router.include_router(memory_forget_controller.router)
 manager_router.include_router(home_page_controller.router)

api/app/controllers/memory_perceptual_controller.py

@@ -0,0 +1,255 @@
+import uuid
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
+from app.core.logging_config import get_api_logger
+from app.core.response_utils import success, fail
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models import User
+from app.models.memory_perceptual_model import PerceptualType
+from app.schemas.memory_perceptual_schema import (
+    PerceptualQuerySchema,
+    PerceptualFilter
+)
+from app.schemas.response_schema import ApiResponse
+from app.services.memory_perceptual_service import MemoryPerceptualService
+
+api_logger = get_api_logger()
+
+router = APIRouter(
+    prefix="/memory/perceptual",
+    tags=["Perceptual Memory System"],
+    dependencies=[Depends(get_current_user)]
+)
+
+
+@router.get("/{group_id}/count", response_model=ApiResponse)
+def get_memory_count(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve perceptual memory statistics for a user group.
+
+    Args:
+        group_id: ID of the user group (usually end_user_id in this context)
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Response containing memory count statistics
+    """
+    api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}")
+
+    try:
+        service = MemoryPerceptualService(db)
+        count_stats = service.get_memory_count(group_id)
+
+        api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}")
+
+        return success(
+            data=count_stats,
+            msg="Memory statistics retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch memory statistics",
+        )
+
+
+@router.get("/{group_id}/last_visual", response_model=ApiResponse)
+def get_last_visual_memory(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve the most recent VISION-type memory for a user.
+
+    Args:
+        group_id: ID of the user group
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Metadata of the latest visual memory
+    """
+    api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}")
+
+    try:
+        service = MemoryPerceptualService(db)
+        visual_memory = service.get_latest_visual_memory(group_id)
+
+        if visual_memory is None:
+            api_logger.info(f"No visual memory found: group_id={group_id}")
+            return success(
+                data=None,
+                msg="No visual memory available"
+            )
+
+        api_logger.info(f"Latest visual memory retrieved successfully: file={visual_memory.get('file_name')}")
+
+        return success(
+            data=visual_memory,
+            msg="Latest visual memory retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch latest visual memory",
+        )
+
+
+@router.get("/{group_id}/last_listen", response_model=ApiResponse)
+def get_last_memory_listen(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve the most recent AUDIO-type memory for a user.
+
+    Args:
+        group_id: ID of the user group
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Metadata of the latest audio memory
+    """
+    api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}")
+
+    try:
+        service = MemoryPerceptualService(db)
+        audio_memory = service.get_latest_audio_memory(group_id)
+
+        if audio_memory is None:
+            api_logger.info(f"No audio memory found: group_id={group_id}")
+            return success(
+                data=None,
+                msg="No audio memory available"
+            )
+
+        api_logger.info(f"Latest audio memory retrieved successfully: file={audio_memory.get('file_name')}")
+
+        return success(
+            data=audio_memory,
+            msg="Latest audio memory retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch latest audio memory",
+        )
+
+
+@router.get("/{group_id}/last_text", response_model=ApiResponse)
+def get_last_text_memory(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve the most recent TEXT-type memory for a user.
+
+    Args:
+        group_id: ID of the user group
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Metadata of the latest text memory
+    """
+    api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}")
+
+    try:
+        # 调用服务层获取最近的文本记忆
+        service = MemoryPerceptualService(db)
+        text_memory = service.get_latest_text_memory(group_id)
+
+        if text_memory is None:
+            api_logger.info(f"No text memory found: group_id={group_id}")
+            return success(
+                data=None,
+                msg="No text memory available"
+            )
+
+        api_logger.info(f"Latest text memory retrieved successfully: file={text_memory.get('file_name')}")
+
+        return success(
+            data=text_memory,
+            msg="Latest text memory retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch latest text memory",
+        )
+
+
+@router.get("/{group_id}/timeline", response_model=ApiResponse)
+def get_memory_time_line(
+        group_id: uuid.UUID,
+        perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"),
+        page: int = Query(1, ge=1, description="页码"),
+        page_size: int = Query(10, ge=1, le=100, description="每页大小"),
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve a timeline of perceptual memories for a user group.
+
+    Args:
+        group_id: ID of the user group
+        perceptual_type: Optional filter for perceptual type
+        page: Page number for pagination
+        page_size: Number of items per page
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Timeline data of perceptual memories
+    """
+    api_logger.info(
+        f"Fetching perceptual memory timeline: user={current_user.username}, "
+        f"group_id={group_id}, type={perceptual_type}, page={page}"
+    )
+
+    try:
+        query = PerceptualQuerySchema(
+            filter=PerceptualFilter(type=perceptual_type),
+            page=page,
+            page_size=page_size
+        )
+
+        service = MemoryPerceptualService(db)
+        timeline_data = service.get_time_line(group_id, query)
+
+        api_logger.info(
+            f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, "
+            f"returned={len(timeline_data.memories)}"
+        )
+
+        return success(
+            data=timeline_data.model_dump(),
+            msg="Perceptual memory timeline retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(
+            f"Failed to fetch perceptual memory timeline: group_id={group_id}, "
+            f"error={str(e)}"
+        )
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch perceptual memory timeline",
+        )

api/app/controllers/memory_short_term_controller.py

@@ -0,0 +1,44 @@
+from fastapi import APIRouter, Depends, HTTPException, status
+from app.core.logging_config import get_api_logger
+from app.core.response_utils import success
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models.user_model import User
+
+from app.services.memory_storage_service import search_entity
+from app.services.memory_short_service import ShortService,LongService
+from dotenv import load_dotenv
+from sqlalchemy.orm import Session
+from typing import Optional
+load_dotenv()
+api_logger = get_api_logger()
+
+router = APIRouter(
+    prefix="/memory/short",
+    tags=["Memory"],
+)
+@router.get("/short_term")
+async def short_term_configs(
+        end_user_id: str,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db),
+):
+    # 获取短期记忆数据
+    short_term=ShortService(end_user_id)
+    short_result=short_term.get_short_databasets()
+    short_count=short_term.get_short_count()
+
+    long_term=LongService(end_user_id)
+    long_result=long_term.get_long_databasets()
+
+    entity_result = await search_entity(end_user_id)
+    result = {
+        'short_term': short_result,
+        'long_term': long_result,
+        'entity': entity_result.get('num', 0),
+        "retrieval_number":short_count,
+        "long_term_number":len(long_result)
+    }
+
+    return success(data=result, msg="短期记忆系统数据获取成功")
+

api/app/core/agent/langchain_agent.py

@@ -7,13 +7,20 @@ LangChain Agent 封装
 - 支持流式输出
 - 使用 RedBearLLM 支持多提供商
 """
+import os
 import time
 from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence
 
