Merge #85 into develop from feature/actr-forget

[feature]actr-记忆遗忘需求开发

* feature/actr-forget: (12 commits squashed)

  - [feature]
    1.Extended fields of the date_config table;
    2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.

  - [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler

  - [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process

  - [feature]
    1.Extended fields of the date_config table;
    2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.

  - [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler

  - [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process

  - Merge branch 'feature/actr-forget' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/actr-forget

  - [fix]Eliminate the interference caused by redundant code

  - [feature]
    1.Extended fields of the date_config table;
    2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.

  - [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler

  - [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process

  - Merge branch 'feature/actr-forget' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/actr-forget

Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com>
Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>

CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/85

api/app/celery_app.py

@@ -85,6 +85,8 @@ health_schedule = timedelta(seconds=settings.HEALTH_CHECK_SECONDS)
 memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
 memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
 workspace_reflection_schedule = timedelta(seconds=30)  # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
+forgetting_cycle_schedule = timedelta(hours=24)  # 每24小时运行一次遗忘周期
+
 # 构建定时任务配置
 beat_schedule_config = {
 
@@ -103,6 +105,13 @@ beat_schedule_config = {
         "schedule": memory_cache_regeneration_schedule,
         "args": (),
     },
+    "run-forgetting-cycle": {
+        "task": "app.tasks.run_forgetting_cycle_task",
+        "schedule": forgetting_cycle_schedule,
+        "kwargs": {
+            "config_id": None,  # 使用默认配置，可以通过环境变量配置
+        },
+    },
 }
 
 # 如果配置了默认工作空间ID，则添加记忆总量统计任务

api/app/controllers/__init__.py

@@ -33,7 +33,7 @@ from . import (
     emotion_config_controller,
     prompt_optimizer_controller,
     tool_controller,
-    home_page_controller,
+    memory_forget_controller,
 )
 from . import user_memory_controllers
 
@@ -71,6 +71,6 @@ 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(tool_controller.router)
-manager_router.include_router(home_page_controller.router)
+manager_router.include_router(memory_forget_controller.router)
 
 __all__ = ["manager_router"]

api/app/controllers/memory_forget_controller.py

@@ -0,0 +1,324 @@
+"""
+遗忘引擎控制器模块
+
+本模块提供遗忘引擎的 REST API 接口，包括：
+1. 手动触发遗忘周期
+2. 获取和更新配置
+3. 获取统计信息
+4. 获取遗忘曲线数据
+
+所有接口都需要用户认证，并自动关联到当前工作空间。
+"""
+
+from typing import Optional
+
+from fastapi import APIRouter, Depends
+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 fail, success
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models.user_model import User
+from app.schemas.memory_storage_schema import (
+    ForgettingTriggerRequest,
+    ForgettingConfigResponse,
+    ForgettingConfigUpdateRequest,
+    ForgettingStatsResponse,
+    ForgettingReportResponse,
+    ForgettingCurveRequest,
+    ForgettingCurveResponse,
+    ForgettingCurvePoint,
+)
+from app.schemas.response_schema import ApiResponse
+from app.services.memory_forget_service import MemoryForgetService
+
+
+# 获取API专用日志器
+api_logger = get_api_logger()
+
+router = APIRouter(
+    prefix="/memory/forget",
+    tags=["Memory Forgetting Engine"],
+    dependencies=[Depends(get_current_user)]  # 所有路由都需要认证
+)
+
+# 初始化服务
+forget_service = MemoryForgetService()
+
+
+# ==================== API 端点 ====================
+
+@router.post("/trigger", response_model=ApiResponse)
+async def trigger_forgetting_cycle(
+    payload: ForgettingTriggerRequest,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db)
+):
+    """
+    手动触发遗忘周期
+    
+    执行一次完整的遗忘周期，识别并融合低激活值节点。
+    
+    Args:
+        payload: 触发请求参数
+        current_user: 当前用户
+        db: 数据库会话
+    
+    Returns:
+        ApiResponse: 包含遗忘报告的响应
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    # 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} 尝试触发遗忘周期但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+    
+    api_logger.info(
+        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求触发遗忘周期: "
+        f"group_id={payload.group_id}, max_batch={payload.max_merge_batch_size}, "
+        f"min_days={payload.min_days_since_access}"
+    )
+    
+    try:
+        # 调用服务层执行遗忘周期
+        report = await forget_service.trigger_forgetting_cycle(
+            db=db,
+            group_id=payload.group_id,
+            max_merge_batch_size=payload.max_merge_batch_size,
+            min_days_since_access=payload.min_days_since_access,
+            config_id=payload.config_id
+        )
+        
+        # 构建响应
+        response_data = ForgettingReportResponse(**report)
+        
+        return success(data=response_data.model_dump(), msg="遗忘周期执行成功")
+    
+    except RuntimeError as e:
+        api_logger.warning(f"遗忘周期执行被拒绝: {str(e)}")
+        return fail(BizCode.INVALID_PARAMETER, str(e), "RuntimeError")
+    
+    except Exception as e:
+        api_logger.error(f"触发遗忘周期失败: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "触发遗忘周期失败", str(e))
+
+
+@router.get("/read_config", response_model=ApiResponse)
+async def read_forgetting_config(
+    config_id: int,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db)
+):
+    """
+    获取遗忘引擎配置
+    
+    读取指定配置ID的遗忘引擎参数。
+    
+    Args:
+        config_id: 配置ID
+        current_user: 当前用户
+        db: 数据库会话
+    
+    Returns:
+        ApiResponse: 包含配置信息的响应
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    # 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} 尝试读取遗忘引擎配置但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+    
+    api_logger.info(
+        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求读取遗忘引擎配置: {config_id}"
+    )
+    
+    try:
+        # 调用服务层读取配置
+        config = forget_service.read_forgetting_config(db=db, config_id=config_id)
+        
+        # 构建响应
+        response_data = ForgettingConfigResponse(**config)
+        
+        return success(data=response_data.model_dump(), msg="查询成功")
+    
+    except ValueError as e:
+        api_logger.warning(f"配置不存在: config_id={config_id}, 错误: {str(e)}")
+        return fail(BizCode.INVALID_PARAMETER, f"配置不存在: {config_id}", str(e))
+    
+    except Exception as e:
+        api_logger.error(f"读取遗忘引擎配置失败: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "查询遗忘引擎配置失败", str(e))
+
+
+@router.post("/update_config", response_model=ApiResponse)
+async def update_forgetting_config(
+    payload: ForgettingConfigUpdateRequest,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db)
+):
+    """
+    更新遗忘引擎配置
+    
+    更新指定配置ID的遗忘引擎参数。
+    
+    Args:
+        payload: 配置更新请求
+        current_user: 当前用户
+        db: 数据库会话
+    
+    Returns:
+        ApiResponse: 包含更新结果的响应
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    # 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} 尝试更新遗忘引擎配置但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+    
+    api_logger.info(
+        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求更新遗忘引擎配置: {payload.config_id}"
+    )
+    
+    try:
+        # 构建更新字段字典（排除 None 值和 config_id）
+        update_data = {
+            key: value 
+            for key, value in payload.model_dump(exclude_none=True).items() 
+            if key != 'config_id'
+        }
+        
+        # 调用服务层更新配置
+        config = forget_service.update_forgetting_config(
+            db=db,
+            config_id=payload.config_id,
+            update_fields=update_data
+        )
+        
+        # 构建响应
+        response_data = ForgettingConfigResponse(**config)
+        
+        return success(data=response_data.model_dump(), msg="更新成功")
+    
+    except ValueError as e:
+        api_logger.warning(f"配置不存在: config_id={payload.config_id}, 错误: {str(e)}")
+        return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError")
+    
+    except Exception as e:
+        db.rollback()
+        api_logger.error(f"更新遗忘引擎配置失败: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "更新遗忘引擎配置失败", str(e))
+
+
+@router.get("/stats", response_model=ApiResponse)
+async def get_forgetting_stats(
+    group_id: Optional[str] = None,
+    config_id: Optional[int] = None,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db)
+):
+    """
+    获取遗忘引擎统计信息
+    
+    返回知识层节点统计、激活值分布等信息。
+    
+    Args:
+        group_id: 组ID（可选）
+        config_id: 配置ID（可选，用于获取遗忘阈值）
+        current_user: 当前用户
+        db: 数据库会话
+    
+    Returns:
+        ApiResponse: 包含统计信息的响应
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    # 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} 尝试获取遗忘引擎统计但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+    
+    api_logger.info(
+        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: "
+        f"group_id={group_id}, config_id={config_id}"
+    )
+    
+    try:
+        # 调用服务层获取统计信息
+        stats = await forget_service.get_forgetting_stats(
+            db=db,
+            group_id=group_id,
+            config_id=config_id
+        )
+        
+        # 构建响应
+        response_data = ForgettingStatsResponse(**stats)
+        
+        return success(data=response_data.model_dump(), msg="查询成功")
+    
+    except Exception as e:
+        api_logger.error(f"获取遗忘引擎统计失败: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "获取遗忘引擎统计失败", str(e))
+
+
+@router.post("/forgetting_curve", response_model=ApiResponse)
+async def get_forgetting_curve(
+    request: ForgettingCurveRequest,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db)
+):
+    """
+    获取遗忘曲线数据
+    
+    生成遗忘曲线数据用于可视化，模拟记忆激活值随时间的衰减。
+    
+    Args:
+        request: 遗忘曲线请求参数
+        current_user: 当前用户
+        db: 数据库会话
+    
+    Returns:
+        ApiResponse: 包含遗忘曲线数据的响应
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    # 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} 尝试获取遗忘曲线但未选择工作空间")
+        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
+    
+    api_logger.info(
+        f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘曲线: "
+        f"importance_score={request.importance_score}, days={request.days}, config_id={request.config_id}"
+    )
+    
+    try:
+        # 调用服务层生成遗忘曲线
+        result = await forget_service.get_forgetting_curve(
+            db=db,
+            importance_score=request.importance_score,
+            days=request.days,
+            config_id=request.config_id
+        )
+        
+        # 转换为响应格式
+        curve_points = [
+            ForgettingCurvePoint(**point)
+            for point in result['curve_data']
+        ]
+        
+        # 构建响应
+        response_data = ForgettingCurveResponse(
+            curve_data=curve_points,
+            config=result['config']
+        )
+        
+        return success(data=response_data.model_dump(), msg="查询成功")
+    
+    except Exception as e:
+        api_logger.error(f"获取遗忘曲线失败: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "获取遗忘曲线失败", str(e))

api/app/controllers/memory_storage_controller.py

@@ -1,4 +1,3 @@
-import datetime
 import os
 import uuid
 from typing import Optional
@@ -9,12 +8,7 @@ from app.core.memory.utils.self_reflexion_utils import self_reflexion
 from app.core.response_utils import fail, success
 from app.db import get_db
 from app.dependencies import get_current_user
-from app.models.end_user_model import EndUser
 from app.models.user_model import User
-from app.schemas.end_user_schema import (
-    EndUserProfileResponse,
-    EndUserProfileUpdate,
-)
 from app.schemas.memory_storage_schema import (
     ConfigKey,
     ConfigParamsCreate,
@@ -22,8 +16,6 @@ from app.schemas.memory_storage_schema import (
     ConfigPilotRun,
     ConfigUpdate,
     ConfigUpdateExtracted,
-    ConfigUpdateForget,
-    GenerateCacheRequest,
 )
 from app.schemas.response_schema import ApiResponse
 from app.services.memory_storage_service import (
@@ -238,28 +230,8 @@ def update_config_extracted(
 
 
 # --- Forget config params ---
-@router.post("/update_config_forget", response_model=ApiResponse) # 更新遗忘引擎配置参数（固定路径）
-def update_config_forget(
-    payload: ConfigUpdateForget,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
-    workspace_id = current_user.current_workspace_id
-    
-    # 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} 尝试更新遗忘引擎配置但未选择工作空间")
-        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
-    
-    api_logger.info(f"用户 {current_user.username} 在工作空间 {workspace_id} 请求更新遗忘引擎配置: {payload.config_id}")
-    try:
-        svc = DataConfigService(db)
-        result = svc.update_forget(payload)
-        return success(data=result, msg="更新成功")
-    except Exception as e:
-        api_logger.error(f"Update config forget failed: {str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "更新遗忘引擎配置失败", str(e))
-
+# 遗忘引擎配置接口已迁移到 memory_forget_controller.py
+# 使用新接口: /api/memory/forget/read_config 和 /api/memory/forget/update_config
 
 @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除
 def read_config_extracted(
@@ -283,28 +255,6 @@ def read_config_extracted(
         api_logger.error(f"Read config extracted failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "查询配置失败", str(e))
 
-@router.get("/read_config_forget", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除
-def read_config_forget(
-    config_id: str,
-    current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db),
-    ) -> dict:
-    workspace_id = current_user.current_workspace_id
-    
-    # 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} 尝试读取遗忘引擎配置但未选择工作空间")
-        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
-    
-    api_logger.info(f"用户 {current_user.username} 在工作空间 {workspace_id} 请求读取遗忘引擎配置: {config_id}")
-    try:
-        svc = DataConfigService(db)
-        result = svc.get_forget(ConfigKey(config_id=config_id))
-        return success(data=result, msg="查询成功")
-    except Exception as e:
-        api_logger.error(f"Read config forget failed: {str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "查询遗忘引擎配置失败", str(e))
-
 @router.get("/read_all_config", response_model=ApiResponse) # 读取所有配置文件列表
 def read_all_config(
     current_user: User = Depends(get_current_user),

api/app/core/memory/agent/mcp_server/services/search_service.py

@@ -106,28 +106,32 @@ class SearchService:
         limit: int = 15,
         search_type: str = "hybrid",
         include: Optional[List[str]] = None,
-        rerank_alpha: float = 0.4,
+        rerank_alpha: float = 0.6,
+        activation_boost_factor: float = 0.8,
         output_path: str = "search_results.json",
         return_raw_results: bool = False,
         memory_config: "MemoryConfig" = None,
     ) -> Tuple[str, str, Optional[dict]]:
         """
-        Execute hybrid search and return clean content.
+        Execute hybrid search with two-stage ranking.
+        
+        Stage 1: Filter by content relevance (BM25 + Embedding)
+        Stage 2: Rerank by activation values (ACTR)
         
         Args:
-            group_id: Group identifier for filtering results
+            group_id: Group identifier for filtering
             question: Search query text
-            limit: Maximum number of results to return (default: 5)
-            search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid")
-            include: List of result types to include (default: ["statements", "chunks", "entities", "summaries"])
-            rerank_alpha: Weight for BM25 scores in reranking (default: 0.4)
-            output_path: Path to save search results (default: "search_results.json")
-            return_raw_results: If True, also return the raw search results as third element (default: False)
-            memory_config: MemoryConfig object for embedding model. Falls back to self.memory_config if not provided.
+            limit: Max results per category (default: 15)
+            search_type: "hybrid", "keyword", or "embedding" (default: "hybrid")
+            include: Result types (default: ["statements", "chunks", "entities", "summaries"])
+            rerank_alpha: BM25 weight (default: 0.6)
+            activation_boost_factor: Activation impact on memory strength (default: 0.8)
+            output_path: JSON output path (default: "search_results.json")
+            return_raw_results: Return full metadata (default: False)
+            memory_config: MemoryConfig for embedding model
         
         Returns:
-            Tuple of (clean_content, cleaned_query, raw_results)
-            raw_results is None if return_raw_results=False
+            Tuple[str, str, Optional[dict]]: (clean_content, cleaned_query, raw_results)
         """
         if include is None:
             include = ["statements", "chunks", "entities", "summaries"]
@@ -151,6 +155,7 @@ class SearchService:
                 output_path=output_path,
                 memory_config=config,
                 rerank_alpha=rerank_alpha,
+                activation_boost_factor=activation_boost_factor,
             )
             
             # Extract results based on search type and include parameter

api/app/core/memory/models/graph_models.py

@@ -228,6 +228,13 @@ class StatementNode(Node):
         chunk_embedding: Optional embedding vector for the parent chunk
         connect_strength: Classification of connection strength ('Strong' or 'Weak')
         config_id: Configuration ID used to process this statement
+        
+        # ACT-R Memory Activation Properties
+        importance_score: Importance score for memory activation (0.0-1.0), default 0.5
+        activation_value: Current activation value calculated by ACT-R engine (0.0-1.0)
+        access_history: List of ISO timestamp strings recording each access
+        last_access_time: ISO timestamp of the most recent access
+        access_count: Total number of times this node has been accessed
     """
     # Core fields (ordered as requested)
     chunk_id: str = Field(..., description="ID of the parent chunk")
@@ -269,6 +276,33 @@ class StatementNode(Node):
     connect_strength: str = Field(..., description="Strong VS Weak classification of this statement")
     config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this statement (integer or string)")
     
+    # ACT-R Memory Activation Properties
+    importance_score: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Importance score for memory activation (0.0-1.0), default 0.5"
+    )
+    activation_value: Optional[float] = Field(
+        None,
+        ge=0.0,
+        le=1.0,
+        description="Current activation value calculated by ACT-R engine (0.0-1.0)"
+    )
+    access_history: List[str] = Field(
+        default_factory=list,
+        description="List of ISO timestamp strings recording each access"
+    )
+    last_access_time: Optional[str] = Field(
+        None,
+        description="ISO timestamp of the most recent access"
+    )
+    access_count: int = Field(
+        default=0,
+        ge=0,
+        description="Total number of times this node has been accessed"
+    )
+    
     @field_validator('valid_at', 'invalid_at', mode='before')
     @classmethod
     def validate_datetime(cls, v):
@@ -351,6 +385,13 @@ class ExtractedEntityNode(Node):
         fact_summary: Summary of facts about this entity
         connect_strength: Classification of connection strength ('Strong', 'Weak', or 'Both')
         config_id: Configuration ID used to process this entity (integer or string)
+        
+        # ACT-R Memory Activation Properties
+        importance_score: Importance score for memory activation (0.0-1.0), default 0.5
+        activation_value: Current activation value calculated by ACT-R engine (0.0-1.0)
+        access_history: List of ISO timestamp strings recording each access
+        last_access_time: ISO timestamp of the most recent access
+        access_count: Total number of times this node has been accessed
     """
     entity_idx: int = Field(..., description="Unique identifier for the entity")
     statement_id: str = Field(..., description="Statement this entity was extracted from")
@@ -365,6 +406,33 @@ class ExtractedEntityNode(Node):
     connect_strength: str = Field(..., description="Strong VS Weak about this entity")
     config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this entity (integer or string)")
     
