feat(memory-api): add end user management and enhance memory API endpoints

- Add end_user_controller with unauthenticated endpoint for creating end users
- Implement get_or_create_end_user logic to handle duplicate end users by other_id
- Register end_user_controller router in main controller initialization
- Add list_memory_configs endpoint to retrieve all workspace memory configurations
- Update MemoryWriteRequest and MemoryReadRequest to make config_id required field
- Refactor memory API endpoints to parse request body directly instead of using Body parameter
- Add CreateEndUserRequest and CreateEndUserResponse schemas for end user creation
- Add ListConfigsResponse schema for configs listing endpoint
- Remove unused config_id and llm_model_id parameters from Neo4j write operation
- Update .gitignore to exclude redbear-mem-metrics and pitch-deck directories

.gitignore

@@ -25,6 +25,8 @@ examples/
 time.log
 celerybeat-schedule.db
 search_results.json
+redbear-mem-metrics/
+pitch-deck/
 
 api/migrations/versions
 tmp

api/Dockerfile

@@ -45,7 +45,8 @@ RUN --mount=type=cache,id=mem_apt,target=/var/cache/apt,sharing=locked \
     apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \
     apt install -y libjemalloc-dev && \
     apt install -y python3-pip pipx nginx unzip curl wget git vim less && \
-    apt install -y ghostscript
+    apt install -y ghostscript && \
+    apt install -y libmagic1
 
 RUN if [ "$NEED_MIRROR" == "1" ]; then \
         pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \

api/alembic.ini

@@ -60,7 +60,12 @@ version_path_separator = os  # Use os.pathsep. Default configuration used for ne
 # are written from script.py.mako
 # output_encoding = utf-8
 
-sqlalchemy.url = postgresql://user:password@localhost/dbname
+# Database connection URL - DO NOT hardcode credentials here!
+# Connection string is set dynamically from environment variables in migrations/env.py
+# Required env vars: DB_USER, DB_PASSWORD, DB_HOST, DB_PORT, DB_NAME
+# Example: postgresql://user:password@localhost:5432/dbname
+; sqlalchemy.url = postgresql://user:password@host:port/dbname
+sqlalchemy.url = driver://user:password@host:port/dbname
 
 
 [post_write_hooks]

api/app/aioRedis.py

@@ -1,10 +1,11 @@
-import os
 import asyncio
 import json
 import logging
 from typing import Dict, Any, Optional
+
 import redis.asyncio as redis
 from redis.asyncio import ConnectionPool
+
 from app.core.config import settings
 
 # 设置日志记录器

api/app/celery_app.py

@@ -63,56 +63,60 @@ celery_app.conf.update(
     accept_content=['json'],
     result_serializer='json',
     
-    # 时区
-    timezone='Asia/Shanghai',
-    enable_utc=True,
+    # # 时区
+    # timezone='Asia/Shanghai',
+    # enable_utc=False,
     
     # 任务追踪
     task_track_started=True,
     task_ignore_result=False,
-    
+
     # 超时设置
     task_time_limit=3600,  # 60分钟硬超时
     task_soft_time_limit=3000,  # 50分钟软超时
-    
+
     # Worker 设置 (per-worker settings are in docker-compose command line)
     worker_prefetch_multiplier=1,  # Don't hoard tasks, fairer distribution
-    
+
     # 结果过期时间
     result_expires=3600,  # 结果保存1小时
-    
+
     # 任务确认设置
     task_acks_late=True,
     task_reject_on_worker_lost=True,
     worker_disable_rate_limits=True,
-    
+
     # FLower setting 
     worker_send_task_events=True,
     task_send_sent_event=True,
-    
+
     # task routing
     task_routes={
         # Memory tasks → memory_tasks queue (threads worker)
         'app.core.memory.agent.read_message_priority': {'queue': 'memory_tasks'},
         'app.core.memory.agent.read_message': {'queue': 'memory_tasks'},
         'app.core.memory.agent.write_message': {'queue': 'memory_tasks'},
-        
+        'app.tasks.write_perceptual_memory': {'queue': 'memory_tasks'},
+
         # Long-term storage tasks → memory_tasks queue (batched write strategies)
         'app.core.memory.agent.long_term_storage.window': {'queue': 'memory_tasks'},
         'app.core.memory.agent.long_term_storage.time': {'queue': 'memory_tasks'},
         'app.core.memory.agent.long_term_storage.aggregate': {'queue': 'memory_tasks'},
-        
+
         # Document tasks → document_tasks queue (prefork worker)
         'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'},
         'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'},
         'app.core.rag.tasks.sync_knowledge_for_kb': {'queue': 'document_tasks'},
-        
+
         # Beat/periodic tasks → periodic_tasks queue (dedicated periodic worker)
         'app.tasks.workspace_reflection_task': {'queue': 'periodic_tasks'},
         'app.tasks.regenerate_memory_cache': {'queue': 'periodic_tasks'},
         'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'},
         'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'},
         'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'},
+        'app.tasks.init_implicit_emotions_for_users': {'queue': 'periodic_tasks'},
+        'app.tasks.init_interest_distribution_for_users': {'queue': 'periodic_tasks'},
+        'app.tasks.init_community_clustering_for_users': {'queue': 'periodic_tasks'},
     },
 )
 
@@ -129,7 +133,7 @@ implicit_emotions_update_schedule = crontab(
     minute=settings.IMPLICIT_EMOTIONS_UPDATE_MINUTE,
 )
 
-#构建定时任务配置
+# 构建定时任务配置
 beat_schedule_config = {
     "run-workspace-reflection": {
         "task": "app.tasks.workspace_reflection_task",

api/app/celery_worker.py

@@ -13,4 +13,4 @@ logger.info("Celery worker logging initialized")
 # 导入任务模块以注册任务
 import app.tasks
 
-__all__ = ['celery_app']
+__all__ = ['celery_app']

api/app/controllers/__init__.py

@@ -13,9 +13,11 @@ from . import (
     document_controller,
     emotion_config_controller,
     emotion_controller,
+    end_user_controller,
     file_controller,
     file_storage_controller,
     home_page_controller,
+    i18n_controller,
     implicit_memory_controller,
     knowledge_controller,
     knowledgeshare_controller,
@@ -94,5 +96,7 @@ manager_router.include_router(memory_working_controller.router)
 manager_router.include_router(file_storage_controller.router)
 manager_router.include_router(ontology_controller.router)
 manager_router.include_router(skill_controller.router)
+manager_router.include_router(i18n_controller.router)
+manager_router.include_router(end_user_controller.router)
 
 __all__ = ["manager_router"]

api/app/controllers/app_controller.py

@@ -1,10 +1,12 @@
 import uuid
+import io
 from typing import Optional, Annotated
 
 import yaml
 from fastapi import APIRouter, Depends, Path, Form, UploadFile, File
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
+from urllib.parse import quote
 
 from app.core.error_codes import BizCode
 from app.core.logging_config import get_business_logger
@@ -25,6 +27,7 @@ from app.services.app_service import AppService
 from app.services.app_statistics_service import AppStatisticsService
 from app.services.workflow_import_service import WorkflowImportService
 from app.services.workflow_service import WorkflowService, get_workflow_service
+from app.services.app_dsl_service import AppDslService
 
 router = APIRouter(prefix="/apps", tags=["Apps"])
 logger = get_business_logger()
@@ -50,6 +53,7 @@ def list_apps(
         status: str | None = None,
         search: str | None = None,
         include_shared: bool = True,
+        shared_only: bool = False,
         page: int = 1,
         pagesize: int = 10,
         ids: Optional[str] = None,
@@ -81,6 +85,7 @@ def list_apps(
         status=status,
         search=search,
         include_shared=include_shared,
+        shared_only=shared_only,
         page=page,
         pagesize=pagesize,
     )
@@ -90,6 +95,37 @@ def list_apps(
     return success(data=PageData(page=meta, items=items))
 
 
+@router.get("/my-shared-out", summary="列出本工作空间主动分享出去的记录")
+@cur_workspace_access_guard()
+def list_my_shared_out(
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """列出本工作空间主动分享给其他工作空间的所有记录（我的共享）"""
+    workspace_id = current_user.current_workspace_id
+    service = app_service.AppService(db)
+    shares = service.list_my_shared_out(workspace_id=workspace_id)
+    data = [app_schema.AppShare.model_validate(s) for s in shares]
+    return success(data=data)
+
+
+@router.delete("/share/{target_workspace_id}", summary="取消对某工作空间的所有应用分享")
+@cur_workspace_access_guard()
+def unshare_all_apps_to_workspace(
+        target_workspace_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """Cancel all app shares from current workspace to a target workspace."""
+    workspace_id = current_user.current_workspace_id
+    service = app_service.AppService(db)
+    count = service.unshare_all_apps_to_workspace(
+        target_workspace_id=target_workspace_id,
+        workspace_id=workspace_id
+    )
+    return success(msg=f"已取消 {count} 个应用的分享", data={"count": count})
+
+
 @router.get("/{app_id}", summary="获取应用详情")
 @cur_workspace_access_guard()
 def get_app(
@@ -158,6 +194,7 @@ def delete_app(
 def copy_app(
         app_id: uuid.UUID,
         new_name: Optional[str] = None,
+        payload: app_schema.CopyAppRequest = None,
         db: Session = Depends(get_db),
         current_user=Depends(get_current_user),
 ):
@@ -169,6 +206,8 @@ def copy_app(
     - 不影响原应用
     """
     workspace_id = current_user.current_workspace_id
+    # body takes precedence over query param for backward compatibility
+    new_name = (payload.new_name if payload else None) or new_name
     logger.info(
         "用户请求复制应用",
         extra={
@@ -218,6 +257,27 @@ def get_agent_config(
     return success(data=app_schema.AgentConfig.model_validate(cfg))
 
 
+@router.get("/{app_id}/opening", summary="获取应用开场白配置")
+@cur_workspace_access_guard()
+def get_opening(
+        app_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """返回开场白文本和预设问题，供前端对话界面初始化时展示"""
+    workspace_id = current_user.current_workspace_id
+    cfg = app_service.get_agent_config(db, app_id=app_id, workspace_id=workspace_id)
+    features = cfg.features or {}
+    if hasattr(features, "model_dump"):
+        features = features.model_dump()
+    opening = features.get("opening_statement", {})
+    return success(data=app_schema.OpeningResponse(
+        enabled=opening.get("enabled", False),
+        statement=opening.get("statement"),
+        suggested_questions=opening.get("suggested_questions", []),
+    ))
+
+
 @router.post("/{app_id}/publish", summary="发布应用（生成不可变快照）")
 @cur_workspace_access_guard()
 def publish_app(
@@ -299,7 +359,8 @@ def share_app(
         app_id=app_id,
         target_workspace_ids=payload.target_workspace_ids,
         user_id=current_user.id,
-        workspace_id=workspace_id
+        workspace_id=workspace_id,
+        permission=payload.permission
     )
 
     data = [app_schema.AppShare.model_validate(s) for s in shares]
@@ -330,6 +391,32 @@ def unshare_app(
     return success(msg="应用分享已取消")
 
 
+@router.patch("/{app_id}/share/{target_workspace_id}", summary="更新共享权限")
+@cur_workspace_access_guard()
+def update_share_permission(
+        app_id: uuid.UUID,
+        target_workspace_id: uuid.UUID,
+        payload: app_schema.UpdateSharePermissionRequest,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """更新共享权限（readonly <-> editable）
+
+    - 只能修改自己工作空间应用的共享权限
+    """
+    workspace_id = current_user.current_workspace_id
+
+    service = app_service.AppService(db)
+    share = service.update_share_permission(
+        app_id=app_id,
+        target_workspace_id=target_workspace_id,
+        permission=payload.permission,
+        workspace_id=workspace_id
+    )
+
+    return success(data=app_schema.AppShare.model_validate(share))
+
+
 @router.get("/{app_id}/shares", summary="列出应用的分享记录")
 @cur_workspace_access_guard()
 def list_app_shares(
@@ -353,6 +440,46 @@ def list_app_shares(
     return success(data=data)
 
 
+@router.delete("/shared/{source_workspace_id}", summary="批量移除某来源工作空间的所有共享应用")
+@cur_workspace_access_guard()
+def remove_all_shared_apps_from_workspace(
+        source_workspace_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """Remove all shared apps from a specific source workspace (recipient operation)."""
+    workspace_id = current_user.current_workspace_id
+    service = app_service.AppService(db)
+    count = service.remove_all_shared_apps_from_workspace(
+        source_workspace_id=source_workspace_id,
+        workspace_id=workspace_id
+    )
+    return success(msg=f"已移除 {count} 个共享应用", data={"count": count})
+
+
+@router.delete("/{app_id}/shared", summary="移除共享给我的应用")
+@cur_workspace_access_guard()
+def remove_shared_app(
+        app_id: uuid.UUID,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """被共享者从自己的工作空间移除共享应用
+
+    - 不会删除源应用，只删除共享记录
+    - 只能移除共享给自己工作空间的应用
+    """
+    workspace_id = current_user.current_workspace_id
+
+    service = app_service.AppService(db)
+    service.remove_shared_app(
+        app_id=app_id,
+        workspace_id=workspace_id
+    )
+
+    return success(msg="已移除共享应用")
+
+
 @router.post("/{app_id}/draft/run", summary="试运行 Agent（使用当前草稿配置）")
 @cur_workspace_access_guard()
 async def draft_run(
@@ -393,7 +520,7 @@ async def draft_run(
     # 提前验证和准备（在流式响应开始前完成）
     from app.services.app_service import AppService
     from app.services.multi_agent_service import MultiAgentService
-    from app.models import AgentConfig, ModelConfig
+    from app.models import AgentConfig, ModelConfig, AppRelease
     from sqlalchemy import select
     from app.core.exceptions import BusinessException
     from app.services.draft_run_service import AgentRunService
@@ -410,11 +537,12 @@ async def draft_run(
     service._validate_app_accessible(app, workspace_id)
 
     if payload.user_id is None:
+        # 先获取 app 的 workspace_id
         end_user_repo = EndUserRepository(db)
         new_end_user = end_user_repo.get_or_create_end_user(
             app_id=app_id,
+            workspace_id=app.workspace_id,
             other_id=str(current_user.id),
-            original_user_id=str(current_user.id)  # Save original user_id to other_id
         )
         payload.user_id = str(new_end_user.id)
 
@@ -431,18 +559,29 @@ async def draft_run(
         service._check_agent_config(app_id)
 
         # 2. 获取 Agent 配置
-        stmt = select(AgentConfig).where(AgentConfig.app_id == app_id)
-        agent_cfg = db.scalars(stmt).first()
-        if not agent_cfg:
-            raise BusinessException("Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING)
+        # 共享应用：从最新发布版本读配置快照，而非草稿
+        is_shared = app.workspace_id != workspace_id
+        if is_shared:
+            if not app.current_release_id:
+                raise BusinessException("该应用尚未发布，无法使用", BizCode.AGENT_CONFIG_MISSING)
+            release = db.get(AppRelease, app.current_release_id)
+            if not release:
+                raise BusinessException("发布版本不存在", BizCode.AGENT_CONFIG_MISSING)
+            agent_cfg = service._agent_config_from_release(release)
+            model_config = db.get(ModelConfig, release.default_model_config_id) if release.default_model_config_id else None
+        else:
+            stmt = select(AgentConfig).where(AgentConfig.app_id == app_id)
+            agent_cfg = db.scalars(stmt).first()
+            if not agent_cfg:
+                raise BusinessException("Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING)
 
-        # 3. 获取模型配置
-        model_config = None
-        if agent_cfg.default_model_config_id:
-            model_config = db.get(ModelConfig, agent_cfg.default_model_config_id)
-            if not model_config:
-                from app.core.exceptions import ResourceNotFoundException
-                raise ResourceNotFoundException("模型配置", str(agent_cfg.default_model_config_id))
+            # 3. 获取模型配置
+            model_config = None
+            if agent_cfg.default_model_config_id:
+                model_config = db.get(ModelConfig, agent_cfg.default_model_config_id)
+                if not model_config:
+                    from app.core.exceptions import ResourceNotFoundException
+                    raise ResourceNotFoundException("模型配置", str(agent_cfg.default_model_config_id))
 
         # 流式返回
         if payload.stream:
@@ -598,7 +737,17 @@ async def draft_run(
             msg="多 Agent 任务执行成功"
         )
     elif app.type == AppType.WORKFLOW:  # 工作流
-        config = workflow_service.check_config(app_id)
+        # 共享应用：从最新发布版本读配置快照，而非草稿
+        is_shared = app.workspace_id != workspace_id
+        if is_shared:
+            if not app.current_release_id:
+                raise BusinessException("该应用尚未发布，无法使用", BizCode.AGENT_CONFIG_MISSING)
+            release = db.get(AppRelease, app.current_release_id)
+            if not release:
+                raise BusinessException("发布版本不存在", BizCode.AGENT_CONFIG_MISSING)
+            config = service._workflow_config_from_release(release)
+        else:
+            config = workflow_service.check_config(app_id)
         # 3. 流式返回
         if payload.stream:
             logger.debug(
@@ -741,6 +890,16 @@ async def draft_run_compare(
         raise BusinessException("只有 Agent 类型应用支持试运行", BizCode.APP_TYPE_NOT_SUPPORTED)
     service._validate_app_accessible(app, workspace_id)
 
+    if payload.user_id is None:
+        # 先获取 app 的 workspace_id
+        end_user_repo = EndUserRepository(db)
+        new_end_user = end_user_repo.get_or_create_end_user(
+            app_id=app_id,
+            workspace_id=app.workspace_id,
+            other_id=str(current_user.id),
+        )
+        payload.user_id = str(new_end_user.id)
+
     # 2. 获取 Agent 配置
     from sqlalchemy import select
     from app.models import AgentConfig
@@ -786,6 +945,13 @@ async def draft_run_compare(
             "conversation_id": model_item.conversation_id  # 传递每个模型的 conversation_id
         })
 
+    # 从 features 中读取功能开关（与 draft_run 保持一致）
+    features_config: dict = agent_cfg.features or {}
+    if hasattr(features_config, 'model_dump'):
+        features_config = features_config.model_dump()
+    web_search_feature = features_config.get("web_search", {})
+    web_search = isinstance(web_search_feature, dict) and web_search_feature.get("enabled", False)
+
     # 流式返回
     if payload.stream:
         async def event_generator():
@@ -797,11 +963,11 @@ async def draft_run_compare(
                     message=payload.message,
                     workspace_id=workspace_id,
                     conversation_id=payload.conversation_id,
-                    user_id=payload.user_id or str(current_user.id),
+                    user_id=payload.user_id,
                     variables=payload.variables,
                     storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id,
-                    web_search=True,
+                    web_search=web_search,
                     memory=True,
                     parallel=payload.parallel,
                     timeout=payload.timeout or 60,
@@ -828,11 +994,11 @@ async def draft_run_compare(
         message=payload.message,
         workspace_id=workspace_id,
         conversation_id=payload.conversation_id,
-        user_id=payload.user_id or str(current_user.id),
+        user_id=payload.user_id,
         variables=payload.variables,
         storage_type=storage_type,
         user_rag_memory_id=user_rag_memory_id,
-        web_search=True,
+        web_search=web_search,
         memory=True,
         parallel=payload.parallel,
         timeout=payload.timeout or 60,
@@ -1010,3 +1176,57 @@ def get_workspace_api_statistics(
     )
 
     return success(data=result)
+
+
+@router.get("/{app_id}/export", summary="导出应用配置为 YAML 文件")
+@cur_workspace_access_guard()
+async def export_app(
+        app_id: uuid.UUID,
+        db: Annotated[Session, Depends(get_db)],
+        current_user: Annotated[User, Depends(get_current_user)],
+        release_id: Optional[uuid.UUID] = None
+):
+    """导出 agent / multi_agent / workflow 应用配置为 YAML 文件流。
+    release_id: 指定发布版本id，不传则导出当前草稿配置。
+    """
+    yaml_str, filename = AppDslService(db).export_dsl(app_id, release_id)
+    encoded = quote(filename, safe=".")
+    yaml_bytes = yaml_str.encode("utf-8")
+    file_stream = io.BytesIO(yaml_bytes)
+    file_stream.seek(0)
+    return StreamingResponse(
+        file_stream,
+        media_type="application/octet-stream; charset=utf-8",
+        headers={"Content-Disposition": f"attachment; filename={encoded}",
+                 "Content-Length": str(len(yaml_bytes))}
+    )
+
+
+@router.post("/import", summary="从 YAML 文件导入应用")
+@cur_workspace_access_guard()
+async def import_app(
+        file: UploadFile = File(...),
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
+):
+    """从 YAML 文件导入 agent / multi_agent / workflow 应用。
+    跨空间/跨租户导入时，模型/工具/知识库会按名称匹配，匹配不到则置空并返回 warnings。
+    """
+    if not file.filename.lower().endswith((".yaml", ".yml")):
+        return fail(msg="仅支持 YAML 文件", code=BizCode.BAD_REQUEST)
+
+    raw = (await file.read()).decode("utf-8")
+    dsl = yaml.safe_load(raw)
+    if not dsl or "app" not in dsl:
+        return fail(msg="YAML 格式无效，缺少 app 字段", code=BizCode.BAD_REQUEST)
+
+    new_app, warnings = AppDslService(db).import_dsl(
+        dsl=dsl,
+        workspace_id=current_user.current_workspace_id,
+        tenant_id=current_user.tenant_id,
+        user_id=current_user.id,
+    )
+    return success(
+        data={"app": app_schema.App.model_validate(new_app), "warnings": warnings},
+        msg="应用导入成功" + ("，但部分资源需手动配置" if warnings else "")
+    )

api/app/controllers/auth_controller.py

@@ -1,4 +1,5 @@
 from datetime import datetime, timedelta, timezone
+from typing import Callable
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
 