+
+from app.db import get_db
 from app.core.logging_config import get_business_logger
 from app.core.memory.agent.utils.redis_tool import store
 from app.core.models import RedBearLLM, RedBearModelConfig
 from app.models.models_model import ModelType
+from app.repositories.memory_short_repository import LongTermMemoryRepository
+from app.services.memory_agent_service import (
+    get_end_user_connected_config,
+)
 from app.services.memory_konwledges_server import write_rag
 from app.services.task_service import get_task_memory_write_result
 from app.tasks import write_message_task
@@ -96,7 +103,8 @@ class LangChainAgent:
                 "temperature": temperature,
                 "streaming": streaming,
                 "tool_count": len(self.tools),
-                "tool_names": [tool.name for tool in self.tools] if self.tools else []
+                "tool_names": [tool.name for tool in self.tools] if self.tools else [],
+                "tool_count": len(self.tools)
             }
         )
 
@@ -137,11 +145,8 @@ class LangChainAgent:
         messages.append(HumanMessage(content=user_content))
 
         return messages
-
     async def term_memory_save(self,messages,end_user_end,aimessages):
-        """
+        '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j'''
-        短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j
-        """
         end_user_end=f"Term_{end_user_end}"
         print(messages)
         print(aimessages)
@@ -155,17 +160,18 @@ class LangChainAgent:
         store.delete_duplicate_sessions()
         # logger.info(f'Redis_Agent:{end_user_end};{session_id}')
         return session_id
-
     async def term_memory_redis_read(self,end_user_end):
         end_user_end = f"Term_{end_user_end}"
         history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end)
         # logger.info(f'Redis_Agent:{end_user_end};{history}')
         messagss_list=[]
+        retrieved_content=[]
         for messages in history:
             query = messages.get("Query")
             aimessages = messages.get("Answer")
             messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
-        return messagss_list
+            retrieved_content.append({query: aimessages})
+        return messagss_list,retrieved_content
 
 
     async def write(self,storage_type,end_user_id,message,user_rag_memory_id,actual_end_user_id,content,actual_config_id):
@@ -205,7 +211,6 @@ class LangChainAgent:
         # If config_id is None, try to get from end_user's connected config
         if actual_config_id is None and end_user_id:
             try:
-                from app.db import get_db
                 from app.services.memory_agent_service import (
                     get_end_user_connected_config,
                 )
@@ -223,11 +228,26 @@ class LangChainAgent:
         logger.info(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
         print(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
 
-        history_term_memory=await self.term_memory_redis_read(end_user_id)
+        history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+        history_term_memory = history_term_memory_result[0]
+        db_for_memory = next(get_db())
         if memory_flag:
             if len(history_term_memory)>=4 and storage_type != "rag":
-                history_term_memory=';'.join(history_term_memory)
+                history_term_memory = ';'.join(history_term_memory)
-                logger.info(f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+                retrieved_content = history_term_memory_result[1]
+                print(retrieved_content)
+                # 为长期记忆操作获取新的数据库连接
+                try:
+                    repo = LongTermMemoryRepository(db_for_memory)
+                    repo.upsert(end_user_id, retrieved_content)
+                    logger.info(
+                        f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+                except Exception as e:
+                    logger.error(f"Failed to write to LongTermMemory: {e}")
+                    raise
+                finally:
+                    db_for_memory.close()
+
                 await self.write(storage_type,end_user_id,history_term_memory,user_rag_memory_id,actual_end_user_id,history_term_memory,actual_config_id)
             await self.write(storage_type,end_user_id,message,user_rag_memory_id,actual_end_user_id,message,actual_config_id)
         try:
@@ -316,10 +336,6 @@ class LangChainAgent:
         # If config_id is None, try to get from end_user's connected config
         if actual_config_id is None and end_user_id:
             try:
-                from app.db import get_db
-                from app.services.memory_agent_service import (
-                    get_end_user_connected_config,
-                )
                 db = next(get_db())
                 try:
                     connected_config = get_end_user_connected_config(end_user_id, db)
@@ -331,14 +347,24 @@ class LangChainAgent:
             except Exception as e:
                 logger.warning(f"Failed to get db session: {e}")
 
-        history_term_memory = await self.term_memory_redis_read(end_user_id)
+        history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+        history_term_memory = history_term_memory_result[0]
         if memory_flag:
             if len(history_term_memory) >= 4 and storage_type != "rag":
                 history_term_memory = ';'.join(history_term_memory)
-                logger.info(
+                retrieved_content = history_term_memory_result[1]
-                    f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+                db_for_memory = next(get_db())
-                await self.write(storage_type, end_user_id, history_term_memory, user_rag_memory_id, end_user_id,
+                try:
-                                 history_term_memory, actual_config_id)
+                    repo = LongTermMemoryRepository(db_for_memory)
+                    repo.upsert(end_user_id, retrieved_content)
+                    logger.info(
+                        f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+                    await self.write(storage_type, end_user_id, history_term_memory, user_rag_memory_id, end_user_id,
+                                     history_term_memory, actual_config_id)
+                except Exception as e:
+                    logger.error(f"Failed to write to long term memory: {e}")
+                finally:
+                    db_for_memory.close()
 
             await self.write(storage_type, end_user_id, message, user_rag_memory_id, end_user_id, message, actual_config_id)
         try:

api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py

@@ -246,7 +246,7 @@ class AccessHistoryManager:
         if not node_data:
             return ConsistencyCheckResult.CONSISTENT, None
         
-        access_history = node_data.get('access_history', [])
+        access_history = node_data.get('access_history') or []
         last_access_time = node_data.get('last_access_time')
         access_count = node_data.get('access_count', 0)
         activation_value = node_data.get('activation_value')
@@ -409,7 +409,7 @@ class AccessHistoryManager:
                 logger.error(f"节点不存在，无法修复: {node_label}[{node_id}]")
                 return False
             
-            access_history = node_data.get('access_history', [])
+            access_history = node_data.get('access_history') or []
             importance_score = node_data.get('importance_score', 0.5)
             
             # 准备修复数据
@@ -530,7 +530,7 @@ class AccessHistoryManager:
         Returns:
             Dict[str, Any]: 更新数据，包含所有需要更新的字段
         """
-        access_history = node_data.get('access_history', [])
+        access_history = node_data.get('access_history') or []
         importance_score = node_data.get('importance_score', 0.5)
         
         # 追加新的访问时间

api/app/core/workflow/nodes/http_request/config.py

@@ -73,8 +73,10 @@ class HttpContentTypeConfig(BaseModel):
         content_type = info.data.get("content_type")
         if content_type == HttpContentType.FROM_DATA and not isinstance(v, HttpFormData):
             raise ValueError("When content_type is 'form-data', data must be of type HttpFormData")
-        elif content_type in [HttpContentType.JSON, HttpContentType.WWW_FORM] and not isinstance(v, dict):
+        elif content_type in [HttpContentType.JSON] and not isinstance(v, str):
-            raise ValueError("When content_type is JSON or x-www-form-urlencoded, data must be a object")
+            raise ValueError("When content_type is JSON, data must be of type str")
+        elif content_type in [HttpContentType.WWW_FORM] and not isinstance(v, dict):
+            raise ValueError("When content_type is x-www-form-urlencoded, data must be a object")
         elif content_type in [HttpContentType.RAW, HttpContentType.BINARY] and not isinstance(v, str):
             raise ValueError("When content_type is raw/binary, data must be a string (File descriptor)")
         return v