+    # ACT-R Memory Activation Properties
+    importance_score: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Importance score for memory activation (0.0-1.0), default 0.5"
+    )
+    activation_value: Optional[float] = Field(
+        None,
+        ge=0.0,
+        le=1.0,
+        description="Current activation value calculated by ACT-R engine (0.0-1.0)"
+    )
+    access_history: List[str] = Field(
+        default_factory=list,
+        description="List of ISO timestamp strings recording each access"
+    )
+    last_access_time: Optional[str] = Field(
+        None,
+        description="ISO timestamp of the most recent access"
+    )
+    access_count: int = Field(
+        default=0,
+        ge=0,
+        description="Total number of times this node has been accessed"
+    )
+    
     @field_validator('aliases', mode='before')
     @classmethod
     def validate_aliases_field(cls, v): # 字段验证器 自动清理和验证 aliases 字段
@@ -401,6 +469,16 @@ class MemorySummaryNode(Node):
         summary_embedding: Optional embedding vector for the summary
         metadata: Additional metadata for the summary
         config_id: Configuration ID used to process this summary
+        original_statement_id: ID of the original statement that was merged (for ACT-R forgetting)
+        original_entity_id: ID of the original entity that was merged (for ACT-R forgetting)
+        merged_at: Timestamp when the nodes were merged
+        
+        # ACT-R Memory Activation Properties
+        importance_score: Importance score for memory activation (0.0-1.0), inherited from merged nodes
+        activation_value: Current activation value calculated by ACT-R engine (0.0-1.0), inherited from merged nodes
+        access_history: List of ISO timestamp strings recording each access (reset on creation)
+        last_access_time: ISO timestamp of the most recent access (set to creation time)
+        access_count: Total number of times this node has been accessed (reset to 1 on creation)
     """
     summary_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for the summary")
     dialog_id: str = Field(..., description="ID of the parent dialog")
@@ -409,3 +487,44 @@ class MemorySummaryNode(Node):
     summary_embedding: Optional[List[float]] = Field(None, description="Embedding vector for the summary")
     metadata: dict = Field(default_factory=dict, description="Additional metadata for the summary")
     config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this summary (integer or string)")
+    
+    # ACT-R Forgetting Engine Properties
+    original_statement_id: Optional[str] = Field(
+        None,
+        description="ID of the original statement that was merged (for traceability)"
+    )
+    original_entity_id: Optional[str] = Field(
+        None,
+        description="ID of the original entity that was merged (for traceability)"
+    )
+    merged_at: Optional[datetime] = Field(
+        None,
+        description="Timestamp when the nodes were merged"
+    )
+    
+    # ACT-R Memory Activation Properties
+    importance_score: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Importance score for memory activation (0.0-1.0), inherited from merged nodes"
+    )
+    activation_value: Optional[float] = Field(
+        None,
+        ge=0.0,
+        le=1.0,
+        description="Current activation value calculated by ACT-R engine (0.0-1.0), inherited from merged nodes"
+    )
+    access_history: List[str] = Field(
+        default_factory=list,
+        description="List of ISO timestamp strings recording each access (reset on creation)"
+    )
+    last_access_time: Optional[str] = Field(
+        None,
+        description="ISO timestamp of the most recent access (set to creation time)"
+    )
+    access_count: int = Field(
+        default=1,
+        ge=0,
+        description="Total number of times this node has been accessed (reset to 1 on creation)"
+    )

api/app/core/memory/src/search.py

@@ -69,6 +69,12 @@ def normalize_scores(results: List[Dict[str, Any]], score_field: str = "score")
     for item in results:
         if score_field in item:
             score = item.get(score_field)
+            # 对于 activation_value，None 值保持为 None，不使用回退值
+            # 这样可以区分有激活值和无激活值的节点
+            if score_field == "activation_value" and score is None:
+                scores.append(None)  # 保持 None，稍后特殊处理
+                continue
+            
             if score is not None and isinstance(score, (int, float)):
                 scores.append(float(score))
             else:
@@ -76,205 +82,433 @@ def normalize_scores(results: List[Dict[str, Any]], score_field: str = "score")
 
     if not scores:
         return results
-
-    if len(scores) == 1:
-        # Single score, set to 1.0
+    
+    # 过滤掉 None 值，只对有效分数进行归一化
+    valid_scores = [s for s in scores if s is not None]
+    
+    if not valid_scores:
+        # 所有分数都是 None，不进行归一化
         for item in results:
-            if score_field in item:
-                item[f"normalized_{score_field}"] = 1.0
+            if score_field in item or score_field == "activation_value":
+                item[f"normalized_{score_field}"] = None
         return results
 
-    # Calculate mean and standard deviation
-    mean_score = sum(scores) / len(scores)
-    variance = sum((score - mean_score) ** 2 for score in scores) / len(scores)
+    if len(valid_scores) == 1:        # Single valid score, set to 1.0
+        for item, score in zip(results, scores):
+            if score_field in item or score_field == "activation_value":
+                if score is None:
+                    item[f"normalized_{score_field}"] = None
+                else:
+                    item[f"normalized_{score_field}"] = 1.0
+        return results
+
+    # Calculate mean and standard deviation (only for valid scores)
+    mean_score = sum(valid_scores) / len(valid_scores)
+    variance = sum((score - mean_score) ** 2 for score in valid_scores) / len(valid_scores)
     std_dev = math.sqrt(variance)
 
     if std_dev == 0:
-        # All scores are the same, set them to 1.0
-        for item in results:
-            if score_field in item:
-                item[f"normalized_{score_field}"] = 1.0
+        # All valid scores are the same, set them to 1.0
+        for item, score in zip(results, scores):
+            if score_field in item or score_field == "activation_value":
+                if score is None:
+                    item[f"normalized_{score_field}"] = None
+                else:
+                    item[f"normalized_{score_field}"] = 1.0
     else:
-        for item in results:
-            if score_field in item:
-                score = item[score_field]
-                # Handle None or non-numeric scores
-                if score is None or not isinstance(score, (int, float)):
-                    score = 0.0
-                # Calculate z-score
-                z_score = (score - mean_score) / std_dev
-                # Transform to positive range using sigmoid function
-                normalized = 1 / (1 + math.exp(-z_score))
-                item[f"normalized_{score_field}"] = normalized
+        for item, score in zip(results, scores):
+            if score_field in item or score_field == "activation_value":
+                if score is None:
+                    # 保持 None，不进行归一化
+                    item[f"normalized_{score_field}"] = None
+                else:
+                    # Calculate z-score
+                    z_score = (score - mean_score) / std_dev
+                    # Transform to positive range using sigmoid function
+                    normalized = 1 / (1 + math.exp(-z_score))
+                    item[f"normalized_{score_field}"] = normalized
 
     return results
 
 
-def rerank_hybrid_results(
-    keyword_results: Dict[str, List[Dict[str, Any]]],
-    embedding_results: Dict[str, List[Dict[str, Any]]],
-    alpha: float = 0.6,
-    limit: int = 10
-) -> Dict[str, List[Dict[str, Any]]]:
-    """
-    Rerank hybrid search results by combining BM25 and embedding scores.
+# ============================================================================
+# 以下函数已被 rerank_with_activation 替代，暂时保留以供参考
+# ============================================================================
 
-    Args:
-        keyword_results: Results from keyword/BM25 search
-        embedding_results: Results from embedding search
-        alpha: Weight for BM25 scores (1-alpha for embedding scores)
-        limit: Maximum number of results to return per category
+# def rerank_hybrid_results(
+#     keyword_results: Dict[str, List[Dict[str, Any]]],
+#     embedding_results: Dict[str, List[Dict[str, Any]]],
+#     alpha: float = 0.6,
+#     limit: int = 10
+# ) -> Dict[str, List[Dict[str, Any]]]:
+#     """
+#     Rerank hybrid search results by combining BM25 and embedding scores.
+#     
+#     已废弃：此函数功能已被 rerank_with_activation 完全替代
+#
+#     Args:
+#         keyword_results: Results from keyword/BM25 search
+#         embedding_results: Results from embedding search
+#         alpha: Weight for BM25 scores (1-alpha for embedding scores)
+#         limit: Maximum number of results to return per category
+#
+#     Returns:
+#         Reranked results with combined scores
+#     """
+#     reranked = {}
+#
+#     for category in ["statements", "chunks", "entities","summaries"]:
+#         keyword_items = keyword_results.get(category, [])
+#         embedding_items = embedding_results.get(category, [])
+#
+#         # Normalize scores within each search type
+#         keyword_items = normalize_scores(keyword_items, "score")
+#         embedding_items = normalize_scores(embedding_items, "score")
+#
+#         # Create a combined pool of unique items
+#         combined_items = {}
+#
+#         # Add keyword results with BM25 scores
+#         for item in keyword_items:
+#             item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
+#             if item_id:
+#                 combined_items[item_id] = item.copy()
+#                 combined_items[item_id]["bm25_score"] = item.get("normalized_score", 0)
+#                 combined_items[item_id]["embedding_score"] = 0  # Default
+#
+#         # Add or update with embedding results
+#         for item in embedding_items:
+#             item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
+#             if item_id:
+#                 if item_id in combined_items:
+#                     # Update existing item with embedding score
+#                     combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
+#                 else:
+#                     # New item from embedding search only
+#                     combined_items[item_id] = item.copy()
+#                     combined_items[item_id]["bm25_score"] = 0  # Default
+#                     combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
+#
+#         # Calculate combined scores and rank
+#         for item_id, item in combined_items.items():
+#             bm25_score = item.get("bm25_score", 0)
+#             embedding_score = item.get("embedding_score", 0)
+#
+#             # Combined score: weighted average of normalized scores
+#             combined_score = alpha * bm25_score + (1 - alpha) * embedding_score
+#             item["combined_score"] = combined_score
+#
+#             # Keep original score for reference
+#             if "score" not in item and bm25_score > 0:
+#                 item["score"] = bm25_score
+#             elif "score" not in item and embedding_score > 0:
+#                 item["score"] = embedding_score
+#
+#         # Sort by combined score and limit results
+#         sorted_items = sorted(
+#             combined_items.values(),
+#             key=lambda x: x.get("combined_score", 0),
+#             reverse=True
+#         )[:limit]
+#
+#         reranked[category] = sorted_items
+#
+#     return reranked
 
-    Returns:
-        Reranked results with combined scores
-    """
-    reranked = {}
+# def rerank_with_forgetting_curve(
+#     keyword_results: Dict[str, List[Dict[str, Any]]],
+#     embedding_results: Dict[str, List[Dict[str, Any]]],
+#     alpha: float = 0.6,
+#     limit: int = 10,
+#     forgetting_config: ForgettingEngineConfig | None = None,
+#     now: datetime | None = None,
+# ) -> Dict[str, List[Dict[str, Any]]]:
+#     """
+#     Rerank hybrid results with a forgetting curve applied to combined scores.
+#     
+#     已废弃：此函数功能已被 rerank_with_activation 完全替代
+#     rerank_with_activation 提供了更完整的遗忘曲线支持（结合激活度）
+#
+#     The forgetting curve reduces scores for older memories or weaker connections.
+#
+#     Args:
+#         keyword_results: Results from keyword/BM25 search
+#         embedding_results: Results from embedding search
+#         alpha: Weight for BM25 scores (1-alpha for embedding scores)
+#         limit: Maximum number of results to return per category
+#         forgetting_config: Configuration for the forgetting engine
+#         now: Optional current time override for testing
+#
+#     Returns:
+#         Reranked results with combined and final scores (after forgetting)
+#     """
+#     engine = ForgettingEngine(forgetting_config or ForgettingEngineConfig())
+#     now_dt = now or datetime.now()
+#
+#     reranked: Dict[str, List[Dict[str, Any]]] = {}
+#
+#     for category in ["statements", "chunks", "entities","summaries"]:
+#         keyword_items = keyword_results.get(category, [])
+#         embedding_items = embedding_results.get(category, [])
+#
+#         # Normalize scores within each search type
+#         keyword_items = normalize_scores(keyword_items, "score")
+#         embedding_items = normalize_scores(embedding_items, "score")
+#
+#         combined_items: Dict[str, Dict[str, Any]] = {}
+#
+#         # Combine two result sets by ID
+#         for src_items, is_embedding in (
+#             (keyword_items, False), (embedding_items, True)
+#         ):
+#             for item in src_items:
+#                 item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
+#                 if not item_id:
+#                     continue
+#                 existing = combined_items.get(item_id)
+#                 if not existing:
+#                     combined_items[item_id] = item.copy()
+#                     combined_items[item_id]["bm25_score"] = 0
+#                     combined_items[item_id]["embedding_score"] = 0
+#                 # Update normalized score from the right source
+#                 if is_embedding:
+#                     combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
+#                 else:
+#                     combined_items[item_id]["bm25_score"] = item.get("normalized_score", 0)
+#
+#         # Calculate scores and apply forgetting weights
+#         for item_id, item in combined_items.items():
+#             bm25_score = float(item.get("bm25_score", 0) or 0)
+#             embedding_score = float(item.get("embedding_score", 0) or 0)
+#             combined_score = alpha * bm25_score + (1 - alpha) * embedding_score
+#
+#             # Estimate time elapsed in days
+#             dt = _parse_datetime(item.get("created_at"))
+#             if dt is None:
+#                 time_elapsed_days = 0.0
+#             else:
+#                 time_elapsed_days = max(0.0, (now_dt - dt).total_seconds() / 86400.0)
+#
+#             # Memory strength (currently set to default value)
+#             memory_strength = 1.0
+#             forgetting_weight = engine.calculate_weight(
+#                 time_elapsed=time_elapsed_days, memory_strength=memory_strength
+#             )
+#             final_score = combined_score * forgetting_weight
+#             item["combined_score"] = final_score
+#
+#         sorted_items = sorted(
+#             combined_items.values(), key=lambda x: x.get("combined_score", 0), reverse=True
+#         )[:limit]
+#
+#         reranked[category] = sorted_items
+#
+#     return reranked
 
-    for category in ["statements", "chunks", "entities","summaries"]:
-        keyword_items = keyword_results.get(category, [])
-        embedding_items = embedding_results.get(category, [])
 
-        # Normalize scores within each search type
-        keyword_items = normalize_scores(keyword_items, "score")
-        embedding_items = normalize_scores(embedding_items, "score")
-
-        # Create a combined pool of unique items
-        combined_items = {}
-
-        # Add keyword results with BM25 scores
-        for item in keyword_items:
-            item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
-            if item_id:
-                combined_items[item_id] = item.copy()
-                combined_items[item_id]["bm25_score"] = item.get("normalized_score", 0)
-                combined_items[item_id]["embedding_score"] = 0  # Default
-
-        # Add or update with embedding results
-        for item in embedding_items:
-            item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
-            if item_id:
-                if item_id in combined_items:
-                    # Update existing item with embedding score
-                    combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
-                else:
-                    # New item from embedding search only
-                    combined_items[item_id] = item.copy()
-                    combined_items[item_id]["bm25_score"] = 0  # Default
-                    combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
-
-        # Calculate combined scores and rank
-        for item_id, item in combined_items.items():
-            bm25_score = item.get("bm25_score", 0)
-            embedding_score = item.get("embedding_score", 0)
-
-            # Combined score: weighted average of normalized scores
-            combined_score = alpha * bm25_score + (1 - alpha) * embedding_score
-            item["combined_score"] = combined_score
-
-            # Keep original score for reference
-            if "score" not in item and bm25_score > 0:
-                item["score"] = bm25_score
-            elif "score" not in item and embedding_score > 0:
-                item["score"] = embedding_score
-
-        # Sort by combined score and limit results
-        sorted_items = sorted(
-            combined_items.values(),
-            key=lambda x: x.get("combined_score", 0),
-            reverse=True
-        )[:limit]
-
-        reranked[category] = sorted_items
-
-    return reranked
-
-def rerank_with_forgetting_curve(
+def rerank_with_activation(
     keyword_results: Dict[str, List[Dict[str, Any]]],
     embedding_results: Dict[str, List[Dict[str, Any]]],
     alpha: float = 0.6,
     limit: int = 10,
     forgetting_config: ForgettingEngineConfig | None = None,
+    activation_boost_factor: float = 0.8,
     now: datetime | None = None,
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
-    Rerank hybrid results with a forgetting curve applied to combined scores.
-
-    The forgetting curve reduces scores for older memories or weaker connections.
-
-    Args:
-        keyword_results: Results from keyword/BM25 search
-        embedding_results: Results from embedding search
-        alpha: Weight for BM25 scores (1-alpha for embedding scores)
-        limit: Maximum number of results to return per category
-        forgetting_config: Configuration for the forgetting engine
-        now: Optional current time override for testing
-
-    Returns:
-        Reranked results with combined and final scores (after forgetting)
+    两阶段排序：先按内容相关性筛选，再按激活值排序。
+    
+    阶段1: content_score = alpha*BM25 + (1-alpha)*Embedding，取 Top-(limit*3)
+    阶段2: 在候选中按 activation_score 排序，取 Top-limit
+           无激活值的节点用于补充不足
+    
+    返回结果中的评分字段说明：
+        - bm25_score: BM25 归一化分数
+        - embedding_score: Embedding 归一化分数
+        - content_score: 内容相关性 = alpha*bm25 + (1-alpha)*embedding
+        - activation_score: ACTR 激活值归一化分数
+        - base_score: 第一阶段基础分数（等于 content_score）
+        - final_score: 最终排序依据
+            * 有激活值的节点：final_score = activation_score
+            * 无激活值的节点：final_score = base_score
+    
+    参数:
+        keyword_results: BM25 检索结果
+        embedding_results: 向量嵌入检索结果
+        alpha: BM25 权重 (默认: 0.6)
+        limit: 每类最大结果数
+        forgetting_config: 遗忘引擎配置（当前未使用）
+        activation_boost_factor: 激活度对记忆强度的影响系数 (默认: 0.8)
+        now: 当前时间（用于遗忘计算）
+        
+    返回:
+        带评分元数据的重排序结果，按 final_score 排序
     """
-    engine = ForgettingEngine(forgetting_config or ForgettingEngineConfig())
+    # 验证权重范围
+    if not (0 <= alpha <= 1):
+        raise ValueError(f"alpha 必须在 [0, 1] 范围内，当前值: {alpha}")
+    
+    # 初始化遗忘引擎（如果需要）
+    engine = None
+    if forgetting_config:
+        engine = ForgettingEngine(forgetting_config)
     now_dt = now or datetime.now()
-
+    
     reranked: Dict[str, List[Dict[str, Any]]] = {}
-
-    for category in ["statements", "chunks", "entities","summaries"]:
+    
+    for category in ["statements", "chunks", "entities", "summaries"]:
         keyword_items = keyword_results.get(category, [])
         embedding_items = embedding_results.get(category, [])
-
-        # Normalize scores within each search type
+        
+        # 步骤 1: 归一化分数
         keyword_items = normalize_scores(keyword_items, "score")
         embedding_items = normalize_scores(embedding_items, "score")
-
+        
+        # 步骤 2: 按 ID 合并结果
         combined_items: Dict[str, Dict[str, Any]] = {}
-
-        # Combine two result sets by ID
-        for src_items, is_embedding in (
-            (keyword_items, False), (embedding_items, True)
-        ):
-            for item in src_items:
-                item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
-                if not item_id:
-                    continue
-                existing = combined_items.get(item_id)
-                if not existing:
-                    combined_items[item_id] = item.copy()
-                    combined_items[item_id]["bm25_score"] = 0
-                    combined_items[item_id]["embedding_score"] = 0
-                # Update normalized score from the right source
-                if is_embedding:
-                    combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
-                else:
-                    combined_items[item_id]["bm25_score"] = item.get("normalized_score", 0)
-
-        # Calculate scores and apply forgetting weights
-        for item_id, item in combined_items.items():
-            bm25_score = float(item.get("bm25_score", 0) or 0)
-            embedding_score = float(item.get("embedding_score", 0) or 0)
-            combined_score = alpha * bm25_score + (1 - alpha) * embedding_score
-
-            # Estimate time elapsed in days
-            dt = _parse_datetime(item.get("created_at"))
-            if dt is None:
-                time_elapsed_days = 0.0
+        
+        # 添加关键词结果
+        for item in keyword_items:
+            item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
+            if not item_id:
+                continue
+            combined_items[item_id] = item.copy()
+            combined_items[item_id]["bm25_score"] = item.get("normalized_score", 0)
+            combined_items[item_id]["embedding_score"] = 0  # 默认值
+        
+        # 添加或更新向量嵌入结果
+        for item in embedding_items:
+            item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
+            if not item_id:
+                continue
+            if item_id in combined_items:
+                # 更新现有项的嵌入分数
+                combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
             else:
-                time_elapsed_days = max(0.0, (now_dt - dt).total_seconds() / 86400.0)
-
-            # Memory strength (currently set to default value)
-            memory_strength = 1.0
-            forgetting_weight = engine.calculate_weight(
-                time_elapsed=time_elapsed_days, memory_strength=memory_strength
-            )
-            # print(f"Forgetting weight for {item_id}: {forgetting_weight}")
-            # print(f"Time elapsed days for {item_id}: {time_elapsed_days}")
-            final_score = combined_score * forgetting_weight
-            item["combined_score"] = final_score
-
-        sorted_items = sorted(
-            combined_items.values(), key=lambda x: x.get("combined_score", 0), reverse=True
-        )[:limit]
-
+                # 仅来自嵌入搜索的新项
+                combined_items[item_id] = item.copy()
+                combined_items[item_id]["bm25_score"] = 0  # 默认值
+                combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
+        
+        # 步骤 3: 归一化激活度分数
+        # 为所有项准备激活度值列表
+        items_list = list(combined_items.values())
+        items_list = normalize_scores(items_list, "activation_value")
+        
+        # 更新 combined_items 中的归一化激活度分数
+        for item in items_list:
+            item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
+            if item_id and item_id in combined_items:
+                combined_items[item_id]["normalized_activation_value"] = item.get("normalized_activation_value", 0)
+        
+        # 步骤 4: 计算基础分数和最终分数
+        for item_id, item in combined_items.items():
+            bm25_norm = float(item.get("bm25_score", 0) or 0)
+            emb_norm = float(item.get("embedding_score", 0) or 0)
+            act_norm = float(item.get("normalized_activation_value", 0) or 0)
+            
+            # 第一阶段：只考虑内容相关性（BM25 + Embedding）
+            # alpha 控制 BM25 权重，(1-alpha) 控制 Embedding 权重
+            content_score = alpha * bm25_norm + (1 - alpha) * emb_norm
+            base_score = content_score  # 第一阶段用内容分数
+            
+            # 存储激活度分数供第二阶段使用
+            item["activation_score"] = act_norm
+            item["content_score"] = content_score
+            item["base_score"] = base_score
+            
+            # 步骤 5: 应用遗忘曲线（可选）
+            if engine:
+                # 计算受激活度影响的记忆强度
+                importance = float(item.get("importance_score", 0.5) or 0.5)
+                
+                # 获取 activation_value
+                activation_val = item.get("activation_value")
+                
+                # 只对有激活值的节点应用遗忘曲线
+                if activation_val is not None and isinstance(activation_val, (int, float)):
+                    activation_val = float(activation_val)
+                    
+                    # 计算记忆强度：importance_score × (1 + activation_value × boost_factor)
+                    memory_strength = importance * (1 + activation_val * activation_boost_factor)
+                    
+                    # 计算经过的时间（天数）
+                    dt = _parse_datetime(item.get("created_at"))
+                    if dt is None:
+                        time_elapsed_days = 0.0
+                    else:
+                        time_elapsed_days = max(0.0, (now_dt - dt).total_seconds() / 86400.0)
+                    
+                    # 获取遗忘权重
+                    forgetting_weight = engine.calculate_weight(
+                        time_elapsed=time_elapsed_days,
+                        memory_strength=memory_strength
+                    )
+                    
+                    # 应用到基础分数
+                    item["forgetting_weight"] = forgetting_weight
+                    item["final_score"] = base_score * forgetting_weight
+                else:
+                    # 无激活值的节点不应用遗忘曲线，保持原始分数
+                    item["final_score"] = base_score
+            else:
+                # 不使用遗忘曲线
+                item["final_score"] = base_score
+        
+        # 步骤 6: 两阶段排序和限制
+        # 第一阶段：按内容相关性（base_score）排序，取 Top-K
+        first_stage_limit = limit * 3  # 可配置，取3倍候选
+        first_stage_sorted = sorted(
+            combined_items.values(),
+            key=lambda x: float(x.get("base_score", 0) or 0),  # 按内容分数排序
+            reverse=True
+        )[:first_stage_limit]
+        
+        # 第二阶段：分离有激活值和无激活值的节点
+        items_with_activation = []
+        items_without_activation = []
+        
+        for item in first_stage_sorted:
+            activation_score = item.get("activation_score")
+            # 检查是否有有效的激活值（不是 None）
+            if activation_score is not None and isinstance(activation_score, (int, float)):
+                items_with_activation.append(item)
+            else:
+                items_without_activation.append(item)
+        
+        # 优先按激活值排序有激活值的节点
+        sorted_with_activation = sorted(
+            items_with_activation,
+            key=lambda x: float(x.get("activation_score", 0) or 0),
+            reverse=True
+        )
+        
+        # 如果有激活值的节点不足 limit，用无激活值的节点补充
+        if len(sorted_with_activation) < limit:
+            needed = limit - len(sorted_with_activation)
+            # 无激活值的节点保持第一阶段的内容相关性排序
+            sorted_items = sorted_with_activation + items_without_activation[:needed]
+        else:
+            sorted_items = sorted_with_activation[:limit]
+        
+        # 两阶段排序完成，更新 final_score 以反映实际排序依据
+        # Stage 1: 按 content_score 筛选候选（已完成）
+        # Stage 2: 按 activation_score 排序（已完成）
+        # 
+        # final_score 语义：反映节点在最终结果中的排序依据
+        #   - 有激活值的节点：final_score = activation_score（第二阶段排序依据）
+        #   - 无激活值的节点：final_score = base_score（保持内容相关性分数）
+        for item in sorted_items:
+            activation_score = item.get("activation_score")
+            if activation_score is not None and isinstance(activation_score, (int, float)):
+                # 有激活值：使用激活度作为最终分数
+                item["final_score"] = activation_score
+            else:
+                # 无激活值：使用内容相关性分数
+                item["final_score"] = item.get("base_score", 0)
+        
         reranked[category] = sorted_items
-
+    
     return reranked
 