@@ -16,6 +17,7 @@ from app.core.exceptions import BusinessException
 from app.core.error_codes import BizCode
 from app.dependencies import get_current_user, oauth2_scheme
 from app.models.user_model import User
+from app.i18n.dependencies import get_translator
 
 # 获取专用日志器
 auth_logger = get_auth_logger()
@@ -26,7 +28,8 @@ router = APIRouter(tags=["Authentication"])
 @router.post("/token", response_model=ApiResponse)
 async def login_for_access_token(
     form_data: TokenRequest,
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator)
 ):
     """用户登录获取token"""
     auth_logger.info(f"用户登录请求: {form_data.email}")
@@ -40,10 +43,10 @@ async def login_for_access_token(
         invite_info = workspace_service.validate_invite_token(db, form_data.invite)
         
         if not invite_info.is_valid:
-            raise BusinessException("邀请码无效或已过期", code=BizCode.BAD_REQUEST)
+            raise BusinessException(t("auth.invite.invalid"), code=BizCode.BAD_REQUEST)
         
         if invite_info.email != form_data.email:
-            raise BusinessException("邀请邮箱与登录邮箱不匹配", code=BizCode.BAD_REQUEST)        
+            raise BusinessException(t("auth.invite.email_mismatch"), code=BizCode.BAD_REQUEST)        
         auth_logger.info(f"邀请码验证成功: workspace={invite_info.workspace_name}")
         try:
             # 尝试认证用户
@@ -69,7 +72,7 @@ async def login_for_access_token(
             elif e.code == BizCode.PASSWORD_ERROR:
                 # 用户存在但密码错误
                 auth_logger.warning(f"接受邀请失败，密码验证错误: {form_data.email}")
-                raise BusinessException("接受邀请失败，密码验证错误", BizCode.LOGIN_FAILED)
+                raise BusinessException(t("auth.invite.password_verification_failed"), BizCode.LOGIN_FAILED)
             else:
                 # 其他认证失败情况，直接抛出
                 raise
@@ -82,7 +85,7 @@ async def login_for_access_token(
         except BusinessException as e:
             
             # 其他认证失败情况，直接抛出
-            raise BusinessException(e.message,BizCode.LOGIN_FAILED)
+            raise BusinessException(e.message, BizCode.LOGIN_FAILED)
 
     # 创建 tokens
     access_token, access_token_id = security.create_access_token(subject=user.id)
@@ -110,14 +113,15 @@ async def login_for_access_token(
             expires_at=access_expires_at,
             refresh_expires_at=refresh_expires_at
         ),
-        msg="登录成功"
+        msg=t("auth.login.success")
     )
 
 
 @router.post("/refresh", response_model=ApiResponse)
 async def refresh_token(
     refresh_request: RefreshTokenRequest,
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator)
 ):
     """刷新token"""
     auth_logger.info("收到token刷新请求")
@@ -125,18 +129,18 @@ async def refresh_token(
     # 验证 refresh token
     userId = security.verify_token(refresh_request.refresh_token, "refresh")
     if not userId:
-        raise BusinessException("无效的refresh token", code=BizCode.TOKEN_INVALID)
+        raise BusinessException(t("auth.token.invalid_refresh_token"), code=BizCode.TOKEN_INVALID)
     
     # 检查用户是否存在
     user = auth_service.get_user_by_id(db, userId)
     if not user:
-        raise BusinessException("用户不存在", code=BizCode.USER_NOT_FOUND)
+        raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND)
     
     # 检查 refresh token 黑名单
     if settings.ENABLE_SINGLE_SESSION:
         refresh_token_id = security.get_token_id(refresh_request.refresh_token)
         if refresh_token_id and await SessionService.is_token_blacklisted(refresh_token_id):
-            raise BusinessException("Refresh token已失效", code=BizCode.TOKEN_BLACKLISTED)
+            raise BusinessException(t("auth.token.refresh_token_blacklisted"), code=BizCode.TOKEN_BLACKLISTED)
     
     # 生成新 tokens
     new_access_token, new_access_token_id = security.create_access_token(subject=user.id)
@@ -167,7 +171,7 @@ async def refresh_token(
             expires_at=access_expires_at,
             refresh_expires_at=refresh_expires_at
         ),
-        msg="token刷新成功"
+        msg=t("auth.token.refresh_success")
     )
 
 
@@ -175,14 +179,15 @@ async def refresh_token(
 async def logout(
     token: str = Depends(oauth2_scheme),
     current_user: User = Depends(get_current_user),
-    db: Session = Depends(get_db)
+    db: Session = Depends(get_db),
+    t: Callable = Depends(get_translator)
 ):
     """登出当前用户：加入token黑名单并清理会话"""
     auth_logger.info(f"用户 {current_user.username} 请求登出")
     
     token_id = security.get_token_id(token)
     if not token_id:
-        raise BusinessException("无效的access token", code=BizCode.TOKEN_INVALID)
+        raise BusinessException(t("auth.token.invalid"), code=BizCode.TOKEN_INVALID)
 
     # 加入黑名单
     await SessionService.blacklist_token(token_id)
@@ -192,5 +197,5 @@ async def logout(
         await SessionService.clear_user_session(current_user.username)
 
     auth_logger.info(f"用户 {current_user.username} 登出成功")
-    return success(msg="登出成功")
+    return success(msg=t("auth.logout.success"))
 

api/app/controllers/end_user_controller.py

@@ -0,0 +1,48 @@
+"""End User 管理接口 - 无需认证"""
+
+from app.core.logging_config import get_business_logger
+from app.core.response_utils import success
+from app.db import get_db
+from app.repositories.end_user_repository import EndUserRepository
+from app.schemas.memory_api_schema import (
+    CreateEndUserRequest,
+    CreateEndUserResponse,
+)
+from fastapi import APIRouter, Depends
+from sqlalchemy.orm import Session
+
+router = APIRouter(prefix="/end_users", tags=["End Users"])
+logger = get_business_logger()
+
+
+@router.post("")
+async def create_end_user(
+    data: CreateEndUserRequest,
+    db: Session = Depends(get_db),
+):
+    """
+    Create an end user.
+    
+    Creates a new end user for the given workspace.
+    If an end user with the same other_id already exists in the workspace,
+    returns the existing one.
+    """
+    logger.info(f"Create end user request - other_id: {data.other_id}, workspace_id: {data.workspace_id}")
+
+    end_user_repo = EndUserRepository(db)
+    end_user = end_user_repo.get_or_create_end_user(
+        app_id=None,
+        workspace_id=data.workspace_id,
+        other_id=data.other_id,
+    )
+
+    logger.info(f"End user ready: {end_user.id}")
+
+    result = {
+        "id": str(end_user.id),
+        "other_id": end_user.other_id or "",
+        "other_name": end_user.other_name or "",
+        "workspace_id": str(end_user.workspace_id),
+    }
+
+    return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")

api/app/controllers/file_storage_controller.py

@@ -15,7 +15,7 @@ import os
 import uuid
 from typing import Any
 
-from fastapi import APIRouter, Depends, File, HTTPException, UploadFile, status
+from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile, status
 from fastapi.responses import FileResponse, RedirectResponse
 from sqlalchemy.orm import Session
 
@@ -47,6 +47,19 @@ router = APIRouter(
 )
 
 
+def _match_scheme(request: Request, url: str) -> str:
+    """
+    将 presigned URL 的协议替换为与当前请求一致的协议（http/https）。
+    解决反向代理场景下 presigned URL 协议与请求协议不匹配的问题。
+    """
+    incoming_scheme = request.headers.get("x-forwarded-proto") or request.url.scheme
+    if url.startswith("http://") and incoming_scheme == "https":
+        return "https://" + url[7:]
+    if url.startswith("https://") and incoming_scheme == "http":
+        return "http://" + url[8:]
+    return url
+
+
 @router.post("/files", response_model=ApiResponse)
 async def upload_file(
     file: UploadFile = File(...),
@@ -280,6 +293,7 @@ async def upload_file_with_share_token(
 
 @router.get("/files/{file_id}", response_model=Any)
 async def download_file(
+    request: Request,
     file_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
@@ -327,6 +341,7 @@ async def download_file(
     else:
         try:
             presigned_url = await storage_service.get_file_url(file_key, expires=3600)
+            presigned_url = _match_scheme(request, presigned_url)
             api_logger.info(f"Redirecting to presigned URL: file_key={file_key}")
             return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
         except FileNotFoundError:
@@ -400,6 +415,7 @@ async def delete_file(
 
 @router.get("/files/{file_id}/url", response_model=ApiResponse)
 async def get_file_url(
+    request: Request,
     file_id: uuid.UUID,
     expires: int = None,
     permanent: bool = False,
@@ -463,6 +479,7 @@ async def get_file_url(
         else:
             # For remote storage (OSS/S3), get presigned URL
             url = await storage_service.get_file_url(file_key, expires=expires)
+            url = _match_scheme(request, url)
 
         api_logger.info(f"Generated file URL: file_id={file_id}")
         return success(
@@ -484,6 +501,7 @@ async def get_file_url(
 
 @router.get("/public/{file_id}", response_model=Any)
 async def public_download_file(
+    request: Request,
     file_id: uuid.UUID,
     expires: int = 0,
     signature: str = "",
@@ -555,6 +573,7 @@ async def public_download_file(
         # For remote storage, redirect to presigned URL
         try:
             presigned_url = await storage_service.get_file_url(file_key, expires=3600)
+            presigned_url = _match_scheme(request, presigned_url)
             return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
         except Exception as e:
             api_logger.error(f"Failed to get presigned URL: {e}")
@@ -566,6 +585,7 @@ async def public_download_file(
 
 @router.get("/permanent/{file_id}", response_model=Any)
 async def permanent_download_file(
+    request: Request,
     file_id: uuid.UUID,
     db: Session = Depends(get_db),
     storage_service: FileStorageService = Depends(get_file_storage_service),
@@ -625,6 +645,7 @@ async def permanent_download_file(
         try:
             # Use a very long expiration (7 days max for most cloud providers)
             presigned_url = await storage_service.get_file_url(file_key, expires=604800)
+            presigned_url = _match_scheme(request, presigned_url)
             return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
         except Exception as e:
             api_logger.error(f"Failed to get presigned URL: {e}")