api/app/core/workflow/nodes/http_request/node.py

@@ -120,7 +120,7 @@ class HttpRequestNode(BaseNode):
                 return {}
             case HttpContentType.JSON:
                 content["json"] = json.loads(self._render_template(
-                    json.dumps(self.typed_config.body.data), state
+                    self.typed_config.body.data, state
                 ))
             case HttpContentType.FROM_DATA:
                 data = {}

api/app/models/__init__.py

@@ -6,6 +6,7 @@ from .document_model import Document
 from .file_model import File
 from .generic_file_model import GenericFile
 from .models_model import ModelConfig, ModelProvider, ModelType, ModelApiKey
+from .memory_short_model import ShortTermMemory, LongTermMemory
 from .knowledgeshare_model import KnowledgeShare
 from .app_model import App
 from .agent_app_config_model import AgentConfig
@@ -67,6 +68,8 @@ __all__ = [
     "BuiltinToolConfig",
     "CustomToolConfig",
     "MCPToolConfig",
+    "ShortTermMemory",
+    "LongTermMemory",
     "ToolExecution",
     "ToolType",
     "ToolStatus",

api/app/models/memory_perceptual_model.py

@@ -0,0 +1,40 @@
+import datetime
+import uuid
+from enum import IntEnum
+
+from sqlalchemy import Column, ForeignKey, Integer, DateTime, String
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.dialects.postgresql import JSONB
+
+from app.db import Base
+
+
+class PerceptualType(IntEnum):
+    VISION = 1
+    AUDIO = 2
+    TEXT = 3
+    CONVERSATION = 4
+
+
+class FileStorageType(IntEnum):
+    LOCAL = 1
+    REMOTE = 2
+
+
+class MemoryPerceptualModel(Base):
+    __tablename__ = "memory_perceptual"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    end_user_id = Column(UUID(as_uuid=True), ForeignKey("end_users.id"), index=True)
+
+    perceptual_type = Column(Integer, index=True, nullable=False, comment="感知类型")
+
+    storage_service = Column(Integer, default=0, comment="存储服务类型")
+    file_path = Column(String, nullable=False, comment="文件路径")
+    file_name = Column(String, nullable=False, comment="文件名称")
+    file_ext = Column(String, nullable=False, comment="文件后缀名")
+
+    summary = Column(String, comment="摘要")
+    meta_data = Column(JSONB, comment="元信息")
+
+    created_time = Column(DateTime, default=datetime.datetime.now, comment="创建时间")

api/app/models/memory_short_model.py

@@ -0,0 +1,60 @@
+"""
+记忆模型 - 短期记忆和长期记忆表
+"""
+import uuid
+import datetime
+from sqlalchemy import Column, String, DateTime, Text, JSON
+from sqlalchemy.dialects.postgresql import UUID
+
+from app.db import Base
+
+
+class ShortTermMemory(Base):
+    """短期记忆表
+    
+    用于存储临时的对话记忆，通常保存较短时间
+    """
+    __tablename__ = "memory_short_term"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="记忆ID")
+    
+    # 用户信息
+    end_user_id = Column(String(255), nullable=False, index=True, comment="终端用户ID")
+    
+    # 对话内容
+    messages = Column(Text, nullable=False, comment="用户消息内容")
+    aimessages = Column(Text, nullable=True, comment="AI回复消息内容")
+    
+    # 搜索开关
+    search_switch = Column(String(50), nullable=True, comment="搜索开关状态")
+    
+    # 检索内容 - 存储为JSON格式的列表，包含字典 [{}, {}]
+    retrieved_content = Column(JSON, nullable=True, default=list, comment="检索到的相关内容，格式为[{}, {}]")
+    
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, index=True, comment="创建时间")
+    
+    def __repr__(self):
+        return f"<ShortTermMemory(id={self.id}, end_user_id={self.end_user_id}, created_at={self.created_at})>"
+
+
+class LongTermMemory(Base):
+    """长期记忆表
+    
+    用于存储重要的对话记忆，长期保存
+    """
+    __tablename__ = "memory_long_term"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True, comment="记忆ID")
+    
+    # 用户信息
+    end_user_id = Column(String(255), nullable=False, index=True, comment="终端用户ID")
+    
+    # 检索内容 - 存储为JSON格式的列表，包含字典 [{}, {}]
+    retrieved_content = Column(JSON, nullable=True, default=list, comment="检索到的相关内容，格式为[{}, {}]")
+    
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, nullable=False, index=True, comment="创建时间")
+    
+    def __repr__(self):
+        return f"<LongTermMemory(id={self.id}, end_user_id={self.end_user_id}, created_at={self.created_at})>"

api/app/repositories/memory_perceptual_repository.py

@@ -0,0 +1,156 @@
+import uuid
+from datetime import datetime
+from typing import List, Tuple, Optional
+
+from sqlalchemy import and_, desc
+from sqlalchemy.orm import Session
+
+from app.core.logging_config import get_db_logger
+from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageType
+from app.schemas.memory_perceptual_schema import PerceptualQuerySchema
+
+db_logger = get_db_logger()
+
+
+class MemoryPerceptualRepository:
+    """Data Access Layer for perceptual memory"""
+
+    def __init__(self, db: Session):
+        self.db = db
+
+    # ==================== Create and update ====================
+    def create_perceptual_memory(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType,
+            file_path: str,
+            file_name: str,
+            file_ext: str,
+            summary: Optional[str] = None,
+            meta_data: Optional[dict] = None,
+            storage_service: FileStorageType = FileStorageType.LOCAL
+
+    ) -> MemoryPerceptualModel:
+
+        """Create perceptual memory"""
+
+        db_logger.debug(f"Creating perceptual memory: end_user_id={end_user_id}, "
+                        f"type={perceptual_type}, file={file_name}")
+
+        try:
+            perceptual_memory = MemoryPerceptualModel(
+                end_user_id=end_user_id,
+                perceptual_type=perceptual_type,
+                storage_service=storage_service,
+                file_path=file_path,
+                file_name=file_name,
+                file_ext=file_ext,
+                summary=summary,
+                meta_data=meta_data,
+                created_time=datetime.now()
+            )
+
+            self.db.add(perceptual_memory)
+            self.db.flush()
+
+            db_logger.info(f"Perceptual memory created successfully: id={perceptual_memory.id}, file={file_name}")
+            return perceptual_memory
+
+        except Exception as e:
+            db_logger.error(f"Failed to create perceptual memory: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    # ==================== Query ====================
+    def get_count_by_user_id(
+            self,
+            end_user_id: uuid.UUID,
+    ):
+        db_logger.debug(f"Querying perceptual memory Count: end_user_id={end_user_id}")
+
+        try:
+            count = self.db.query(MemoryPerceptualModel).filter(
+                MemoryPerceptualModel.end_user_id == end_user_id
+            ).count()
+            return count
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memory count: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    def get_count_by_type(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType,
+    ):
+        db_logger.debug(f"Querying perceptual memory Count: end_user_id={end_user_id}, type={perceptual_type}")
+
+        try:
+            count = self.db.query(MemoryPerceptualModel).filter(
+                MemoryPerceptualModel.end_user_id == end_user_id,
+                MemoryPerceptualModel.perceptual_type == perceptual_type
+            ).count()
+            return count
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memory count: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    def get_timeline(
+            self,
+            end_user_id: uuid.UUID,
+            query: PerceptualQuerySchema
+    ) -> Tuple[int, List[MemoryPerceptualModel]]:
+        """Get the timeline of a user's perceptual memories"""
+        db_logger.debug(f"Querying perceptual memory timeline: end_user_id={end_user_id}, filter={query.filter}")
+
+        try:
+            base_query = self.db.query(MemoryPerceptualModel).filter(
+                MemoryPerceptualModel.end_user_id == end_user_id
+            )
+
+            if query.filter.type is not None:
+                base_query = base_query.filter(
+                    MemoryPerceptualModel.perceptual_type == query.filter.type
+                )
+
+            total_count = base_query.count()
+
+            memories = base_query.order_by(
+                desc(MemoryPerceptualModel.created_time)
+            ).offset(
+                (query.page - 1) * query.page_size
+            ).limit(query.page_size).all()
+
+            db_logger.info(
+                f"Perceptual memory timeline query succeeded: end_user_id={end_user_id}, total={total_count}, returned={len(memories)}")
+            return total_count, memories
+
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memory timeline: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    def get_by_type(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType,
+            limit: int = 10,
+            offset: int = 0
+    ) -> List[MemoryPerceptualModel]:
+        """Get memories by perceptual type"""
+        db_logger.debug(f"Querying perceptual memories by type: end_user_id={end_user_id}, type={perceptual_type}")
+
+        try:
+            memories = self.db.query(MemoryPerceptualModel).filter(
+                and_(
+                    MemoryPerceptualModel.end_user_id == end_user_id,
+                    MemoryPerceptualModel.perceptual_type == perceptual_type
+                )
+            ).order_by(
+                desc(MemoryPerceptualModel.created_time)
+            ).offset(offset).limit(limit).all()
+
+            db_logger.debug(f"Query by type succeeded: count={len(memories)}")
+            return memories
+
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memories by type: end_user_id={end_user_id}, "
+                            f"type={perceptual_type} - {str(e)}")
+            raise