 
@@ -560,6 +794,7 @@ async def run_hybrid_search(
     output_path: str | None,
     memory_config: "MemoryConfig",
     rerank_alpha: float = 0.6,
+    activation_boost_factor: float = 0.8,
     use_forgetting_rerank: bool = False,
     use_llm_rerank: bool = False,
 ):
@@ -685,30 +920,28 @@ async def run_hybrid_search(
                 "search_timestamp": datetime.now().isoformat()
             }
 
-            # Apply reranking (optionally with forgetting curve)
+            # Apply two-stage reranking with ACTR activation calculation
             rerank_start = time.time()
-            if use_forgetting_rerank:
-                # Load forgetting parameters from pipeline config
-                try:
-                    pc = get_pipeline_config(memory_config)
-                    forgetting_cfg = pc.forgetting_engine
-                except Exception as e:
-                    logger.debug(f"Failed to load forgetting config, using defaults: {e}")
-                    forgetting_cfg = ForgettingEngineConfig()
-                reranked_results = rerank_with_forgetting_curve(
-                    keyword_results=keyword_results,
-                    embedding_results=embedding_results,
-                    alpha=rerank_alpha,
-                    limit=limit,
-                    forgetting_config=forgetting_cfg,
-                )
-            else:
-                reranked_results = rerank_hybrid_results(
-                    keyword_results=keyword_results,
-                    embedding_results=embedding_results,
-                    alpha=rerank_alpha,  # Configurable weight for BM25 vs embedding
-                    limit=limit
-                )
+            logger.info("Using two-stage reranking with ACTR activation")
+            
+            # 加载遗忘引擎配置
+            try:
+                pc = get_pipeline_config(memory_config)
+                forgetting_cfg = pc.forgetting_engine
+            except Exception as e:
+                logger.debug(f"Failed to load forgetting config, using defaults: {e}")
+                forgetting_cfg = ForgettingEngineConfig()
+            
+            # 统一使用激活度重排序（两阶段：检索 + ACTR计算）
+            reranked_results = rerank_with_activation(
+                keyword_results=keyword_results,
+                embedding_results=embedding_results,
+                alpha=rerank_alpha,
+                limit=limit,
+                forgetting_config=forgetting_cfg,
+                activation_boost_factor=activation_boost_factor,
+            )
+            
             rerank_latency = time.time() - rerank_start
             latency_metrics["reranking_latency"] = round(rerank_latency, 4)
             logger.info(f"Reranking completed in {rerank_latency:.4f}s")
@@ -737,6 +970,7 @@ async def run_hybrid_search(
                 "search_query": query_text,
                 "search_timestamp": datetime.now().isoformat(),
                 "reranking_alpha": rerank_alpha,
+                "activation_boost_factor": activation_boost_factor,
                 "forgetting_rerank": use_forgetting_rerank,
                 "llm_rerank": llm_rerank_applied,
             }

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

@@ -1,8 +1,40 @@
 """遗忘引擎模块
 
-该模块实现记忆的遗忘机制，基于改进的艾宾浩斯遗忘曲线。
+该模块实现记忆的遗忘机制，基于改进的艾宾浩斯遗忘曲线和 ACT-R 认知架构理论。
 """
 
 from app.core.memory.storage_services.forgetting_engine.forgetting_engine import ForgettingEngine
+from app.core.memory.storage_services.forgetting_engine.actr_calculator import (
+    ACTRCalculator,
+    calculate_activation,
+    generate_forgetting_curve
+)
+from app.core.memory.storage_services.forgetting_engine.access_history_manager import (
+    AccessHistoryManager,
+    ConsistencyCheckResult
+)
+from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import (
+    ForgettingStrategy
+)
+from app.core.memory.storage_services.forgetting_engine.forgetting_scheduler import (
+    ForgettingScheduler
+)
+from app.core.memory.storage_services.forgetting_engine.config_utils import (
+    calculate_forgetting_rate,
+    load_actr_config_from_db,
+    create_actr_calculator_from_config
+)
 
-__all__ = ["ForgettingEngine"]
+__all__ = [
+    "ForgettingEngine",
+    "ACTRCalculator",
+    "calculate_activation",
+    "generate_forgetting_curve",
+    "AccessHistoryManager",
+    "ConsistencyCheckResult",
+    "ForgettingStrategy",
+    "ForgettingScheduler",
+    "calculate_forgetting_rate",
+    "load_actr_config_from_db",
+    "create_actr_calculator_from_config"
+]

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

@@ -0,0 +1,691 @@
+"""
+访问历史管理器模块
+
+本模块实现访问历史的追踪、更新和一致性保证。
+负责在知识节点被访问时原子性地更新激活值相关的所有字段。
+
+Classes:
+    AccessHistoryManager: 访问历史管理器，提供并发安全的访问记录和一致性检查
+"""
+
+import logging
+from typing import List, Dict, Any, Optional, Tuple
+from datetime import datetime
+from enum import Enum
+
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
+
+
+logger = logging.getLogger(__name__)
+
+
+class ConsistencyCheckResult(Enum):
+    """一致性检查结果枚举"""
+    CONSISTENT = "consistent"  # 数据一致
+    INCONSISTENT_HISTORY_TIME = "inconsistent_history_time"  # access_history[-1] != last_access_time
+    INCONSISTENT_HISTORY_COUNT = "inconsistent_history_count"  # len(access_history) != access_count
+    MISSING_ACTIVATION = "missing_activation"  # 有访问历史但无激活值
+    INVALID_ACTIVATION_RANGE = "invalid_activation_range"  # 激活值超出有效范围
+
+
+class AccessHistoryManager:
+    """
+    访问历史管理器
+    
+    负责追踪知识节点的访问历史，并在访问时原子性地更新所有相关字段：
+    - activation_value: 激活值
+    - access_history: 访问历史时间戳数组
+    - last_access_time: 最后访问时间
+    - access_count: 访问次数
+    
+    特性：
+    - 原子性更新：使用Neo4j事务确保所有字段同时更新或回滚
+    - 并发安全：使用乐观锁机制防止并发冲突
+    - 一致性保证：提供一致性检查和自动修复功能
+    - 智能修剪：自动修剪过长的访问历史
+    
+    Attributes:
+        connector: Neo4j连接器实例
+        actr_calculator: ACT-R激活值计算器实例
+        max_retries: 并发冲突时的最大重试次数
+    """
+    
+    def __init__(
+        self,
+        connector: Neo4jConnector,
+        actr_calculator: ACTRCalculator,
+        max_retries: int = 3
+    ):
+        """
+        初始化访问历史管理器
+        
+        Args:
+            connector: Neo4j连接器实例
+            actr_calculator: ACT-R激活值计算器实例
+            max_retries: 并发冲突时的最大重试次数（默认3次）
+        """
+        self.connector = connector
+        self.actr_calculator = actr_calculator
+        self.max_retries = max_retries
+    
+    async def record_access(
+        self,
+        node_id: str,
+        node_label: str,
+        group_id: Optional[str] = None,
+        current_time: Optional[datetime] = None
+    ) -> Dict[str, Any]:
+        """
+        记录节点访问并原子性更新所有相关字段
+        
+        这是核心方法，实现了：
+        1. 首次访问：初始化access_history，计算初始激活值
+        2. 后续访问：追加访问历史，重新计算激活值
+        3. 历史修剪：当历史过长时自动修剪
+        4. 原子性：所有字段在单个事务中更新
+        5. 并发安全：使用乐观锁重试机制
+        
+        Args:
+            node_id: 节点ID
+            node_label: 节点标签（Statement, ExtractedEntity, MemorySummary）
+            group_id: 组ID（可选，用于过滤）
+            current_time: 当前时间（可选，默认使用系统时间）
+        
+        Returns:
+            Dict[str, Any]: 更新后的节点数据，包含：
+                - id: 节点ID
+                - activation_value: 更新后的激活值
+                - access_history: 更新后的访问历史
+                - last_access_time: 最后访问时间
+                - access_count: 访问次数
+                - importance_score: 重要性分数
+        
+        Raises:
+            ValueError: 如果节点不存在或节点标签无效
+            RuntimeError: 如果重试次数耗尽仍然失败
+        """
+        if current_time is None:
+            current_time = datetime.now()
+        
+        current_time_iso = current_time.isoformat()
+        
+        # 验证节点标签
+        valid_labels = ["Statement", "ExtractedEntity", "MemorySummary"]
+        if node_label not in valid_labels:
+            raise ValueError(
+                f"Invalid node_label: {node_label}. Must be one of {valid_labels}"
+            )
+        
+        # 使用乐观锁重试机制处理并发冲突
+        for attempt in range(self.max_retries):
+            try:
+                # 步骤1：读取当前节点状态
+                node_data = await self._fetch_node(node_id, node_label, group_id)
+                
+                if not node_data:
+                    raise ValueError(
+                        f"Node not found: {node_label} with id={node_id}"
+                    )
+                
+                # 步骤2：计算新的访问历史和激活值
+                update_data = await self._calculate_update(
+                    node_data=node_data,
+                    current_time=current_time,
+                    current_time_iso=current_time_iso
+                )
+                
+                # 步骤3：原子性更新节点（使用事务）
+                updated_node = await self._atomic_update(
+                    node_id=node_id,
+                    node_label=node_label,
+                    update_data=update_data,
+                    group_id=group_id
+                )
+                
+                logger.info(
+                    f"成功记录访问: {node_label}[{node_id}], "
+                    f"activation={update_data['activation_value']:.4f}, "
+                    f"access_count={update_data['access_count']}"
+                )
+                
+                return updated_node
+                
+            except Exception as e:
+                if attempt < self.max_retries - 1:
+                    logger.warning(
+                        f"访问记录失败（尝试 {attempt + 1}/{self.max_retries}）: {str(e)}"
+                    )
+                    continue
+                else:
+                    logger.error(
+                        f"访问记录失败，重试次数耗尽: {node_label}[{node_id}], "
+                        f"错误: {str(e)}"
+                    )
+                    raise RuntimeError(
+                        f"Failed to record access after {self.max_retries} attempts: {str(e)}"
+                    )
+    
+    async def record_batch_access(
+        self,
+        node_ids: List[str],
+        node_label: str,
+        group_id: Optional[str] = None,
+        current_time: Optional[datetime] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        批量记录多个节点的访问
+        
+        为提高性能，批量更新多个节点的访问历史。
+        每个节点独立更新，失败的节点不影响其他节点。
+        
+        Args:
+            node_ids: 节点ID列表
+            node_label: 节点标签（所有节点必须是同一类型）
+            group_id: 组ID（可选）
+            current_time: 当前时间（可选）
+        
+        Returns:
+            List[Dict[str, Any]]: 成功更新的节点列表
+        """
+        if current_time is None:
+            current_time = datetime.now()
+        
+        results = []
+        failed_count = 0
+        
+        for node_id in node_ids:
+            try:
+                updated_node = await self.record_access(
+                    node_id=node_id,
+                    node_label=node_label,
+                    group_id=group_id,
+                    current_time=current_time
+                )
+                results.append(updated_node)
+            except Exception as e:
+                failed_count += 1
+                logger.warning(
+                    f"批量访问记录失败: {node_label}[{node_id}], 错误: {str(e)}"
+                )
+        
+        logger.info(
+            f"批量访问记录完成: 成功 {len(results)}/{len(node_ids)}, "
+            f"失败 {failed_count}"
+        )
+        
+        return results
+    
+    async def check_consistency(
+        self,
+        node_id: str,
+        node_label: str,
+        group_id: Optional[str] = None
+    ) -> Tuple[ConsistencyCheckResult, Optional[str]]:
+        """
+        检查节点数据的一致性
+        
+        验证以下一致性规则：
+        1. access_history[-1] == last_access_time
+        2. len(access_history) == access_count
+        3. 如果有访问历史，必须有激活值
+        4. 激活值必须在有效范围内 [offset, 1.0]
+        
+        Args:
+            node_id: 节点ID
+            node_label: 节点标签
+            group_id: 组ID（可选）
+        
+        Returns:
+            Tuple[ConsistencyCheckResult, Optional[str]]: 
+                - 一致性检查结果枚举
+                - 错误描述（如果不一致）
+        """
+        node_data = await self._fetch_node(node_id, node_label, group_id)
+        
+        if not node_data:
+            return ConsistencyCheckResult.CONSISTENT, None
+        
+        access_history = node_data.get('access_history', [])
+        last_access_time = node_data.get('last_access_time')
+        access_count = node_data.get('access_count', 0)
+        activation_value = node_data.get('activation_value')
+        
+        # 检查1：access_history[-1] == last_access_time
+        if access_history and last_access_time:
+            if access_history[-1] != last_access_time:
+                return (
+                    ConsistencyCheckResult.INCONSISTENT_HISTORY_TIME,
+                    f"access_history[-1]={access_history[-1]} != "
+                    f"last_access_time={last_access_time}"
+                )
+        
+        # 检查2：len(access_history) == access_count
+        if len(access_history) != access_count:
+            return (
+                ConsistencyCheckResult.INCONSISTENT_HISTORY_COUNT,
+                f"len(access_history)={len(access_history)} != "
+                f"access_count={access_count}"
+            )
+        
+        # 检查3：有访问历史必须有激活值
+        if access_history and activation_value is None:
+            return (
+                ConsistencyCheckResult.MISSING_ACTIVATION,
+                "Node has access_history but activation_value is None"
+            )
+        
+        # 检查4：激活值范围
+        if activation_value is not None:
+            offset = self.actr_calculator.offset
+            if not (offset <= activation_value <= 1.0):
+                return (
+                    ConsistencyCheckResult.INVALID_ACTIVATION_RANGE,
+                    f"activation_value={activation_value} out of range "
+                    f"[{offset}, 1.0]"
+                )
+        
+        return ConsistencyCheckResult.CONSISTENT, None
+    
+    async def check_batch_consistency(
+        self,
+        node_label: str,
+        group_id: Optional[str] = None,
+        limit: int = 1000
+    ) -> Dict[str, Any]:
+        """
+        批量检查多个节点的一致性
+        
+        Args:
+            node_label: 节点标签
+            group_id: 组ID（可选）
+            limit: 检查的最大节点数
+        
+        Returns:
+            Dict[str, Any]: 一致性检查报告，包含：
+                - total_checked: 检查的节点总数
+                - consistent_count: 一致的节点数
+                - inconsistent_count: 不一致的节点数
+                - inconsistencies: 不一致节点的详细信息列表
+                - consistency_rate: 一致性率（0-1）
+        """
+        # 查询所有相关节点
+        query = f"""
+        MATCH (n:{node_label})
+        WHERE n.access_history IS NOT NULL
+        """
+        if group_id:
+            query += " AND n.group_id = $group_id"
+        query += """
+        RETURN n.id as id
+        LIMIT $limit
+        """
+        
+        params = {"limit": limit}
+        if group_id:
+            params["group_id"] = group_id
+        
+        results = await self.connector.execute_query(query, **params)
+        node_ids = [r['id'] for r in results]
+        
+        # 检查每个节点
+        inconsistencies = []
+        consistent_count = 0
+        
+        for node_id in node_ids:
+            result, message = await self.check_consistency(
+                node_id=node_id,
+                node_label=node_label,
+                group_id=group_id
+            )
+            
+            if result == ConsistencyCheckResult.CONSISTENT:
+                consistent_count += 1
+            else:
+                inconsistencies.append({
+                    'node_id': node_id,
+                    'result': result.value,
+                    'message': message
+                })
+        
+        total_checked = len(node_ids)
+        inconsistent_count = len(inconsistencies)
+        consistency_rate = consistent_count / total_checked if total_checked > 0 else 1.0
+        
+        report = {
+            'total_checked': total_checked,
+            'consistent_count': consistent_count,
+            'inconsistent_count': inconsistent_count,
+            'inconsistencies': inconsistencies,
+            'consistency_rate': consistency_rate
+        }
+        
+        logger.info(
+            f"一致性检查完成: {node_label}, "
+            f"一致率={consistency_rate:.2%}, "
+            f"不一致节点={inconsistent_count}/{total_checked}"
+        )
+        
+        return report
+    
+    async def repair_inconsistency(
+        self,
+        node_id: str,
+        node_label: str,
+        group_id: Optional[str] = None
+    ) -> bool:
+        """
+        自动修复节点的数据不一致问题
+        
+        修复策略：
+        1. 如果access_history[-1] != last_access_time：使用access_history[-1]
+        2. 如果len(access_history) != access_count：使用len(access_history)
+        3. 如果有历史但无激活值：重新计算激活值
+        4. 如果激活值超出范围：重新计算激活值
+        
+        Args:
+            node_id: 节点ID
+            node_label: 节点标签
+            group_id: 组ID（可选）
+        
+        Returns:
+            bool: 修复成功返回True，否则返回False
+        """
+        try:
+            # 检查一致性
+            result, message = await self.check_consistency(
+                node_id=node_id,
+                node_label=node_label,
+                group_id=group_id
+            )
+            
+            if result == ConsistencyCheckResult.CONSISTENT:
+                logger.info(f"节点数据一致，无需修复: {node_label}[{node_id}]")
+                return True
+            
+            # 获取节点数据
+            node_data = await self._fetch_node(node_id, node_label, group_id)
+            if not node_data:
+                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', [])
+            importance_score = node_data.get('importance_score', 0.5)
+            
+            # 准备修复数据
+            repair_data = {}
+            
+            # 修复last_access_time
+            if access_history:
+                repair_data['last_access_time'] = access_history[-1]
+            
+            # 修复access_count
+            repair_data['access_count'] = len(access_history)
+            
+            # 修复activation_value
+            if access_history:
+                current_time = datetime.now()
+                last_access_dt = datetime.fromisoformat(access_history[-1])
+                access_history_dt = [
+                    datetime.fromisoformat(ts) for ts in access_history
+                ]
+                
+                activation_value = self.actr_calculator.calculate_memory_activation(
+                    access_history=access_history_dt,
+                    current_time=current_time,
+                    last_access_time=last_access_dt,
+                    importance_score=importance_score
+                )
+                repair_data['activation_value'] = activation_value
+            
+            # 执行修复
+            query = f"""
+            MATCH (n:{node_label} {{id: $node_id}})
+            """
+            if group_id:
+                query += " WHERE n.group_id = $group_id"
+            query += """
+            SET n += $repair_data
+            RETURN n
+            """
+            
+            params = {
+                'node_id': node_id,
+                'repair_data': repair_data
+            }
+            if group_id:
+                params['group_id'] = group_id
+            
+            await self.connector.execute_query(query, **params)
+            
+            logger.info(
+                f"成功修复节点不一致: {node_label}[{node_id}], "
+                f"问题类型={result.value}"
+            )
+            return True
+            
+        except Exception as e:
+            logger.error(
+                f"修复节点失败: {node_label}[{node_id}], 错误: {str(e)}"
+            )
+            return False
+    
+    # ==================== 私有辅助方法 ====================
+    
+    async def _fetch_node(
+        self,
+        node_id: str,
+        node_label: str,
+        group_id: Optional[str] = None
+    ) -> Optional[Dict[str, Any]]:
+        """
+        获取节点数据
+        
+        Args:
+            node_id: 节点ID
+            node_label: 节点标签
+            group_id: 组ID（可选）
+        
+        Returns:
+            Optional[Dict[str, Any]]: 节点数据，如果不存在返回None
+        """
+        query = f"""
+        MATCH (n:{node_label} {{id: $node_id}})
+        """
+        if group_id:
+            query += " WHERE n.group_id = $group_id"
+        query += """
+        RETURN n.id as id,
+               n.importance_score as importance_score,
+               n.activation_value as activation_value,
+               n.access_history as access_history,
+               n.last_access_time as last_access_time,
+               n.access_count as access_count
+        """
+        
+        params = {'node_id': node_id}
+        if group_id:
+            params['group_id'] = group_id
+        
+        results = await self.connector.execute_query(query, **params)
+        
+        if results:
+            return results[0]
+        return None
+    
+    async def _calculate_update(
+        self,
+        node_data: Dict[str, Any],
+        current_time: datetime,
+        current_time_iso: str
+    ) -> Dict[str, Any]:
+        """
+        计算更新数据
+        
+        Args:
+            node_data: 当前节点数据
+            current_time: 当前时间（datetime对象）
+            current_time_iso: 当前时间（ISO格式字符串）
+        
+        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', [])
+        importance_score = node_data.get('importance_score', 0.5)
+        
+        # 追加新的访问时间
+        new_access_history = access_history + [current_time_iso]
+        
+        # 修剪访问历史（如果过长）
+        access_history_dt = [
+            datetime.fromisoformat(ts) for ts in new_access_history
+        ]
+        trimmed_history_dt = self.actr_calculator.trim_access_history(
+            access_history=access_history_dt,
+            current_time=current_time
+        )
+        trimmed_history = [ts.isoformat() for ts in trimmed_history_dt]
+        
+        # 计算新的激活值
+        activation_value = self.actr_calculator.calculate_memory_activation(
+            access_history=trimmed_history_dt,
+            current_time=current_time,
+            last_access_time=current_time,  # 最后访问时间就是当前时间
+            importance_score=importance_score
+        )
+        
+        # 返回所有需要更新的字段
+        return {
+            'activation_value': activation_value,
+            'access_history': trimmed_history,
+            'last_access_time': current_time_iso,
+            'access_count': len(trimmed_history)
+        }
+    
+    async def _atomic_update(
+        self,
+        node_id: str,
+        node_label: str,
+        update_data: Dict[str, Any],
+        group_id: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        原子性更新节点（使用乐观锁）
+        
+        使用Neo4j事务和版本号确保所有字段同时更新或回滚。
+        实现乐观锁机制防止并发冲突。
+        
+        Args:
+            node_id: 节点ID
+            node_label: 节点标签
+            update_data: 更新数据
+            group_id: 组ID（可选）
+        
+        Returns:
+            Dict[str, Any]: 更新后的节点数据
+        
+        Raises:
+            RuntimeError: 如果更新失败或发生版本冲突
+        """
+        # 定义事务函数
+        async def update_transaction(tx, node_id, node_label, update_data, group_id):
+            # 步骤1：读取当前节点并获取版本号
+            read_query = f"""
+            MATCH (n:{node_label} {{id: $node_id}})
+            """
+            if group_id:
+                read_query += " WHERE n.group_id = $group_id"
+            read_query += """
+            RETURN n.id as id,
+                   n.version as version,
+                   n.activation_value as activation_value,
+                   n.access_history as access_history,
+                   n.last_access_time as last_access_time,
+                   n.access_count as access_count,
+                   n.importance_score as importance_score
+            """
+            
+            read_params = {'node_id': node_id}
+            if group_id:
+                read_params['group_id'] = group_id
+            
+            read_result = await tx.run(read_query, **read_params)
+            current_node = await read_result.single()
+            
+            if not current_node:
+                raise RuntimeError(f"Node not found: {node_label}[{node_id}]")
+            
+            # 获取当前版本号（如果不存在则为0）
+            current_version = current_node.get('version', 0) or 0
+            new_version = current_version + 1
+            
+            # 步骤2：使用乐观锁更新节点
+            # 只有当版本号匹配时才更新
+            update_query = f"""
+            MATCH (n:{node_label} {{id: $node_id}})
+            """
+            if group_id:
+                update_query += " WHERE n.group_id = $group_id"
+            
+            # 添加版本检查
+            if current_version > 0:
+                update_query += " AND n.version = $current_version"
+            else:
+                # 如果节点没有版本号，检查是否为首次更新
+                update_query += " AND (n.version IS NULL OR n.version = 0)"
+            
+            update_query += """
+            SET n.activation_value = $activation_value,
+                n.access_history = $access_history,
+                n.last_access_time = $last_access_time,
+                n.access_count = $access_count,
+                n.version = $new_version
+            RETURN n.id as id,
+                   n.activation_value as activation_value,
+                   n.access_history as access_history,
+                   n.last_access_time as last_access_time,
+                   n.access_count as access_count,
+                   n.importance_score as importance_score,
+                   n.version as version
+            """
+            
+            update_params = {
+                'node_id': node_id,
+                'current_version': current_version,
+                'new_version': new_version,
+                'activation_value': update_data['activation_value'],
+                'access_history': update_data['access_history'],
+                'last_access_time': update_data['last_access_time'],
+                'access_count': update_data['access_count']
+            }
+            if group_id:
+                update_params['group_id'] = group_id
+            
+            update_result = await tx.run(update_query, **update_params)
+            updated_node = await update_result.single()
+            
+            if not updated_node:
+                raise RuntimeError(
+                    f"Version conflict detected for {node_label}[{node_id}]. "
+                    f"Expected version {current_version}, but node was modified by another transaction."
+                )
+            
+            return dict(updated_node)
+        
+        # 执行事务
+        try:
+            result = await self.connector.execute_write_transaction(
+                update_transaction,
+                node_id=node_id,
+                node_label=node_label,
+                update_data=update_data,
+                group_id=group_id
+            )
+            return result
+        except Exception as e:
+            logger.error(
+                f"原子性更新失败: {node_label}[{node_id}], 错误: {str(e)}"
+            )
+            raise RuntimeError(
+                f"Failed to atomically update node: {str(e)}"
+            ) from e