api/app/controllers/i18n_controller.py

@@ -0,0 +1,833 @@
+"""
+I18n Management API Controller
+
+This module provides management APIs for:
+- Language management (list, get, add, update languages)
+- Translation management (get, update, reload translations)
+"""
+
+from fastapi import APIRouter, Depends, HTTPException, status
+from sqlalchemy.orm import Session
+from typing import Callable, Optional
+
+from app.core.logging_config import get_api_logger
+from app.core.response_utils import success
+from app.db import get_db
+from app.dependencies import get_current_user, get_current_superuser
+from app.i18n.dependencies import get_translator
+from app.i18n.service import get_translation_service
+from app.models.user_model import User
+from app.schemas.i18n_schema import (
+    LanguageInfo,
+    LanguageListResponse,
+    LanguageCreateRequest,
+    LanguageUpdateRequest,
+    TranslationResponse,
+    TranslationUpdateRequest,
+    MissingTranslationsResponse,
+    ReloadResponse
+)
+from app.schemas.response_schema import ApiResponse
+
+api_logger = get_api_logger()
+
+router = APIRouter(
+    prefix="/i18n",
+    tags=["I18n Management"],
+)
+
+
+# ============================================================================
+# Language Management APIs
+# ============================================================================
+
+@router.get("/languages", response_model=ApiResponse)
+def get_languages(
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    Get list of all supported languages.
+    
+    Returns:
+        List of language information including code, name, and status
+    """
+    api_logger.info(f"Get languages request from user: {current_user.username}")
+    
+    from app.core.config import settings
+    translation_service = get_translation_service()
+    
+    # Get available locales from translation service
+    available_locales = translation_service.get_available_locales()
+    
+    # Build language info list
+    languages = []
+    for locale in available_locales:
+        is_default = locale == settings.I18N_DEFAULT_LANGUAGE
+        is_enabled = locale in settings.I18N_SUPPORTED_LANGUAGES
+        
+        # Get native names
+        native_names = {
+            "zh": "中文（简体）",
+            "en": "English",
+            "ja": "日本語",
+            "ko": "한국어",
+            "fr": "Français",
+            "de": "Deutsch",
+            "es": "Español"
+        }
+        
+        language_info = LanguageInfo(
+            code=locale,
+            name=f"{locale.upper()}",
+            native_name=native_names.get(locale, locale),
+            is_enabled=is_enabled,
+            is_default=is_default
+        )
+        languages.append(language_info)
+    
+    response = LanguageListResponse(languages=languages)
+    
+    api_logger.info(f"Returning {len(languages)} languages")
+    return success(data=response.dict(), msg=t("common.success.retrieved"))
+
+
+@router.get("/languages/{locale}", response_model=ApiResponse)
+def get_language(
+    locale: str,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    Get information about a specific language.
+    
+    Args:
+        locale: Language code (e.g., 'zh', 'en')
+        
+    Returns:
+        Language information
+    """
+    api_logger.info(f"Get language info request: locale={locale}, user={current_user.username}")
+    
+    from app.core.config import settings
+    translation_service = get_translation_service()
+    
+    # Check if locale exists
+    available_locales = translation_service.get_available_locales()
+    if locale not in available_locales:
+        api_logger.warning(f"Language not found: {locale}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=t("i18n.language.not_found", locale=locale)
+        )
+    
+    # Build language info
+    is_default = locale == settings.I18N_DEFAULT_LANGUAGE
+    is_enabled = locale in settings.I18N_SUPPORTED_LANGUAGES
+    
+    native_names = {
+        "zh": "中文（简体）",
+        "en": "English",
+        "ja": "日本語",
+        "ko": "한국어",
+        "fr": "Français",
+        "de": "Deutsch",
+        "es": "Español"
+    }
+    
+    language_info = LanguageInfo(
+        code=locale,
+        name=f"{locale.upper()}",
+        native_name=native_names.get(locale, locale),
+        is_enabled=is_enabled,
+        is_default=is_default
+    )
+    
+    api_logger.info(f"Returning language info for: {locale}")
+    return success(data=language_info.dict(), msg=t("common.success.retrieved"))
+
+
+@router.post("/languages", response_model=ApiResponse)
+def add_language(
+    request: LanguageCreateRequest,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Add a new language (admin only).
+    
+    Note: This endpoint validates the request but actual language addition
+    requires creating translation files in the locales directory.
+    
+    Args:
+        request: Language creation request
+        
+    Returns:
+        Success message
+    """
+    api_logger.info(
+        f"Add language request: code={request.code}, admin={current_user.username}"
+    )
+    
+    from app.core.config import settings
+    translation_service = get_translation_service()
+    
+    # Check if language already exists
+    available_locales = translation_service.get_available_locales()
+    if request.code in available_locales:
+        api_logger.warning(f"Language already exists: {request.code}")
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=t("i18n.language.already_exists", locale=request.code)
+        )
+    
+    # Note: Actual language addition requires creating translation files
+    # This endpoint serves as a validation and documentation point
+    
+    api_logger.info(
+        f"Language addition validated: {request.code}. "
+        "Translation files need to be created manually."
+    )
+    
+    return success(
+        msg=t(
+            "i18n.language.add_instructions",
+            locale=request.code,
+            dir=settings.I18N_CORE_LOCALES_DIR
+        )
+    )
+
+
+@router.put("/languages/{locale}", response_model=ApiResponse)
+def update_language(
+    locale: str,
+    request: LanguageUpdateRequest,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Update language configuration (admin only).
+    
+    Note: This endpoint validates the request but actual configuration
+    changes require updating environment variables or config files.
+    
+    Args:
+        locale: Language code
+        request: Language update request
+        
+    Returns:
+        Success message
+    """
+    api_logger.info(
+        f"Update language request: locale={locale}, admin={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    
+    # Check if language exists
+    available_locales = translation_service.get_available_locales()
+    if locale not in available_locales:
+        api_logger.warning(f"Language not found: {locale}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=t("i18n.language.not_found", locale=locale)
+        )
+    
+    # Note: Actual configuration changes require updating settings
+    # This endpoint serves as a validation and documentation point
+    
+    api_logger.info(
+        f"Language update validated: {locale}. "
+        "Configuration changes require environment variable updates."
+    )
+    
+    return success(msg=t("i18n.language.update_instructions", locale=locale))
+
+
+# ============================================================================
+# Translation Management APIs
+# ============================================================================
+
+@router.get("/translations", response_model=ApiResponse)
+def get_all_translations(
+    locale: Optional[str] = None,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    Get all translations for all or specific locale.
+    
+    Args:
+        locale: Optional locale filter
+        
+    Returns:
+        All translations organized by locale and namespace
+    """
+    api_logger.info(
+        f"Get all translations request: locale={locale}, user={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    
+    if locale:
+        # Get translations for specific locale
+        available_locales = translation_service.get_available_locales()
+        if locale not in available_locales:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=t("i18n.language.not_found", locale=locale)
+            )
+        
+        translations = {
+            locale: translation_service._cache.get(locale, {})
+        }
+    else:
+        # Get all translations
+        translations = translation_service._cache
+    
+    response = TranslationResponse(translations=translations)
+    
+    api_logger.info(f"Returning translations for: {locale or 'all locales'}")
+    return success(data=response.dict(), msg=t("common.success.retrieved"))
+
+
+@router.get("/translations/{locale}", response_model=ApiResponse)
+def get_locale_translations(
+    locale: str,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    Get all translations for a specific locale.
+    
+    Args:
+        locale: Language code
+        
+    Returns:
+        All translations for the locale organized by namespace
+    """
+    api_logger.info(
+        f"Get locale translations request: locale={locale}, user={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    
+    # Check if locale exists
+    available_locales = translation_service.get_available_locales()
+    if locale not in available_locales:
+        api_logger.warning(f"Language not found: {locale}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=t("i18n.language.not_found", locale=locale)
+        )
+    
+    translations = translation_service._cache.get(locale, {})
+    
+    api_logger.info(f"Returning {len(translations)} namespaces for locale: {locale}")
+    return success(data={"locale": locale, "translations": translations}, msg=t("common.success.retrieved"))
+
+
+@router.get("/translations/{locale}/{namespace}", response_model=ApiResponse)
+def get_namespace_translations(
+    locale: str,
+    namespace: str,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    Get translations for a specific namespace in a locale.
+    
+    Args:
+        locale: Language code
+        namespace: Translation namespace (e.g., 'common', 'auth')
+        
+    Returns:
+        Translations for the specified namespace
+    """
+    api_logger.info(
+        f"Get namespace translations request: locale={locale}, "
+        f"namespace={namespace}, user={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    
+    # Check if locale exists
+    available_locales = translation_service.get_available_locales()
+    if locale not in available_locales:
+        api_logger.warning(f"Language not found: {locale}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=t("i18n.language.not_found", locale=locale)
+        )
+    
+    # Get namespace translations
+    locale_translations = translation_service._cache.get(locale, {})
+    namespace_translations = locale_translations.get(namespace, {})
+    
+    if not namespace_translations:
+        api_logger.warning(f"Namespace not found: {namespace} in locale: {locale}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=t("i18n.namespace.not_found", namespace=namespace, locale=locale)
+        )
+    
+    api_logger.info(
+        f"Returning translations for namespace: {namespace} in locale: {locale}"
+    )
+    return success(
+        data={
+            "locale": locale,
+            "namespace": namespace,
+            "translations": namespace_translations
+        },
+        msg=t("common.success.retrieved")
+    )
+
+
+@router.put("/translations/{locale}/{key:path}", response_model=ApiResponse)
+def update_translation(
+    locale: str,
+    key: str,
+    request: TranslationUpdateRequest,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Update a single translation (admin only).
+    
+    Note: This endpoint validates the request but actual translation updates
+    require modifying translation files in the locales directory.
+    
+    Args:
+        locale: Language code
+        key: Translation key (format: "namespace.key.subkey")
+        request: Translation update request
+        
+    Returns:
+        Success message
+    """
+    api_logger.info(
+        f"Update translation request: locale={locale}, key={key}, "
+        f"admin={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    
+    # Check if locale exists
+    available_locales = translation_service.get_available_locales()
+    if locale not in available_locales:
+        api_logger.warning(f"Language not found: {locale}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=t("i18n.language.not_found", locale=locale)
+        )
+    
+    # Validate key format
+    if "." not in key:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=t("i18n.translation.invalid_key_format", key=key)
+        )
+    
+    # Note: Actual translation updates require modifying JSON files
+    # This endpoint serves as a validation and documentation point
+    
+    api_logger.info(
+        f"Translation update validated: {locale}/{key}. "
+        "Translation files need to be updated manually."
+    )
+    
+    return success(
+        msg=t("i18n.translation.update_instructions", locale=locale, key=key)
+    )
+
+
+@router.get("/translations/missing", response_model=ApiResponse)
+def get_missing_translations(
+    locale: Optional[str] = None,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    Get list of missing translations.
+    
+    Compares translations across locales to find missing keys.
+    
+    Args:
+        locale: Optional locale to check (defaults to checking all non-default locales)
+        
+    Returns:
+        List of missing translation keys
+    """
+    api_logger.info(
+        f"Get missing translations request: locale={locale}, user={current_user.username}"
+    )
+    
+    from app.core.config import settings
+    translation_service = get_translation_service()
+    
+    default_locale = settings.I18N_DEFAULT_LANGUAGE
+    available_locales = translation_service.get_available_locales()
+    
+    # Get default locale translations as reference
+    default_translations = translation_service._cache.get(default_locale, {})
+    
+    # Collect all keys from default locale
+    def collect_keys(data, prefix=""):
+        keys = []
+        for key, value in data.items():
+            full_key = f"{prefix}.{key}" if prefix else key
+            if isinstance(value, dict):
+                keys.extend(collect_keys(value, full_key))
+            else:
+                keys.append(full_key)
+        return keys
+    
+    default_keys = set()
+    for namespace, translations in default_translations.items():
+        namespace_keys = collect_keys(translations, namespace)
+        default_keys.update(namespace_keys)
+    
+    # Find missing keys in target locale(s)
+    missing_by_locale = {}
+    
+    target_locales = [locale] if locale else [
+        loc for loc in available_locales if loc != default_locale
+    ]
+    
+    for target_locale in target_locales:
+        if target_locale not in available_locales:
+            continue
+        
+        target_translations = translation_service._cache.get(target_locale, {})
+        target_keys = set()
+        
+        for namespace, translations in target_translations.items():
+            namespace_keys = collect_keys(translations, namespace)
+            target_keys.update(namespace_keys)
+        
+        missing_keys = default_keys - target_keys
+        if missing_keys:
+            missing_by_locale[target_locale] = sorted(list(missing_keys))
+    
+    response = MissingTranslationsResponse(missing_translations=missing_by_locale)
+    
+    total_missing = sum(len(keys) for keys in missing_by_locale.values())
+    api_logger.info(f"Found {total_missing} missing translations across {len(missing_by_locale)} locales")
+    
+    return success(data=response.dict(), msg=t("common.success.retrieved"))
+
+
+@router.post("/reload", response_model=ApiResponse)
+def reload_translations(
+    locale: Optional[str] = None,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Trigger hot reload of translation files (admin only).
+    
+    Args:
+        locale: Optional locale to reload (defaults to reloading all locales)
+        
+    Returns:
+        Reload status and statistics
+    """
+    api_logger.info(
+        f"Reload translations request: locale={locale or 'all'}, "
+        f"admin={current_user.username}"
+    )
+    
+    from app.core.config import settings
+    
+    if not settings.I18N_ENABLE_HOT_RELOAD:
+        api_logger.warning("Hot reload is disabled in configuration")
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail=t("i18n.reload.disabled")
+        )
+    
+    translation_service = get_translation_service()
+    
+    try:
+        # Reload translations
+        translation_service.reload(locale)
+        
+        # Get statistics
+        available_locales = translation_service.get_available_locales()
+        reloaded_locales = [locale] if locale else available_locales
+        
+        response = ReloadResponse(
+            success=True,
+            reloaded_locales=reloaded_locales,
+            total_locales=len(available_locales)
+        )
+        
+        api_logger.info(
+            f"Successfully reloaded translations for: {', '.join(reloaded_locales)}"
+        )
+        
+        return success(data=response.dict(), msg=t("i18n.reload.success"))
+        
+    except Exception as e:
+        api_logger.error(f"Failed to reload translations: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=t("i18n.reload.failed", error=str(e))
+        )
+
+
+# ============================================================================
+# Performance Monitoring APIs
+# ============================================================================
+
+@router.get("/metrics", response_model=ApiResponse)
+def get_metrics(
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Get i18n performance metrics (admin only).
+    
+    Returns:
+        Performance metrics including:
+        - Request counts
+        - Missing translations
+        - Timing statistics
+        - Locale usage
+        - Error counts
+    """
+    api_logger.info(f"Get metrics request: admin={current_user.username}")
+    
+    translation_service = get_translation_service()
+    metrics = translation_service.get_metrics_summary()
+    
+    api_logger.info("Returning i18n metrics")
+    return success(data=metrics, msg=t("common.success.retrieved"))
+
+
+@router.get("/metrics/cache", response_model=ApiResponse)
+def get_cache_stats(
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Get cache statistics (admin only).
+    
+    Returns:
+        Cache statistics including:
+        - Hit/miss rates
+        - LRU cache performance
+        - Loaded locales
+        - Memory usage
+    """
+    api_logger.info(f"Get cache stats request: admin={current_user.username}")
+    
+    translation_service = get_translation_service()
+    cache_stats = translation_service.get_cache_stats()
+    memory_usage = translation_service.get_memory_usage()
+    
+    data = {
+        "cache": cache_stats,
+        "memory": memory_usage
+    }
+    
+    api_logger.info("Returning cache statistics")
+    return success(data=data, msg=t("common.success.retrieved"))
+
+
+@router.get("/metrics/prometheus")
+def get_prometheus_metrics(
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Get metrics in Prometheus format (admin only).
+    
+    Returns:
+        Prometheus-formatted metrics as plain text
+    """
+    api_logger.info(f"Get Prometheus metrics request: admin={current_user.username}")
+    
+    from app.i18n.metrics import get_metrics
+    metrics = get_metrics()
+    prometheus_output = metrics.export_prometheus()
+    
+    from fastapi.responses import PlainTextResponse
+    return PlainTextResponse(content=prometheus_output)
+
+
+@router.post("/metrics/reset", response_model=ApiResponse)
+def reset_metrics(
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Reset all metrics (admin only).
+    
+    Returns:
+        Success message
+    """
+    api_logger.info(f"Reset metrics request: admin={current_user.username}")
+    
+    from app.i18n.metrics import get_metrics
+    metrics = get_metrics()
+    metrics.reset()
+    
+    translation_service = get_translation_service()
+    translation_service.cache.reset_stats()
+    
+    api_logger.info("Metrics reset completed")
+    return success(msg=t("i18n.metrics.reset_success"))
+
+
+# ============================================================================
+# Missing Translation Logging and Reporting APIs
+# ============================================================================
+
+@router.get("/logs/missing", response_model=ApiResponse)
+def get_missing_translation_logs(
+    locale: Optional[str] = None,
+    limit: Optional[int] = 100,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Get missing translation logs (admin only).
+    
+    Returns logged missing translations with context information.
+    
+    Args:
+        locale: Optional locale filter
+        limit: Maximum number of entries to return (default: 100)
+        
+    Returns:
+        Missing translation logs with context
+    """
+    api_logger.info(
+        f"Get missing translation logs request: locale={locale}, "
+        f"limit={limit}, admin={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    translation_logger = translation_service.translation_logger
+    
+    # Get missing translations
+    missing_translations = translation_logger.get_missing_translations(locale)
+    
+    # Get missing with context
+    missing_with_context = translation_logger.get_missing_with_context(locale, limit)
+    
+    # Get statistics
+    statistics = translation_logger.get_statistics()
+    
+    data = {
+        "missing_translations": missing_translations,
+        "recent_context": missing_with_context,
+        "statistics": statistics
+    }
+    
+    api_logger.info(
+        f"Returning {statistics['total_missing']} missing translations"
+    )
+    return success(data=data, msg=t("common.success.retrieved"))
+
+
+@router.get("/logs/missing/report", response_model=ApiResponse)
+def generate_missing_translation_report(
+    locale: Optional[str] = None,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Generate a comprehensive missing translation report (admin only).
+    
+    Args:
+        locale: Optional locale filter
+        
+    Returns:
+        Comprehensive report with missing translations and statistics
+    """
+    api_logger.info(
+        f"Generate missing translation report request: locale={locale}, "
+        f"admin={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    translation_logger = translation_service.translation_logger
+    
+    # Generate report
+    report = translation_logger.generate_report(locale)
+    
+    api_logger.info(
+        f"Generated report with {report['total_missing']} missing translations"
+    )
+    return success(data=report, msg=t("common.success.retrieved"))
+
+
+@router.post("/logs/missing/export", response_model=ApiResponse)
+def export_missing_translations(
+    locale: Optional[str] = None,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Export missing translations to JSON file (admin only).
+    
+    Args:
+        locale: Optional locale filter
+        
+    Returns:
+        Export status and file path
+    """
+    api_logger.info(
+        f"Export missing translations request: locale={locale}, "
+        f"admin={current_user.username}"
+    )
+    
+    from datetime import datetime
+    translation_service = get_translation_service()
+    translation_logger = translation_service.translation_logger
+    
+    # Generate filename with timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    locale_suffix = f"_{locale}" if locale else "_all"
+    output_file = f"logs/i18n/missing_translations{locale_suffix}_{timestamp}.json"
+    
+    # Export to file
+    translation_logger.export_to_json(output_file)
+    
+    api_logger.info(f"Missing translations exported to: {output_file}")
+    return success(
+        data={"file_path": output_file},
+        msg=t("i18n.logs.export_success", file=output_file)
+    )
+
+
+@router.delete("/logs/missing", response_model=ApiResponse)
+def clear_missing_translation_logs(
+    locale: Optional[str] = None,
+    t: Callable = Depends(get_translator),
+    current_user: User = Depends(get_current_superuser)
+):
+    """
+    Clear missing translation logs (admin only).
+    
+    Args:
+        locale: Optional locale to clear (clears all if not specified)
+        
+    Returns:
+        Success message
+    """
+    api_logger.info(
+        f"Clear missing translation logs request: locale={locale or 'all'}, "
+        f"admin={current_user.username}"
+    )
+    
+    translation_service = get_translation_service()
+    translation_logger = translation_service.translation_logger
+    
+    # Clear logs
+    translation_logger.clear(locale)
+    
+    api_logger.info(f"Cleared missing translation logs for: {locale or 'all locales'}")
+    return success(msg=t("i18n.logs.clear_success"))

api/app/controllers/mcp_market_config_controller.py

@@ -19,7 +19,7 @@ from app.models import mcp_market_config_model
 from app.models.user_model import User
 from app.schemas import mcp_market_config_schema
 from app.schemas.response_schema import ApiResponse
-from app.services import mcp_market_config_service
+from app.services import mcp_market_config_service, mcp_market_service
 
 # Obtain a dedicated API logger
 api_logger = get_api_logger()
@@ -55,6 +55,12 @@ async def get_mcp_servers(
             status_code=status.HTTP_400_BAD_REQUEST,
             detail="The paging parameter must be greater than 0"
         )
+    if page * pagesize > 100:
+        api_logger.warning(f"Paging parameters exceed ModelScope limit: page={page}, pagesize={pagesize}")
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"The maximum number of MCP services can view is 100. Please visit the ModelScope MCP Plaza."
+        )
 
     # 2. Query mcp market config information from the database
     api_logger.debug(f"Query mcp market config: {mcp_market_config_id}")
@@ -64,14 +70,16 @@ async def get_mcp_servers(
     if not db_mcp_market_config:
         api_logger.warning(
             f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or access is denied"
-        )
+        return success(msg='The mcp market config does not exist or access is denied')
 
     # 3. Execute paged query
-    api = MCPApi()
     token = db_mcp_market_config.token
+    if not token:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="MCP market config token is not configured"
+        )
+    api = MCPApi()
     api.login(token)
 
     body = {
@@ -115,6 +123,17 @@ async def get_mcp_servers(
             "has_next": True if page * pagesize < total else False
         }
     }
+    # 5. Update mck_market.mcp_count
+    db_mcp_market = mcp_market_service.get_mcp_market_by_id(db, mcp_market_id=db_mcp_market_config.mcp_market_id, current_user=current_user)
+    if not db_mcp_market:
+        api_logger.warning(f"The mcp market does not exist or access is denied: mcp_market_id={db_mcp_market_config.mcp_market_id}")
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="The mcp market does not exist or access is denied"
+        )
+    db_mcp_market.mcp_count = total
+    db.commit()
+    db.refresh(db_mcp_market)
     return success(data=result, msg="Query of mcp servers list successful")
 
 
@@ -140,14 +159,16 @@ async def get_operational_mcp_servers(
     if not db_mcp_market_config:
         api_logger.warning(
             f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or access is denied"
-        )
+        return success(msg='The mcp market config does not exist or access is denied')
 
     # 2. Execute paged query
-    api = MCPApi()
     token = db_mcp_market_config.token
+    if not token:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="MCP market config token is not configured"
+        )
+    api = MCPApi()
     api.login(token)
 
     url = f'{api.mcp_base_url}/operational'
@@ -198,14 +219,16 @@ async def get_mcp_server(
     if not db_mcp_market_config:
         api_logger.warning(
             f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or access is denied"
-        )
+        return success(msg='The mcp market config does not exist or access is denied')
 
     # 2. Get detailed information for a specific MCP Server
-    api = MCPApi()
     token = db_mcp_market_config.token
+    if not token:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="MCP market config token is not configured"
+        )
+    api = MCPApi()
     api.login(token)
 
     result = api.get_mcp_server(server_id=server_id)
@@ -226,7 +249,26 @@ async def create_mcp_market_config(
 
     try:
         api_logger.debug(f"Start creating the mcp market config: {create_data.mcp_market_id}")
-        # 1. Check if the mcp market name already exists
+        # 1. Validate token can access ModelScope MCP market
+        if not create_data.token:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="Token is required to access ModelScope MCP market"
+            )
+        try:
+            api = MCPApi()
+            api.login(create_data.token)
+            body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None}
+            cookies = api.get_cookies(create_data.token)
+            r = api.session.put(url=api.mcp_base_url, headers=api.builder_headers(api.headers), json=body, cookies=cookies)
+            raise_for_http_status(r)
+        except Exception as e:
+            api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}")
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Unable to access ModelScope MCP market with the provided token: {str(e)}"
+            )
+        # 2. Check if the mcp market name already exists
         db_mcp_market_config_exist = mcp_market_config_service.get_mcp_market_config_by_mcp_market_id(db, mcp_market_id=create_data.mcp_market_id, current_user=current_user)
         if db_mcp_market_config_exist:
             api_logger.warning(f"The mcp market id already exists: {create_data.mcp_market_id}")
@@ -234,6 +276,30 @@ async def create_mcp_market_config(
                 status_code=status.HTTP_400_BAD_REQUEST,
                 detail=f"The mcp market id already exists: {create_data.mcp_market_id}"
             )
+        # 2. verify token
+        create_data.status = 1
+        try:
+            api = MCPApi()
+            token = create_data.token
+            api.login(token)
+
+            body = {
+                'filter': {},
+                'page_number': 1,
+                'page_size': 20,
+                'search': ""
+            }
+            cookies = api.get_cookies(token)
+            r = api.session.put(
+                url=api.mcp_base_url,
+                headers=api.builder_headers(api.headers),
+                json=body,
+                cookies=cookies)
+            raise_for_http_status(r)
+        except requests.exceptions.RequestException as e:
+            api_logger.error(f"Failed to get MCP servers: {str(e)}")
+            create_data.status = 0
+        # 3. create mcp_market_config
         db_mcp_market_config = mcp_market_config_service.create_mcp_market_config(db=db, mcp_market_config=create_data, current_user=current_user)
         api_logger.info(
             f"The mcp market config has been successfully created: (ID: {db_mcp_market_config.id})")
@@ -262,10 +328,7 @@ async def get_mcp_market_config(
         db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user)
         if not db_mcp_market_config:
             api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_config_id={mcp_market_config_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market config does not exist or access is denied"
-            )
+            return success(msg='The mcp market config does not exist or access is denied')
 
         api_logger.info(f"mcp market config query successful: (ID: {db_mcp_market_config.id})")
         return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
@@ -295,10 +358,7 @@ async def get_mcp_market_config_by_mcp_market_id(
         db_mcp_market_config = mcp_market_config_service.get_mcp_market_config_by_mcp_market_id(db, mcp_market_id=mcp_market_id, current_user=current_user)
         if not db_mcp_market_config:
             api_logger.warning(f"The mcp market config does not exist or access is denied: mcp_market_id={mcp_market_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market config does not exist or access is denied"
-            )
+            return success(msg='The mcp market config does not exist or access is denied')
 
         api_logger.info(f"mcp market config query successful: (ID: {db_mcp_market_config.id})")
         return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
@@ -324,12 +384,25 @@ async def update_mcp_market_config(
     if not db_mcp_market_config:
         api_logger.warning(
             f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}")
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="The mcp market config does not exist or you do not have permission to access it"
-        )
+        return success(msg='The mcp market config does not exist or access is denied')
 
-    # 2. Update fields (only update non-null fields)
+    # 2. Validate new token if provided
+    if update_data.token is not None:
+        try:
+            api = MCPApi()
+            api.login(update_data.token)
+            body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None}
+            cookies = api.get_cookies(update_data.token)
+            r = api.session.put(url=api.mcp_base_url, headers=api.builder_headers(api.headers), json=body, cookies=cookies)
+            raise_for_http_status(r)
+        except Exception as e:
+            api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}")
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Unable to access ModelScope MCP market with the provided token: {str(e)}"
+            )
+
+    # 3. Update fields (only update non-null fields)
     api_logger.debug(f"Start updating the mcp market config fields: {mcp_market_config_id}")
     update_dict = update_data.dict(exclude_unset=True)
     updated_fields = []
@@ -344,7 +417,7 @@ async def update_mcp_market_config(
     if updated_fields:
         api_logger.debug(f"updated fields: {', '.join(updated_fields)}")
 
-    # 3. Save to database
+    # 4. Save to database
     try:
         db.commit()
         db.refresh(db_mcp_market_config)
@@ -357,7 +430,7 @@ async def update_mcp_market_config(
             detail=f"The mcp market config update failed: {str(e)}"
         )
 
-    # 4. Return the updated mcp market config
+    # 5. Return the updated mcp market config
     return success(data=jsonable_encoder(mcp_market_config_schema.McpMarketConfig.model_validate(db_mcp_market_config)),
                    msg="The mcp market config information updated successfully")
 
@@ -381,10 +454,7 @@ async def delete_mcp_market_config(
         if not db_mcp_market_config:
             api_logger.warning(
                 f"The mcp market config does not exist or you do not have permission to access it: mcp_market_config_id={mcp_market_config_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The mcp market config does not exist or you do not have permission to access it"
-            )
+            return success(msg='The mcp market config does not exist or access is denied')
 