api/app/repositories/memory_short_repository.py

@@ -0,0 +1,503 @@
+"""
+记忆仓储模块 - 短期记忆和长期记忆的数据访问层
+"""
+from sqlalchemy.orm import Session
+from typing import List, Optional, Dict, Any
+import uuid
+import datetime
+
+from app.models.memory_short_model import ShortTermMemory, LongTermMemory
+from app.core.logging_config import get_db_logger
+
+# 获取数据库专用日志器
+db_logger = get_db_logger()
+
+
+class ShortTermMemoryRepository:
+    """短期记忆仓储类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+
+    def create(self, end_user_id: str, messages: str, aimessages: str = None, search_switch: str = None, retrieved_content: List[Dict] = None) -> ShortTermMemory:
+        """创建短期记忆记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            messages: 用户消息内容
+            aimessages: AI回复消息内容
+            search_switch: 搜索开关状态
+            retrieved_content: 检索到的相关内容，格式为[{}, {}]
+            
+        Returns:
+            ShortTermMemory: 创建的短期记忆对象
+        """
+        try:
+            memory = ShortTermMemory(
+                end_user_id=end_user_id,
+                messages=messages,
+                aimessages=aimessages,
+                search_switch=search_switch,
+                retrieved_content=retrieved_content or []
+            )
+            
+            self.db.add(memory)
+            self.db.commit()
+            self.db.refresh(memory)
+            
+            db_logger.info(f"成功创建短期记忆记录: {memory.id} for user {end_user_id}")
+            return memory
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"创建短期记忆记录时出错: {str(e)}")
+            raise
+
+    def count_by_user_id(self,end_user_id: str) -> int:
+        """根据ID获取短期记忆记录
+        
+        Args:
+            memory_id: 记忆ID
+            
+        Returns:
+            Optional[ShortTermMemory]: 记忆对象，如果不存在则返回None
+        """
+        try:
+            count = (
+                self.db.query(ShortTermMemory)
+                .filter(ShortTermMemory.end_user_id == end_user_id)
+                .count()
+            )
+            db_logger.debug(f"成功统计用户 {end_user_id} 的短期记忆数量: {count}")
+                
+            return count
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"查询短期记忆记录 {count} 时出错: {str(e)}")
+            raise
+
+
+    def get_latest_by_user_id(self, end_user_id: str, limit: int = 5) -> List[ShortTermMemory]:
+        """获取用户最新的短期记忆记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            limit: 返回记录数限制，默认5条
+            
+        Returns:
+            List[ShortTermMemory]: 最新的记忆记录列表，按创建时间倒序
+        """
+        try:
+            # 使用复合索引 ix_memory_short_term_user_time 优化查询
+            memories = (
+                self.db.query(ShortTermMemory)
+                .filter(ShortTermMemory.end_user_id == end_user_id)
+                .order_by(ShortTermMemory.created_at.desc())
+                .limit(limit)
+                .all()
+            )
+            
+            db_logger.info(f"成功查询用户 {end_user_id} 的最新 {len(memories)} 条短期记忆记录")
+            return memories
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"查询用户 {end_user_id} 的最新短期记忆记录时出错: {str(e)}")
+            raise
+
+    def get_recent_by_user_id(self, end_user_id: str, hours: int = 24) -> List[ShortTermMemory]:
+        """获取用户最近指定小时内的短期记忆记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            hours: 时间范围（小时），默认24小时
+            
+        Returns:
+            List[ShortTermMemory]: 记忆记录列表，按创建时间倒序
+        """
+        try:
+            cutoff_time = datetime.datetime.now() - datetime.timedelta(hours=hours)
+            
+            # 使用复合索引 ix_memory_short_term_user_time 优化查询
+            memories = (
+                self.db.query(ShortTermMemory)
+                .filter(
+                    ShortTermMemory.end_user_id == end_user_id,
+                    ShortTermMemory.created_at >= cutoff_time
+                )
+                .order_by(ShortTermMemory.created_at.desc())
+                .all()
+            )
+            
+            db_logger.info(f"成功查询用户 {end_user_id} 最近 {hours} 小时的 {len(memories)} 条短期记忆记录")
+            return memories
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"查询用户 {end_user_id} 最近 {hours} 小时的短期记忆记录时出错: {str(e)}")
+            raise
+
+    def delete_by_id(self, memory_id: uuid.UUID) -> bool:
+        """删除指定ID的短期记忆记录
+        
+        Args:
+            memory_id: 记忆ID
+            
+        Returns:
+            bool: 删除成功返回True，否则返回False
+        """
+        try:
+            deleted_count = (
+                self.db.query(ShortTermMemory)
+                .filter(ShortTermMemory.id == memory_id)
+                .delete(synchronize_session=False)
+            )
+            
+            self.db.commit()
+            
+            if deleted_count > 0:
+                db_logger.info(f"成功删除短期记忆记录 {memory_id}")
+                return True
+            else:
+                db_logger.warning(f"未找到短期记忆记录 {memory_id}，无法删除")
+                return False
+                
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"删除短期记忆记录 {memory_id} 时出错: {str(e)}")
+            raise
+
+    def delete_old_memories(self, days: int = 7) -> int:
+        """删除指定天数之前的短期记忆记录
+        
+        Args:
+            days: 保留天数，默认7天
+            
+        Returns:
+            int: 删除的记录数
+        """
+        try:
+            cutoff_time = datetime.datetime.now() - datetime.timedelta(days=days)
+            
+            deleted_count = (
+                self.db.query(ShortTermMemory)
+                .filter(ShortTermMemory.created_at < cutoff_time)
+                .delete(synchronize_session=False)
+            )
+            
+            self.db.commit()
+            
+            db_logger.info(f"成功删除 {days} 天前的 {deleted_count} 条短期记忆记录")
+            return deleted_count
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"删除 {days} 天前的短期记忆记录时出错: {str(e)}")
+            raise
+
+    def upsert(self, end_user_id: str, messages: str, aimessages: str = None, search_switch: str = None, retrieved_content: List[Dict] = None) -> ShortTermMemory:
+        """创建或更新短期记忆记录
+        
+        根据 end_user_id、messages 和 aimessages 查找现有记录：
+        - 如果找到匹配的记录，则更新 messages、aimessages、search_switch 和 retrieved_content
+        - 如果没有找到匹配的记录，则创建新记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            messages: 用户消息内容
+            aimessages: AI回复消息内容
+            search_switch: 搜索开关状态
+            retrieved_content: 检索到的相关内容，格式为[{}, {}]
+            
+        Returns:
+            ShortTermMemory: 创建或更新的短期记忆对象
+        """
+        try:
+            # 构建查询条件，使用复合索引 ix_memory_short_term_user_messages 优化查询
+            query_filters = [
+                ShortTermMemory.end_user_id == end_user_id,
+                ShortTermMemory.messages == messages
+            ]
+            
+            # 如果 aimessages 不为空，则加入查询条件
+            if aimessages is not None:
+                query_filters.append(ShortTermMemory.aimessages == aimessages)
+            else:
+                # 如果 aimessages 为 None，则查找 aimessages 为 NULL 的记录
+                query_filters.append(ShortTermMemory.aimessages.is_(None))
+            
+            # 查找现有记录
+            existing_memory = (
+                self.db.query(ShortTermMemory)
+                .filter(*query_filters)
+                .first()
+            )
+            
+            if existing_memory:
+                # 更新现有记录
+                existing_memory.messages = messages
+                existing_memory.aimessages = aimessages
+                existing_memory.search_switch = search_switch
+                existing_memory.retrieved_content = retrieved_content or []
+                
+                self.db.commit()
+                self.db.refresh(existing_memory)
+                
+                db_logger.info(f"成功更新短期记忆记录: {existing_memory.id} for user {end_user_id}")
+                return existing_memory
+            else:
+                # 创建新记录