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

@@ -0,0 +1,359 @@
+"""
+ACT-R Memory Activation Calculator
+
+This module implements the unified Memory Activation model based on ACT-R
+(Adaptive Control of Thought-Rational) cognitive architecture theory.
+
+The calculator integrates BLA (Base-Level Activation) computation into the
+Memory Activation formula, providing a single coherent model for memory strength
+calculation that reflects both recency and frequency of access.
+
+Formula: R(i) = offset + (1-offset) * exp(-λ*t / Σ(I·t_k^(-d)))
+
+Where:
+- R(i): Memory activation value (0 to 1)
+- offset: Minimum retention rate (prevents complete forgetting)
+- λ: Forgetting rate (lambda_time / lambda_mem)
+- t: Time since last access
+- I: Importance score (0 to 1)
+- t_k: Time since k-th access
+- d: Decay constant (typically 0.5)
+
+Reference: Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe?
+"""
+
+import math
+from typing import List, Dict, Any, Optional
+from datetime import datetime, timedelta
+
+
+class ACTRCalculator:
+    """
+    Unified ACT-R Memory Activation Calculator.
+    
+    This calculator implements the Memory Activation model that combines
+    recency and frequency effects into a single activation value computation.
+    It replaces the separate BLA calculation with an integrated approach.
+    
+    Attributes:
+        decay_constant: Decay parameter d (typically 0.5)
+        forgetting_rate: Lambda parameter λ controlling forgetting speed
+        offset: Minimum retention rate (baseline memory strength)
+        max_history_length: Maximum number of access records to keep
+    """
+
+    def __init__(
+        self,
+        decay_constant: float = 0.5,
+        forgetting_rate: float = 0.3,
+        offset: float = 0.1,
+        max_history_length: int = 100
+    ):
+        """
+        Initialize the ACT-R calculator.
+        
+        Args:
+            decay_constant: Decay parameter d (default 0.5)
+            forgetting_rate: Forgetting rate λ (default 0.3)
+            offset: Minimum retention rate (default 0.1)
+            max_history_length: Maximum access history length (default 100)
+        """
+        self.decay_constant = decay_constant
+        self.forgetting_rate = forgetting_rate
+        self.offset = offset
+        self.max_history_length = max_history_length
+
+    def calculate_memory_activation(
+        self,
+        access_history: List[datetime],
+        current_time: datetime,
+        last_access_time: datetime,
+        importance_score: float = 0.5
+    ) -> float:
+        """
+        Calculate memory activation value using the unified Memory Activation formula.
+        
+        This method computes R(i) = offset + (1-offset) * exp(-λ*t / Σ(I·t_k^(-d)))
+        
+        The formula integrates:
+        - Recency effect: Recent accesses contribute more (via t)
+        - Frequency effect: Multiple accesses strengthen memory (via Σ)
+        - Importance weighting: Important memories decay slower (via I)
+        
+        Args:
+            access_history: List of access timestamps (ISO format or datetime objects)
+            current_time: Current time for calculation
+            last_access_time: Time of most recent access
+            importance_score: Importance weight (0 to 1, default 0.5)
+        
+        Returns:
+            float: Memory activation value between offset and 1.0
+        
+        Raises:
+            ValueError: If access_history is empty or contains invalid data
+        """
+        if not access_history:
+            raise ValueError("access_history cannot be empty")
+        
+        if not (0.0 <= importance_score <= 1.0):
+            raise ValueError(f"importance_score must be between 0 and 1, got {importance_score}")
+        
+        # Calculate time since last access (in days)
+        time_since_last = (current_time - last_access_time).total_seconds() / 86400.0
+        time_since_last = max(time_since_last, 0.0001)  # Avoid division by zero
+        
+        # Calculate BLA component: Σ(I·t_k^(-d))
+        bla_sum = 0.0
+        for access_time in access_history:
+            # Calculate time since this access (in days)
+            time_diff = (current_time - access_time).total_seconds() / 86400.0
+            time_diff = max(time_diff, 0.0001)  # Avoid division by zero
+            
+            # Add weighted power-law term: I * t_k^(-d)
+            bla_sum += importance_score * (time_diff ** (-self.decay_constant))
+        
+        # Avoid division by zero in case of numerical issues
+        if bla_sum <= 0:
+            bla_sum = 0.0001
+        
+        # Calculate Memory Activation: R(i) = offset + (1-offset) * exp(-λ*t / BLA)
+        exponent = -self.forgetting_rate * time_since_last / bla_sum
+        
+        # Clamp exponent to avoid numerical overflow/underflow
+        exponent = max(min(exponent, 100), -100)
+        
+        activation = self.offset + (1 - self.offset) * math.exp(exponent)
+        
+        # Ensure activation is within valid range [offset, 1.0]
+        return max(self.offset, min(1.0, activation))
+
+    def trim_access_history(
+        self,
+        access_history: List[datetime],
+        current_time: datetime
+    ) -> List[datetime]:
+        """
+        Intelligently trim access history to prevent unbounded growth.
+        
+        Strategy:
+        - Keep all records if under max_history_length
+        - If over limit, keep most recent 50% and sample from older records
+        - Preserves both recent accesses (high importance) and historical pattern
+        
+        Args:
+            access_history: List of access timestamps (sorted or unsorted)
+            current_time: Current time for calculation
+        
+        Returns:
+            List[datetime]: Trimmed access history
+        """
+        if len(access_history) <= self.max_history_length:
+            return access_history
+        
+        # Sort by time (most recent first)
+        sorted_history = sorted(access_history, reverse=True)
+        
+        # Calculate split point (keep most recent 50%)
+        keep_recent_count = self.max_history_length // 2
+        
+        # Keep most recent 50%
+        recent_records = sorted_history[:keep_recent_count]
+        
+        # Sample from older records
+        older_records = sorted_history[keep_recent_count:]
+        sample_count = self.max_history_length - keep_recent_count
+        
+        if len(older_records) <= sample_count:
+            # If older records fit, keep them all
+            sampled_older = older_records
+        else:
+            # Sample evenly from older records
+            step = len(older_records) / sample_count
+            sampled_older = [
+                older_records[int(i * step)]
+                for i in range(sample_count)
+            ]
+        
+        # Combine and return
+        trimmed_history = recent_records + sampled_older
+        return sorted(trimmed_history, reverse=True)
+
+    def get_forgetting_curve(   # 预测激活值，决定复习；测试不同配置效果，选择合适的d
+        self,
+        initial_time: datetime,
+        importance_score: float = 0.5,
+        days: int = 60
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate forgetting curve data for visualization.
+        
+        This method simulates how memory activation decays over time
+        for a single initial access, useful for understanding and
+        visualizing the forgetting behavior.
+        
+        Args:
+            initial_time: Time of initial memory creation/access
+            importance_score: Importance weight (0 to 1, default 0.5)
+            days: Number of days to simulate (default 60)
+        
+        Returns:
+            List of dictionaries with keys:
+            - 'day': Day number (0 to days)
+            - 'activation': Memory activation value
+            - 'retention_rate': Same as activation (for compatibility)
+        """
+        curve_data = []
+        access_history = [initial_time]
+        
+        for day in range(days + 1):
+            current_time = initial_time + timedelta(days=day)
+            
+            try:
+                activation = self.calculate_memory_activation(
+                    access_history=access_history,
+                    current_time=current_time,
+                    last_access_time=initial_time,
+                    importance_score=importance_score
+                )
+            except ValueError:
+                # Handle edge cases
+                activation = self.offset
+            
+            curve_data.append({
+                'day': day,
+                'activation': activation,
+                'retention_rate': activation  # Alias for compatibility
+            })
+        
+        return curve_data
+
+    def calculate_forgetting_score(
+        self,
+        access_history: List[datetime],
+        current_time: datetime,
+        last_access_time: datetime,
+        importance_score: float = 0.5
+    ) -> float:
+        """
+        Calculate forgetting score (inverse of activation).
+        
+        Forgetting score = 1 - activation value
+        Higher score means more likely to be forgotten.
+        
+        Args:
+            access_history: List of access timestamps
+            current_time: Current time for calculation
+            last_access_time: Time of most recent access
+            importance_score: Importance weight (0 to 1, default 0.5)
+        
+        Returns:
+            float: Forgetting score between 0 and (1 - offset)
+        """
+        activation = self.calculate_memory_activation(
+            access_history=access_history,
+            current_time=current_time,
+            last_access_time=last_access_time,
+            importance_score=importance_score
+        )
+        return 1.0 - activation
+
+    def should_forget(
+        self,
+        access_history: List[datetime],
+        current_time: datetime,
+        last_access_time: datetime,
+        importance_score: float = 0.5,
+        threshold: float = 0.3
+    ) -> bool:
+        """
+        Determine if a memory should be forgotten based on activation threshold.
+        
+        Args:
+            access_history: List of access timestamps
+            current_time: Current time for calculation
+            last_access_time: Time of most recent access
+            importance_score: Importance weight (0 to 1, default 0.5)
+            threshold: Activation threshold below which memory should be forgotten
+        
+        Returns:
+            bool: True if activation < threshold (should forget), False otherwise
+        """
+        activation = self.calculate_memory_activation(
+            access_history=access_history,
+            current_time=current_time,
+            last_access_time=last_access_time,
+            importance_score=importance_score
+        )
+        return activation < threshold
+
+
+# Convenience functions for quick calculations
+def calculate_activation(
+    access_history: List[datetime],
+    current_time: datetime,
+    last_access_time: datetime,
+    importance_score: float = 0.5,
+    decay_constant: float = 0.5,
+    forgetting_rate: float = 0.3,
+    offset: float = 0.1
+) -> float:
+    """
+    Quick function to calculate activation without creating a calculator instance.
+    
+    Args:
+        access_history: List of access timestamps
+        current_time: Current time for calculation
+        last_access_time: Time of most recent access
+        importance_score: Importance weight (0 to 1, default 0.5)
+        decay_constant: Decay parameter d (default 0.5)
+        forgetting_rate: Forgetting rate λ (default 0.3)
+        offset: Minimum retention rate (default 0.1)
+    
+    Returns:
+        float: Memory activation value between offset and 1.0
+    """
+    calculator = ACTRCalculator(
+        decay_constant=decay_constant,
+        forgetting_rate=forgetting_rate,
+        offset=offset
+    )
+    return calculator.calculate_memory_activation(
+        access_history=access_history,
+        current_time=current_time,
+        last_access_time=last_access_time,
+        importance_score=importance_score
+    )
+
+
+def generate_forgetting_curve(
+    initial_time: datetime,
+    importance_score: float = 0.5,
+    days: int = 60,
+    decay_constant: float = 0.5,
+    forgetting_rate: float = 0.3,
+    offset: float = 0.1
+) -> List[Dict[str, Any]]:
+    """
+    Quick function to generate forgetting curve data.
+    
+    Args:
+        initial_time: Time of initial memory creation/access
+        importance_score: Importance weight (0 to 1, default 0.5)
+        days: Number of days to simulate (default 60)
+        decay_constant: Decay parameter d (default 0.5)
+        forgetting_rate: Forgetting rate λ (default 0.3)
+        offset: Minimum retention rate (default 0.1)
+    
+    Returns:
+        List of dictionaries with forgetting curve data
+    """
+    calculator = ACTRCalculator(
+        decay_constant=decay_constant,
+        forgetting_rate=forgetting_rate,
+        offset=offset
+    )
+    return calculator.get_forgetting_curve(
+        initial_time=initial_time,
+        importance_score=importance_score,
+        days=days
+    )