         # 2. Deleting mcp market config
         mcp_market_config_service.delete_mcp_market_config_by_id(db, mcp_market_config_id=mcp_market_config_id, current_user=current_user)

api/app/controllers/memory_dashboard_controller.py

@@ -1,4 +1,5 @@
 from fastapi import APIRouter, Depends, HTTPException, status, Query
+from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session
 from typing import Optional
 from app.core.response_utils import success
@@ -149,6 +150,21 @@ async def get_workspace_end_users(
         
         return {uid: {"total": 0} for uid in end_user_ids}
     
+    # 触发按需初始化：为 implicit_emotions_storage 中没有记录的用户异步生成数据
+    try:
+        from app.celery_app import celery_app as _celery_app
+        _celery_app.send_task(
+            "app.tasks.init_implicit_emotions_for_users",
+            kwargs={"end_user_ids": end_user_ids},
+        )
+        _celery_app.send_task(
+            "app.tasks.init_interest_distribution_for_users",
+            kwargs={"end_user_ids": end_user_ids},
+        )
+        api_logger.info(f"已触发按需初始化任务，候选用户数: {len(end_user_ids)}")
+    except Exception as e:
+        api_logger.warning(f"触发按需初始化任务失败（不影响主流程）: {e}")
+
     # 并发执行配置查询和记忆数量查询
     memory_configs_map, memory_nums_map = await asyncio.gather(
         get_memory_configs(),
@@ -177,7 +193,16 @@ async def get_workspace_end_users(
         await aio_redis_set(cache_key, json.dumps(result), expire=30)
     except Exception as e:
         api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
-    
+
+    # 触发社区聚类补全任务（异步，不阻塞接口响应）
+    # 对有 ExtractedEntity 但无 Community 节点的存量用户自动补跑全量聚类
+    try:
+        from app.tasks import init_community_clustering_for_users
+        init_community_clustering_for_users.delay(end_user_ids=end_user_ids)
+        api_logger.info(f"已触发社区聚类补全任务，候选用户数: {len(end_user_ids)}")
+    except Exception as e:
+        api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}")
+
     api_logger.info(f"成功获取 {len(end_users)} 个宿主记录")
     return success(data=result, msg="宿主列表获取成功")
 
@@ -387,14 +412,15 @@ def get_current_user_rag_total_num(
 @router.get("/rag_content", response_model=ApiResponse)
 def get_rag_content(
     end_user_id: str = Query(..., description="宿主ID"),
-    limit: int = Query(15, description="返回记录数"),
+    page: int = Query(1, gt=0, description="页码，从1开始"),
+    pagesize: int = Query(15, gt=0, le=100, description="每页返回记录数"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取当前宿主知识库中的chunk内容
+    获取当前宿主知识库中的chunk内容（分页）
     """
-    data = memory_dashboard_service.get_rag_content(end_user_id, limit, db, current_user)
+    data = memory_dashboard_service.get_rag_content(end_user_id, page, pagesize, db, current_user)
     return success(data=data, msg="宿主RAGchunk数据获取成功")
 
 
@@ -407,26 +433,18 @@ async def get_chunk_summary_tag(
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取chunk总结、提取的标签和人物形象
-    
+    读取RAG摘要、标签和人物形象（纯读库，不触发生成）。
+
     返回格式：
     {
-        "summary": "chunk内容的总结",
-        "tags": [
-            {"tag": "标签1", "frequency": 5},
-            {"tag": "标签2", "frequency": 3},
-            ...
-        ],
-        "personas": [
-            "产品设计师",
-            "旅行爱好者",
-            "摄影发烧友",
-            ...
-        ]
+        "summary": "用户摘要",
+        "tags": [{"tag": "标签1", "frequency": 5}, ...],
+        "personas": ["产品设计师", ...],
+        "generated": true/false  // false表示尚未生产，请调用 /generate_rag_profile
     }
     """
-    api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk摘要、标签和人物形象")
-    
+    api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG摘要/标签/人物形象")
+
     data = await memory_dashboard_service.get_chunk_summary_and_tags(
         end_user_id=end_user_id,
         limit=limit,
@@ -434,9 +452,8 @@ async def get_chunk_summary_tag(
         db=db,
         current_user=current_user
     )
-    
-    api_logger.info(f"成功获取chunk摘要、{len(data.get('tags', []))} 个标签和 {len(data.get('personas', []))} 个人物形象")
-    return success(data=data, msg="chunk摘要、标签和人物形象获取成功")
+
+    return success(data=data, msg="获取成功")
 
 
 @router.get("/chunk_insight", response_model=ApiResponse)
@@ -447,24 +464,57 @@ async def get_chunk_insight(
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取chunk的洞察内容
-    
+    读取RAG洞察报告（纯读库，不触发生成）。
+
     返回格式：
     {
-        "insight": "对chunk内容的深度洞察分析"
+        "insight": "总体概述",
+        "behavior_pattern": "行为模式",
+        "key_findings": "关键发现",
+        "growth_trajectory": "成长轨迹",
+        "generated": true/false  // false表示尚未生产，请调用 /generate_rag_profile
     }
     """
-    api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk洞察")
-    
+    api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG洞察")
+
     data = await memory_dashboard_service.get_chunk_insight(
         end_user_id=end_user_id,
         limit=limit,
         db=db,
         current_user=current_user
     )
-    
-    api_logger.info("成功获取chunk洞察")
-    return success(data=data, msg="chunk洞察获取成功")
+
+    return success(data=data, msg="获取成功")
+
+
+class GenerateRagProfileRequest(BaseModel):
+    end_user_id: str = Field(..., description="宿主ID")
+    limit: int = Field(15, description="参与生成的chunk数量上限")
+    max_tags: int = Field(10, description="最大标签数量")
+
+
+@router.post("/generate_rag_profile", response_model=ApiResponse)
+async def generate_rag_profile(
+    body: GenerateRagProfileRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """
+    生产接口：为RAG存储模式的宿主全量重新生成完整画像并持久化到end_user表。
+    每次请求都会重新生成，覆盖已有数据。
+    """
+    api_logger.info(f"用户 {current_user.username} 触发RAG画像生产: end_user_id={body.end_user_id}")
+
+    data = await memory_dashboard_service.generate_rag_profile(
+        end_user_id=body.end_user_id,
+        limit=body.limit,
+        max_tags=body.max_tags,
+        db=db,
+        current_user=current_user,
+    )
+
+    api_logger.info(f"RAG画像生产完成: {data}")
+    return success(data=data, msg="RAG画像生产完成")
 
 
 @router.get("/dashboard_data", response_model=ApiResponse)
@@ -553,9 +603,12 @@ async def dashboard_data(
                 )
                 neo4j_data["total_memory"] = total_memory_data.get("total_memory_count", 0)
                 # total_app: 统计当前空间下的所有app数量
-                from app.repositories import app_repository
-                apps_orm = app_repository.get_apps_by_workspace_id(db, workspace_id)
-                neo4j_data["total_app"] = len(apps_orm)
+                # 包含自有app + 被分享给本工作空间的app
+                from app.services import app_service as _app_svc
+                _, total_app = _app_svc.AppService(db).list_apps(
+                    workspace_id=workspace_id, include_shared=True, pagesize=1
+                )
+                neo4j_data["total_app"] = total_app
                 api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}, 应用数量: {neo4j_data['total_app']}")
             except Exception as e:
                 api_logger.warning(f"获取记忆总量失败: {str(e)}")

api/app/controllers/memory_reflection_controller.py

@@ -1,3 +1,19 @@
+"""
+Memory Reflection Controller
+
+This module provides REST API endpoints for managing memory reflection configurations
+and operations. It handles reflection engine setup, configuration management, and
+execution of self-reflection processes across memory systems.
+
+Key Features:
+- Reflection configuration management (save, retrieve, update)
+- Workspace-wide reflection execution across multiple applications
+- Individual configuration-based reflection runs
+- Multi-language support for reflection outputs
+- Integration with Neo4j memory storage and LLM models
+- Comprehensive error handling and logging
+"""
+
 import asyncio
 import time
 import uuid
@@ -28,9 +44,13 @@ from sqlalchemy.orm import Session
 
 from app.utils.config_utils import resolve_config_id
 
+# Load environment variables for configuration
 load_dotenv()
+
+# Initialize API logger for request tracking and debugging
 api_logger = get_api_logger()
 
+# Configure router with prefix and tags for API organization
 router = APIRouter(
     prefix="/memory",
     tags=["Memory"],
@@ -43,7 +63,38 @@ async def save_reflection_config(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """Save reflection configuration to data_comfig table"""
+    """
+    Save reflection configuration to memory config table
+    
+    Persists reflection engine configuration settings to the data_config table,
+    including reflection parameters, model settings, and evaluation criteria.
+    Validates configuration parameters and ensures data consistency.
+    
+    Args:
+        request: Memory reflection configuration data including:
+            - config_id: Configuration identifier to update
+            - reflection_enabled: Whether reflection is enabled
+            - reflection_period_in_hours: Reflection execution interval
+            - reflexion_range: Scope of reflection (partial/all)
+            - baseline: Reflection strategy (time/fact/hybrid)
+            - reflection_model_id: LLM model for reflection operations
+            - memory_verify: Enable memory verification checks
+            - quality_assessment: Enable quality assessment evaluation
+        current_user: Authenticated user saving the configuration
+        db: Database session for data operations
+    
+    Returns:
+        dict: Success response with saved reflection configuration data
+        
+    Raises:
+        HTTPException 400: If config_id is missing or parameters are invalid
+        HTTPException 500: If configuration save operation fails
+        
+    Database Operations:
+        - Updates memory_config table with reflection settings
+        - Commits transaction and refreshes entity
+        - Maintains configuration consistency
+    """
     try:
         config_id = request.config_id
         config_id = resolve_config_id(config_id, db)
@@ -54,6 +105,7 @@ async def save_reflection_config(
             )
         api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}")
 
+        # Update reflection configuration in database
         memory_config = MemoryConfigRepository.update_reflection_config(
             db,
             config_id=config_id,
@@ -66,6 +118,7 @@ async def save_reflection_config(
             quality_assessment=request.quality_assessment
         )
 
+        # Commit transaction and refresh entity
         db.commit()
         db.refresh(memory_config)
 
@@ -102,13 +155,55 @@ async def start_workspace_reflection(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """启动工作空间中所有匹配应用的反思功能"""
+    """
+    Start reflection functionality for all matching applications in workspace
+    
+    Initiates reflection processes across all applications within the user's current
+    workspace that have valid memory configurations. Processes each application's
+    configurations and associated end users, executing reflection operations
+    with proper error isolation and transaction management.
+    
+    This endpoint serves as a workspace-wide reflection orchestrator, ensuring
+    that reflection failures for individual users don't affect other operations.
+    
+    Args:
+        current_user: Authenticated user initiating workspace reflection
+        db: Database session for configuration queries
+    
+    Returns:
+        dict: Success response with reflection results for all processed applications:
+            - app_id: Application identifier
+            - config_id: Memory configuration identifier
+            - end_user_id: End user identifier
+            - reflection_result: Individual reflection operation result
+    
+    Processing Logic:
+        1. Retrieve all applications in the current workspace
+        2. Filter applications with valid memory configurations
+        3. For each configuration, find matching releases
+        4. Execute reflection for each end user with isolated transactions
+        5. Aggregate results with error handling per user
+    
+    Error Handling:
+        - Individual user reflection failures are isolated
+        - Failed operations are logged and included in results
+        - Database transactions are isolated per user to prevent cascading failures
+        - Comprehensive error reporting for debugging
+    
+    Raises:
+        HTTPException 500: If workspace reflection initialization fails
+        
+    Performance Notes:
+        - Uses independent database sessions for each user operation
+        - Prevents transaction failures from affecting other users
+        - Comprehensive logging for operation tracking
+    """
     workspace_id = current_user.current_workspace_id
 
     try:
         api_logger.info(f"用户 {current_user.username} 启动workspace反思，workspace_id: {workspace_id}")
 
-        # 使用独立的数据库会话来获取工作空间应用详情，避免事务失败
+        # Use independent database session to get workspace app details, avoiding transaction failures
         from app.db import get_db_context
         with get_db_context() as query_db:
             service = WorkspaceAppService(query_db)
@@ -116,8 +211,9 @@ async def start_workspace_reflection(
         
         reflection_results = []
         
+        # Process each application in the workspace
         for data in result['apps_detailed_info']:
-            # 跳过没有配置的应用
+            # Skip applications without configurations
             if not data['memory_configs']:
                 api_logger.debug(f"应用 {data['id']} 没有memory_configs，跳过")
                 continue
@@ -126,22 +222,22 @@ async def start_workspace_reflection(
             memory_configs = data['memory_configs']
             end_users = data['end_users']
 
-            # 为每个配置和用户组合执行反思
+            # Execute reflection for each configuration and user combination
             for config in memory_configs:
                 config_id_str = str(config['config_id'])
 
-                # 找到匹配此配置的所有release
+                # Find all releases matching this configuration
                 matching_releases = [r for r in releases if str(r['config']) == config_id_str]
 
                 if not matching_releases:
                     api_logger.debug(f"配置 {config_id_str} 没有匹配的release")
                     continue
 
-                # 为每个用户执行反思 - 使用独立的数据库会话
+                # Execute reflection for each user - using independent database sessions
                 for user in end_users:
                     api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}")
 
-                    # 为每个用户创建独立的数据库会话，避免事务失败影响其他用户
+                    # Create independent database session for each user to avoid transaction failure impact
                     with get_db_context() as user_db:
                         try:
                             reflection_service = MemoryReflectionService(user_db)
@@ -184,14 +280,51 @@ async def start_reflection_configs(
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ) -> dict:
-    """通过config_id查询memory_config表中的反思配置信息"""
+    """
+    Query reflection configuration information by config_id
+    
+    Retrieves detailed reflection configuration settings from the memory_config
+    table for a specific configuration ID. Provides comprehensive reflection
+    parameters including model settings, evaluation criteria, and operational flags.
+    
+    Args:
+        config_id: Configuration identifier (UUID or integer) to query
+        current_user: Authenticated user making the request
+        db: Database session for data operations
+    
+    Returns:
+        dict: Success response with detailed reflection configuration:
+            - config_id: Resolved configuration identifier
+            - reflection_enabled: Whether reflection is enabled for this config
+            - reflection_period_in_hours: Reflection execution interval
+            - reflexion_range: Scope of reflection operations (partial/all)
+            - baseline: Reflection strategy (time/fact/hybrid)
+            - reflection_model_id: LLM model identifier for reflection
+            - memory_verify: Memory verification flag
+            - quality_assessment: Quality assessment flag
+    
+    Database Operations:
+        - Queries memory_config table by resolved config_id
+        - Retrieves all reflection-related configuration fields
+        - Resolves configuration ID for consistent formatting
+    
+    Raises:
+        HTTPException 404: If configuration with specified ID is not found
+        HTTPException 500: If configuration query operation fails
+        
+    ID Resolution:
+        - Supports both UUID and integer config_id formats
+        - Automatically resolves to appropriate internal format
+        - Maintains consistency across different ID representations
+    """
     config_id = resolve_config_id(config_id, db)
     try:
         config_id=resolve_config_id(config_id,db)
         api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
         result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
         memory_config_id = resolve_config_id(result.config_id, db)
-        # 构建返回数据
+        
+        # Build response data with comprehensive configuration details
         reflection_config = {
             "config_id": memory_config_id,
             "reflection_enabled": result.enable_self_reflexion,
@@ -204,10 +337,12 @@ async def start_reflection_configs(
         }
         api_logger.info(f"成功查询反思配置，config_id: {config_id}")
         return success(data=reflection_config, msg="反思配置查询成功")
-        
 
+        api_logger.info(f"Successfully queried reflection config, config_id: {config_id}")
+        return success(data=reflection_config, msg="Reflection configuration query successful")
+        
     except HTTPException:
-        # 重新抛出HTTP异常
+        # Re-raise HTTP exceptions without modification
         raise
     except Exception as e:
         api_logger.error(f"查询反思配置失败: {str(e)}")
@@ -223,13 +358,66 @@ async def reflection_run(
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
-    """Activate the reflection function for all matching applications in the workspace"""
-    # 使用集中化的语言校验
+    """
+    Execute reflection engine with specified configuration
+    
+    Runs the reflection engine using configuration parameters from the database.
+    Validates model availability, sets up the reflection engine with proper
+    configuration, and executes the reflection process with multi-language support.
+    
+    This endpoint provides a test run capability for reflection configurations,
+    allowing users to validate their reflection settings and see results before
+    deploying to production environments.
+    
+    Args:
+        config_id: Configuration identifier (UUID or integer) for reflection settings
+        language_type: Language preference header for output localization (optional)
+        current_user: Authenticated user executing the reflection
+        db: Database session for configuration queries
+    
+    Returns:
+        dict: Success response with reflection execution results including:
+            - baseline: Reflection strategy used
+            - source_data: Input data processed
+            - memory_verifies: Memory verification results (if enabled)
+            - quality_assessments: Quality assessment results (if enabled)
+            - reflexion_data: Generated reflection insights and solutions
+    
+    Configuration Validation:
+        - Verifies configuration exists in database
+        - Validates LLM model availability
+        - Falls back to default model if specified model is unavailable
+        - Ensures all required parameters are properly set
+    
+    Reflection Engine Setup:
+        - Creates ReflectionConfig with database parameters
+        - Initializes Neo4j connector for memory access
+        - Sets up ReflectionEngine with validated model
+        - Configures language preferences for output
+    
+    Error Handling:
+        - Model validation with fallback to default
+        - Configuration validation and error reporting
+        - Comprehensive logging for debugging
+        - Graceful handling of missing configurations
+    
+    Raises:
+        HTTPException 404: If configuration is not found
+        HTTPException 500: If reflection execution fails
+        
+    Performance Notes:
+        - Direct database query for configuration retrieval
+        - Model validation to prevent runtime failures
+        - Efficient reflection engine initialization
+        - Language-aware output processing
+    """
+    # Use centralized language validation for consistent localization
     language = get_language_from_header(language_type)
 
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
     config_id = resolve_config_id(config_id, db)
-    # 使用MemoryConfigRepository查询反思配置
+    
+    # Query reflection configuration using MemoryConfigRepository
     result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
     if not result:
         raise HTTPException(
@@ -239,7 +427,7 @@ async def reflection_run(
 
     api_logger.info(f"成功查询反思配置，config_id: {config_id}")
 
-    # 验证模型ID是否存在
+    # Validate model ID existence
     model_id = result.reflection_model_id
     if model_id:
         try:
@@ -250,6 +438,7 @@ async def reflection_run(
             # 可以设置为None，让反思引擎使用默认模型
             model_id = None
 
+    # Create reflection configuration with database parameters
     config = ReflectionConfig(
         enabled=result.enable_self_reflexion,
         iteration_period=result.iteration_period,
@@ -262,11 +451,13 @@ async def reflection_run(
         model_id=model_id,
         language_type=language_type
     )
+    
+    # Initialize Neo4j connector and reflection engine
     connector = Neo4jConnector()
     engine = ReflectionEngine(
         config=config,
         neo4j_connector=connector,
-        llm_client=model_id  # 传入验证后的 model_id
+        llm_client=model_id  # Pass validated model_id
     )
 
     result=await (engine.reflection_run())

api/app/controllers/memory_short_term_controller.py

@@ -1,3 +1,18 @@
+"""
+Memory Short Term Controller
+
+This module provides REST API endpoints for managing short-term and long-term memory
+data retrieval and analysis. It handles memory system statistics, data aggregation,
+and provides comprehensive memory insights for end users.
+
+Key Features:
+- Short-term memory data retrieval and statistics
+- Long-term memory data aggregation
+- Entity count integration
+- Multi-language response support
+- Memory system analytics and reporting
+"""
+
 from typing import Optional
 
 from dotenv import load_dotenv
@@ -13,9 +28,13 @@ from app.models.user_model import User
 from app.services.memory_short_service import LongService, ShortService
 from app.services.memory_storage_service import search_entity
 
+# Load environment variables for configuration
 load_dotenv()
+
+# Initialize API logger for request tracking and debugging
 api_logger = get_api_logger()
 