+                new_memory = ShortTermMemory(
+                    end_user_id=end_user_id,
+                    messages=messages,
+                    aimessages=aimessages,
+                    search_switch=search_switch,
+                    retrieved_content=retrieved_content or []
+                )
+                
+                self.db.add(new_memory)
+                self.db.commit()
+                self.db.refresh(new_memory)
+                
+                db_logger.info(f"成功创建新的短期记忆记录: {new_memory.id} for user {end_user_id}")
+                return new_memory
+                
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"创建或更新短期记忆记录时出错: {str(e)}")
+            raise
+
+
+class LongTermMemoryRepository:
+    """长期记忆仓储类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+
+    def create(self, end_user_id: str, retrieved_content: List[Dict] = None) -> LongTermMemory:
+        """创建长期记忆记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            retrieved_content: 检索到的相关内容，格式为[{}, {}]
+            
+        Returns:
+            LongTermMemory: 创建的长期记忆对象
+        """
+        try:
+            memory = LongTermMemory(
+                end_user_id=end_user_id,
+                retrieved_content=retrieved_content or []
+            )
+            
+            self.db.add(memory)
+            self.db.commit()
+            self.db.refresh(memory)
+            
+            db_logger.info(f"成功创建长期记忆记录: {memory.id} for user {end_user_id}")
+            return memory
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"创建长期记忆记录时出错: {str(e)}")
+            raise
+
+    def get_by_id(self, memory_id: uuid.UUID) -> Optional[LongTermMemory]:
+        """根据ID获取长期记忆记录
+        
+        Args:
+            memory_id: 记忆ID
+            
+        Returns:
+            Optional[LongTermMemory]: 记忆对象，如果不存在则返回None
+        """
+        try:
+            memory = (
+                self.db.query(LongTermMemory)
+                .filter(LongTermMemory.id == memory_id)
+                .first()
+            )
+            
+            if memory:
+                db_logger.debug(f"成功查询到长期记忆记录 {memory_id}")
+            else:
+                db_logger.debug(f"未找到长期记忆记录 {memory_id}")
+                
+            return memory
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"查询长期记忆记录 {memory_id} 时出错: {str(e)}")
+            raise
+
+    def get_by_user_id(self, end_user_id: str, limit: int = 100, offset: int = 0) -> List[LongTermMemory]:
+        """根据用户ID获取长期记忆记录列表
+        
+        Args:
+            end_user_id: 终端用户ID
+            limit: 返回记录数限制，默认100
+            offset: 偏移量，默认0
+            
+        Returns:
+            List[LongTermMemory]: 记忆记录列表，按创建时间倒序
+        """
+        try:
+            # 使用复合索引 ix_memory_long_term_user_time 优化查询
+            memories = (
+                self.db.query(LongTermMemory)
+                .filter(LongTermMemory.end_user_id == end_user_id)
+                .order_by(LongTermMemory.created_at.desc())
+                .limit(limit)
+                .offset(offset)
+                .all()
+            )
+            
+            db_logger.info(f"成功查询用户 {end_user_id} 的 {len(memories)} 条长期记忆记录")
+            return memories
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"查询用户 {end_user_id} 的长期记忆记录时出错: {str(e)}")
+            raise
+
+    def search_by_content(self, end_user_id: str, keyword: str, limit: int = 50) -> List[LongTermMemory]:
+        """根据内容关键词搜索长期记忆记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            keyword: 搜索关键词
+            limit: 返回记录数限制，默认50
+            
+        Returns:
+            List[LongTermMemory]: 匹配的记忆记录列表，按创建时间倒序
+        """
+        try:
+            # 使用 GIN 索引 ix_memory_long_term_retrieved_content_gin 优化 JSON 搜索
+            # 同时使用复合索引 ix_memory_long_term_user_time 优化用户过滤
+            memories = (
+                self.db.query(LongTermMemory)
+                .filter(
+                    LongTermMemory.end_user_id == end_user_id,
+                    LongTermMemory.retrieved_content.astext.contains(keyword)
+                )
+                .order_by(LongTermMemory.created_at.desc())
+                .limit(limit)
+                .all()
+            )
+            
+            db_logger.info(f"成功搜索用户 {end_user_id} 包含关键词 '{keyword}' 的 {len(memories)} 条长期记忆记录")
+            return memories
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"搜索用户 {end_user_id} 包含关键词 '{keyword}' 的长期记忆记录时出错: {str(e)}")
+            raise
+
+    def delete_by_id(self, memory_id: uuid.UUID) -> bool:
+        """删除指定ID的长期记忆记录
+        
+        Args:
+            memory_id: 记忆ID
+            
+        Returns:
+            bool: 删除成功返回True，否则返回False
+        """
+        try:
+            deleted_count = (
+                self.db.query(LongTermMemory)
+                .filter(LongTermMemory.id == memory_id)
+                .delete(synchronize_session=False)
+            )
+            
+            self.db.commit()
+            
+            if deleted_count > 0:
+                db_logger.info(f"成功删除长期记忆记录 {memory_id}")
+                return True
+            else:
+                db_logger.warning(f"未找到长期记忆记录 {memory_id}，无法删除")
+                return False
+                
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"删除长期记忆记录 {memory_id} 时出错: {str(e)}")
+            raise
+
+    def count_by_user_id(self, end_user_id: str) -> int:
+        """统计用户的长期记忆记录数量
+        
+        Args:
+            end_user_id: 终端用户ID
+            
+        Returns:
+            int: 记录数量
+        """
+        try:
+            count = (
+                self.db.query(LongTermMemory)
+                .filter(LongTermMemory.end_user_id == end_user_id)
+                .count()
+            )
+            
+            db_logger.debug(f"用户 {end_user_id} 共有 {count} 条长期记忆记录")
+            return count
+            
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"统计用户 {end_user_id} 的长期记忆记录数量时出错: {str(e)}")
+            raise
+
+    def upsert(self, end_user_id: str, retrieved_content: List[Dict] = None) -> Optional[LongTermMemory]:
+        """创建或更新长期记忆记录
+        
+        根据 end_user_id 和 retrieved_content 判断是否需要写入：
+        - 如果找到相同的 end_user_id 和 retrieved_content，则不写入，返回 None
+        - 如果没有找到相同的记录，则创建新记录
+        
+        Args:
+            end_user_id: 终端用户ID
+            retrieved_content: 检索到的相关内容，格式为[{}, {}]
+            
+        Returns:
+            Optional[LongTermMemory]: 创建的长期记忆对象，如果不需要写入则返回 None
+        """
+        try:
+            retrieved_content = retrieved_content or []
+            
+            # 优化查询：使用复合索引 ix_memory_long_term_user_time 先过滤用户
+            # 然后在应用层比较 JSON 内容，避免复杂的数据库 JSON 比较
+            existing_memories = (
+                self.db.query(LongTermMemory)
+                .filter(LongTermMemory.end_user_id == end_user_id)
+                .order_by(LongTermMemory.created_at.desc())
+                .limit(100)  # 限制查询数量，避免加载过多数据
+                .all()
+            )
+            
+            # 在 Python 中比较 retrieved_content
+            for memory in existing_memories:
+                if memory.retrieved_content == retrieved_content:
+                    # 如果找到相同的记录，不写入
+                    db_logger.info(f"长期记忆记录已存在，跳过写入: user {end_user_id}")
+                    return None
+            
+            # 如果没有找到相同的记录，创建新记录
+            new_memory = LongTermMemory(
+                end_user_id=end_user_id,
+                retrieved_content=retrieved_content
+            )
+            
+            self.db.add(new_memory)
+            self.db.commit()
+            self.db.refresh(new_memory)
+            
+            db_logger.info(f"成功创建新的长期记忆记录: {new_memory.id} for user {end_user_id}")
+            return new_memory
+                
+        except Exception as e:
+            self.db.rollback()
+            db_logger.error(f"创建或更新长期记忆记录时出错: {str(e)}")
+            raise
+
+