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

@@ -0,0 +1,195 @@
+"""
+遗忘引擎配置工具模块
+
+本模块提供从数据库加载配置并创建遗忘引擎组件的辅助函数。
+
+Functions:
+    calculate_forgetting_rate: 计算遗忘速率（lambda_time / lambda_mem）
+    load_actr_config_from_db: 从数据库加载 ACT-R 配置参数
+    create_actr_calculator_from_config: 从配置创建 ACTRCalculator 实例
+"""
+
+import logging
+from typing import Optional, Dict, Any
+from sqlalchemy.orm import Session
+
+from app.repositories.data_config_repository import DataConfigRepository
+from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
+
+
+logger = logging.getLogger(__name__)
+
+
+def calculate_forgetting_rate(lambda_time: float, lambda_mem: float) -> float:
+    """
+    计算遗忘速率
+    
+    公式：forgetting_rate = lambda_time / lambda_mem
+    
+    这个计算将两个独立的 lambda 参数组合成一个统一的遗忘速率参数，
+    用于 ACT-R 激活值计算。
+    
+    Args:
+        lambda_time: 时间衰减参数（0-1）
+        lambda_mem: 记忆衰减参数（0-1）
+    
+    Returns:
+        float: 遗忘速率
+    
+    Raises:
+        ValueError: 如果 lambda_mem 为 0
+    
+    Examples:
+        >>> calculate_forgetting_rate(0.5, 0.5)
+        1.0
+        >>> calculate_forgetting_rate(0.3, 0.5)
+        0.6
+    """
+    if lambda_mem == 0:
+        raise ValueError("lambda_mem 不能为 0")
+    
+    forgetting_rate = lambda_time / lambda_mem
+    
+    logger.debug(
+        f"计算遗忘速率: lambda_time={lambda_time}, "
+        f"lambda_mem={lambda_mem}, "
+        f"forgetting_rate={forgetting_rate:.4f}"
+    )
+    
+    return forgetting_rate
+
+
+def load_actr_config_from_db(
+    db: Session,
+    config_id: Optional[int] = None
+) -> Dict[str, Any]:
+    """
+    从数据库加载 ACT-R 配置参数
+    
+    从 PostgreSQL 的 data_config 表读取配置参数，
+    并计算派生参数（如 forgetting_rate）。
+    
+    Args:
+        db: 数据库会话
+        config_id: 配置 ID（可选，如果为 None 则使用默认值）
+    
+    Returns:
+        Dict[str, Any]: 配置参数字典，包含：
+            - decay_constant: 衰减常数 d
+            - lambda_time: 时间衰减参数
+            - lambda_mem: 记忆衰减参数
+            - forgetting_rate: 遗忘速率（根据 lambda_time / lambda_mem 计算得出）
+            - offset: 偏移量
+            - max_history_length: 访问历史最大长度
+            - forgetting_threshold: 遗忘阈值
+            - min_days_since_access: 最小未访问天数
+            - enable_llm_summary: 是否使用 LLM 生成摘要
+            - max_merge_batch_size: 单次最大融合节点对数
+            - forgetting_interval_hours: 遗忘周期间隔
+            
+        注意：llm_id 不包含在返回的配置中，需要时由 forgetting_strategy 直接从数据库读取
+    
+    Raises:
+        ValueError: 如果指定的 config_id 不存在
+    """
+    # 必须指定 config_id
+    if config_id is None:
+        logger.error("未指定 config_id，无法加载配置")
+        raise ValueError("config_id 不能为空，必须指定一个有效的配置 ID")
+    
+    # 从数据库加载配置
+    try:
+        repository = DataConfigRepository()
+        db_config = repository.get_by_id(db, config_id)
+        
+        if db_config is None:
+            logger.error(f"配置不存在: config_id={config_id}")
+            raise ValueError(f"配置不存在: config_id={config_id}")
+        
+        # 读取配置参数（信任数据库默认值）
+        lambda_time = db_config.lambda_time
+        lambda_mem = db_config.lambda_mem
+        decay_constant = db_config.decay_constant
+        offset = db_config.offset
+        max_history_length = db_config.max_history_length
+        forgetting_threshold = db_config.forgetting_threshold
+        min_days_since_access = db_config.min_days_since_access
+        enable_llm_summary = db_config.enable_llm_summary
+        max_merge_batch_size = db_config.max_merge_batch_size
+        forgetting_interval_hours = db_config.forgetting_interval_hours
+        
+        # 计算 forgetting_rate
+        forgetting_rate = calculate_forgetting_rate(lambda_time, lambda_mem)
+        
+        config = {
+            'decay_constant': decay_constant,
+            'lambda_time': lambda_time,
+            'lambda_mem': lambda_mem,
+            'forgetting_rate': forgetting_rate,
+            'offset': offset,
+            'max_history_length': max_history_length,
+            'forgetting_threshold': forgetting_threshold,
+            'min_days_since_access': min_days_since_access,
+            'enable_llm_summary': enable_llm_summary,
+            'max_merge_batch_size': max_merge_batch_size,
+            'forgetting_interval_hours': forgetting_interval_hours
+            # 注意：llm_id 不包含在配置响应中，仅在内部使用
+        }
+        
+        logger.info(
+            f"成功加载 ACT-R 配置: config_id={config_id}, "
+            f"forgetting_rate={forgetting_rate:.4f}"
+        )
+        
+        return config
+    
+    except Exception as e:
+        logger.error(f"加载 ACT-R 配置失败: config_id={config_id}, 错误: {str(e)}")
+        raise
+
+
+def create_actr_calculator_from_config(
+    db: Session,
+    config_id: Optional[int] = None
+) -> ACTRCalculator:
+    """
+    从数据库配置创建 ACTRCalculator 实例
+    
+    这是创建 ACTRCalculator 的推荐方式，确保使用数据库中的配置参数。
+    
+    Args:
+        db: 数据库会话
+        config_id: 配置 ID（可选，如果为 None 则使用默认值）
+    
+    Returns:
+        ACTRCalculator: 配置好的 ACT-R 计算器实例
+    
+    Raises:
+        ValueError: 如果指定的 config_id 不存在
+    
+    Examples:
+        >>> from sqlalchemy.orm import Session
+        >>> db = Session()
+        >>> calculator = create_actr_calculator_from_config(db, config_id=1)
+        >>> # 使用计算器
+        >>> activation = calculator.calculate_memory_activation(...)
+    """
+    # 加载配置
+    config = load_actr_config_from_db(db, config_id)
+    
+    # 创建计算器
+    calculator = ACTRCalculator(
+        decay_constant=config['decay_constant'],
+        forgetting_rate=config['forgetting_rate'],
+        offset=config['offset'],
+        max_history_length=config['max_history_length']
+    )
+    
+    logger.info(
+        f"创建 ACTRCalculator: config_id={config_id}, "
+        f"decay_constant={config['decay_constant']}, "
+        f"forgetting_rate={config['forgetting_rate']:.4f}, "
+        f"offset={config['offset']}"
+    )
+    
+    return calculator

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

@@ -0,0 +1,351 @@
+"""
+遗忘调度器模块
+
+本模块实现遗忘周期的调度和管理，负责：
+1. 手动触发遗忘周期
+2. 批量处理可遗忘节点（限制批量大小）
+3. 按激活值优先级排序（激活值最低的优先）
+4. 进度跟踪和日志记录
+5. 生成遗忘报告
+
+注意：定期调度功能已迁移到 Celery Beat，见 app/tasks.py 中的 run_forgetting_cycle_task
+
+Classes:
+    ForgettingScheduler: 遗忘调度器，提供遗忘周期管理功能
+"""
+
+import logging
+from typing import Dict, Any, Optional
+from datetime import datetime
+
+from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+
+logger = logging.getLogger(__name__)
+
+
+class ForgettingScheduler:
+    """
+    遗忘调度器
+    
+    管理遗忘周期的执行，实现批量处理、优先级排序和进度跟踪功能。
+    
+    核心功能：
+    1. 运行遗忘周期：识别可遗忘节点并批量融合
+    2. 优先级排序：优先处理激活值最低的节点对
+    3. 批量限制：限制单次处理的节点对数量
+    4. 进度跟踪：每完成 10% 记录一次日志
+    5. 遗忘报告：生成详细的执行报告
+    
+    注意：定期调度功能已迁移到 Celery Beat 定时任务
+    
+    Attributes:
+        forgetting_strategy: 遗忘策略执行器实例
+        connector: Neo4j 连接器实例
+        is_running: 是否正在运行遗忘周期
+    """
+    
+    def __init__(
+        self,
+        forgetting_strategy: ForgettingStrategy,
+        connector: Neo4jConnector
+    ):
+        """
+        初始化遗忘调度器
+        
+        Args:
+            forgetting_strategy: 遗忘策略执行器实例
+            connector: Neo4j 连接器实例
+        """
+        self.forgetting_strategy = forgetting_strategy
+        self.connector = connector
+        self.is_running = False
+        
+        logger.info("初始化遗忘调度器")
+    
+    async def run_forgetting_cycle(
+        self,
+        group_id: Optional[str] = None,
+        max_merge_batch_size: int = 100,
+        min_days_since_access: int = 30,
+        config_id: Optional[int] = None,
+        db = None
+    ) -> Dict[str, Any]:
+        """
+        运行一次完整的遗忘周期
+        
+        
+        Args:
+            group_id: 组 ID（可选，用于过滤特定组的节点）
+            max_merge_batch_size: 单次最大融合节点对数（默认 100）
+            min_days_since_access: 最小未访问天数（默认 30 天）
+            config_id: 配置ID（可选，用于获取 llm_id）
+            db: 数据库会话（可选，用于获取 llm_id）
+        
+        Returns:
+            Dict[str, Any]: 遗忘报告，包含：
+                - merged_count: 融合的节点对数量
+                - nodes_before: 遗忘前的节点总数
+                - nodes_after: 遗忘后的节点总数
+                - reduction_rate: 节点减少率（0-1）
+                - duration_seconds: 执行耗时（秒）
+                - start_time: 开始时间（ISO 格式）
+                - end_time: 结束时间（ISO 格式）
+                - failed_count: 失败的融合数量
+                - success_rate: 成功率（0-1）
+        
+        Raises:
+            RuntimeError: 如果已有遗忘周期正在运行
+        """
+        # 检查是否已有遗忘周期在运行
+        if self.is_running:
+            raise RuntimeError("遗忘周期已在运行中，请等待当前周期完成")
+        
+        self.is_running = True
+        start_time = datetime.now()
+        start_time_iso = start_time.isoformat()
+        
+        logger.info(
+            f"开始遗忘周期: group_id={group_id}, "
+            f"max_batch={max_merge_batch_size}, "
+            f"min_days={min_days_since_access}"
+        )
+        
+        try:
+            # 步骤1：统计遗忘前的节点数量
+            nodes_before = await self._count_knowledge_nodes(group_id)
+            logger.info(f"遗忘前节点总数: {nodes_before}")
+            
+            # 步骤2：识别可遗忘的节点对
+            forgettable_pairs = await self.forgetting_strategy.find_forgettable_nodes(
+                group_id=group_id,
+                min_days_since_access=min_days_since_access
+            )
+            
+            total_forgettable = len(forgettable_pairs)
+            logger.info(f"识别到 {total_forgettable} 个可遗忘节点对")
+            
+            if total_forgettable == 0:
+                logger.info("没有可遗忘的节点对，遗忘周期结束")
+                end_time = datetime.now()
+                duration = (end_time - start_time).total_seconds()
+                
+                report = {
+                    'merged_count': 0,
+                    'nodes_before': nodes_before,
+                    'nodes_after': nodes_before,
+                    'reduction_rate': 0.0,
+                    'duration_seconds': duration,
+                    'start_time': start_time_iso,
+                    'end_time': end_time.isoformat(),
+                    'failed_count': 0,
+                    'success_rate': 1.0
+                }
+                
+                logger.info("没有可遗忘的节点对，遗忘周期结束")
+                
+                return report
+            
+            # 步骤3：按激活值排序（激活值最低的优先）
+            # avg_activation 已经在 find_forgettable_nodes 中计算并排序
+            # 这里只需要确认排序是正确的（升序）
+            sorted_pairs = sorted(
+                forgettable_pairs,
+                key=lambda x: x['avg_activation']
+            )
+            
+            # 步骤4：限制批量大小
+            pairs_to_process = sorted_pairs[:max_merge_batch_size]
+            actual_batch_size = len(pairs_to_process)
+            
+            logger.info(
+                f"将处理 {actual_batch_size} 个节点对 "
+                f"(限制: {max_merge_batch_size})"
+            )
+            
+            # 步骤5：批量融合节点，每 10% 记录进度
+            merged_count = 0
+            failed_count = 0
+            skipped_count = 0  # 跳过的节点对数量（节点已被处理）
+            progress_interval = max(1, actual_batch_size // 10)  # 每 10% 记录一次
+            
+            # 跟踪已处理的节点 ID，避免重复处理
+            processed_statement_ids = set()
+            processed_entity_ids = set()
+            
+            # 预先过滤掉重复的节点对
+            unique_pairs = []
+            for pair in pairs_to_process:
+                statement_id = pair['statement_id']
+                entity_id = pair['entity_id']
+                
+                # 如果节点已被标记为处理，跳过
+                if statement_id in processed_statement_ids or entity_id in processed_entity_ids:
+                    skipped_count += 1
+                    logger.debug(
+                        f"预过滤：跳过重复节点对 Statement[{statement_id}] + Entity[{entity_id}]"
+                    )
+                    continue
+                
+                # 标记节点为已处理
+                processed_statement_ids.add(statement_id)
+                processed_entity_ids.add(entity_id)
+                unique_pairs.append(pair)
+            
+            logger.info(
+                f"预过滤完成：原始 {actual_batch_size} 对，去重后 {len(unique_pairs)} 对，"
+                f"跳过 {skipped_count} 对重复节点"
+            )
+            
+            # 更新实际处理的批次大小
+            actual_batch_size = len(unique_pairs)
+            progress_interval = max(1, actual_batch_size // 10)  # 重新计算进度间隔
+            
+            for idx, pair in enumerate(unique_pairs, start=1):
+                statement_id = pair['statement_id']
+                entity_id = pair['entity_id']
+                
+                try:
+                    # 准备节点数据
+                    statement_node = {
+                        'statement_id': statement_id,
+                        'statement_text': pair['statement_text'],
+                        'statement_activation': pair['statement_activation'],
+                        'statement_importance': pair['statement_importance'],
+                        'group_id': group_id
+                    }
+                    
+                    entity_node = {
+                        'entity_id': entity_id,
+                        'entity_name': pair['entity_name'],
+                        'entity_type': pair['entity_type'],
+                        'entity_activation': pair['entity_activation'],
+                        'entity_importance': pair['entity_importance'],
+                        'group_id': group_id
+                    }
+                    
+                    # 融合节点
+                    await self.forgetting_strategy.merge_nodes_to_summary(
+                        statement_node=statement_node,
+                        entity_node=entity_node,
+                        config_id=config_id,
+                        db=db
+                    )
+                    
+                    merged_count += 1
+                    
+                    # 进度跟踪：每 10% 记录一次
+                    if actual_batch_size > 0 and (idx % progress_interval == 0 or idx == actual_batch_size):
+                        progress_pct = (idx / actual_batch_size) * 100
+                        logger.info(
+                            f"遗忘进度: {idx}/{actual_batch_size} "
+                            f"({progress_pct:.1f}%), "
+                            f"已融合: {merged_count}, 失败: {failed_count}"
+                        )
+                
+                except Exception as e:
+                    failed_count += 1
+                    # 检查是否是节点不存在的错误
+                    if "nodes may not exist" in str(e):
+                        logger.warning(
+                            f"节点对 ({idx}/{actual_batch_size}) 的节点不存在（可能已被其他操作删除）: "
+                            f"Statement[{statement_id}] + Entity[{entity_id}]"
+                        )
+                    else:
+                        logger.error(
+                            f"融合节点对失败 ({idx}/{actual_batch_size}): "
+                            f"Statement[{statement_id}] + Entity[{entity_id}], "
+                            f"错误: {str(e)}"
+                        )
+                    # 继续处理剩余节点
+                    continue
+            
+            # 步骤6：统计遗忘后的节点数量
+            nodes_after = await self._count_knowledge_nodes(group_id)
+            logger.info(f"遗忘后节点总数: {nodes_after}")
+            
+            # 步骤7：生成遗忘报告
+            end_time = datetime.now()
+            duration = (end_time - start_time).total_seconds()
+            
+            # 计算节点减少率
+            if nodes_before > 0:
+                reduction_rate = (nodes_before - nodes_after) / nodes_before
+            else:
+                reduction_rate = 0.0
+            
+            # 计算成功率
+            if actual_batch_size > 0:
+                success_rate = merged_count / actual_batch_size
+            else:
+                success_rate = 1.0
+            
+            report = {
+                'merged_count': merged_count,
+                'nodes_before': nodes_before,
+                'nodes_after': nodes_after,
+                'reduction_rate': reduction_rate,
+                'duration_seconds': duration,
+                'start_time': start_time_iso,
+                'end_time': end_time.isoformat(),
+                'failed_count': failed_count,
+                'success_rate': success_rate
+            }
+            
+            logger.info(
+                f"遗忘周期完成: "
+                f"融合 {merged_count} 对节点, "
+                f"失败 {failed_count} 对, "
+                f"节点减少 {nodes_before - nodes_after} 个 "
+                f"({reduction_rate:.2%}), "
+                f"耗时 {duration:.2f} 秒"
+            )
+            
+            return report
+        
+        except Exception as e:
+            logger.error(f"遗忘周期执行失败: {str(e)}")
+            raise
+        
+        finally:
+            self.is_running = False
+    
+    # ==================== 私有辅助方法 ====================
+    
+    async def _count_knowledge_nodes(
+        self,
+        group_id: Optional[str] = None
+    ) -> int:
+        """
+        统计知识层节点总数
+        
+        统计 Statement、ExtractedEntity 和 MemorySummary 节点的总数。
+        
+        Args:
+            group_id: 组 ID（可选，用于过滤特定组的节点）
+        
+        Returns:
+            int: 知识层节点总数
+        """
+        query = """
+        MATCH (n)
+        WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
+        """
+        
+        if group_id:
+            query += " AND n.group_id = $group_id"
+        
+        query += """
+        RETURN count(n) as total
+        """
+        
+        params = {}
+        if group_id:
+            params['group_id'] = group_id
+        
+        results = await self.connector.execute_query(query, **params)
+        
+        if results:
+            return results[0]['total']
+        return 0