+# Configure router with prefix and tags for API organization
 router = APIRouter(
     prefix="/memory/short",
     tags=["Memory"],
@@ -27,24 +46,73 @@ async def short_term_configs(
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ):
-    # 使用集中化的语言校验
+    """
+    Retrieve comprehensive short-term and long-term memory statistics
+    
+    Provides a comprehensive overview of memory system data for a specific end user,
+    including short-term memory entries, long-term memory aggregations, entity counts,
+    and retrieval statistics. Supports multi-language responses based on request headers.
+    
+    This endpoint serves as a central dashboard for memory system analytics, combining
+    data from multiple memory subsystems to provide a holistic view of user memory state.
+    
+    Args:
+        end_user_id: Unique identifier for the end user whose memory data to retrieve
+        language_type: Language preference header for response localization (optional)
+        current_user: Authenticated user making the request (injected by dependency)
+        db: Database session for data operations (injected by dependency)
+    
+    Returns:
+        dict: Success response containing comprehensive memory statistics:
+            - short_term: List of short-term memory entries with detailed data
+            - long_term: List of long-term memory aggregations and summaries
+            - entity: Count of entities associated with the end user
+            - retrieval_number: Total count of short-term memory retrievals
+            - long_term_number: Total count of long-term memory entries
+    
+    Response Structure:
+        {
+            "code": 200,
+            "msg": "Short-term memory system data retrieved successfully",
+            "data": {
+                "short_term": [...],      # Short-term memory entries
+                "long_term": [...],       # Long-term memory data
+                "entity": 42,             # Entity count
+                "retrieval_number": 156,  # Short-term retrieval count
+                "long_term_number": 23    # Long-term memory count
+            }
+        }
+    
+    Raises:
+        HTTPException: If end_user_id is invalid or data retrieval fails
+        
+    Performance Notes:
+        - Combines multiple service calls for comprehensive data
+        - Entity search is performed asynchronously for better performance
+        - Response time depends on memory data volume for the specified user
+    """
+    # Use centralized language validation for consistent localization
     language = get_language_from_header(language_type)
     
-    # 获取短期记忆数据
-    short_term=ShortService(end_user_id, db)
-    short_result=short_term.get_short_databasets()
-    short_count=short_term.get_short_count()
+    # Retrieve short-term memory data and statistics
+    short_term = ShortService(end_user_id, db)
+    short_result = short_term.get_short_databasets()  # Get short-term memory entries
+    short_count = short_term.get_short_count()        # Get short-term retrieval count
 
-    long_term=LongService(end_user_id, db)
-    long_result=long_term.get_long_databasets()
+    # Retrieve long-term memory data and aggregations
+    long_term = LongService(end_user_id, db)
+    long_result = long_term.get_long_databasets()     # Get long-term memory entries
 
+    # Get entity count for the specified end user
     entity_result = await search_entity(end_user_id)
+    
+    # Compile comprehensive memory statistics response
     result = {
-        'short_term': short_result,
-        'long_term': long_result,
-        'entity': entity_result.get('num', 0),
-        "retrieval_number":short_count,
-        "long_term_number":len(long_result)
+        'short_term': short_result,                    # Short-term memory entries
+        'long_term': long_result,                      # Long-term memory data
+        'entity': entity_result.get('num', 0),        # Entity count (default to 0 if not found)
+        "retrieval_number": short_count,               # Short-term retrieval statistics
+        "long_term_number": len(long_result)          # Long-term memory entry count
     }
 
     return success(data=result, msg="短期记忆系统数据获取成功")

api/app/controllers/memory_working_controller.py

@@ -8,6 +8,7 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models import User
+from app.schemas import conversation_schema
 from app.schemas.response_schema import ApiResponse
 from app.services.conversation_service import ConversationService
 
@@ -90,11 +91,7 @@ def get_messages(
         conversation_id,
     )
     messages = [
-        {
-            "role": message.role,
-            "content": message.content,
-            "created_at": int(message.created_at.timestamp() * 1000),
-        }
+        conversation_schema.Message.model_validate(message)
         for message in messages_obj
     ]
     return success(data=messages, msg="get conversation history success")

api/app/controllers/public_share_controller.py

@@ -13,7 +13,6 @@ from app.core.logging_config import get_business_logger
 from app.core.response_utils import success, fail
 from app.db import get_db, get_db_read
 from app.dependencies import get_share_user_id, ShareTokenData
-from app.models.app_model import App
 from app.models.app_model import AppType
 from app.repositories import knowledge_repository
 from app.repositories.end_user_repository import EndUserRepository
@@ -22,6 +21,7 @@ from app.schemas import release_share_schema, conversation_schema
 from app.schemas.response_schema import PageData, PageMeta
 from app.services import workspace_service
 from app.services.app_chat_service import AppChatService, get_app_chat_service
+from app.services.app_service import AppService
 from app.services.auth_service import create_access_token
 from app.services.conversation_service import ConversationService
 from app.services.release_share_service import ReleaseShareService
@@ -215,8 +215,11 @@ def list_conversations(
     service = SharedChatService(db)
     share, release = service.get_release_by_share_token(share_data.share_token, password)
     end_user_repo = EndUserRepository(db)
+    app_service = AppService(db)
+    app = app_service._get_app_or_404(share.app_id)
     new_end_user = end_user_repo.get_or_create_end_user(
         app_id=share.app_id,
+        workspace_id=app.workspace_id,
         other_id=other_id
     )
     logger.debug(new_end_user.id)
@@ -308,25 +311,29 @@ async def chat(
 
         # Store end_user_id in database with original user_id
         end_user_repo = EndUserRepository(db)
+        app_service = AppService(db)
+        app = app_service._get_app_or_404(share.app_id)
+        workspace_id = app.workspace_id
         new_end_user = end_user_repo.get_or_create_end_user(
             app_id=share.app_id,
+            workspace_id=workspace_id,
             other_id=other_id,
-            original_user_id=user_id  # Save original user_id to other_id
+            original_user_id=user_id
         )
         end_user_id = str(new_end_user.id)
 
-        appid = share.app_id
+        # appid = share.app_id
         """获取存储类型和工作空间的ID"""
 
         # 直接通过 SQLAlchemy 查询 app（仅查询未删除的应用）
-        app = db.query(App).filter(
-            App.id == appid,
-            App.is_active.is_(True)
-        ).first()
-        if not app:
-            raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND)
+        # app = db.query(App).filter(
+        #     App.id == appid,
+        #     App.is_active.is_(True)
+        # ).first()
+        # if not app:
+        #     raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND)
 
-        workspace_id = app.workspace_id
+        # workspace_id = app.workspace_id
 
         # 直接从 workspace 获取 storage_type（公开分享场景无需权限检查）
         storage_type = workspace_service.get_workspace_storage_type_without_auth(
@@ -610,11 +617,11 @@ async def chat(
 
         # 多 Agent 非流式返回
         result = await app_chat_service.workflow_chat(
-
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
             user_id=end_user_id,  # 转换为字符串
             variables=payload.variables,
+            files=payload.files,
             config=config,
             web_search=payload.web_search,
             memory=payload.memory,
@@ -654,17 +661,21 @@ async def config_query(
         workflow_service = WorkflowService(db)
         content = {
             "app_type": release.app.type,
-            "variables": workflow_service.get_start_node_variables(release.config)
+            "variables": workflow_service.get_start_node_variables(release.config),
+            "memory":  workflow_service.is_memory_enable(release.config),
+            "features": release.config.get("features")
         }
     elif release.app.type == AppType.AGENT:
         content = {
             "app_type": release.app.type,
-            "variables": release.config.get("variables")
+            "variables": release.config.get("variables"),
+            "features": release.config.get("features")
         }
     elif release.app.type == AppType.MULTI_AGENT:
         content = {
             "app_type": release.app.type,
-            "variables": []
+            "variables": [],
+            "features": release.config.get("features")
         }
     else:
         return fail(msg="Unsupported app type", code=BizCode.APP_TYPE_NOT_SUPPORTED)

api/app/controllers/service/app_api_controller.py

@@ -95,8 +95,8 @@ async def chat(
     end_user_repo = EndUserRepository(db)
     new_end_user = end_user_repo.get_or_create_end_user(
         app_id=app.id,
+        workspace_id=workspace_id,
         other_id=other_id,
-        original_user_id=other_id  # Save original user_id to other_id
     )
     end_user_id = str(new_end_user.id)
     web_search = True
@@ -280,6 +280,7 @@ async def chat(
             memory=memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
+            files=payload.files,
             app_id=app.id,
             workspace_id=workspace_id,
             release_id=app.current_release.id

api/app/controllers/service/memory_api_controller.py

@@ -6,6 +6,7 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.schemas.api_key_schema import ApiKeyAuth
 from app.schemas.memory_api_schema import (
+    ListConfigsResponse,
     MemoryReadRequest,
     MemoryReadResponse,
     MemoryWriteRequest,
@@ -31,14 +32,15 @@ async def write_memory_api_service(
     request: Request,
     api_key_auth: ApiKeyAuth = None,
     db: Session = Depends(get_db),
-    payload: MemoryWriteRequest = Body(..., embed=False),
-
+    message: str = Body(..., description="Message content"),
 ):
     """
     Write memory to storage.
     
     Stores memory content for the specified end user using the Memory API Service.
     """
+    body = await request.json()
+    payload = MemoryWriteRequest(**body)
     logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, workspace_id: {api_key_auth.workspace_id}")
     
     memory_api_service = MemoryAPIService(db)
@@ -62,13 +64,15 @@ async def read_memory_api_service(
     request: Request,
     api_key_auth: ApiKeyAuth = None,
     db: Session = Depends(get_db),
-    payload: MemoryReadRequest = Body(..., embed=False),
+    message: str = Body(..., description="Query message"),
 ):
     """
     Read memory from storage.
     
     Queries and retrieves memories for the specified end user with context-aware responses.
     """
+    body = await request.json()
+    payload = MemoryReadRequest(**body)
     logger.info(f"Memory read request - end_user_id: {payload.end_user_id}")
     
     memory_api_service = MemoryAPIService(db)
@@ -85,3 +89,27 @@ async def read_memory_api_service(
     
     logger.info(f"Memory read successful for end_user: {payload.end_user_id}")
     return success(data=MemoryReadResponse(**result).model_dump(), msg="Memory read successfully")
+
+
+@router.get("/configs")
+@require_api_key(scopes=["memory"])
+async def list_memory_configs(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    List all memory configs for the workspace.
+    
+    Returns all available memory configurations associated with the authorized workspace.
+    """
+    logger.info(f"List configs request - workspace_id: {api_key_auth.workspace_id}")
+
+    memory_api_service = MemoryAPIService(db)
+
+    result = memory_api_service.list_memory_configs(
+        workspace_id=api_key_auth.workspace_id,
+    )
+
+    logger.info(f"Listed {result['total']} configs for workspace: {api_key_auth.workspace_id}")
+    return success(data=ListConfigsResponse(**result).model_dump(), msg="Configs listed successfully")

api/app/controllers/tool_controller.py

@@ -3,8 +3,11 @@ from typing import Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
 from app.schemas.tool_schema import (
-    ToolCreateRequest, ToolUpdateRequest, ToolExecuteRequest, ParseSchemaRequest, CustomToolTestRequest
+    ToolCreateRequest, ToolUpdateRequest, ToolExecuteRequest, ParseSchemaRequest,
+    CustomToolTestRequest, ToolActiveUpdate
 )
 
 from app.core.response_utils import success
@@ -14,6 +17,7 @@ from app.models import User
 from app.models.tool_model import ToolType, ToolStatus, AuthType
 from app.services.tool_service import ToolService
 from app.schemas.response_schema import ApiResponse
+from app.core.exceptions import BusinessException
 
 router = APIRouter(prefix="/tools", tags=["Tool System"])
 
@@ -97,7 +101,13 @@ async def create_tool(
 ):
     """创建工具"""
     try:
-        tool_id = service.create_tool(
+        # 将 MCP 来源字段合并进 config
+        if request.tool_type == ToolType.MCP:
+            for key in ("source_channel", "market_id", "market_config_id", "mcp_service_id"):
+                val = getattr(request, key, None)
+                if val is not None:
+                    request.config[key] = val
+        tool_id = await service.create_tool(
             name=request.name,
             tool_type=request.tool_type,
             tenant_id=current_user.tenant_id,
@@ -107,6 +117,8 @@ async def create_tool(
             tags=request.tags
         )
         return success(data={"tool_id": tool_id}, msg="工具创建成功")
+    except BusinessException as e:
+        raise HTTPException(status_code=400, detail=e.message)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     except Exception as e:
@@ -147,7 +159,7 @@ async def delete_tool(
         current_user: User = Depends(get_current_user),
         service: ToolService = Depends(get_tool_service)
 ):
-    """删除工具"""
+    """删除工具（逻辑删除，is_active=False）"""
     try:
         success_flag = service.delete_tool(tool_id, current_user.tenant_id)
         if not success_flag:
@@ -159,6 +171,30 @@ async def delete_tool(
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@router.patch("/{tool_id}/active", response_model=ApiResponse)
+async def set_tool_active(
+        tool_id: str,
+        request: ToolActiveUpdate,
+        current_user: User = Depends(get_current_user),
+        service: ToolService = Depends(get_tool_service)
+):
+    """设置工具可用状态（启用/禁用）
+
+    - is_active=true: 启用工具
+    - is_active=false: 禁用工具（等同于删除，但可恢复）
+    """
+    try:
+        success_flag = service.set_tool_active(tool_id, current_user.tenant_id, request.is_active)
+        if not success_flag:
+            raise HTTPException(status_code=404, detail="工具不存在")
+        action = "启用" if request.is_active else "禁用"
+        return success(msg=f"工具已{action}")
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @router.post("/execution/execute", response_model=ApiResponse)
 async def execute_tool(
         request: ToolExecuteRequest,
@@ -216,8 +252,10 @@ async def sync_mcp_tools(
     try:
         result = await service.sync_mcp_tools(tool_id, current_user.tenant_id)
         if not result.get("success", False):
-            raise HTTPException(status_code=400, detail=result.get("message", "同步失败"))
+            raise BusinessException(result.get("message", "工具列表同步失败"), BizCode.BAD_REQUEST)
         return success(data=result, msg="MCP工具列表同步完成")
+    except BusinessException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -240,8 +278,10 @@ async def test_tool_connection(
             # 普通连接测试
             result = await service.test_connection(tool_id, current_user.tenant_id)
         if result["success"] is False:
-            raise HTTPException(status_code=400, detail=result["message"])
+            raise BusinessException(result["message"], BizCode.SERVICE_UNAVAILABLE)
         return success(data=result, msg="连接测试完成")
+    except BusinessException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 

api/app/controllers/user_controller.py

@@ -1,6 +1,7 @@
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
 import uuid
+from typing import Callable
 
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
@@ -19,6 +20,7 @@ from app.services import user_service
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success
 from app.core.security import verify_password
+from app.i18n.dependencies import get_translator
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -33,7 +35,8 @@ router = APIRouter(
 def create_superuser(
     user: user_schema.UserCreate,
     db: Session = Depends(get_db),
-    current_superuser: User = Depends(get_current_superuser)
+    current_superuser: User = Depends(get_current_superuser),
+    t: Callable = Depends(get_translator)
 ):
     """创建超级管理员（仅超级管理员可访问）"""
     api_logger.info(f"超级管理员创建请求: {user.username}, email: {user.email}")
@@ -42,7 +45,7 @@ def create_superuser(
     api_logger.info(f"超级管理员创建成功: {result.username} (ID: {result.id})")
     
     result_schema = user_schema.User.model_validate(result)
-    return success(data=result_schema, msg="超级管理员创建成功")
+    return success(data=result_schema, msg=t("users.create.superuser_success"))
 
 
 @router.delete("/{user_id}", response_model=ApiResponse)
@@ -50,6 +53,7 @@ def delete_user(
     user_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """停用用户（软删除）"""
     api_logger.info(f"用户停用请求: user_id={user_id}, 操作者: {current_user.username}")
@@ -57,13 +61,14 @@ def delete_user(
         db=db, user_id_to_deactivate=user_id, current_user=current_user
     )
     api_logger.info(f"用户停用成功: {result.username} (ID: {result.id})")
-    return success(msg="用户停用成功")
+    return success(msg=t("users.delete.deactivate_success"))
 
 @router.post("/{user_id}/activate", response_model=ApiResponse)
 def activate_user(
     user_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """激活用户"""
     api_logger.info(f"用户激活请求: user_id={user_id}, 操作者: {current_user.username}")
@@ -74,13 +79,14 @@ def activate_user(
     api_logger.info(f"用户激活成功: {result.username} (ID: {result.id})")
     
     result_schema = user_schema.User.model_validate(result)
-    return success(data=result_schema, msg="用户激活成功")
+    return success(data=result_schema, msg=t("users.activate.success"))
 
 
 @router.get("", response_model=ApiResponse)
 def get_current_user_info(
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """获取当前用户信息"""
     api_logger.info(f"当前用户信息请求: {current_user.username}")
@@ -105,7 +111,7 @@ def get_current_user_info(
                 break
     
     api_logger.info(f"当前用户信息获取成功: {result.username}, 角色: {result_schema.role}, 工作空间: {result_schema.current_workspace_name}")
-    return success(data=result_schema, msg="用户信息获取成功")
+    return success(data=result_schema, msg=t("users.info.get_success"))
 
 
 @router.get("/superusers", response_model=ApiResponse)
@@ -113,6 +119,7 @@ def get_tenant_superusers(
     include_inactive: bool = False,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
+    t: Callable = Depends(get_translator)
 ):
     """获取当前租户下的超管账号列表（仅超级管理员可访问）"""
     api_logger.info(f"获取租户超管列表请求: {current_user.username}")
@@ -125,7 +132,7 @@ def get_tenant_superusers(
     api_logger.info(f"租户超管列表获取成功: count={len(superusers)}")
     
     superusers_schema = [user_schema.User.model_validate(u) for u in superusers]
-    return success(data=superusers_schema, msg="租户超管列表获取成功")
+    return success(data=superusers_schema, msg=t("users.list.superusers_success"))
 
 
 
@@ -134,6 +141,7 @@ def get_user_info_by_id(
     user_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """根据用户ID获取用户信息"""
     api_logger.info(f"获取用户信息请求: user_id={user_id}, 操作者: {current_user.username}")
@@ -144,7 +152,7 @@ def get_user_info_by_id(
     api_logger.info(f"用户信息获取成功: {result.username}")
     
     result_schema = user_schema.User.model_validate(result)
-    return success(data=result_schema, msg="用户信息获取成功")
+    return success(data=result_schema, msg=t("users.info.get_success"))
 
 
 @router.put("/change-password", response_model=ApiResponse)
@@ -152,6 +160,7 @@ async def change_password(
     request: ChangePasswordRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """修改当前用户密码"""
     api_logger.info(f"用户密码修改请求: {current_user.username}")
@@ -164,7 +173,7 @@ async def change_password(
         current_user=current_user
     )
     api_logger.info(f"用户密码修改成功: {current_user.username}")
-    return success(msg="密码修改成功")
+    return success(msg=t("auth.password.change_success"))
 
 
 @router.put("/admin/change-password", response_model=ApiResponse)
@@ -172,6 +181,7 @@ async def admin_change_password(
     request: AdminChangePasswordRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
+    t: Callable = Depends(get_translator)
 ):
     """超级管理员修改指定用户的密码"""
     api_logger.info(f"管理员密码修改请求: 管理员 {current_user.username} 修改用户 {request.user_id}")
@@ -186,16 +196,17 @@ async def admin_change_password(
     # 根据是否生成了随机密码来构造响应
     if request.new_password:
         api_logger.info(f"管理员密码修改成功: 用户 {request.user_id}")
-        return success(msg="密码修改成功")
+        return success(msg=t("auth.password.change_success"))
     else:
         api_logger.info(f"管理员密码重置成功: 用户 {request.user_id}, 随机密码已生成")
-        return success(data=generated_password, msg="密码重置成功")
+        return success(data=generated_password, msg=t("auth.password.reset_success"))
 
 
 @router.post("/verify_pwd", response_model=ApiResponse)
 def verify_pwd(
     request: VerifyPasswordRequest,
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """验证当前用户密码"""
     api_logger.info(f"用户验证密码请求: {current_user.username}")
@@ -203,8 +214,8 @@ def verify_pwd(
     is_valid = verify_password(request.password, current_user.hashed_password)
     api_logger.info(f"用户密码验证结果: {current_user.username}, valid={is_valid}")
     if not is_valid:
-        raise BusinessException("密码验证失败", code=BizCode.VALIDATION_FAILED)
-    return success(data={"valid": is_valid}, msg="验证完成")
+        raise BusinessException(t("users.errors.password_verification_failed"), code=BizCode.VALIDATION_FAILED)
+    return success(data={"valid": is_valid}, msg=t("common.success.retrieved"))
 