api/app/repositories/neo4j/cypher_queries.py

@@ -722,7 +722,12 @@ SET m += {
     chunk_ids: summary.chunk_ids,
     content: summary.content,
     summary_embedding: summary.summary_embedding,
-    config_id: summary.config_id
+    config_id: summary.config_id,
+    importance_score: CASE WHEN summary.importance_score IS NOT NULL THEN summary.importance_score ELSE coalesce(m.importance_score, 0.5) END,
+    activation_value: CASE WHEN summary.activation_value IS NOT NULL THEN summary.activation_value ELSE m.activation_value END,
+    access_history: CASE WHEN summary.access_history IS NOT NULL THEN summary.access_history ELSE coalesce(m.access_history, []) END,
+    last_access_time: CASE WHEN summary.last_access_time IS NOT NULL THEN summary.last_access_time ELSE m.last_access_time END,
+    access_count: CASE WHEN summary.access_count IS NOT NULL THEN summary.access_count ELSE coalesce(m.access_count, 0) END
 }
 RETURN m.id AS uuid
 """

api/app/repositories/neo4j/entity_repository.py

@@ -58,7 +58,7 @@ class EntityRepository(BaseNeo4jRepository[ExtractedEntityNode]):
         # 处理 ACT-R 属性 - 确保字段存在且有默认值
         n['importance_score'] = n.get('importance_score', 0.5)
         n['activation_value'] = n.get('activation_value')
-        n['access_history'] = n.get('access_history', [])
+        n['access_history'] = n.get('access_history') or []
         n['last_access_time'] = n.get('last_access_time')
         n['access_count'] = n.get('access_count', 0)
         

api/app/repositories/neo4j/statement_repository.py

@@ -78,7 +78,7 @@ class StatementRepository(BaseNeo4jRepository[StatementNode]):
         # 处理 ACT-R 属性 - 确保字段存在且有默认值
         n['importance_score'] = n.get('importance_score', 0.5)
         n['activation_value'] = n.get('activation_value')
-        n['access_history'] = n.get('access_history', [])
+        n['access_history'] = n.get('access_history') or []
         n['last_access_time'] = n.get('last_access_time')
         n['access_count'] = n.get('access_count', 0)
         