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

@@ -0,0 +1,611 @@
+"""
+遗忘策略执行器模块
+
+本模块实现基于 ACT-R 激活值的遗忘策略，负责：
+1. 识别低激活值的节点对（Statement-Entity）
+2. 将低激活值节点融合为 MemorySummary 节点
+3. 使用 LLM 生成高质量摘要（可选）
+4. 保留溯源信息并删除原始节点
+
+Classes:
+    ForgettingStrategy: 遗忘策略执行器，提供节点识别和融合功能
+"""
+
+import logging
+from typing import List, Dict, Any, Optional
+from datetime import datetime, timedelta
+
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
+
+
+logger = logging.getLogger(__name__)
+
+
+class ForgettingStrategy:
+    """
+    遗忘策略执行器
+    
+    基于 ACT-R 激活值识别和融合低价值记忆节点。
+    实现了完整的遗忘周期：识别 → 融合 → 删除。
+    
+    核心功能：
+    1. 识别可遗忘节点：激活值低于阈值且长期未访问的 Statement-Entity 对
+    2. 节点融合：创建 MemorySummary 节点，继承较高的激活值和重要性
+    3. LLM 摘要生成：使用 LLM 生成语义摘要（可降级到简单拼接）
+    4. 溯源保留：记录原始节点 ID，保持可追溯性
+    
+    Attributes:
+        connector: Neo4j 连接器实例
+        actr_calculator: ACT-R 激活值计算器实例
+        forgetting_threshold: 遗忘阈值（激活值低于此值的节点可被遗忘）
+    """
+    
+    def __init__(
+        self,
+        connector: Neo4jConnector,
+        actr_calculator: ACTRCalculator,
+        forgetting_threshold: float = 0.3,
+        enable_llm_summary: bool = True
+    ):
+        """
+        初始化遗忘策略执行器
+        
+        Args:
+            connector: Neo4j 连接器实例
+            actr_calculator: ACT-R 激活值计算器实例
+            forgetting_threshold: 遗忘阈值（默认 0.3）
+            enable_llm_summary: 是否启用 LLM 摘要生成（默认 True）
+        """
+        self.connector = connector
+        self.actr_calculator = actr_calculator
+        self.forgetting_threshold = forgetting_threshold
+        self.enable_llm_summary = enable_llm_summary
+        
+        logger.info(
+            f"初始化遗忘策略执行器: threshold={forgetting_threshold}, "
+            f"enable_llm_summary={enable_llm_summary}"
+        )
+    
+    async def calculate_forgetting_score(
+        self,
+        activation_value: float
+    ) -> float:
+        """
+        计算遗忘分数
+        
+        遗忘分数 = 1 - 激活值
+        分数越高，越容易被遗忘。
+        
+        注意：激活值已经包含了 importance_score 的权重，
+        因此不需要单独考虑重要性分数。
+        
+        Args:
+            activation_value: 节点的激活值（0-1）
+        
+        Returns:
+            float: 遗忘分数（0-1），值越高越容易被遗忘
+        """
+        return 1.0 - activation_value
+    
+    async def find_forgettable_nodes(
+        self,
+        group_id: Optional[str] = None,
+        min_days_since_access: int = 30
+    ) -> List[Dict[str, Any]]:
+        """
+        识别可遗忘的节点对
+        
+        查找满足以下条件的 Statement-Entity 节点对：
+        1. 两个节点的激活值都低于遗忘阈值
+        2. 两个节点都至少 min_days_since_access 天未被访问
+        3. Statement 和 Entity 之间存在关系边
+        
+        Args:
+            group_id: 组 ID（可选，用于过滤特定组的节点）
+            min_days_since_access: 最小未访问天数（默认 30 天）
+        
+        Returns:
+            List[Dict[str, Any]]: 可遗忘节点对列表，每个元素包含：
+                - statement_id: Statement 节点 ID
+                - statement_text: Statement 文本内容
+                - statement_activation: Statement 激活值
+                - statement_importance: Statement 重要性分数
+                - statement_last_access: Statement 最后访问时间
+                - entity_id: Entity 节点 ID
+                - entity_name: Entity 名称
+                - entity_type: Entity 类型
+                - entity_activation: Entity 激活值
+                - entity_importance: Entity 重要性分数
+                - entity_last_access: Entity 最后访问时间
+                - avg_activation: 平均激活值（用于排序）
+        """
+        # 计算时间阈值
+        cutoff_time = datetime.now() - timedelta(days=min_days_since_access)
+        cutoff_time_iso = cutoff_time.isoformat()
+        
+        # 构建查询
+        query = """
+        MATCH (s:Statement)-[r]-(e:ExtractedEntity)
+        WHERE s.activation_value IS NOT NULL
+          AND e.activation_value IS NOT NULL
+          AND s.activation_value < $threshold
+          AND e.activation_value < $threshold
+          AND s.last_access_time < $cutoff_time
+          AND e.last_access_time < $cutoff_time
+          AND (e.entity_type IS NULL OR e.entity_type <> 'Person')
+        """
+        
+        if group_id:
+            query += " AND s.group_id = $group_id AND e.group_id = $group_id"
+        
+        query += """
+        RETURN s.id as statement_id,
+               s.statement as statement_text,
+               s.activation_value as statement_activation,
+               s.importance_score as statement_importance,
+               s.last_access_time as statement_last_access,
+               e.id as entity_id,
+               e.name as entity_name,
+               e.entity_type as entity_type,
+               e.activation_value as entity_activation,
+               e.importance_score as entity_importance,
+               e.last_access_time as entity_last_access,
+               (s.activation_value + e.activation_value) / 2.0 as avg_activation
+        ORDER BY avg_activation ASC
+        """
+        
+        params = {
+            'threshold': self.forgetting_threshold,
+            'cutoff_time': cutoff_time_iso
+        }
+        if group_id:
+            params['group_id'] = group_id
+        
+        results = await self.connector.execute_query(query, **params)
+        
+        logger.info(
+            f"识别到 {len(results)} 个可遗忘节点对 "
+            f"(threshold={self.forgetting_threshold}, "
+            f"min_days={min_days_since_access})"
+        )
+        
+        return results
+    
+    async def merge_nodes_to_summary(
+        self,
+        statement_node: Dict[str, Any],
+        entity_node: Dict[str, Any],
+        config_id: Optional[int] = None,
+        db = None
+    ) -> str:
+        """
+        将 Statement 和 Entity 节点融合为 MemorySummary 节点
+        
+        融合过程：
+        1. 生成摘要内容（使用 LLM 或简单拼接）
+        2. 创建 MemorySummary 节点，继承较高的激活值和重要性分数
+        3. 删除原始 Statement 和 Entity 节点
+        4. 保留溯源信息（original_statement_id, original_entity_id）
+        
+        Args:
+            statement_node: Statement 节点数据，必须包含：
+                - statement_id: 节点 ID
+                - statement_text: 文本内容
+                - statement_activation: 激活值
+                - statement_importance: 重要性分数
+            entity_node: Entity 节点数据，必须包含：
+                - entity_id: 节点 ID
+                - entity_name: 实体名称
+                - entity_type: 实体类型
+                - entity_activation: 激活值
+                - entity_importance: 重要性分数
+            config_id: 配置ID（可选，用于获取 llm_id）
+            db: 数据库会话（可选，用于获取 llm_id）
+        
+        Returns:
+            str: 创建的 MemorySummary 节点 ID
+        
+        Raises:
+            ValueError: 如果节点数据不完整
+            RuntimeError: 如果融合操作失败
+        """
+        # 验证输入数据
+        required_statement_keys = [
+            'statement_id', 'statement_text', 
+            'statement_activation', 'statement_importance'
+        ]
+        required_entity_keys = [
+            'entity_id', 'entity_name', 'entity_type',
+            'entity_activation', 'entity_importance'
+        ]
+        
+        for key in required_statement_keys:
+            if key not in statement_node:
+                raise ValueError(f"Statement 节点缺少必需字段: {key}")
+        
+        for key in required_entity_keys:
+            if key not in entity_node:
+                raise ValueError(f"Entity 节点缺少必需字段: {key}")
+        
+        # 验证实体类型：不允许融合 Person 类型的实体
+        if entity_node.get('entity_type') == 'Person':
+            raise ValueError(
+                f"不允许融合 Person 类型的实体: entity_id={entity_node.get('entity_id')}, "
+                f"entity_name={entity_node.get('entity_name')}"
+            )
+        
+        # 提取节点信息
+        statement_id = statement_node['statement_id']
+        statement_text = statement_node['statement_text']
+        statement_activation = statement_node['statement_activation']
+        statement_importance = statement_node['statement_importance']
+        
+        entity_id = entity_node['entity_id']
+        entity_name = entity_node['entity_name']
+        entity_type = entity_node['entity_type']
+        entity_activation = entity_node['entity_activation']
+        entity_importance = entity_node['entity_importance']
+        
+        # 生成摘要内容
+        summary_text = await self._generate_summary(
+            statement_text=statement_text,
+            entity_name=entity_name,
+            entity_type=entity_type,
+            config_id=config_id,
+            db=db
+        )
+        
+        # 计算继承的激活值和重要性（取较高值）
+        inherited_activation = max(statement_activation, entity_activation)
+        inherited_importance = max(statement_importance, entity_importance)
+        
+        # 创建 MemorySummary 节点
+        current_time = datetime.now()
+        current_time_iso = current_time.isoformat()
+        
+        # 生成新的 MemorySummary ID
+        import uuid
+        summary_id = f"summary_{uuid.uuid4().hex[:16]}"
+        
+        # 获取 group_id（从 statement 或 entity 节点）
+        group_id = statement_node.get('group_id') or entity_node.get('group_id')
+        
+        # 使用事务创建 MemorySummary 并删除原节点
+        async def merge_transaction(tx, **params):
+            """事务函数：创建摘要节点并删除原节点"""
+            query = """
+            // 首先检查节点是否存在
+            OPTIONAL MATCH (s:Statement {id: $statement_id})
+            OPTIONAL MATCH (e:ExtractedEntity {id: $entity_id})
+            
+            // 如果任一节点不存在，直接返回 null（不执行后续操作）
+            WITH s, e
+            WHERE s IS NOT NULL AND e IS NOT NULL
+            
+            // 创建 MemorySummary 节点
+            CREATE (ms:MemorySummary {
+                id: $summary_id,
+                summary: $summary_text,
+                original_statement_id: $statement_id,
+                original_entity_id: $entity_id,
+                activation_value: $inherited_activation,
+                importance_score: $inherited_importance,
+                access_history: [$current_time],
+                last_access_time: $current_time,
+                access_count: 1,
+                version: 1,
+                group_id: $group_id,
+                created_at: datetime($current_time),
+                merged_at: datetime($current_time)
+            })
+            
+            // 转移 Statement 的出边到 MemorySummary（只转移目标节点仍存在的边）
+            WITH ms, s, e
+            CALL (ms, s, e) {
+                OPTIONAL MATCH (s)-[r_out]->(target)
+                WHERE target <> e AND r_out IS NOT NULL AND target IS NOT NULL
+                FOREACH (_ IN CASE WHEN target IS NOT NULL THEN [1] ELSE [] END |
+                    MERGE (ms)-[new_rel:DERIVED_FROM]->(target)
+                    ON CREATE SET 
+                        new_rel = properties(r_out),
+                        new_rel.original_relationship_type = type(r_out),
+                        new_rel.merged_from_statement = true,
+                        new_rel.merge_count = 1
+                    ON MATCH SET
+                        new_rel.merge_count = coalesce(new_rel.merge_count, 0) + 1
+                )
+            }
+            
+            // 转移 Statement 的入边到 MemorySummary（只转移源节点仍存在的边）
+            WITH ms, s, e
+            CALL (ms, s, e) {
+                OPTIONAL MATCH (source)-[r_in]->(s)
+                WHERE r_in IS NOT NULL AND source IS NOT NULL
+                FOREACH (_ IN CASE WHEN source IS NOT NULL THEN [1] ELSE [] END |
+                    MERGE (source)-[new_rel:DERIVED_FROM]->(ms)
+                    ON CREATE SET 
+                        new_rel = properties(r_in),
+                        new_rel.original_relationship_type = type(r_in),
+                        new_rel.merged_from_statement = true,
+                        new_rel.merge_count = 1
+                    ON MATCH SET
+                        new_rel.merge_count = coalesce(new_rel.merge_count, 0) + 1
+                )
+            }
+            
+            // 转移 Entity 的出边到 MemorySummary（只转移目标节点仍存在的边）
+            WITH ms, s, e
+            CALL (ms, s, e) {
+                OPTIONAL MATCH (e)-[r_out]->(target)
+                WHERE target <> s AND r_out IS NOT NULL AND target IS NOT NULL
+                FOREACH (_ IN CASE WHEN target IS NOT NULL THEN [1] ELSE [] END |
+                    MERGE (ms)-[new_rel:DERIVED_FROM]->(target)
+                    ON CREATE SET 
+                        new_rel = properties(r_out),
+                        new_rel.original_relationship_type = type(r_out),
+                        new_rel.merged_from_entity = true,
+                        new_rel.merge_count = 1
+                    ON MATCH SET
+                        new_rel.merge_count = coalesce(new_rel.merge_count, 0) + 1
+                )
+            }
+            
+            // 转移 Entity 的入边到 MemorySummary（只转移源节点仍存在的边）
+            WITH ms, s, e
+            CALL (ms, s, e) {
+                OPTIONAL MATCH (source)-[r_in]->(e)
+                WHERE source <> s AND r_in IS NOT NULL AND source IS NOT NULL
+                FOREACH (_ IN CASE WHEN source IS NOT NULL THEN [1] ELSE [] END |
+                    MERGE (source)-[new_rel:DERIVED_FROM]->(ms)
+                    ON CREATE SET 
+                        new_rel = properties(r_in),
+                        new_rel.original_relationship_type = type(r_in),
+                        new_rel.merged_from_entity = true,
+                        new_rel.merge_count = 1
+                    ON MATCH SET
+                        new_rel.merge_count = coalesce(new_rel.merge_count, 0) + 1
+                )
+            }
+            
+            // 删除原始节点
+            WITH ms, s, e
+            DETACH DELETE s, e
+            
+            RETURN ms.id as summary_id
+            """
+            
+            result = await tx.run(query, **params)
+            record = await result.single()
+            
+            if not record:
+                raise RuntimeError("Failed to create MemorySummary node - nodes may not exist")
+            
+            return record['summary_id']
+        
+        params = {
+            'summary_id': summary_id,
+            'summary_text': summary_text,
+            'statement_id': statement_id,
+            'entity_id': entity_id,
+            'inherited_activation': inherited_activation,
+            'inherited_importance': inherited_importance,
+            'current_time': current_time_iso,
+            'group_id': group_id
+        }
+        
+        try:
+            created_summary_id = await self.connector.execute_write_transaction(
+                merge_transaction,
+                **params
+            )
+            
+            logger.info(
+                f"成功融合节点: Statement[{statement_id}] + Entity[{entity_id}] "
+                f"-> MemorySummary[{created_summary_id}], "
+                f"activation={inherited_activation:.4f}, "
+                f"importance={inherited_importance:.4f}"
+            )
+            
+            return created_summary_id
+            
+        except Exception as e:
+            # 记录详细的错误信息，包括异常类型和堆栈
+            import traceback
+            error_details = traceback.format_exc()
+            logger.error(
+                f"融合节点失败: Statement[{statement_id}] + Entity[{entity_id}], "
+                f"错误类型: {type(e).__name__}, "
+                f"错误信息: {str(e)}, "
+                f"详细堆栈:\n{error_details}"
+            )
+            raise RuntimeError(
+                f"融合节点失败: {str(e)}"
+            ) from e
+    
+    # ==================== 私有辅助方法 ====================
+    
+    async def _generate_summary(
+        self,
+        statement_text: str,
+        entity_name: str,
+        entity_type: str,
+        config_id: Optional[int] = None,
+        db = None
+    ) -> str:
+        """
+        生成摘要内容
+        
+        优先使用 LLM 生成高质量摘要，如果 LLM 不可用或失败，
+        则降级到简单文本拼接。
+        
+        Args:
+            statement_text: Statement 文本内容
+            entity_name: Entity 名称
+            entity_type: Entity 类型
+            config_id: 配置ID（可选，用于获取 llm_id）
+            db: 数据库会话（可选，用于获取 llm_id）
+        
+        Returns:
+            str: 生成的摘要文本（最多 200 个字符）
+        """
+        # 如果配置禁用 LLM 摘要，直接使用简单拼接
+        if not self.enable_llm_summary:
+            logger.info("LLM 摘要生成已禁用，使用简单拼接")
+            return self._simple_concatenation(
+                statement_text, entity_name, entity_type
+            )
+        
+        # 尝试获取 LLM 客户端
+        llm_client = None
+        if config_id is not None and db is not None:
+            try:
+                llm_client = await self._get_llm_client(db, config_id)
+            except Exception as e:
+                logger.warning(f"获取 LLM 客户端失败: {str(e)}")
+        
+        # 如果没有 LLM 客户端，直接使用简单拼接
+        if llm_client is None:
+            logger.info("未能获取 LLM 客户端，使用简单拼接")
+            return self._simple_concatenation(
+                statement_text, entity_name, entity_type
+            )
+        
+        # 尝试使用 LLM 生成摘要
+        try:
+            summary = await self._generate_llm_summary(
+                statement_text=statement_text,
+                entity_name=entity_name,
+                entity_type=entity_type,
+                llm_client=llm_client
+            )
+            
+            # 限制长度为 200 个字符
+            if len(summary) > 200:
+                summary = f"{summary[:197]}..."
+            
+            logger.info(f"使用 LLM 生成摘要: {summary}")
+            return summary
+            
+        except Exception as e:
+            logger.warning(
+                f"LLM 摘要生成失败，降级到简单拼接: {str(e)}"
+            )
+            return self._simple_concatenation(
+                statement_text, entity_name, entity_type
+            )
+    
+    async def _get_llm_client(self, db, config_id: int):
+        """
+        从数据库获取 LLM 客户端
+        
+        Args:
+            db: 数据库会话
+            config_id: 配置ID
+        
+        Returns:
+            LLM 客户端实例，如果无法获取则返回 None
+        """
+        try:
+            from app.repositories.data_config_repository import DataConfigRepository
+            from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+            
+            # 从数据库读取配置
+            repository = DataConfigRepository()
+            db_config = repository.get_by_id(db, config_id)
+            
+            if db_config is None or db_config.llm_id is None:
+                logger.warning(f"配置 {config_id} 不存在或未设置 llm_id")
+                return None
+            
+            # 创建 LLM 客户端
+            factory = MemoryClientFactory(db)
+            llm_client = factory.get_llm_client(str(db_config.llm_id))
+            
+            logger.info(f"成功获取 LLM 客户端: config_id={config_id}, llm_id={db_config.llm_id}")
+            return llm_client
+            
+        except Exception as e:
+            logger.error(f"获取 LLM 客户端失败: {str(e)}")
+            return None
+    
+    async def _generate_llm_summary(
+        self,
+        statement_text: str,
+        entity_name: str,
+        entity_type: str,
+        llm_client
+    ) -> str:
+        """
+        使用 LLM 生成高质量摘要
+        
+        Args:
+            statement_text: Statement 文本内容
+            entity_name: Entity 名称
+            entity_type: Entity 类型
+            llm_client: LLM 客户端实例
+        
+        Returns:
+            str: LLM 生成的摘要文本
+        
+        Raises:
+            Exception: 如果 LLM 调用失败
+        """
+        # 构建提示词
+        prompt = f"""请为以下记忆片段生成一个简洁的摘要（不超过200个字符）：
+
+实体名称: {entity_name}
+实体类型: {entity_type}
+陈述内容: {statement_text}
+
+要求：
+1. 摘要应该保留核心语义信息
+2. 长度不超过200个字符
+3. 使用简洁、自然的中文表达
+4. 只返回摘要文本，不要包含其他内容
+
+摘要："""
+        
+        # 调用 LLM（直接传递 prompt 字符串）
+        response = await llm_client.chat(prompt)
+        
+        # 提取摘要文本
+        if isinstance(response, str):
+            summary = response.strip()
+        elif hasattr(response, 'content'):
+            summary = response.content.strip()
+        else:
+            summary = str(response).strip()
+        
+        return summary
+    
+    def _simple_concatenation(
+        self,
+        statement_text: str,
+        entity_name: str,
+        entity_type: str
+    ) -> str:
+        """
+        简单文本拼接生成摘要
+        
+        降级策略：当 LLM 不可用时使用。
+        格式：[实体类型]实体名称: 陈述内容
+        
+        Args:
+            statement_text: Statement 文本内容
+            entity_name: Entity 名称
+            entity_type: Entity 类型
+        
+        Returns:
+            str: 拼接的摘要文本（最多 200 个字符）
+        """
+        # 构建简单摘要
+        summary = f"[{entity_type}]{entity_name}: {statement_text}"
+        
+        # 限制长度为 200 个字符（注意：这里的长度是字符数，不是字节数）
+        if len(summary) > 200:
+            # 截断并添加省略号
+            summary = f"{summary[:197]}..."
+        
+        return summary
+

api/app/models/data_config_model.py

@@ -65,6 +65,15 @@ class DataConfig(Base):
     lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数")
     offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数")
     
+    # ACT-R 遗忘引擎配置
+    decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5")
+    forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3")
+    forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24")
+    enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True")
+    max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100")
+    max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100")
+    min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30")
+    
     # 情绪引擎配置
     emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取")
     emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID")

api/app/repositories/neo4j/add_nodes.py

@@ -106,7 +106,13 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC
                 "emotion_intensity": statement.emotion_intensity,
                 "emotion_keywords": statement.emotion_keywords if statement.emotion_keywords else [],
                 "emotion_subject": statement.emotion_subject,
-                "emotion_target": statement.emotion_target
+                "emotion_target": statement.emotion_target,
+                # 添加 ACT-R 记忆激活属性
+                "importance_score": statement.importance_score,
+                "activation_value": statement.activation_value,
+                "access_history": statement.access_history if statement.access_history else [],
+                "last_access_time": statement.last_access_time,
+                "access_count": statement.access_count
             }
             flattened_statements.append(flattened_statement)
 

api/app/repositories/neo4j/cypher_queries.py

@@ -38,7 +38,12 @@ SET s += {
     valid_at: statement.valid_at,
     invalid_at: statement.invalid_at,
     statement_embedding: statement.statement_embedding,
-    relevence_info: statement.relevence_info
+    relevence_info: statement.relevence_info,
+    importance_score: statement.importance_score,
+    activation_value: statement.activation_value,
+    access_history: statement.access_history,
+    last_access_time: statement.last_access_time,
+    access_count: statement.access_count
 }
 RETURN s.id AS uuid
 """
@@ -111,7 +116,12 @@ SET e.name = CASE WHEN entity.name IS NOT NULL AND entity.name <> '' THEN entity
             WHEN e.connect_strength IS NULL OR e.connect_strength = '' THEN entity.connect_strength
             ELSE e.connect_strength
         END
-    END
+    END,
+    e.importance_score = CASE WHEN entity.importance_score IS NOT NULL THEN entity.importance_score ELSE coalesce(e.importance_score, 0.5) END,
+    e.activation_value = CASE WHEN entity.activation_value IS NOT NULL THEN entity.activation_value ELSE e.activation_value END,
+    e.access_history = CASE WHEN entity.access_history IS NOT NULL THEN entity.access_history ELSE coalesce(e.access_history, []) END,
+    e.last_access_time = CASE WHEN entity.last_access_time IS NOT NULL THEN entity.last_access_time ELSE e.last_access_time END,
+    e.access_count = CASE WHEN entity.access_count IS NOT NULL THEN entity.access_count ELSE coalesce(e.access_count, 0) END
 RETURN e.id AS uuid
 """
 