 
 @router.post("/send-email-code", response_model=ApiResponse)
@@ -212,6 +223,7 @@ async def send_email_code(
     request: SendEmailCodeRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """发送邮箱验证码"""
     api_logger.info(f"用户请求发送邮箱验证码: {current_user.username}, email={request.email}")
@@ -219,7 +231,7 @@ async def send_email_code(
     await user_service.send_email_code_method(db=db, email=request.email, user_id=current_user.id)
     
     api_logger.info(f"邮箱验证码已发送: {current_user.username}")
-    return success(msg="验证码已发送到您的邮箱，请查收")
+    return success(msg=t("users.email.code_sent"))
 
 
 @router.put("/change-email", response_model=ApiResponse)
@@ -227,6 +239,7 @@ async def change_email(
     request: VerifyEmailCodeRequest,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
 ):
     """验证验证码并修改邮箱"""
     api_logger.info(f"用户修改邮箱: {current_user.username}, new_email={request.new_email}")
@@ -239,4 +252,51 @@ async def change_email(
     )
     
     api_logger.info(f"用户邮箱修改成功: {current_user.username}")
-    return success(msg="邮箱修改成功")
+    return success(msg=t("users.email.change_success"))
+
+
+
+@router.get("/me/language", response_model=ApiResponse)
+def get_current_user_language(
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
+):
+    """获取当前用户的语言偏好"""
+    api_logger.info(f"获取用户语言偏好: {current_user.username}")
+    
+    language = user_service.get_user_language_preference(
+        db=db,
+        user_id=current_user.id,
+        current_user=current_user
+    )
+    
+    api_logger.info(f"用户语言偏好获取成功: {current_user.username}, language={language}")
+    return success(
+        data=user_schema.LanguagePreferenceResponse(language=language),
+        msg=t("users.language.get_success")
+    )
+
+
+@router.put("/me/language", response_model=ApiResponse)
+def update_current_user_language(
+    request: user_schema.LanguagePreferenceRequest,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+    t: Callable = Depends(get_translator)
+):
+    """设置当前用户的语言偏好"""
+    api_logger.info(f"更新用户语言偏好: {current_user.username}, language={request.language}")
+    
+    updated_user = user_service.update_user_language_preference(
+        db=db,
+        user_id=current_user.id,
+        language=request.language,
+        current_user=current_user
+    )
+    
+    api_logger.info(f"用户语言偏好更新成功: {current_user.username}, language={request.language}")
+    return success(
+        data=user_schema.LanguagePreferenceResponse(language=updated_user.preferred_language),
+        msg=t("users.language.update_success")
+    )

api/app/controllers/user_memory_controllers.py

@@ -17,6 +17,7 @@ from app.services.user_memory_service import (
     UserMemoryService,
     analytics_memory_types,
     analytics_graph_data,
+    analytics_community_graph_data,
 )
 from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction
 from app.schemas.response_schema import ApiResponse
@@ -295,6 +296,42 @@ async def get_graph_data_api(
         return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e))
 
 
+@router.get("/analytics/community_graph", response_model=ApiResponse)
+async def get_community_graph_data_api(
+    end_user_id: str,
+    current_user: User = Depends(get_current_user),
+    db: Session = Depends(get_db),
+) -> dict:
+    workspace_id = current_user.current_workspace_id
+
+    if workspace_id is None:
+        api_logger.warning(f"用户 {current_user.username} 尝试查询社区图谱但未选择工作空间")
+        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"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, "
+        f"workspace={workspace_id}"
+    )
+
+    try:
+        result = await analytics_community_graph_data(db=db, end_user_id=end_user_id)
+
+        if "message" in result and result["statistics"]["total_nodes"] == 0:
+            api_logger.warning(f"社区图谱查询返回空结果: {result.get('message')}")
+            return success(data=result, msg=result.get("message", "查询成功"))
+
+        api_logger.info(
+            f"成功获取社区图谱: end_user_id={end_user_id}, "
+            f"nodes={result['statistics']['total_nodes']}, "
+            f"edges={result['statistics']['total_edges']}"
+        )
+        return success(data=result, msg="查询成功")
+
+    except Exception as e:
+        api_logger.error(f"社区图谱查询失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "社区图谱查询失败", str(e))
+
+
 @router.get("/read_end_user/profile", response_model=ApiResponse)
 async def get_end_user_profile(
     end_user_id: str,

api/app/controllers/workspace_controller.py

@@ -14,6 +14,12 @@ from app.dependencies import (
     get_current_user,
     workspace_access_guard,
 )
+from app.i18n.dependencies import get_current_language, get_translator
+from app.i18n.serializers import (
+    WorkspaceSerializer,
+    WorkspaceMemberSerializer,
+    WorkspaceInviteSerializer
+)
 from app.models.tenant_model import Tenants
 from app.models.user_model import User
 from app.models.workspace_model import InviteStatus
@@ -65,7 +71,9 @@ def get_workspaces(
     include_current: bool = Query(True, description="是否包含当前工作空间"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
-    current_tenant: Tenants = Depends(get_current_tenant)
+    current_tenant: Tenants = Depends(get_current_tenant),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """获取当前租户下用户参与的所有工作空间
 
@@ -88,8 +96,13 @@ def get_workspaces(
         )
 
     api_logger.info(f"成功获取 {len(workspaces)} 个工作空间")
-    workspaces_schema = [WorkspaceResponse.model_validate(w) for w in workspaces]
-    return success(data=workspaces_schema, msg="工作空间列表获取成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceSerializer()
+    workspaces_data = [WorkspaceResponse.model_validate(w).model_dump() for w in workspaces]
+    workspaces_i18n = serializer.serialize_list(workspaces_data, language)
+    
+    return success(data=workspaces_i18n, msg=t("workspace.list_retrieved"))
 
 
 @router.post("", response_model=ApiResponse)
@@ -98,6 +111,8 @@ def create_workspace(
     language_type: str = Header(default="zh", alias="X-Language-Type"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_superuser),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """创建新的工作空间"""
     from app.core.language_utils import get_language_from_header
@@ -118,8 +133,13 @@ def create_workspace(
         f"工作空间创建成功 - 名称: {workspace.name}, ID: {result.id}, "
         f"创建者: {current_user.username}, language={language}"
     )
-    result_schema = WorkspaceResponse.model_validate(result)
-    return success(data=result_schema, msg="工作空间创建成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceSerializer()
+    result_data = WorkspaceResponse.model_validate(result).model_dump()
+    result_i18n = serializer.serialize(result_data, language)
+    
+    return success(data=result_i18n, msg=t("workspace.created"))
 
 @router.put("", response_model=ApiResponse)
 @cur_workspace_access_guard()
@@ -127,6 +147,8 @@ def update_workspace(
     workspace: WorkspaceUpdate,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """更新工作空间"""
     workspace_id = current_user.current_workspace_id
@@ -139,14 +161,21 @@ def update_workspace(
         user=current_user,
     )
     api_logger.info(f"工作空间更新成功 - ID: {workspace_id}, 用户: {current_user.username}")
-    result_schema = WorkspaceResponse.model_validate(result)
-    return success(data=result_schema, msg="工作空间更新成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceSerializer()
+    result_data = WorkspaceResponse.model_validate(result).model_dump()
+    result_i18n = serializer.serialize(result_data, language)
+    
+    return success(data=result_i18n, msg=t("workspace.updated"))
 
 @router.get("/members", response_model=ApiResponse)
 @cur_workspace_access_guard()
 def get_cur_workspace_members(
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """获取工作空间成员列表（关系序列化）"""
     api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {current_user.current_workspace_id} 的成员列表")
@@ -157,8 +186,14 @@ def get_cur_workspace_members(
         user=current_user,
     )
     api_logger.info(f"工作空间成员列表获取成功 - ID: {current_user.current_workspace_id}, 数量: {len(members)}")
+    
+    # 转换为表格项并使用序列化器添加国际化字段
     table_items = _convert_members_to_table_items(members)
-    return success(data=table_items, msg="工作空间成员列表获取成功")
+    serializer = WorkspaceMemberSerializer()
+    members_data = [item.model_dump() for item in table_items]
+    members_i18n = serializer.serialize_list(members_data, language)
+    
+    return success(data=members_i18n, msg=t("workspace.members.list_retrieved"))
 
 
 @router.put("/members", response_model=ApiResponse)
@@ -168,6 +203,7 @@ def update_workspace_members(
     updates: List[WorkspaceMemberUpdate],
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: callable = Depends(get_translator)
 ):
     workspace_id = current_user.current_workspace_id
     api_logger.info(f"用户 {current_user.username} 请求更新工作空间 {workspace_id} 的成员角色")
@@ -178,7 +214,7 @@ def update_workspace_members(
         user=current_user,
     )
     api_logger.info(f"工作空间成员角色更新成功 - ID: {workspace_id}, 数量: {len(members)}")
-    return success(msg="成员角色更新成功")
+    return success(msg=t("workspace.members.role_updated"))
 
 
 @router.delete("/members/{member_id}", response_model=ApiResponse)
@@ -187,6 +223,7 @@ def delete_workspace_member(
     member_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: callable = Depends(get_translator)
 ):
     workspace_id = current_user.current_workspace_id
     api_logger.info(f"用户 {current_user.username} 请求删除工作空间 {workspace_id} 的成员 {member_id}")
@@ -198,7 +235,7 @@ def delete_workspace_member(
         user=current_user,
     )
     api_logger.info(f"工作空间成员删除成功 - ID: {workspace_id}, 成员: {member_id}")
-    return success(msg="成员删除成功")
+    return success(msg=t("workspace.members.deleted"))
 
 
 # 创建空间协作邀请
@@ -208,6 +245,8 @@ def create_workspace_invite(
     invite_data: WorkspaceInviteCreate,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """创建工作空间邀请"""
     workspace_id = current_user.current_workspace_id
@@ -220,7 +259,12 @@ def create_workspace_invite(
         user=current_user
     )
     api_logger.info(f"工作空间邀请创建成功 - 工作空间: {workspace_id}, 邮箱: {invite_data.email}")
-    return success(data=result, msg="邀请创建成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceInviteSerializer()
+    result_i18n = serializer.serialize(result, language)
+    
+    return success(data=result_i18n, msg=t("workspace.invites.created"))
 
 
 @router.get("/invites", response_model=ApiResponse)
@@ -232,6 +276,8 @@ def get_workspace_invites(
     offset: int = Query(0, ge=0),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """获取工作空间邀请列表"""
     workspace_id = current_user.current_workspace_id
@@ -246,18 +292,30 @@ def get_workspace_invites(
         offset=offset
     )
     api_logger.info(f"成功获取 {len(invites)} 个邀请记录")
-    return success(data=invites, msg="邀请列表获取成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceInviteSerializer()
+    invites_i18n = serializer.serialize_list(invites, language)
+    
+    return success(data=invites_i18n, msg=t("workspace.invites.list_retrieved"))
 
 
 @public_router.get("/invites/validate/{token}", response_model=ApiResponse)
 def get_workspace_invite_info(
     token: str,
     db: Session = Depends(get_db),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """获取工作空间邀请用户信息（无需认证）"""
     result = workspace_service.validate_invite_token(db=db, token=token)
     api_logger.info(f"工作空间邀请验证成功 - 邀请: {token}")
-    return success(data=result, msg="邀请验证成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceInviteSerializer()
+    result_i18n = serializer.serialize(result, language)
+    
+    return success(data=result_i18n, msg=t("workspace.invites.validated"))
 
 
 @router.delete("/invites/{invite_id}", response_model=ApiResponse)
@@ -267,6 +325,8 @@ def revoke_workspace_invite(
     invite_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    language: str = Depends(get_current_language),
+    t: callable = Depends(get_translator)
 ):
     """撤销工作空间邀请"""
     workspace_id = current_user.current_workspace_id
@@ -279,7 +339,12 @@ def revoke_workspace_invite(
         user=current_user
     )
     api_logger.info(f"工作空间邀请撤销成功 - 邀请: {invite_id}")
-    return success(data=result, msg="邀请撤销成功")
+    
+    # 使用序列化器添加国际化字段
+    serializer = WorkspaceInviteSerializer()
+    result_i18n = serializer.serialize(result, language)
+    
+    return success(data=result_i18n, msg=t("workspace.invites.revoked"))
 
 # ==================== 公开邀请接口（无需认证） ====================
 
@@ -302,6 +367,7 @@ def switch_workspace(
     workspace_id: uuid.UUID,
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
+    t: callable = Depends(get_translator)
 ):
     """切换工作空间"""
     api_logger.info(f"用户 {current_user.username} 请求切换工作空间为 {workspace_id}")
@@ -312,7 +378,7 @@ def switch_workspace(
         user=current_user,
     )
     api_logger.info(f"成功切换工作空间为 {workspace_id}")
-    return success(msg="工作空间切换成功")
+    return success(msg=t("workspace.switched"))
 
 
 @router.get("/storage", response_model=ApiResponse)
@@ -320,6 +386,7 @@ def switch_workspace(
 def get_workspace_storage_type(
         db: Session = Depends(get_db),
         current_user: User = Depends(get_current_user),
+        t: callable = Depends(get_translator)
 ):
     """获取当前工作空间的存储类型"""
     workspace_id = current_user.current_workspace_id
@@ -331,7 +398,7 @@ def get_workspace_storage_type(
         user=current_user
     )
     api_logger.info(f"成功获取工作空间 {workspace_id} 的存储类型: {storage_type}")
-    return success(data={"storage_type": storage_type}, msg="存储类型获取成功")
+    return success(data={"storage_type": storage_type}, msg=t("workspace.storage.type_retrieved"))
 
 
 @router.get("/workspace_models", response_model=ApiResponse)
@@ -339,6 +406,8 @@ def get_workspace_storage_type(
 def workspace_models_configs(
         db: Session = Depends(get_db),
         current_user: User = Depends(get_current_user),
+        language: str = Depends(get_current_language),
+        t: callable = Depends(get_translator)
 ):
     """获取当前工作空间的模型配置（llm, embedding, rerank）"""
     workspace_id = current_user.current_workspace_id
@@ -354,14 +423,14 @@ def workspace_models_configs(
         api_logger.warning(f"工作空间 {workspace_id} 不存在或无权访问")
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,
-            detail="工作空间不存在或无权访问"
+            detail=t("workspace.not_found")
         )
 
     api_logger.info(
         f"成功获取工作空间 {workspace_id} 的模型配置: "
         f"llm={configs.get('llm')}, embedding={configs.get('embedding')}, rerank={configs.get('rerank')}"
     )
-    return success(data=WorkspaceModelsConfig.model_validate(configs), msg="模型配置获取成功")
+    return success(data=WorkspaceModelsConfig.model_validate(configs), msg=t("workspace.models.config_retrieved"))
 
 
 @router.put("/workspace_models", response_model=ApiResponse)
@@ -370,6 +439,7 @@ def update_workspace_models_configs(
         models_update: WorkspaceModelsUpdate,
         db: Session = Depends(get_db),
         current_user: User = Depends(get_current_user),
+        t: callable = Depends(get_translator)
 ):
     """更新当前工作空间的模型配置（llm, embedding, rerank）"""
     workspace_id = current_user.current_workspace_id
@@ -386,5 +456,5 @@ def update_workspace_models_configs(
         f"成功更新工作空间 {workspace_id} 的模型配置: "
         f"llm={updated_workspace.llm}, embedding={updated_workspace.embedding}, rerank={updated_workspace.rerank}"
     )
-    return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg="模型配置更新成功")
+    return success(data=WorkspaceModelsConfig.model_validate(updated_workspace), msg=t("workspace.models.config_updated"))
 

api/app/core/config.py

@@ -1,7 +1,6 @@
-import json
 import os
 from pathlib import Path
-from typing import Annotated, Any, Dict, Optional
+from typing import Annotated, Optional
 
 from dotenv import load_dotenv
 from pydantic import Field, TypeAdapter
@@ -115,6 +114,7 @@ class Settings:
     S3_ACCESS_KEY_ID: str = os.getenv("S3_ACCESS_KEY_ID", "")
     S3_SECRET_ACCESS_KEY: str = os.getenv("S3_SECRET_ACCESS_KEY", "")
     S3_BUCKET_NAME: str = os.getenv("S3_BUCKET_NAME", "")
+    S3_ENDPOINT_URL: str = os.getenv("S3_ENDPOINT_URL", "")
 
     # VOLC ASR settings
     VOLC_APP_KEY: str = os.getenv("VOLC_APP_KEY", "")
@@ -162,6 +162,44 @@ class Settings:
     # This controls the language used for memory summary titles and other generated content
     DEFAULT_LANGUAGE: str = os.getenv("DEFAULT_LANGUAGE", "zh")
 
+    # ========================================================================
+    # Internationalization (i18n) Configuration
+    # ========================================================================
+    # Default language for API responses
+    I18N_DEFAULT_LANGUAGE: str = os.getenv("I18N_DEFAULT_LANGUAGE", "zh")
+    
+    # Supported languages (comma-separated)
+    I18N_SUPPORTED_LANGUAGES: list[str] = [
+        lang.strip()
+        for lang in os.getenv("I18N_SUPPORTED_LANGUAGES", "zh,en").split(",")
+        if lang.strip()
+    ]
+    
+    # Core locales directory (community edition)
+    # Use absolute path to work from any working directory
+    I18N_CORE_LOCALES_DIR: str = os.getenv(
+        "I18N_CORE_LOCALES_DIR",
+        os.path.join(os.path.dirname(os.path.dirname(__file__)), "locales")
+    )
+    
+    # Premium locales directory (enterprise edition, optional)
+    I18N_PREMIUM_LOCALES_DIR: Optional[str] = os.getenv("I18N_PREMIUM_LOCALES_DIR", None)
+    
+    # Enable translation cache
+    I18N_ENABLE_TRANSLATION_CACHE: bool = os.getenv("I18N_ENABLE_TRANSLATION_CACHE", "true").lower() == "true"
+    
+    # LRU cache size for hot translations
+    I18N_LRU_CACHE_SIZE: int = int(os.getenv("I18N_LRU_CACHE_SIZE", "1000"))
+    
+    # Enable hot reload of translation files
+    I18N_ENABLE_HOT_RELOAD: bool = os.getenv("I18N_ENABLE_HOT_RELOAD", "false").lower() == "true"
+    
+    # Fallback language when translation is missing
+    I18N_FALLBACK_LANGUAGE: str = os.getenv("I18N_FALLBACK_LANGUAGE", "zh")
+    
+    # Log missing translations
+    I18N_LOG_MISSING_TRANSLATIONS: bool = os.getenv("I18N_LOG_MISSING_TRANSLATIONS", "true").lower() == "true"
+
     # Logging settings
     LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
     LOG_FORMAT: str = os.getenv("LOG_FORMAT", "%(asctime)s - %(name)s - %(levelname)s - %(message)s")

api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py

@@ -1,16 +1,45 @@
 from app.core.memory.agent.utils.llm_tools import ReadState, WriteState
+from app.schemas.memory_agent_schema import AgentMemoryDataset
 
 
 def content_input_node(state: ReadState) -> ReadState:
-    """开始节点 - 提取内容并保持状态信息"""
+    """
+    Start node - Extract content and maintain state information
+
+    Extracts the content from the first message in the state and returns it
+    as the data field while preserving all other state information.
+
+    Args:
+        state: ReadState containing messages and other state data
+
+    Returns:
+        ReadState: Updated state with extracted content in data field
+    """
 
     content = state['messages'][0].content if state.get('messages') else ''
-    # 返回内容并保持所有状态信息
+    # Return content and maintain all state information
+    for pronoun in AgentMemoryDataset.PRONOUN:
+        content = content.replace(pronoun, AgentMemoryDataset.NAME)
+
     return {"data": content}
 