api/app/schemas/memory_perceptual_schema.py

@@ -0,0 +1,133 @@
+import uuid
+from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from app.models.memory_perceptual_model import PerceptualType, FileStorageType
+
+
+class PerceptualFilter(BaseModel):
+    type: PerceptualType | None = Field(
+        default=None,
+        description="Perceptual type used for filtering the query; optional"
+    )
+
+
+class PerceptualQuerySchema(BaseModel):
+    filter: PerceptualFilter = Field(
+        default_factory=lambda: PerceptualFilter(),
+        description="Query filter containing perceptual type criteria"
+    )
+
+    page: int = Field(
+        default=1,
+        ge=1,
+        description="Page number for pagination, starting from 1"
+    )
+
+    page_size: int = Field(
+        default=10,
+        ge=1,
+        le=100,
+        description="Number of records per page, range 1-100"
+    )
+
+
+class PerceptualMemoryItem(BaseModel):
+    """感知记忆项"""
+    id: uuid.UUID = Field(..., description="Unique memory ID")
+    perceptual_type: PerceptualType = Field(..., description="Type of perception, e.g., text, audio, or video")
+    file_path: str = Field(..., description="File path in the storage service")
+    file_name: str = Field(..., description="File name")
+    summary: Optional[str] = Field(None, description="摘要")
+    storage_type: FileStorageType = Field(..., description="Storage type for file")
+    created_time: Optional[datetime] = Field(None, description="创建时间")
+
+    class Config:
+        from_attributes = True
+
+
+class PerceptualTimelineResponse(BaseModel):
+    """感知记忆时间线响应"""
+    total: int = Field(..., description="总数量")
+    page: int = Field(..., description="当前页码")
+    page_size: int = Field(..., description="每页大小")
+    total_pages: int = Field(..., description="总页数")
+    memories: list[PerceptualMemoryItem] = Field(..., description="记忆列表")
+
+    class Config:
+        from_attributes = True
+
+
+# --------------------------
+#  TODO: FileMetaData
+# --------------------------
+class Identity(BaseModel):
+    title: str
+    filename: str
+    source: str  # upload | crawl | system
+    author: Optional[str] = None
+
+
+class Semantic(BaseModel):
+    topic: str
+    domain: str
+    difficulty: str  # beginner | intermediate | advanced
+    intent: str  # informative | instructional | promotional
+    sentiment: str  # positive | neutral | negative
+
+
+class Content(BaseModel):
+    summary: str
+    keywords: list[str]
+    topic: str
+    domain: str
+
+
+class Usage(BaseModel):
+    target_audience: list[str]
+    use_cases: list[str]
+
+
+class Stats(BaseModel):
+    duration_sec: Optional[int] = None
+    char_count: int
+    word_count: int
+
+
+class Processing(BaseModel):
+    transcribed: bool
+    ocr_applied: bool
+    chunked: bool
+    vectorized: bool
+    embedding_model: Optional[str] = None
+
+
+class VideoModal(BaseModel):
+    scene: list[str]
+
+
+class AudioModal(BaseModel):
+    speaker_count: int
+
+
+class TextModal(BaseModel):
+    section_count: int
+
+
+class Asset(BaseModel):
+    type: str
+    modality: str  # text | audio | video
+    format: str  # docx | mp3 | mp4
+    language: str
+    encoding: str
+
+    identity: Identity
+    semantic: Semantic
+    content: Content
+    usage: Usage
+    stats: Stats
+    processing: Processing
+    created_at: str
+    modalities: AudioModal | TextModal | VideoModal

api/app/services/memory_agent_service.py

@@ -4,6 +4,7 @@ Memory Agent Service
 Handles business logic for memory agent operations including read/write services,
 health checks, and message type classification.
 """
+import datetime
 import json
 import os
 import re
@@ -24,6 +25,7 @@ from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.models.knowledge_model import Knowledge, KnowledgeType
+from app.repositories.memory_short_repository import ShortTermMemoryRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import ConfigurationError, MemoryConfig
 from app.services.memory_config_service import MemoryConfigService
@@ -393,7 +395,7 @@ class MemoryAgentService:
 
         import time
         start_time = time.time()
-
+        ori_message=message
         # Resolve config_id if None using end_user's connected config
         if config_id is None:
             try:
@@ -596,6 +598,30 @@ class MemoryAgentService:
                         "has_answer": bool(final_answer)
                     }
                 )
+            retrieved_content=[]
+            repo = ShortTermMemoryRepository(db)
+            if str(search_switch)!="2":
+                for intermediate in intermediate_outputs:
+                    intermediate_type=intermediate['type']
+                    if intermediate_type=="search_result":
+                        query=intermediate['query']
+                        raw_results=intermediate['raw_results']
+                        reranked_results=raw_results.get('reranked_results',[])
+                        statements=[statement['statement'] for statement in reranked_results.get('statements', [])]
+                        statements=list(set(statements))
+                        retrieved_content.append({query:statements})
+            if   '信息不足，无法回答' in str(final_answer) or retrieved_content!=[]:
+                # 使用 upsert 方法
+                repo.upsert(
+                    end_user_id=group_id,  # 确保这个变量在作用域内
+                    messages=ori_message,
+                    aimessages=final_answer,
+                    retrieved_content=retrieved_content,
+                    search_switch=str(search_switch)
+                )
+                print("写入成功")
+
+
 
             return {
                 "answer": final_answer,

api/app/services/memory_perceptual_service.py

@@ -0,0 +1,166 @@
+import uuid
+from typing import Dict, Any, Optional
+
+from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
+from app.core.exceptions import BusinessException
+from app.core.logging_config import get_business_logger
+from app.models.memory_perceptual_model import PerceptualType, FileStorageType
+from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository
+from app.schemas.memory_perceptual_schema import (
+    PerceptualQuerySchema,
+    PerceptualTimelineResponse,
+    PerceptualMemoryItem,
+    AudioModal, Content, VideoModal, TextModal
+)
+
+business_logger = get_business_logger()
+
+
+class MemoryPerceptualService:
+    def __init__(self, db: Session):
+        self.db = db
+        self.repository = MemoryPerceptualRepository(db)
+
+    def get_memory_count(self, end_user_id: uuid.UUID) -> Dict[str, Any]:
+        """Retrieve perceptual memory statistics for a user."""
+        business_logger.info(f"Fetching perceptual memory statistics: end_user_id={end_user_id}")
+        try:
+            total_count = self.repository.get_count_by_user_id(end_user_id=end_user_id)
+
+            vision_count = self.repository.get_count_by_type(end_user_id, PerceptualType.VISION)
+            audio_count = self.repository.get_count_by_type(end_user_id, PerceptualType.AUDIO)
+            text_count = self.repository.get_count_by_type(end_user_id, PerceptualType.TEXT)
+            conversation_count = self.repository.get_count_by_type(end_user_id, PerceptualType.CONVERSATION)
+
+            stats = {
+                "total": total_count,
+                "by_type": {
+                    "vision": vision_count,
+                    "audio": audio_count,
+                    "text": text_count,
+                    "conversation": conversation_count
+                }
+            }
+
+            business_logger.info(f"Memory statistics fetched successfully: total={total_count}")
+            return stats
+
+        except Exception as e:
+            business_logger.error(f"Failed to fetch memory statistics: {str(e)}")
+            raise BusinessException(f"Failed to fetch memory statistics: {str(e)}", BizCode.DB_ERROR)
+
+    def _get_latest_memory_by_type(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType
+    ) -> Optional[dict[str, Any]]:
+        """Internal helper to retrieve the latest memory by type."""
+        business_logger.info(f"Fetching latest {perceptual_type.name.lower()} memory: end_user_id={end_user_id}")
+        try:
+            memories = self.repository.get_by_type(
+                end_user_id=end_user_id,
+                perceptual_type=perceptual_type,
+                limit=1,
+                offset=0
+            )
+            if not memories:
+                business_logger.info(f"No {perceptual_type.name.lower()} memory found: end_user_id={end_user_id}")
+                return None
+
+            memory = memories[0]
+            meta_data = memory.meta_data or {}
+            modalities = meta_data.get("modalities")
+            content = meta_data.get("content")
+
+            if not modalities:
+                raise BusinessException(f"Modalities not defined, perceptual memory_id={memory.id}", BizCode.DB_ERROR)
+            if not content:
+                raise BusinessException(f"Content not defined, perceptual memory_id={memory.id}", BizCode.DB_ERROR)
+            content = Content(**content)
+            match perceptual_type:
+                case PerceptualType.VISION:
+                    modal = VideoModal(**modalities)
+                case PerceptualType.AUDIO:
+                    modal = AudioModal(**modalities)
+                case PerceptualType.TEXT:
+                    modal = TextModal(**modalities)
+                case _:
+                    raise BusinessException("Unsupported perceptual type", BizCode.DB_ERROR)
+            detail = modal.model_dump()
+
+            result = {
+                "id": str(memory.id),
+                "file_name": memory.file_name,
+                "file_path": memory.file_path,
+                "storage_type": memory.storage_service,
+                "summary": memory.summary,
+                "keywords": content.keywords,
+                "topic": content.topic,
+                "domain": content.domain,
+                "created_time": memory.created_time.isoformat() if memory.created_time else None,
+                **detail
+            }
+
+            business_logger.info(
+                f"Latest {perceptual_type.name.lower()} memory retrieved successfully: file={memory.file_name}")
+            return result
+
+        except Exception as e:
+            business_logger.error(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}")
+            raise BusinessException(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}",
+                                    BizCode.DB_ERROR)
+
+    def get_latest_visual_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
+        return self._get_latest_memory_by_type(end_user_id, PerceptualType.VISION)
+
+    def get_latest_audio_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
+        return self._get_latest_memory_by_type(end_user_id, PerceptualType.AUDIO)
+
+    def get_latest_text_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
+        return self._get_latest_memory_by_type(end_user_id, PerceptualType.TEXT)
+
+    def get_time_line(self, end_user_id: uuid.UUID, query: PerceptualQuerySchema) -> PerceptualTimelineResponse:
+        """Retrieve a timeline of perceptual memories for a user."""
+        business_logger.info(f"Fetching perceptual memory timeline: "
+                             f"end_user_id={end_user_id}, filter={query.filter}")
+
+        try:
+            if query.page < 1:
+                raise BusinessException("Page number must be greater than 0", BizCode.INVALID_PARAMETER)
+            if query.page_size < 1 or query.page_size > 100:
+                raise BusinessException("Page size must be between 1 and 100", BizCode.INVALID_PARAMETER)
+
+            total_count, memories = self.repository.get_timeline(end_user_id, query)
+
+            memory_items = []
+            for memory in memories:
+                memory_item = PerceptualMemoryItem(
+                    id=memory.id,
+                    perceptual_type=PerceptualType(memory.perceptual_type),
+                    file_path=memory.file_path,
+                    file_name=memory.file_name,
+                    summary=memory.summary,
+                    created_time=memory.created_time,
+                    storage_type=FileStorageType(memory.storage_service),
+                )
+                memory_items.append(memory_item)
+
+            timeline_response = PerceptualTimelineResponse(
+                total=total_count,
+                page=query.page,
+                page_size=query.page_size,
+                total_pages=(total_count + query.page_size - 1) // query.page_size,
+                memories=memory_items
+            )
+
+            business_logger.info(f"Perceptual memory timeline retrieved successfully: "
+                                 f"total={total_count}, returned={len(memories)}")
+            return timeline_response
+
+        except BusinessException:
+            raise
+        except Exception as e:
+            business_logger.error(f"Failed to fetch perceptual memory timeline: {str(e)}")
+            raise BusinessException(f"Failed to fetch perceptual memory timeline: {str(e)}", BizCode.DB_ERROR)