@@ -225,6 +235,10 @@ RETURN e.id AS id,
        e.name AS name,
        e.group_id AS group_id,
        e.entity_type AS entity_type,
+       COALESCE(e.activation_value, e.importance_score, 0.5) AS activation_value,
+       COALESCE(e.importance_score, 0.5) AS importance_score,
+       e.last_access_time AS last_access_time,
+       COALESCE(e.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -243,6 +257,10 @@ RETURN s.id AS id,
        s.expired_at AS expired_at,
        s.valid_at AS valid_at,
        s.invalid_at AS invalid_at,
+       COALESCE(s.activation_value, s.importance_score, 0.5) AS activation_value,
+       COALESCE(s.importance_score, 0.5) AS importance_score,
+       s.last_access_time AS last_access_time,
+       COALESCE(s.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -258,6 +276,9 @@ RETURN c.id AS chunk_id,
        c.group_id AS group_id,
        c.content AS content,
        c.dialog_id AS dialog_id,
+       COALESCE(c.activation_value, 0.5) AS activation_value,
+       c.last_access_time AS last_access_time,
+       COALESCE(c.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -278,6 +299,10 @@ RETURN s.id AS id,
        s.invalid_at AS invalid_at,
        c.id AS chunk_id_from_rel,
        collect(DISTINCT e.id) AS entity_ids,
+       COALESCE(s.activation_value, s.importance_score, 0.5) AS activation_value,
+       COALESCE(s.importance_score, 0.5) AS importance_score,
+       s.last_access_time AS last_access_time,
+       COALESCE(s.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -305,6 +330,10 @@ RETURN e.id AS id,
        e.connect_strength AS connect_strength,
        collect(DISTINCT s.id) AS statement_ids,
        collect(DISTINCT c.id) AS chunk_ids,
+       COALESCE(e.activation_value, e.importance_score, 0.5) AS activation_value,
+       COALESCE(e.importance_score, 0.5) AS importance_score,
+       e.last_access_time AS last_access_time,
+       COALESCE(e.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -322,6 +351,9 @@ RETURN c.id AS chunk_id,
        c.sequence_number AS sequence_number,
        collect(DISTINCT s.id) AS statement_ids,
        collect(DISTINCT e.id) AS entity_ids,
+       COALESCE(c.activation_value, 0.5) AS activation_value,
+       c.last_access_time AS last_access_time,
+       COALESCE(c.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -419,7 +451,11 @@ RETURN s.id AS id,
        s.created_at AS created_at,
        s.valid_at AS valid_at,
        s.invalid_at AS invalid_at,
-       collect(DISTINCT s.id) AS statement_ids
+       collect(DISTINCT s.id) AS statement_ids,
+       COALESCE(s.activation_value, s.importance_score, 0.5) AS activation_value,
+       COALESCE(s.importance_score, 0.5) AS importance_score,
+       s.last_access_time AS last_access_time,
+       COALESCE(s.access_count, 0) AS access_count
 ORDER BY datetime(s.created_at) DESC
 LIMIT $limit
 """
@@ -446,6 +482,10 @@ RETURN s.id AS id,
        s.invalid_at AS invalid_at,
        c.id AS chunk_id_from_rel,
        collect(DISTINCT e.id) AS entity_ids,
+       COALESCE(s.activation_value, s.importance_score, 0.5) AS activation_value,
+       COALESCE(s.importance_score, 0.5) AS importance_score,
+       s.last_access_time AS last_access_time,
+       COALESCE(s.access_count, 0) AS access_count,
        score
 ORDER BY s.created_at DESC, score DESC
 LIMIT $limit
@@ -635,6 +675,10 @@ RETURN m.id AS id,
        m.chunk_ids AS chunk_ids,
        m.content AS content,
        m.created_at AS created_at,
+       COALESCE(m.activation_value, m.importance_score, 0.5) AS activation_value,
+       COALESCE(m.importance_score, 0.5) AS importance_score,
+       m.last_access_time AS last_access_time,
+       COALESCE(m.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit
@@ -653,6 +697,10 @@ RETURN m.id AS id,
        m.chunk_ids AS chunk_ids,
        m.content AS content,
        m.created_at AS created_at,
+       COALESCE(m.activation_value, m.importance_score, 0.5) AS activation_value,
+       COALESCE(m.importance_score, 0.5) AS importance_score,
+       m.last_access_time AS last_access_time,
+       COALESCE(m.access_count, 0) AS access_count,
        score
 ORDER BY score DESC
 LIMIT $limit

api/app/repositories/neo4j/entity_repository.py

@@ -55,6 +55,13 @@ class EntityRepository(BaseNeo4jRepository[ExtractedEntityNode]):
         if 'aliases' not in n or n['aliases'] is None:
             n['aliases'] = []
         
+        # 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['last_access_time'] = n.get('last_access_time')
+        n['access_count'] = n.get('access_count', 0)
+        
         return ExtractedEntityNode(**n)
     
     async def find_by_type(self, entity_type: str, limit: int = 100) -> List[ExtractedEntityNode]:

api/app/repositories/neo4j/graph_search.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List, Optional
 import asyncio
+import logging
 
 # 使用新的仓储层
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
@@ -24,6 +25,157 @@ from app.repositories.neo4j.cypher_queries import (
     SEARCH_STATEMENTS_L_VALID_AT,
 )
 
+logger = logging.getLogger(__name__)
+
+
+async def _update_activation_values_batch(
+    connector: Neo4jConnector,
+    nodes: List[Dict[str, Any]],
+    node_label: str,
+    group_id: Optional[str] = None,
+    max_retries: int = 3
+) -> List[Dict[str, Any]]:
+    """
+    批量更新节点的激活值
+    
+    为提高性能，批量更新多个节点的访问历史和激活值。
+    使用重试机制处理更新失败的情况。
+    
+    Args:
+        connector: Neo4j连接器
+        nodes: 节点列表，每个节点必须包含 'id' 字段
+        node_label: 节点标签（Statement, ExtractedEntity, MemorySummary）
+        group_id: 组ID（可选）
+        max_retries: 最大重试次数
+    
+    Returns:
+        List[Dict[str, Any]]: 成功更新的节点列表
+    """
+    if not nodes:
+        return []
+    
+    # 延迟导入以避免循环依赖
+    from app.core.memory.storage_services.forgetting_engine.access_history_manager import AccessHistoryManager
+    from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
+    
+    # 创建计算器和管理器实例
+    actr_calculator = ACTRCalculator()
+    access_manager = AccessHistoryManager(
+        connector=connector,
+        actr_calculator=actr_calculator,
+        max_retries=max_retries
+    )
+    
+    # 提取节点ID列表
+    node_ids = [node.get('id') for node in nodes if node.get('id')]
+    
+    if not node_ids:
+        logger.warning(f"批量更新激活值：没有有效的节点ID")
+        return nodes
+    
+    # 批量记录访问
+    try:
+        updated_nodes = await access_manager.record_batch_access(
+            node_ids=node_ids,
+            node_label=node_label,
+            group_id=group_id
+        )
+        
+        logger.info(
+            f"批量更新激活值成功: {node_label}, "
+            f"更新数量={len(updated_nodes)}/{len(node_ids)}"
+        )
+        
+        return updated_nodes
+        
+    except Exception as e:
+        logger.error(
+            f"批量更新激活值失败: {node_label}, 错误: {str(e)}"
+        )
+        # 失败时返回原始节点列表
+        return nodes
+
+
+async def _update_search_results_activation(
+    connector: Neo4jConnector,
+    results: Dict[str, List[Dict[str, Any]]],
+    group_id: Optional[str] = None
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    更新搜索结果中所有知识节点的激活值
+    
+    对 Statement、ExtractedEntity、MemorySummary 节点进行批量激活值更新。
+    ChunkNode 和 DialogueNode 不参与激活值更新（数据层隔离）。
+    
+    Args:
+        connector: Neo4j连接器
+        results: 搜索结果字典，包含不同类型节点的列表
+        group_id: 组ID（可选）
+    
+    Returns:
+        Dict[str, List[Dict[str, Any]]]: 更新后的搜索结果
+    """
+    # 定义需要更新激活值的节点类型
+    knowledge_node_types = {
+        'statements': 'Statement',
+        'entities': 'ExtractedEntity',
+        'summaries': 'MemorySummary'
+    }
+    
+    # 并行更新所有类型的节点
+    update_tasks = []
+    update_keys = []
+    
+    for key, label in knowledge_node_types.items():
+        if key in results and results[key]:
+            update_tasks.append(
+                _update_activation_values_batch(
+                    connector=connector,
+                    nodes=results[key],
+                    node_label=label,
+                    group_id=group_id
+                )
+            )
+            update_keys.append(key)
+    
+    if not update_tasks:
+        return results
+    
+    # 并行执行所有更新
+    update_results = await asyncio.gather(*update_tasks, return_exceptions=True)
+    
+    # 更新结果字典，保留原始搜索分数
+    updated_results = results.copy()
+    for key, update_result in zip(update_keys, update_results):
+        if not isinstance(update_result, Exception):
+            # 更新成功，合并原始搜索结果和更新后的激活值数据
+            # 保留原始的 score 字段（BM25/Embedding 分数）
+            original_nodes = results[key]
+            updated_nodes = update_result
+            
+            # 创建 ID 到原始节点的映射（用于快速查找 score）
+            original_map = {node.get('id'): node for node in original_nodes if node.get('id')}
+            
+            # 合并数据：激活值来自更新结果，score 来自原始结果
+            merged_nodes = []
+            for updated_node in updated_nodes:
+                node_id = updated_node.get('id')
+                if node_id and node_id in original_map:
+                    # 保留原始的 score 字段
+                    original_score = original_map[node_id].get('score')
+                    if original_score is not None:
+                        updated_node['score'] = original_score
+                merged_nodes.append(updated_node)
+            
+            updated_results[key] = merged_nodes
+        else:
+            # 更新失败，记录错误但保留原始结果
+            logger.warning(
+                f"更新 {key} 激活值失败: {str(update_result)}"
+            )
+    
+    return updated_results
+
 
 async def search_graph(
     connector: Neo4jConnector,
@@ -36,6 +188,7 @@ async def search_graph(
     Search across Statements, Entities, Chunks, and Summaries using a free-text query.
     
     OPTIMIZED: Runs all queries in parallel using asyncio.gather()
+    INTEGRATED: Updates activation values for knowledge nodes before returning results
 
     - Statements: matches s.statement CONTAINS q
     - Entities: matches e.name CONTAINS q
@@ -50,7 +203,7 @@ async def search_graph(
         include: List of categories to search (default: all)
 
     Returns:
-        Dictionary with search results per category
+        Dictionary with search results per category (with updated activation values)
     """
     if include is None:
         include = ["statements", "chunks", "entities", "summaries"]
@@ -106,6 +259,13 @@ async def search_graph(
         else:
             results[key] = result
     
+    # 更新知识节点的激活值（Statement, ExtractedEntity, MemorySummary）
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
     return results
 
 
@@ -121,6 +281,7 @@ async def search_graph_by_embedding(
     Embedding-based semantic search across Statements, Chunks, and Entities.
     
     OPTIMIZED: Runs all queries in parallel using asyncio.gather()
+    INTEGRATED: Updates activation values for knowledge nodes before returning results
 
     - Computes query embedding with the provided embedder_client
     - Ranks by cosine similarity in Cypher
@@ -203,6 +364,16 @@ async def search_graph_by_embedding(
         else:
             results[key] = result
 
+    # 更新知识节点的激活值（Statement, ExtractedEntity, MemorySummary）
+    update_start = time.time()
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    update_time = time.time() - update_start
+    print(f"[PERF] Activation value updates took: {update_time:.4f}s")
+
     return results
 async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全文索引按名称检索候选实体
     connector: Neo4jConnector,
@@ -304,6 +475,8 @@ async def search_graph_by_keyword_temporal(
 ) -> Dict[str, List[Any]]:
     """
     Temporal keyword search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements containing query_text created between start_date and end_date
     - Optionally filters by group_id, apply_id, user_id
@@ -326,7 +499,15 @@ async def search_graph_by_keyword_temporal(
     )
     print(f"查询结果为：\n{statements}")
 
-    return {"statements": statements}
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+
+    return results
 
 
 async def search_graph_by_temporal(
@@ -342,6 +523,8 @@ async def search_graph_by_temporal(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created between start_date and end_date
     - Optionally filters by group_id, apply_id, user_id
@@ -362,7 +545,16 @@ async def search_graph_by_temporal(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_TEMPORAL}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results
 
 
 async def search_graph_by_dialog_id(
@@ -419,6 +611,8 @@ async def search_graph_by_created_at(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
     - Optionally filters by group_id, apply_id, user_id
@@ -436,7 +630,16 @@ async def search_graph_by_created_at(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_CREATED_AT}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, created_at: {created_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results
 
 async def search_graph_by_valid_at(
     connector: Neo4jConnector,
@@ -448,6 +651,8 @@ async def search_graph_by_valid_at(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
     - Optionally filters by group_id, apply_id, user_id
@@ -465,7 +670,16 @@ async def search_graph_by_valid_at(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_VALID_AT}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, valid_at: {valid_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results
 
 async def search_graph_g_created_at(
     connector: Neo4jConnector,
@@ -477,6 +691,8 @@ async def search_graph_g_created_at(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
     - Optionally filters by group_id, apply_id, user_id
@@ -494,7 +710,16 @@ async def search_graph_g_created_at(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_G_CREATED_AT}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, created_at: {created_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results
 
 async def search_graph_g_valid_at(
     connector: Neo4jConnector,
@@ -506,6 +731,8 @@ async def search_graph_g_valid_at(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
     - Optionally filters by group_id, apply_id, user_id
@@ -523,7 +750,16 @@ async def search_graph_g_valid_at(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_G_VALID_AT}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, valid_at: {valid_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results
 
 async def search_graph_l_created_at(
     connector: Neo4jConnector,
@@ -535,6 +771,8 @@ async def search_graph_l_created_at(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
     - Optionally filters by group_id, apply_id, user_id
@@ -552,7 +790,16 @@ async def search_graph_l_created_at(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_L_CREATED_AT}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, created_at: {created_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results
 
 async def search_graph_l_valid_at(
     connector: Neo4jConnector,
@@ -564,6 +811,8 @@ async def search_graph_l_valid_at(
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Statements.
+    
+    INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
     - Optionally filters by group_id, apply_id, user_id
@@ -581,4 +830,13 @@ async def search_graph_l_valid_at(
     print(f"查询语句为：\n{SEARCH_STATEMENTS_L_VALID_AT}")
     print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, valid_at: {valid_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
-    return {"statements": statements}
+    
+    # 更新 Statement 节点的激活值
+    results = {"statements": statements}
+    results = await _update_search_results_activation(
+        connector=connector,
+        results=results,
+        group_id=group_id
+    )
+    
+    return results

api/app/repositories/neo4j/neo4j_connector.py

@@ -8,7 +8,6 @@ Classes:
     Neo4jConnector: Neo4j数据库连接器，提供异步查询接口
 """
 
-import os
 from typing import Any, List, Dict
 
 from neo4j import AsyncGraphDatabase, basic_auth
@@ -85,6 +84,63 @@ class Neo4jConnector:
         records, summary, keys = result
         return [record.data() for record in records]
     
+    async def execute_write_transaction(self, transaction_func, **kwargs: Any) -> Any:
+        """在写事务中执行操作
+        
+        提供显式事务支持，确保操作的原子性。
+        如果事务函数抛出异常，所有更改将自动回滚。
+        
+        Args:
+            transaction_func: 事务函数，接收 tx 参数并执行查询
+            **kwargs: 传递给事务函数的额外参数
+            
+        Returns:
+            Any: 事务函数的返回值
+            
+        Example:
+            >>> async def create_node(tx, name):
+            ...     result = await tx.run(
+            ...         "CREATE (n:Person {name: $name}) RETURN n",
+            ...         name=name
+            ...     )
+            ...     return await result.single()
+            >>> 
+            >>> connector = Neo4jConnector()
+            >>> result = await connector.execute_write_transaction(
+            ...     create_node, name="Alice"
+            ... )
+        """
+        async with self.driver.session(database="neo4j") as session:
+            return await session.execute_write(transaction_func, **kwargs)
+    
+    async def execute_read_transaction(self, transaction_func, **kwargs: Any) -> Any:
+        """在读事务中执行操作
+        
+        提供显式事务支持用于读操作。
+        
+        Args:
+            transaction_func: 事务函数，接收 tx 参数并执行查询
+            **kwargs: 传递给事务函数的额外参数
+            
+        Returns:
+            Any: 事务函数的返回值
+            
+        Example:
+            >>> async def get_node(tx, name):
+            ...     result = await tx.run(
+            ...         "MATCH (n:Person {name: $name}) RETURN n",
+            ...         name=name
+            ...     )
+            ...     return await result.single()
+            >>> 
+            >>> connector = Neo4jConnector()
+            >>> result = await connector.execute_read_transaction(
+            ...     get_node, name="Alice"
+            ... )
+        """
+        async with self.driver.session(database="neo4j") as session:
+            return await session.execute_read(transaction_func, **kwargs)
+    
     async def delete_group(self, group_id: str):
         """删除指定组的所有数据
         

api/app/repositories/neo4j/statement_repository.py

@@ -75,6 +75,13 @@ class StatementRepository(BaseNeo4jRepository[StatementNode]):
         n['emotion_subject'] = n.get('emotion_subject')
         n['emotion_target'] = n.get('emotion_target')
         
+        # 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['last_access_time'] = n.get('last_access_time')
+        n['access_count'] = n.get('access_count', 0)
+        
         return StatementNode(**n)
     
     async def find_by_chunk_id(self, chunk_id: str) -> List[StatementNode]:

api/app/schemas/memory_storage_schema.py

@@ -399,3 +399,104 @@ class GenerateCacheRequest(BaseModel):
         None, 
         description="终端用户ID（UUID格式）。如果提供，只为该用户生成；如果不提供，为当前工作空间的所有用户生成"
     )
+
+
+# ============================================================================
+# 遗忘引擎相关 Schema
+# ============================================================================
+
+class ForgettingTriggerRequest(BaseModel):
+    """手动触发遗忘周期请求模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    group_id: Optional[str] = Field(None, description="组ID（可选，用于过滤特定组的节点）")
+    max_merge_batch_size: int = Field(100, ge=1, le=1000, description="单次最大融合节点对数（默认100）")
+    min_days_since_access: int = Field(30, ge=1, le=365, description="最小未访问天数（默认30天）")
+    config_id: Optional[int] = Field(None, description="配置ID（可选，用于指定遗忘引擎配置）") # TODO 后续group_id更换成enduser_id，自动与config_id关联 ，要删除此行
+
+
+class ForgettingConfigResponse(BaseModel):
+    """遗忘引擎配置响应模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    config_id: int = Field(..., description="配置ID")
+    decay_constant: float = Field(..., description="衰减常数 d")
+    lambda_time: float = Field(..., description="时间衰减参数")
+    lambda_mem: float = Field(..., description="记忆衰减参数")
+    forgetting_rate: float = Field(..., description="遗忘速率（根据 lambda_time / lambda_mem 计算得出）")
+    offset: float = Field(..., description="偏移量")
+    max_history_length: int = Field(..., description="访问历史最大长度")
+    forgetting_threshold: float = Field(..., description="遗忘阈值")
+    min_days_since_access: int = Field(..., description="最小未访问天数")
+    enable_llm_summary: bool = Field(..., description="是否使用 LLM 生成摘要")
+    max_merge_batch_size: int = Field(..., description="单次最大融合节点对数")
+    forgetting_interval_hours: int = Field(..., description="遗忘周期间隔（小时）")
+
+
+class ForgettingConfigUpdateRequest(BaseModel):
+    """遗忘引擎配置更新请求模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    config_id: int = Field(..., description="配置ID")
+    decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d")
+    lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数")
+    lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数")
+    offset: Optional[float] = Field(None, ge=0.0, le=1.0, description="偏移量")
+    max_history_length: Optional[int] = Field(None, ge=10, le=1000, description="访问历史最大长度")
+    forgetting_threshold: Optional[float] = Field(None, ge=0.0, le=1.0, description="遗忘阈值")
+    min_days_since_access: Optional[int] = Field(None, ge=1, le=365, description="最小未访问天数")
+    enable_llm_summary: Optional[bool] = Field(None, description="是否使用 LLM 生成摘要")
+    max_merge_batch_size: Optional[int] = Field(None, ge=1, le=1000, description="单次最大融合节点对数")
+    forgetting_interval_hours: Optional[int] = Field(None, ge=1, le=168, description="遗忘周期间隔（小时）")
+
+
+class ForgettingStatsResponse(BaseModel):
+    """遗忘引擎统计信息响应模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    activation_metrics: Dict[str, Any] = Field(..., description="激活值相关指标")
+    node_distribution: Dict[str, int] = Field(..., description="节点类型分布")
+    consistency_check: Optional[Dict[str, Any]] = Field(None, description="数据一致性检查结果")
+    nodes_merged_total: int = Field(..., description="累计融合节点对数")
+    recent_cycles: List[Dict[str, Any]] = Field(..., description="最近的遗忘周期记录")
+    timestamp: str = Field(..., description="统计时间（ISO格式）")
+
+
+class ForgettingReportResponse(BaseModel):
+    """遗忘周期报告响应模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    merged_count: int = Field(..., description="融合的节点对数量")
+    nodes_before: int = Field(..., description="遗忘前的节点总数")
+    nodes_after: int = Field(..., description="遗忘后的节点总数")
+    reduction_rate: float = Field(..., description="节点减少率（0-1）")
+    duration_seconds: float = Field(..., description="执行耗时（秒）")
+    start_time: str = Field(..., description="开始时间（ISO格式）")
+    end_time: str = Field(..., description="结束时间（ISO格式）")
+    failed_count: int = Field(..., description="失败的融合数量")
+    success_rate: float = Field(..., description="成功率（0-1）")
+
+
+class ForgettingCurvePoint(BaseModel):
+    """遗忘曲线数据点模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    day: int = Field(..., description="天数")
+    activation: float = Field(..., description="激活值")
+    retention_rate: float = Field(..., description="保持率（与激活值相同）")
+
+
+class ForgettingCurveRequest(BaseModel):
+    """遗忘曲线请求模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）")
+    days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）")
+    config_id: Optional[int] = Field(None, description="配置ID（可选，如果为None则使用默认配置）")
+
+
+class ForgettingCurveResponse(BaseModel):
+    """遗忘曲线响应模型"""
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+    
+    curve_data: List[ForgettingCurvePoint] = Field(..., description="遗忘曲线数据点列表")
+    config: Dict[str, Any] = Field(..., description="使用的配置参数")