+
 def content_input_write(state: WriteState) -> WriteState:
-    """开始节点 - 提取内容并保持状态信息"""
+    """
+    Start node - Extract content and maintain state information for write operations
+
+    Extracts the content from the first message in the state for write operations.
+
+    Args:
+        state: WriteState containing messages and other state data
+
+    Returns:
+        WriteState: Updated state with extracted content in data field
+    """
 
     content = state['messages'][0].content if state.get('messages') else ''
-    # 返回内容并保持所有状态信息
-    return {"data": content}
+    # Return content and maintain all state information
+    for pronoun in AgentMemoryDataset.PRONOUN:
+        content = content.replace(pronoun, AgentMemoryDataset.NAME)
+
+    return {"data": content}

api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py

@@ -19,19 +19,39 @@ logger = get_agent_logger(__name__)
 
 
 class ProblemNodeService(LLMServiceMixin):
-    """问题处理节点服务类"""
+    """
+    Problem processing node service class
+    
+    Handles problem decomposition and extension operations using LLM services.
+    Inherits from LLMServiceMixin to provide structured LLM calling capabilities.
+    
+    Attributes:
+        template_service: Service for rendering Jinja2 templates
+    """
 
     def __init__(self):
         super().__init__()
         self.template_service = TemplateService(template_root)
 
 
-# 创建全局服务实例
+# Create global service instance
 problem_service = ProblemNodeService()
 
 
 async def Split_The_Problem(state: ReadState) -> ReadState:
-    """问题分解节点"""
+    """
+    Problem decomposition node
+    
+    Breaks down complex user queries into smaller, more manageable sub-problems.
+    Uses LLM to analyze the input and generate structured problem decomposition
+    with question types and reasoning.
+    
+    Args:
+        state: ReadState containing user input and configuration
+        
+    Returns:
+        ReadState: Updated state with problem decomposition results
+    """
     # 从状态中获取数据
     content = state.get('data', '')
     end_user_id = state.get('end_user_id', '')
@@ -64,7 +84,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
         # 添加更详细的日志记录
         logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
 
-        # 验证结构化响应
+        # Validate structured response
         if not structured or not hasattr(structured, 'root'):
             logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
             split_result = json.dumps([], ensure_ascii=False)
@@ -106,7 +126,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
             exc_info=True
         )
 
-        # 提供更详细的错误信息
+        # Provide more detailed error information
         error_details = {
             "error_type": type(e).__name__,
             "error_message": str(e),
@@ -116,7 +136,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
 
         logger.error(f"Split_The_Problem error details: {error_details}")
 
-        # 创建默认的空结果
+        # Create default empty result
         result = {
             "context": json.dumps([], ensure_ascii=False),
             "original": content,
@@ -130,13 +150,25 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
             }
         }
 
-    # 返回更新后的状态，包含spit_context字段
+    # Return updated state including spit_context field
     return {"spit_data": result}
 
 
 async def Problem_Extension(state: ReadState) -> ReadState:
-    """问题扩展节点"""
-    # 获取原始数据和分解结果
+    """
+    Problem extension node
+    
+    Extends the decomposed problems from Split_The_Problem node by generating
+    additional related questions and organizing them by original question.
+    Uses LLM to create comprehensive question extensions for better memory retrieval.
+    
+    Args:
+        state: ReadState containing decomposed problems and configuration
+        
+    Returns:
+        ReadState: Updated state with extended problem results
+    """
+    # Get original data and decomposition results
     start = time.time()
     content = state.get('data', '')
     data = state.get('spit_data', '')['context']
@@ -182,7 +214,7 @@ async def Problem_Extension(state: ReadState) -> ReadState:
 
         logger.info(f"Problem_Extension: 开始处理问题扩展，问题数量: {len(databasets)}")
 
-        # 验证结构化响应
+        # Validate structured response
         if not response_content or not hasattr(response_content, 'root'):
             logger.warning("Problem_Extension: 结构化响应为空或格式不正确")
             aggregated_dict = {}
@@ -216,7 +248,7 @@ async def Problem_Extension(state: ReadState) -> ReadState:
             exc_info=True
         )
 
-        # 提供更详细的错误信息
+        # Provide more detailed error information
         error_details = {
             "error_type": type(e).__name__,
             "error_message": str(e),

api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py

@@ -29,6 +29,18 @@ logger = get_agent_logger(__name__)
 
 
 async def rag_config(state):
+    """
+    Configure RAG (Retrieval-Augmented Generation) settings
+    
+    Creates configuration for knowledge base retrieval including similarity thresholds,
+    weights, and reranker settings.
+    
+    Args:
+        state: Current state containing user_rag_memory_id
+        
+    Returns:
+        dict: RAG configuration dictionary
+    """
     user_rag_memory_id = state.get('user_rag_memory_id', '')
     kb_config = {
         "knowledge_bases": [
@@ -48,6 +60,19 @@ async def rag_config(state):
 
 
 async def rag_knowledge(state, question):
+    """
+    Retrieve knowledge using RAG approach
+    
+    Performs knowledge retrieval from configured knowledge bases using the
+    provided question and returns formatted results.
+    
+    Args:
+        state: Current state containing configuration
+        question: Question to search for
+        
+    Returns:
+        tuple: (retrieval_knowledge, clean_content, cleaned_query, raw_results)
+    """
     kb_config = await rag_config(state)
     end_user_id = state.get('end_user_id', '')
     user_rag_memory_id = state.get("user_rag_memory_id", '')
@@ -68,12 +93,24 @@ async def rag_knowledge(state, question):
 
 
 async def llm_infomation(state: ReadState) -> ReadState:
+    """
+    Get LLM configuration information from state
+    
+    Retrieves model configuration details including model ID and tenant ID
+    from the memory configuration in the current state.
+    
+    Args:
+        state: ReadState containing memory configuration
+        
+    Returns:
+        ReadState: Model configuration as Pydantic model
+    """
     memory_config = state.get('memory_config', None)
     model_id = memory_config.llm_model_id
     tenant_id = memory_config.tenant_id
 
-    # 使用现有的 memory_config 而不是重新查询数据库
-    # 或者使用线程安全的数据库访问
+    # Use existing memory_config instead of re-querying database
+    # or use thread-safe database access
     with get_db_context() as db:
         result_orm = ModelConfigService.get_model_by_id(db=db, model_id=model_id, tenant_id=tenant_id)
         result_pydantic = model_schema.ModelConfig.model_validate(result_orm)
@@ -82,16 +119,20 @@ async def llm_infomation(state: ReadState) -> ReadState:
 
 async def clean_databases(data) -> str:
     """
-    简化的数据库搜索结果清理函数
+    Simplified database search result cleaning function
+    
+    Processes and cleans search results from various sources including
+    reranked results and time-based search results. Extracts text content
+    from structured data and returns as formatted string.
     
     Args:
-        data: 搜索结果数据
+        data: Search result data (can be string, dict, or other types)
         
     Returns:
-        清理后的内容字符串
+        str: Cleaned content string
     """
     try:
-        # 解析JSON字符串
+        # Parse JSON string
         if isinstance(data, str):
             try:
                 data = json.loads(data)
@@ -101,24 +142,24 @@ async def clean_databases(data) -> str:
         if not isinstance(data, dict):
             return str(data)
 
-        # 获取结果数据
+        # Get result data
         # with open("搜索结果.json","w",encoding='utf-8') as f:
         #     f.write(json.dumps(data, indent=4, ensure_ascii=False))
         results = data.get('results', data)
         if not isinstance(results, dict):
             return str(results)
 
-        # 收集所有内容
+        # Collect all content
         content_list = []
 
-        # 处理重排序结果
+        # Process reranked results
         reranked = results.get('reranked_results', {})
         if reranked:
-            for category in ['summaries', 'statements', 'chunks', 'entities']:
+            for category in ['summaries', 'communities', 'statements', 'chunks', 'entities']:
                 items = reranked.get(category, [])
                 if isinstance(items, list):
                     content_list.extend(items)
-        # 处理时间搜索结果
+        # Process time search results
         time_search = results.get('time_search', {})
         if time_search:
             if isinstance(time_search, dict):
@@ -128,11 +169,18 @@ async def clean_databases(data) -> str:
             elif isinstance(time_search, list):
                 content_list.extend(time_search)
 
-        # 提取文本内容
+        # Extract text content，对 community 按 name 去重（多次 tool 调用会产生重复）
         text_parts = []
+        seen_community_names = set()
         for item in content_list:
             if isinstance(item, dict):
-                text = item.get('statement') or item.get('content', '')
+                # community 节点用 name 去重
+                if 'member_count' in item or 'core_entities' in item:
+                    community_name = item.get('name') or item.get('id', '')
+                    if community_name in seen_community_names:
+                        continue
+                    seen_community_names.add(community_name)
+                text = item.get('statement') or item.get('content') or item.get('summary', '')
                 if text:
                     text_parts.append(text)
             elif isinstance(item, str):
@@ -146,10 +194,19 @@ async def clean_databases(data) -> str:
 
 
 async def retrieve_nodes(state: ReadState) -> ReadState:
-    '''
-
-    模型信息
-    '''
+    """
+    Retrieve information using simplified search approach
+    
+    Processes extended problems from previous nodes and performs retrieval
+    using either RAG or hybrid search based on storage type. Handles concurrent
+    processing of multiple questions and deduplicates results.
+    
+    Args:
+        state: ReadState containing problem extensions and configuration
+        
+    Returns:
+        ReadState: Updated state with retrieval results and intermediate outputs
+    """
 
     problem_extension = state.get('problem_extension', '')['context']
     storage_type = state.get('storage_type', '')
@@ -163,7 +220,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
             problem_list.append(data)
     logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
 
-    # 创建异步任务处理单个问题
+    # Create async task to process individual questions
     async def process_question_nodes(idx, question):
         try:
             # Prepare search parameters based on storage type
@@ -209,7 +266,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
                 }
             }
 
-    # 并发处理所有问题
+    # Process all questions concurrently
     tasks = [process_question_nodes(idx, question) for idx, question in enumerate(problem_list)]
     databases_anser = await asyncio.gather(*tasks)
     databases_data = {
@@ -257,7 +314,20 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
 
 
 async def retrieve(state: ReadState) -> ReadState:
-    # 从state中获取end_user_id
+    """
+    Advanced retrieve function using LangChain agents and tools
+    
+    Uses LangChain agents with specialized retrieval tools (time-based and hybrid)
+    to perform sophisticated information retrieval. Supports both RAG and traditional
+    memory storage approaches with concurrent processing and result deduplication.
+    
+    Args:
+        state: ReadState containing problem extensions and configuration
+        
+    Returns:
+        ReadState: Updated state with retrieval results and intermediate outputs
+    """
+    # Get end_user_id from state
     import time
     start = time.time()
     problem_extension = state.get('problem_extension', '')['context']
@@ -291,7 +361,11 @@ async def retrieve(state: ReadState) -> ReadState:
     )
 
     time_retrieval_tool = create_time_retrieval_tool(end_user_id)
-    search_params = {"end_user_id": end_user_id, "return_raw_results": True}
+    search_params = {
+        "end_user_id": end_user_id,
+        "return_raw_results": True,
+        "include": ["summaries", "statements", "chunks", "entities", "communities"],
+    }
     hybrid_retrieval = create_hybrid_retrieval_tool_sync(memory_config, **search_params)
     agent = create_agent(
         llm,
@@ -299,21 +373,21 @@ async def retrieve(state: ReadState) -> ReadState:
         system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}"
     )
 
-    # 创建异步任务处理单个问题
+    # Create async task to process individual questions
     import asyncio
 
-    # 在模块级别定义信号量，限制最大并发数
-    SEMAPHORE = asyncio.Semaphore(5)  # 限制最多5个并发数据库操作
+    # Define semaphore at module level to limit maximum concurrency
+    SEMAPHORE = asyncio.Semaphore(5)  # Limit to maximum 5 concurrent database operations
 
     async def process_question(idx, question):
-        async with SEMAPHORE:  # 限制并发
+        async with SEMAPHORE:  # Limit concurrency
             try:
                 if storage_type == "rag" and user_rag_memory_id:
                     retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state,
                                                                                                          question)
                 else:
                     cleaned_query = question
-                    # 使用 asyncio 在线程池中运行同步的 agent.invoke
+                    # Use asyncio to run synchronous agent.invoke in thread pool
                     import asyncio
                     response = await asyncio.get_event_loop().run_in_executor(
                         None,
@@ -327,8 +401,32 @@ async def retrieve(state: ReadState) -> ReadState:
                         raw_results = tool_results['content']
                         clean_content = await clean_databases(raw_results)
 
+                        # 社区展开：从 tool 返回结果中提取命中的 community，
+                        # 沿 BELONGS_TO_COMMUNITY 关系拉取关联 Statement 追加到 clean_content
+                        _expanded_stmts_to_write = []
+                        try:
+                            results_dict = raw_results.get('results', {}) if isinstance(raw_results, dict) else {}
+                            reranked = results_dict.get('reranked_results', {})
+                            community_hits = reranked.get('communities', [])
+                            if not community_hits:
+                                community_hits = results_dict.get('communities', [])
+                            if community_hits:
+                                from app.core.memory.agent.services.search_service import expand_communities_to_statements
+                                _expanded_stmts_to_write, new_texts = await expand_communities_to_statements(
+                                    community_results=community_hits,
+                                    end_user_id=end_user_id,
+                                    existing_content=clean_content,
+                                )
+                                if new_texts:
+                                    clean_content = clean_content + '\n' + '\n'.join(new_texts)
+                        except Exception as parse_err:
+                            logger.warning(f"[Retrieve] 解析社区命中结果失败，跳过展开: {parse_err}")
+
                         try:
                             raw_results = raw_results['results']
+                            # 写回展开结果，接口返回中可见（已在 helper 中清洗过字段）
+                            if _expanded_stmts_to_write and isinstance(raw_results, dict):
+                                raw_results.setdefault('reranked_results', {})['expanded_statements'] = _expanded_stmts_to_write
                         except Exception:
                             raw_results = []
 
@@ -362,7 +460,7 @@ async def retrieve(state: ReadState) -> ReadState:
                     }
                 }
 
-    # 并发处理所有问题
+    # Process all questions concurrently
     import asyncio
     tasks = [process_question(idx, question) for idx, question in enumerate(problem_list)]
     databases_anser = await asyncio.gather(*tasks)

api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py

@@ -23,18 +23,39 @@ logger = get_agent_logger(__name__)
 
 
 class SummaryNodeService(LLMServiceMixin):
-    """总结节点服务类"""
+    """
+    Summary node service class
+    
+    Handles summary generation operations using LLM services. Inherits from 
+    LLMServiceMixin to provide structured LLM calling capabilities for 
+    generating summaries from retrieved information.
+    
+    Attributes:
+        template_service: Service for rendering Jinja2 templates
+    """
 
     def __init__(self):
         super().__init__()
         self.template_service = TemplateService(template_root)
 
 
-# 创建全局服务实例
+# Create global service instance
 summary_service = SummaryNodeService()
 
 
 async def rag_config(state):
+    """
+    Configure RAG (Retrieval-Augmented Generation) settings for summary operations
+    
+    Creates configuration for knowledge base retrieval including similarity thresholds,
+    weights, and reranker settings specifically for summary generation.
+    
+    Args:
+        state: Current state containing user_rag_memory_id
+        
+    Returns:
+        dict: RAG configuration dictionary with knowledge base settings
+    """
     user_rag_memory_id = state.get('user_rag_memory_id', '')
     kb_config = {
         "knowledge_bases": [
@@ -54,6 +75,23 @@ async def rag_config(state):
 
 
 async def rag_knowledge(state, question):
+    """
+    Retrieve knowledge using RAG approach for summary generation
+    
+    Performs knowledge retrieval from configured knowledge bases using the
+    provided question and returns formatted results for summary processing.
+    
+    Args:
+        state: Current state containing configuration
+        question: Question to search for in knowledge base
+        
+    Returns:
+        tuple: (retrieval_knowledge, clean_content, cleaned_query, raw_results)
+            - retrieval_knowledge: List of retrieved knowledge chunks
+            - clean_content: Formatted content string
+            - cleaned_query: Processed query string
+            - raw_results: Raw retrieval results
+    """
     kb_config = await rag_config(state)
     end_user_id = state.get('end_user_id', '')
     user_rag_memory_id = state.get("user_rag_memory_id", '')
@@ -74,6 +112,18 @@ async def rag_knowledge(state, question):
 
 
 async def summary_history(state: ReadState) -> ReadState:
+    """
+    Retrieve conversation history for summary context
+    
+    Gets the conversation history for the current user to provide context
+    for summary generation operations.
+    
+    Args:
+        state: ReadState containing end_user_id
+        
+    Returns:
+        ReadState: Conversation history data
+    """
     end_user_id = state.get("end_user_id", '')
     history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
     return history
@@ -82,11 +132,26 @@ async def summary_history(state: ReadState) -> ReadState:
 async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,
                       search_mode) -> str:
     """
-    增强的summary_llm函数，包含更好的错误处理和数据验证
+    Enhanced summary_llm function with better error handling and data validation
+    
+    Generates summaries using LLM with structured output. Includes fallback mechanisms
+    for handling LLM failures and provides robust error recovery.
+    
+    Args:
+        state: ReadState containing current context
+        history: Conversation history for context
+        retrieve_info: Retrieved information to summarize
+        template_name: Jinja2 template name for prompt generation
+        operation_name: Type of operation (summary, input_summary, retrieve_summary)
+        response_model: Pydantic model for structured output
+        search_mode: Search mode flag ("0" for simple, "1" for complex)
+        
+    Returns:
+        str: Generated summary text or fallback message
     """
     data = state.get("data", '')
 
-    # 构建系统提示词
+    # Build system prompt
     if str(search_mode) == "0":
         system_prompt = await summary_service.template_service.render_template(
             template_name=template_name,
@@ -103,7 +168,7 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
             retrieve_info=retrieve_info
         )
     try:
-        # 使用优化的LLM服务进行结构化输出
+        # Use optimized LLM service for structured output
         with get_db_context() as db_session:
             structured = await summary_service.call_llm_structured(
                 state=state,
@@ -112,23 +177,23 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
                 response_model=response_model,
                 fallback_value=None
             )
-        # 验证结构化响应
+        # Validate structured response
         if structured is None:
             logger.warning("LLM返回None，使用默认回答")
             return "信息不足，无法回答"
 
-        # 根据操作类型提取答案
+        # Extract answer based on operation type
         if operation_name == "summary":
             aimessages = getattr(structured, 'query_answer', None) or "信息不足，无法回答"
         else:
-            # 处理RetrieveSummaryResponse
+            # Handle RetrieveSummaryResponse
             if hasattr(structured, 'data') and structured.data:
                 aimessages = getattr(structured.data, 'query_answer', None) or "信息不足，无法回答"
             else:
                 logger.warning("结构化响应缺少data字段")
                 aimessages = "信息不足，无法回答"
 
-        # 验证答案不为空
+        # Validate answer is not empty
         if not aimessages or aimessages.strip() == "":
             aimessages = "信息不足，无法回答"
 
@@ -137,7 +202,7 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
     except Exception as e:
         logger.error(f"结构化输出失败: {e}", exc_info=True)
 