api/app/services/memory_short_service.py

@@ -0,0 +1,56 @@
+
+from app.core.logging_config import get_api_logger
+from app.db import get_db
+from app.repositories.memory_short_repository import LongTermMemoryRepository
+from app.repositories.memory_short_repository import ShortTermMemoryRepository
+
+
+api_logger = get_api_logger()
+db=next(get_db())
+class ShortService:
+    def __init__(self, end_user_id):
+        self.short_repo = ShortTermMemoryRepository(db)
+        self.end_user_id = end_user_id
+
+    def get_short_databasets(self):
+        short_memories = self.short_repo.get_latest_by_user_id(self.end_user_id, 3)
+        short_result = []
+        for memory in short_memories:
+            deep_expanded = {}  # Create a new dictionary for each memory
+            messages = memory.messages
+            aimessages = memory.aimessages
+            retrieved_content = memory.retrieved_content or []
+
+            api_logger.debug(f"Retrieved content: {retrieved_content}")
+
+            retrieval_source = []
+            for item in retrieved_content:
+                if isinstance(item, dict):
+                    for key, values in item.items():
+                        retrieval_source.append({"query": key, "retrieval": values})
+
+            deep_expanded['retrieval'] = retrieval_source
+            deep_expanded['message'] = messages  # 修正拼写错误
+            deep_expanded['answer'] = aimessages
+            short_result.append(deep_expanded)
+        return short_result
+    def get_short_count(self):
+        short_count = self.short_repo.count_by_user_id(self.end_user_id)
+        return short_count
+
+class LongService:
+    def __init__(self, end_user_id):
+        self.long_repo = LongTermMemoryRepository(db)
+        self.end_user_id = end_user_id
+    def get_long_databasets(self):
+        # 获取长期记忆数据
+        long_memories = self.long_repo.get_by_user_id(self.end_user_id, 1)
+
+        long_result = []
+        for long_memory in long_memories:
+            if long_memory.retrieved_content:
+                for memory_item in long_memory.retrieved_content:
+                    if isinstance(memory_item, dict):
+                        for key, values in memory_item.items():
+                            long_result.append({"query": key, "retrieval": values})
+        return long_result

api/app/services/prompt_optimizer_service.py

@@ -166,6 +166,8 @@ class PromptOptimizerService:
         model_config = self.get_model_config(tenant_id, model_id)
         session_history = self.get_session_message_history(session_id=session_id, user_id=user_id)
 
+        logger.info(f"Prompt optimization started, user_id={user_id}, session_id={session_id}")
+
         # Create LLM instance
         api_config: ModelApiKey = model_config.api_keys[0]
         llm = RedBearLLM(RedBearModelConfig(
@@ -203,7 +205,6 @@ class PromptOptimizerService:
 
         messages.extend(session_history[:-1])  # last message is current message
         messages.extend([(RoleType.USER.value, rendered_user_message)])
-        logger.info(f"Prompt optimization message: {messages}")
         buffer = ""
         prompt_started = False
         prompt_finished = False
@@ -250,6 +251,7 @@ class PromptOptimizerService:
             content=desc
         )
         variables = self.parser_prompt_variables(optim_result.get("prompt"))
+        logger.info(f"Prompt optimization completed, user_id={user_id}, session_id={session_id}")
         yield {"desc": optim_result.get("desc"), "variables": variables}
 
     @staticmethod

api/app/services/user_memory_service.py

@@ -1496,8 +1496,8 @@ def _extract_node_properties(label: str, properties: Dict[str, Any]) -> Dict[str
     field_whitelist = {
         "Dialogue": ["content", "created_at"],
         "Chunk": ["content", "created_at"],
-        "Statement": ["temporal_info", "stmt_type", "statement", "valid_at", "created_at", "caption"],
+        "Statement": ["temporal_info", "stmt_type", "statement", "valid_at", "created_at", "caption","emotion_keywords","emotion_type","emotion_subject"],
-        "ExtractedEntity": ["description", "name", "entity_type", "created_at", "caption"],
+        "ExtractedEntity": ["description", "name", "entity_type", "created_at", "caption","aliases","connect_strength"],
         "MemorySummary": ["summary", "content", "created_at", "caption"]  # 添加 content 字段
     }