api/app/services/memory_forget_service.py

@@ -0,0 +1,460 @@
+"""
+遗忘引擎服务层模块
+
+本模块提供遗忘引擎的业务逻辑实现，包括：
+1. 遗忘周期执行
+2. 配置管理
+3. 统计信息查询
+4. 遗忘曲线生成
+
+所有业务逻辑从控制器层分离到此服务层。
+"""
+
+from typing import Optional, Dict, Any, Tuple
+from datetime import datetime
+
+from sqlalchemy.orm import Session
+
+from app.core.logging_config import get_api_logger
+from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
+from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy
+from app.core.memory.storage_services.forgetting_engine.forgetting_scheduler import ForgettingScheduler
+from app.core.memory.storage_services.forgetting_engine.config_utils import (
+    load_actr_config_from_db,
+)
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+from app.repositories.data_config_repository import DataConfigRepository
+
+
+# 获取API专用日志器
+api_logger = get_api_logger()
+
+
+class MemoryForgetService:
+    """遗忘引擎服务类"""
+    
+    def __init__(self):
+        """初始化服务"""
+        self.config_repository = DataConfigRepository()
+    
+    def _get_neo4j_connector(self) -> Neo4jConnector:
+        """
+        获取 Neo4j 连接器实例
+        
+        Returns:
+            Neo4jConnector: Neo4j 连接器实例
+        """
+        # 这里应该从配置或依赖注入获取连接器
+        # 暂时创建新实例（实际应该使用单例或连接池）
+        return Neo4jConnector()
+    
+    async def _get_forgetting_components(
+        self,
+        db: Session,
+        config_id: Optional[int] = None
+    ) -> Tuple[ACTRCalculator, ForgettingStrategy, ForgettingScheduler, Dict[str, Any]]:
+        """
+        获取遗忘引擎组件（计算器、策略、调度器）
+        
+        Args:
+            db: 数据库会话
+            config_id: 配置ID（可选）
+        
+        Returns:
+            tuple: (actr_calculator, forgetting_strategy, forgetting_scheduler, config)
+        """
+        # 加载配置
+        config = load_actr_config_from_db(db, config_id)
+        
+        # 创建 ACT-R 计算器
+        actr_calculator = ACTRCalculator(
+            decay_constant=config['decay_constant'],
+            forgetting_rate=config['forgetting_rate'],
+            offset=config['offset'],
+            max_history_length=config['max_history_length']
+        )
+        
+        # 获取 Neo4j 连接器
+        connector = self._get_neo4j_connector()
+        
+        # 创建遗忘策略执行器
+        forgetting_strategy = ForgettingStrategy(
+            connector=connector,
+            actr_calculator=actr_calculator,
+            forgetting_threshold=config['forgetting_threshold'],
+            enable_llm_summary=config['enable_llm_summary']
+        )
+        
+        # 创建遗忘调度器
+        forgetting_scheduler = ForgettingScheduler(
+            forgetting_strategy=forgetting_strategy,
+            connector=connector
+        )
+        
+        return actr_calculator, forgetting_strategy, forgetting_scheduler, config
+    
+    async def _get_knowledge_stats(
+        self,
+        connector: Neo4jConnector,
+        group_id: Optional[str] = None,
+        forgetting_threshold: float = 0.3
+    ) -> Dict[str, Any]:
+        """
+        获取知识层统计信息
+        
+        Args:
+            connector: Neo4j 连接器
+            group_id: 组ID（可选）
+            forgetting_threshold: 遗忘阈值
+        
+        Returns:
+            dict: 统计信息字典
+        """
+        # 构建查询
+        query = """
+        MATCH (n)
+        WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
+        """
+        
+        if group_id:
+            query += " AND n.group_id = $group_id"
+        
+        query += """
+        WITH n,
+             CASE 
+               WHEN n:Statement THEN 'statement'
+               WHEN n:ExtractedEntity THEN 'entity'
+               WHEN n:MemorySummary THEN 'summary'
+             END as node_type
+        RETURN 
+            count(n) as total_nodes,
+            sum(CASE WHEN node_type = 'statement' THEN 1 ELSE 0 END) as statement_count,
+            sum(CASE WHEN node_type = 'entity' THEN 1 ELSE 0 END) as entity_count,
+            sum(CASE WHEN node_type = 'summary' THEN 1 ELSE 0 END) as summary_count,
+            avg(n.activation_value) as average_activation,
+            sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
+        """
+        
+        params = {'threshold': forgetting_threshold}
+        if group_id:
+            params['group_id'] = group_id
+        
+        results = await connector.execute_query(query, **params)
+        
+        if results:
+            result = results[0]
+            return {
+                'total_nodes': result['total_nodes'] or 0,
+                'statement_count': result['statement_count'] or 0,
+                'entity_count': result['entity_count'] or 0,
+                'summary_count': result['summary_count'] or 0,
+                'average_activation': result['average_activation'],
+                'low_activation_nodes': result['low_activation_nodes'] or 0
+            }
+        
+        return {
+            'total_nodes': 0,
+            'statement_count': 0,
+            'entity_count': 0,
+            'summary_count': 0,
+            'average_activation': None,
+            'low_activation_nodes': 0
+        }
+    
+    async def trigger_forgetting_cycle(
+        self,
+        db: Session,
+        group_id: Optional[str] = None,
+        max_merge_batch_size: Optional[int] = None,
+        min_days_since_access: Optional[int] = None,
+        config_id: Optional[int] = None
+    ) -> Dict[str, Any]:
+        """
+        手动触发遗忘周期
+        
+        执行一次完整的遗忘周期，识别并融合低激活值节点。
+        
+        Args:
+            db: 数据库会话
+            group_id: 组ID（可选）
+            max_merge_batch_size: 最大融合批次大小（可选）
+            min_days_since_access: 最小未访问天数（可选）
+            config_id: 配置ID（可选）
+        
+        Returns:
+            dict: 遗忘报告
+        """
+        # 获取遗忘引擎组件
+        _, _, forgetting_scheduler, config = await self._get_forgetting_components(db, config_id)
+        
+        # 运行遗忘周期（LLM 客户端将在需要时由 forgetting_strategy 内部获取）
+        report = await forgetting_scheduler.run_forgetting_cycle(
+            group_id=group_id,
+            max_merge_batch_size=max_merge_batch_size,
+            min_days_since_access=min_days_since_access,
+            config_id=config_id,
+            db=db
+        )
+        
+        api_logger.info(
+            f"遗忘周期完成: 融合 {report['merged_count']} 对节点, "
+            f"失败 {report['failed_count']} 对, "
+            f"耗时 {report['duration_seconds']:.2f} 秒"
+        )
+        
+        return report
+    
+    def read_forgetting_config(
+        self,
+        db: Session,
+        config_id: int
+    ) -> Dict[str, Any]:
+        """
+        获取遗忘引擎配置
+        
+        读取指定配置ID的遗忘引擎参数。
+        
+        Args:
+            db: 数据库会话
+            config_id: 配置ID
+        
+        Returns:
+            dict: 配置信息字典
+        """
+        # 加载配置
+        config = load_actr_config_from_db(db, config_id)
+        
+        # 添加 config_id 到返回结果
+        config['config_id'] = config_id
+        
+        api_logger.info(f"成功读取遗忘引擎配置: config_id={config_id}")
+        
+        return config
+    
+    def update_forgetting_config(
+        self,
+        db: Session,
+        config_id: int,
+        update_fields: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        更新遗忘引擎配置
+        
+        更新指定配置ID的遗忘引擎参数。
+        
+        Args:
+            db: 数据库会话
+            config_id: 配置ID
+            update_fields: 要更新的字段字典
+        
+        Returns:
+            dict: 更新后的配置信息
+        
+        Raises:
+            ValueError: 配置不存在
+        """
+        # 检查配置是否存在
+        db_config = self.config_repository.get_by_id(db, config_id)
+        if db_config is None:
+            raise ValueError(f"配置不存在: {config_id}")
+        
+        # 执行更新
+        if update_fields:
+            for key, value in update_fields.items():
+                if hasattr(db_config, key):
+                    setattr(db_config, key, value)
+            
+            db.commit()
+            db.refresh(db_config)
+            
+            api_logger.info(
+                f"成功更新遗忘引擎配置: config_id={config_id}, "
+                f"更新字段: {list(update_fields.keys())}"
+            )
+        else:
+            api_logger.info(f"没有字段需要更新: config_id={config_id}")
+        
+        # 重新加载配置并返回
+        config = load_actr_config_from_db(db, config_id)
+        config['config_id'] = config_id
+        
+        return config
+    
+    async def get_forgetting_stats(
+        self,
+        db: Session,
+        group_id: Optional[str] = None,
+        config_id: Optional[int] = None
+    ) -> Dict[str, Any]:
+        """
+        获取遗忘引擎统计信息
+        
+        返回知识层节点统计、激活值分布等信息。
+        
+        Args:
+            db: 数据库会话
+            group_id: 组ID（可选）
+            config_id: 配置ID（可选，用于获取遗忘阈值）
+        
+        Returns:
+            dict: 统计信息字典
+        """
+        # 获取遗忘引擎组件
+        _, _, forgetting_scheduler, config = await self._get_forgetting_components(db, config_id)
+        
+        connector = forgetting_scheduler.connector
+        forgetting_threshold = config['forgetting_threshold']
+        
+        # 收集激活值指标
+        activation_query = """
+        MATCH (n)
+        WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
+        """
+        
+        if group_id:
+            activation_query += " AND n.group_id = $group_id"
+        
+        activation_query += """
+        RETURN 
+            count(n) as total_nodes,
+            sum(CASE WHEN n.activation_value IS NOT NULL THEN 1 ELSE 0 END) as nodes_with_activation,
+            sum(CASE WHEN n.activation_value IS NULL THEN 1 ELSE 0 END) as nodes_without_activation,
+            avg(n.activation_value) as average_activation,
+            sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
+        """
+        
+        params = {'threshold': forgetting_threshold}
+        if group_id:
+            params['group_id'] = group_id
+        
+        activation_results = await connector.execute_query(activation_query, **params)
+        
+        if activation_results:
+            result = activation_results[0]
+            activation_metrics = {
+                'total_nodes': result['total_nodes'] or 0,
+                'nodes_with_activation': result['nodes_with_activation'] or 0,
+                'nodes_without_activation': result['nodes_without_activation'] or 0,
+                'average_activation_value': result['average_activation'],
+                'low_activation_nodes': result['low_activation_nodes'] or 0,
+                'timestamp': datetime.now().isoformat()
+            }
+        else:
+            activation_metrics = {
+                'total_nodes': 0,
+                'nodes_with_activation': 0,
+                'nodes_without_activation': 0,
+                'average_activation_value': None,
+                'low_activation_nodes': 0,
+                'timestamp': datetime.now().isoformat()
+            }
+        
+        # 收集节点类型分布
+        distribution_query = """
+        MATCH (n)
+        WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
+        """
+        
+        if group_id:
+            distribution_query += " AND n.group_id = $group_id"
+        
+        distribution_query += """
+        WITH n,
+             CASE 
+               WHEN n:Statement THEN 'statement'
+               WHEN n:ExtractedEntity THEN 'entity'
+               WHEN n:MemorySummary THEN 'summary'
+               WHEN n:Chunk THEN 'chunk'
+             END as node_type
+        RETURN 
+            sum(CASE WHEN node_type = 'statement' THEN 1 ELSE 0 END) as statement_count,
+            sum(CASE WHEN node_type = 'entity' THEN 1 ELSE 0 END) as entity_count,
+            sum(CASE WHEN node_type = 'summary' THEN 1 ELSE 0 END) as summary_count,
+            sum(CASE WHEN node_type = 'chunk' THEN 1 ELSE 0 END) as chunk_count
+        """
+        
+        dist_params = {}
+        if group_id:
+            dist_params['group_id'] = group_id
+        
+        distribution_results = await connector.execute_query(distribution_query, **dist_params)
+        
+        if distribution_results:
+            result = distribution_results[0]
+            node_distribution = {
+                'statement_count': result['statement_count'] or 0,
+                'entity_count': result['entity_count'] or 0,
+                'summary_count': result['summary_count'] or 0,
+                'chunk_count': result['chunk_count'] or 0
+            }
+        else:
+            node_distribution = {
+                'statement_count': 0,
+                'entity_count': 0,
+                'summary_count': 0,
+                'chunk_count': 0
+            }
+        
+        # 构建统计信息（不包含监控历史数据）
+        stats = {
+            'activation_metrics': activation_metrics,
+            'node_distribution': node_distribution,
+            'consistency_check': None,  # 不再提供一致性检查
+            'nodes_merged_total': 0,  # 不再跟踪累计融合数
+            'recent_cycles': [],  # 不再提供历史记录
+            'timestamp': datetime.now().isoformat()
+        }
+        
+        api_logger.info(
+            f"成功获取遗忘引擎统计: total_nodes={stats['activation_metrics']['total_nodes']}, "
+            f"low_activation_nodes={stats['activation_metrics']['low_activation_nodes']}"
+        )
+        
+        return stats
+    
+    async def get_forgetting_curve(
+        self,
+        db: Session,
+        importance_score: float,
+        days: int,
+        config_id: Optional[int] = None
+    ) -> Dict[str, Any]:
+        """
+        获取遗忘曲线数据
+        
+        生成遗忘曲线数据用于可视化，模拟记忆激活值随时间的衰减。
+        
+        Args:
+            db: 数据库会话
+            importance_score: 重要性分数（0-1）
+            days: 模拟天数
+            config_id: 配置ID（可选）
+        
+        Returns:
+            dict: 包含曲线数据和配置的字典
+        """
+        # 获取 ACT-R 计算器
+        actr_calculator, _, _, config = await self._get_forgetting_components(db, config_id)
+        
+        # 生成遗忘曲线数据
+        initial_time = datetime.now()
+        curve_data = actr_calculator.get_forgetting_curve(
+            initial_time=initial_time,
+            importance_score=importance_score,
+            days=days
+        )
+        
+        api_logger.info(
+            f"成功生成遗忘曲线数据: {len(curve_data)} 个数据点"
+        )
+        
+        return {
+            'curve_data': curve_data,
+            'config': {
+                'decay_constant': config['decay_constant'],
+                'forgetting_rate': config['forgetting_rate'],
+                'offset': config['offset'],
+                'importance_score': importance_score,
+                'days': days
+            }
+        }

api/app/services/memory_storage_service.py

@@ -8,7 +8,6 @@ import asyncio
 import json
 import os
 import time
-import uuid
 from datetime import datetime
 from typing import Any, AsyncGenerator, Dict, List, Optional
 
@@ -26,7 +25,6 @@ from app.schemas.memory_storage_schema import (
     ConfigPilotRun,
     ConfigUpdate,
     ConfigUpdateExtracted,
-    ConfigUpdateForget,
 )
 from app.services.memory_config_service import MemoryConfigService
 from app.utils.sse_utils import format_sse_message
@@ -159,11 +157,8 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
         return {"affected": 1}
 
     # --- Forget config params ---
-    def update_forget(self, update: ConfigUpdateForget) -> Dict[str, Any]: # 保存遗忘引擎的配置
-        config = DataConfigRepository.update_forget(self.db, update)
-        if not config:
-            raise ValueError("未找到配置")
-        return {"affected": 1}
+    # 遗忘引擎配置方法已迁移到 memory_forget_service.py
+    # 使用新方法: MemoryForgetService.read_forgetting_config() 和 MemoryForgetService.update_forgetting_config()
 
     # --- Read ---
     def get_extracted(self, key: ConfigKey) -> Dict[str, Any]: # 获取萃取配置参数
@@ -172,12 +167,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
             raise ValueError("未找到配置")
         return result
 
-    def get_forget(self, key: ConfigKey) -> Dict[str, Any]: # 获取遗忘配置参数
-        result = DataConfigRepository.get_forget_config(self.db, key.config_id)
-        if not result:
-            raise ValueError("未找到配置")
-        return result
-
     # --- Read All ---
     def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数
         configs = DataConfigRepository.get_all(self.db, workspace_id)

api/app/tasks.py

@@ -412,7 +412,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str,
                 actual_config_id = connected_config.get("memory_config_id")
             finally:
                 db.close()
-        except Exception as e:
+        except Exception:
             # Log but continue - will fail later with proper error
             pass
     
@@ -499,7 +499,7 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
                 actual_config_id = connected_config.get("memory_config_id")
             finally:
                 db.close()
-        except Exception as e:
+        except Exception:
             # Log but continue - will fail later with proper error
             pass
     
@@ -1064,4 +1064,74 @@ def workspace_reflection_task(self) -> Dict[str, Any]:
             "error": str(e),
             "elapsed_time": elapsed_time,
             "task_id": self.request.id
-        }
+        }
+
+
+
+@celery_app.task(name="app.tasks.run_forgetting_cycle_task", bind=True)
+def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str, Any]:
+    """定时任务：运行遗忘周期
+    
+    定期执行遗忘周期，识别并融合低激活值的知识节点。
+    
+    Args:
+        config_id: 配置ID（可选，如果为None则使用默认配置）
+    
+    Returns:
+        包含任务执行结果的字典
+    """
+    start_time = time.time()
+    
+    async def _run() -> Dict[str, Any]:
+        from app.core.logging_config import get_api_logger
+        from app.services.memory_forget_service import MemoryForgetService
+        
+        api_logger = get_api_logger()
+        
+        with get_db_context() as db:
+            try:
+                api_logger.info(f"开始执行遗忘周期定时任务，config_id: {config_id}")
+                
+                forget_service = MemoryForgetService()
+                
+                # 运行遗忘周期
+                report = await forget_service.trigger_forgetting(
+                    db=db,
+                    group_id=None,  # 处理所有组
+                    config_id=config_id
+                )
+                
+                duration = time.time() - start_time
+                
+                api_logger.info(
+                    f"遗忘周期定时任务完成: "
+                    f"融合 {report['merged_count']} 对节点, "
+                    f"失败 {report['failed_count']} 对, "
+                    f"耗时 {duration:.2f} 秒"
+                )
+                
+                return {
+                    "status": "SUCCESS",
+                    "message": "遗忘周期执行成功",
+                    "report": report,
+                    "duration_seconds": duration
+                }
+                
+            except Exception as e:
+                duration = time.time() - start_time
+                api_logger.error(f"遗忘周期定时任务失败: {str(e)}", exc_info=True)
+                
+                return {
+                    "status": "FAILED",
+                    "message": f"遗忘周期执行失败: {str(e)}",
+                    "duration_seconds": duration
+                }
+    
+    # 运行异步函数
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        result = loop.run_until_complete(_run())
+        return result
+    finally:
+        loop.close()