-        # 尝试非结构化输出作为fallback
+        # Try unstructured output as fallback
         try:
             logger.info("尝试非结构化输出作为fallback")
             response = await summary_service.call_llm_simple(
@@ -148,9 +213,9 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
             )
 
             if response and response.strip():
-                # 简单清理响应
+                # Simple response cleaning
                 cleaned_response = response.strip()
-                # 移除可能的JSON标记
+                # Remove possible JSON markers
                 if cleaned_response.startswith('```'):
                     lines = cleaned_response.split('\n')
                     cleaned_response = '\n'.join(lines[1:-1])
@@ -165,6 +230,19 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
 
 
 async def summary_redis_save(state: ReadState, aimessages) -> ReadState:
+    """
+    Save summary results to Redis session storage
+    
+    Stores the generated summary and user query in Redis for session management
+    and conversation history tracking.
+    
+    Args:
+        state: ReadState containing user and query information
+        aimessages: Generated summary message to save
+        
+    Returns:
+        ReadState: Updated state after saving to Redis
+    """
     data = state.get("data", '')
     end_user_id = state.get("end_user_id", '')
     await SessionService(store).save_session(
@@ -179,6 +257,20 @@ async def summary_redis_save(state: ReadState, aimessages) -> ReadState:
 
 
 async def summary_prompt(state: ReadState, aimessages, raw_results) -> ReadState:
+    """
+    Format summary results for different output types
+    
+    Creates structured output formats for both input summary and retrieval summary
+    operations, including metadata and intermediate results for frontend display.
+    
+    Args:
+        state: ReadState containing storage and user information
+        aimessages: Generated summary message
+        raw_results: Raw search/retrieval results
+        
+    Returns:
+        tuple: (input_summary, retrieve_summary) formatted result dictionaries
+    """
     storage_type = state.get("storage_type", '')
     user_rag_memory_id = state.get("user_rag_memory_id", '')
     data = state.get("data", '')
@@ -217,6 +309,19 @@ async def summary_prompt(state: ReadState, aimessages, raw_results) -> ReadState
 
 
 async def Input_Summary(state: ReadState) -> ReadState:
+    """
+    Generate quick input summary from retrieved information
+    
+    Performs fast retrieval and generates a quick summary response for user queries.
+    This function prioritizes speed by only searching summary nodes and provides
+    immediate feedback to users.
+    
+    Args:
+        state: ReadState containing user query, storage configuration, and context
+        
+    Returns:
+        ReadState: Dictionary containing summary results with status and metadata
+    """
     start = time.time()
     storage_type = state.get("storage_type", '')
     memory_config = state.get('memory_config', None)
@@ -229,13 +334,22 @@ async def Input_Summary(state: ReadState) -> ReadState:
         "end_user_id": end_user_id,
         "question": data,
         "return_raw_results": True,
-        "include": ["summaries"]  # Only search summary nodes for faster performance
+        "include": ["summaries", "communities"]  # MemorySummary 和 Community 同为高维度概括节点
     }
 
     try:
         if storage_type != "rag":
-            retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params,
-                                                                                               memory_config=memory_config)
+            retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(
+                **search_params,
+                memory_config=memory_config,
+                expand_communities=False,  # 路径 "2" 只需要 community 的 summary 文本，不展开到 Statement
+            )
+            # 调试：打印 community 检索结果数量
+            if raw_results and isinstance(raw_results, dict):
+                reranked = raw_results.get('reranked_results', {})
+                community_hits = reranked.get('communities', [])
+                logger.debug(f"[Input_Summary] community 命中数: {len(community_hits)}, "
+                             f"summary 命中数: {len(reranked.get('summaries', []))}")
         else:
             retrieval_knowledge, retrieve_info, question, raw_results = await rag_knowledge(state, data)
     except Exception as e:
@@ -266,6 +380,19 @@ async def Input_Summary(state: ReadState) -> ReadState:
 
 
 async def Retrieve_Summary(state: ReadState) -> ReadState:
+    """
+    Generate comprehensive summary from retrieved expansion issues
+    
+    Processes retrieved expansion issues and generates a detailed summary using LLM.
+    This function handles complex retrieval results and provides comprehensive answers
+    based on expanded query results.
+    
+    Args:
+        state: ReadState containing retrieve data with expansion issues
+        
+    Returns:
+        ReadState: Dictionary containing comprehensive summary results
+    """
     retrieve = state.get("retrieve", '')
     history = await summary_history(state)
     import json
@@ -299,13 +426,26 @@ async def Retrieve_Summary(state: ReadState) -> ReadState:
         duration = 0.0
     log_time('Retrieval summary', duration)
 
-    # 修复协程调用 - 先await，然后访问返回值
+    # Fixed coroutine call - await first, then access return value
     summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
     summary = summary_result[1]
     return {"summary": summary}
 
 
 async def Summary(state: ReadState) -> ReadState:
+    """
+    Generate final comprehensive summary from verified data
+    
+    Creates the final summary using verified expansion issues and conversation history.
+    This function processes verified data to generate the most comprehensive and
+    accurate response to user queries.
+    
+    Args:
+        state: ReadState containing verified data and query information
+        
+    Returns:
+        ReadState: Dictionary containing final summary results
+    """
     start = time.time()
     query = state.get("data", '')
     verify = state.get("verify", '')
@@ -336,13 +476,26 @@ async def Summary(state: ReadState) -> ReadState:
         duration = 0.0
     log_time('Retrieval summary', duration)
 
-    # 修复协程调用 - 先await，然后访问返回值
+    # Fixed coroutine call - await first, then access return value
     summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
     summary = summary_result[1]
     return {"summary": summary}
 
 
 async def Summary_fails(state: ReadState) -> ReadState:
+    """
+    Generate fallback summary when normal summary process fails
+    
+    Provides a fallback summary generation mechanism when the standard summary
+    process encounters errors or fails to produce satisfactory results. Uses
+    a specialized failure template to handle edge cases.
+    
+    Args:
+        state: ReadState containing verified data and failure context
+        
+    Returns:
+        ReadState: Dictionary containing fallback summary results
+    """
     storage_type = state.get("storage_type", '')
     user_rag_memory_id = state.get("user_rag_memory_id", '')
     history = await summary_history(state)

api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py

@@ -18,24 +18,46 @@ logger = get_agent_logger(__name__)
 
 
 class VerificationNodeService(LLMServiceMixin):
-    """验证节点服务类"""
+    """
+    Verification node service class
+    
+    Handles data verification operations using LLM services. Inherits from 
+    LLMServiceMixin to provide structured LLM calling capabilities for 
+    verifying and validating retrieved information.
+    
+    Attributes:
+        template_service: Service for rendering Jinja2 templates
+    """
 
     def __init__(self):
         super().__init__()
         self.template_service = TemplateService(template_root)
 
 
-# 创建全局服务实例
+# Create global service instance
 verification_service = VerificationNodeService()
 
 
 async def Verify_prompt(state: ReadState, messages_deal: VerificationResult):
-    """处理验证结果并生成输出格式"""
+    """
+    Process verification results and generate output format
+    
+    Transforms VerificationResult objects into structured output format suitable
+    for frontend consumption. Handles conversion of VerificationItem objects to
+    dictionary format and adds metadata for tracking.
+    
+    Args:
+        state: ReadState containing storage and user configuration
+        messages_deal: VerificationResult containing verification outcomes
+        
+    Returns:
+        dict: Formatted verification result with status and metadata
+    """
     storage_type = state.get('storage_type', '')
     user_rag_memory_id = state.get('user_rag_memory_id', '')
     data = state.get('data', '')
 
-    # 将 VerificationItem 对象转换为字典列表
+    # Convert VerificationItem objects to dictionary list
     verified_data = []
     if messages_deal.expansion_issue:
         for item in messages_deal.expansion_issue:
@@ -89,7 +111,7 @@ async def Verify(state: ReadState):
 
         logger.info("Verify: 开始渲染模板")
 
-        # 生成 JSON schema 以指导 LLM 输出正确格式
+        # Generate JSON schema to guide LLM output format
         json_schema = VerificationResult.model_json_schema()
 
         system_prompt = await verification_service.template_service.render_template(
@@ -104,8 +126,8 @@ async def Verify(state: ReadState):
         # 使用优化的LLM服务，添加超时保护
         logger.info("Verify: 开始调用 LLM")
         try:
-            # 添加 asyncio.wait_for 超时包裹，防止无限等待
-            # 超时时间设置为 150 秒（比 LLM 配置的 120 秒稍长）
+            # Add asyncio.wait_for timeout wrapper to prevent infinite waiting
+            # Timeout set to 150 seconds (slightly longer than LLM config's 120 seconds)
 
             with get_db_context() as db_session:
                 structured = await asyncio.wait_for(
@@ -122,7 +144,7 @@ async def Verify(state: ReadState):
                             "reason": "验证失败或超时"
                         }
                     ),
-                    timeout=150.0  # 150秒超时
+                    timeout=150.0  # 150 second timeout
                 )
             logger.info(f"Verify: LLM 调用完成，result={structured}")
         except asyncio.TimeoutError:

api/app/core/memory/agent/langgraph_graph/read_graph.py

@@ -33,7 +33,19 @@ from app.core.memory.agent.langgraph_graph.routing.routers import (
 
 @asynccontextmanager
 async def make_read_graph():
-    """创建并返回 LangGraph 工作流"""
+    """
+    Create and return a LangGraph workflow for memory reading operations
+    
+    Builds a state graph workflow that handles memory retrieval, problem analysis,
+    verification, and summarization. The workflow includes nodes for content input,
+    problem splitting, retrieval, verification, and various summary operations.
+    
+    Yields:
+        StateGraph: Compiled LangGraph workflow for memory reading
+        
+    Raises:
+        Exception: If workflow creation fails
+    """
     try:
         # Build workflow graph
         workflow = StateGraph(ReadState)
@@ -48,7 +60,7 @@ async def make_read_graph():
         workflow.add_node("Summary", Summary)
         workflow.add_node("Summary_fails", Summary_fails)
 
-        # 添加边
+        # Add edges to define workflow flow
         workflow.add_edge(START, "content_input")
         workflow.add_conditional_edges("content_input", Split_continue)
         workflow.add_edge("Input_Summary", END)
@@ -63,7 +75,7 @@ async def make_read_graph():
         '''-----'''
         # workflow.add_edge("Retrieve", END)
 
-        # 编译工作流
+        # Compile workflow
         graph = workflow.compile()
         yield graph
 
@@ -72,108 +84,3 @@ async def make_read_graph():
         raise
     finally:
         print("工作流创建完成")
-
-
-async def main():
-    """主函数 - 运行工作流"""
-    message = "昨天有什么好看的电影"
-    end_user_id = '88a459f5_text09'  # 组ID
-    storage_type = 'neo4j'  # 存储类型
-    search_switch = '1'  # 搜索开关
-    user_rag_memory_id = 'wwwwwwww'  # 用户RAG记忆ID
-
-    # 获取数据库会话
-    db_session = next(get_db())
-    config_service = MemoryConfigService(db_session)
-    memory_config = config_service.load_memory_config(
-        config_id=17,  # 改为整数
-        service_name="MemoryAgentService"
-    )
-    import time
-    start = time.time()
-    try:
-        async with make_read_graph() as graph:
-            config = {"configurable": {"thread_id": end_user_id}}
-            # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch,
-                             "end_user_id": end_user_id
-                , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id,
-                             "memory_config": memory_config}
-            # 获取节点更新信息
-            _intermediate_outputs = []
-            summary = ''
-
-            async for update_event in graph.astream(
-                    initial_state,
-                    stream_mode="updates",
-                    config=config
-            ):
-                for node_name, node_data in update_event.items():
-                    print(f"处理节点: {node_name}")
-
-                    # 处理不同Summary节点的返回结构
-                    if 'Summary' in node_name:
-                        if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']:
-                            summary = node_data['InputSummary']['summary_result']
-                        elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']:
-                            summary = node_data['RetrieveSummary']['summary_result']
-                        elif 'summary' in node_data and 'summary_result' in node_data['summary']:
-                            summary = node_data['summary']['summary_result']
-                        elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']:
-                            summary = node_data['SummaryFails']['summary_result']
-
-                    spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
-                    if spit_data and spit_data != [] and spit_data != {}:
-                        _intermediate_outputs.append(spit_data)
-
-                    # Problem_Extension 节点
-                    problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None)
-                    if problem_extension and problem_extension != [] and problem_extension != {}:
-                        _intermediate_outputs.append(problem_extension)
-
-                    # Retrieve 节点
-                    retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None)
-                    if retrieve_node and retrieve_node != [] and retrieve_node != {}:
-                        _intermediate_outputs.extend(retrieve_node)
-
-                    # Verify 节点
-                    verify_n = node_data.get('verify', {}).get('_intermediate', None)
-                    if verify_n and verify_n != [] and verify_n != {}:
-                        _intermediate_outputs.append(verify_n)
-
-                    # Summary 节点
-                    summary_n = node_data.get('summary', {}).get('_intermediate', None)
-                    if summary_n and summary_n != [] and summary_n != {}:
-                        _intermediate_outputs.append(summary_n)
-
-            # # 过滤掉空值
-            # _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
-            #
-            # # 优化搜索结果
-            # print("=== 开始优化搜索结果 ===")
-            # optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
-            # result=reorder_output_results(optimized_outputs)
-            # # 保存优化后的结果到文件
-            # with open('_intermediate_outputs_optimized.json', 'w', encoding='utf-8') as f:
-            #     import json
-            #     f.write(json.dumps(result, indent=4, ensure_ascii=False))
-            #
-            print(f"=== 最终摘要 ===")
-            print(summary)
-
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-    finally:
-        db_session.close()
-
-    end = time.time()
-    print(100 * 'y')
-    print(f"总耗时: {end - start}s")
-    print(100 * 'y')
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    asyncio.run(main())

api/app/core/memory/agent/langgraph_graph/routing/routers.py

@@ -1,13 +1,13 @@
-
 from typing import Literal
 
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.llm_tools import ReadState, COUNTState
 
-
 logger = get_agent_logger(__name__)
 counter = COUNTState(limit=3)
-def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
+
+
+def Split_continue(state: ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
     """
     Determine routing based on search_switch value.
 
@@ -25,6 +25,7 @@ def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summa
             return 'Input_Summary'
     return 'Split_The_Problem'  # 默认情况
 
+
 def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
     """
     Determine routing based on search_switch value.
@@ -43,8 +44,10 @@ def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
         elif search_switch == '1':
             return 'Retrieve_Summary'
     return 'Retrieve_Summary'  # Default based on business logic
+
+
 def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
-    status=state.get('verify', '')['status']
+    status = state.get('verify', '')['status']
     # loop_count = counter.get_total()
     if "success" in status:
         # counter.reset()
@@ -53,7 +56,7 @@ def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "co
         # if loop_count < 2:  # Maximum loop count is 3
         #     return "content_input"
         # else:
-            # counter.reset()
+        # counter.reset()
         return "Summary_fails"
     else:
         # Add default return value to avoid returning None

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -2,77 +2,104 @@ import json
 import os
 
 from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.langgraph_graph.tools.write_tool import  format_parsing,  messages_parse
-from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph, long_term_storage
-
+from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, messages_parse
 from app.core.memory.agent.models.write_aggregate_model import WriteAggregateModel
 from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
-from app.core.memory.agent.utils.redis_tool import write_store
 from app.core.memory.agent.utils.redis_tool import count_store
+from app.core.memory.agent.utils.redis_tool import write_store
 from app.core.memory.agent.utils.template_tools import TemplateService
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.db import get_db_context, get_db
+from app.db import get_db_context
 from app.repositories.memory_short_repository import LongTermMemoryRepository
 from app.schemas.memory_agent_schema import AgentMemory_Long_Term
 from app.services.memory_konwledges_server import write_rag
 from app.services.task_service import get_task_memory_write_result
 from app.tasks import write_message_task
 from app.utils.config_utils import resolve_config_id
+
 logger = get_agent_logger(__name__)
 template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
 
+
 async def write_rag_agent(end_user_id, user_message, ai_message, user_rag_memory_id):
-    # RAG 模式：组合消息为字符串格式（保持原有逻辑）
+    """
+    Write messages to RAG storage system
+
+    Combines user and AI messages into a single string format and stores them
+    in the RAG (Retrieval-Augmented Generation) knowledge base for future retrieval.
+
+    Args:
+        end_user_id: User identifier for the conversation
+        user_message: User's input message content
+        ai_message: AI's response message content
+        user_rag_memory_id: RAG memory identifier for storage location
+    """
+    # RAG mode: combine messages into string format (maintain original logic)
     combined_message = f"user: {user_message}\nassistant: {ai_message}"
     await write_rag(end_user_id, combined_message, user_rag_memory_id)
     logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
-async def write(storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id,
-                actual_config_id, long_term_messages=[]):
+
+
+async def write(
+        storage_type,
+        end_user_id,
+        user_message,
+        ai_message,
+        user_rag_memory_id,
+        actual_end_user_id,
+        actual_config_id,
+        long_term_messages=None
+):
     """
-    写入记忆（支持结构化消息）
+    Write memory with structured message support
+
+    Handles memory writing operations for different storage types (Neo4j/RAG).
+    Supports both individual message pairs and batch long-term message processing.
 
     Args:
-        storage_type: 存储类型 (neo4j/rag)
-        end_user_id: 终端用户ID
-        user_message: 用户消息内容
-        ai_message: AI 回复内容
-        user_rag_memory_id: RAG 记忆ID
-        actual_end_user_id: 实际用户ID
-        actual_config_id: 配置ID
+        storage_type: Storage type identifier ("neo4j" or "rag")
+        end_user_id: Terminal user identifier
+        user_message: User message content
+        ai_message: AI response content
+        user_rag_memory_id: RAG memory identifier
+        actual_end_user_id: Actual user identifier for storage
+        actual_config_id: Configuration identifier
+        long_term_messages: Optional list of structured messages for batch processing
 
-    逻辑说明：
-    - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变
-    - Neo4j 模式：使用结构化消息列表
-      1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant]
-      2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景）
-      3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段
+    Logic explanation:
+    - RAG mode: Combines user_message and ai_message into string format, maintains original logic
+    - Neo4j mode: Uses structured message lists
+      1. If both user_message and ai_message are not empty: Creates paired messages [user, assistant]
+      2. If only user_message exists: Creates single user message [user] (for historical memory scenarios)
+      3. Each message is converted to independent Chunk, preserving speaker field
     """
 
-    db = next(get_db())
-    try:
+    if long_term_messages is None:
+        long_term_messages = []
+    with get_db_context() as db:
         actual_config_id = resolve_config_id(actual_config_id, db)
-        # Neo4j 模式：使用结构化消息列表
+        # Neo4j mode: Use structured message lists
         structured_messages = []
 
-        # 始终添加用户消息（如果不为空）
+        # Always add user message (if not empty)
         if isinstance(user_message, str) and user_message.strip() != "":
             structured_messages.append({"role": "user", "content": user_message})
 
-        # 只有当 AI 回复不为空时才添加 assistant 消息
+        # Only add assistant message when AI reply is not empty
         if isinstance(ai_message, str) and ai_message.strip() != "":
             structured_messages.append({"role": "assistant", "content": ai_message})
 
-        # 如果提供了 long_term_messages，使用它替代 structured_messages
+        # If long_term_messages provided, use it to replace structured_messages
         if long_term_messages and isinstance(long_term_messages, list):
             structured_messages = long_term_messages
         elif long_term_messages and isinstance(long_term_messages, str):
-            # 如果是 JSON 字符串，先解析
+            # If it's a JSON string, parse it first
             try:
                 structured_messages = json.loads(long_term_messages)
             except json.JSONDecodeError:
                 logger.error(f"Failed to parse long_term_messages as JSON: {long_term_messages}")
 
-        # 如果没有消息，直接返回
+        # If no messages, return directly
         if not structured_messages:
             logger.warning(f"No messages to write for user {actual_end_user_id}")
             return
@@ -80,29 +107,41 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me
         logger.info(
             f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
         write_id = write_message_task.delay(
-            actual_end_user_id,  # end_user_id: 用户ID
-            structured_messages,  # message: JSON 字符串格式的消息列表
-            str(actual_config_id),  # config_id: 配置ID字符串
+            actual_end_user_id,  # end_user_id: User ID
+            structured_messages,  # message: JSON string format message list
+            str(actual_config_id),  # config_id: Configuration ID string
             storage_type,  # storage_type: "neo4j"
-            user_rag_memory_id or ""  # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
+            user_rag_memory_id or ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
         )
         logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
         write_status = get_task_memory_write_result(str(write_id))
         logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
-    finally:
-        db.close()
 
-async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type,scope):
+
+async def term_memory_save(long_term_messages, actual_config_id, end_user_id, type, scope):
+    """
+    Save long-term memory data to database
+
+    Handles the storage of long-term memory data based on different strategies
+    (chunk-based or aggregate-based) and manages the transition from short-term
+    to long-term memory storage.
+
+    Args:
+        long_term_messages: Long-term message data to be saved
+        actual_config_id: Configuration identifier for memory settings
+        end_user_id: User identifier for memory association
+        type: Memory storage strategy type (STRATEGY_CHUNK or STRATEGY_AGGREGATE)
+        scope: Scope/window size for memory processing
+    """
     with get_db_context() as db_session:
         repo = LongTermMemoryRepository(db_session)
 
-
         from app.core.memory.agent.utils.redis_tool import write_store
         result = write_store.get_session_by_userid(end_user_id)
-        if type==AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE:
+        if type == AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE:
             data = await format_parsing(result, "dict")
             chunk_data = data[:scope]
-            if len(chunk_data)==scope:
+            if len(chunk_data) == scope:
                 repo.upsert(end_user_id, chunk_data)
                 logger.info(f'---------写入短长期-----------')
         else:
@@ -112,18 +151,23 @@ async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type,
             logger.info(f'写入短长期：')
 
 
+"""Window-based dialogue processing"""
 
-'''根据窗口'''
-async def window_dialogue(end_user_id,langchain_messages,memory_config,scope):
-    '''
-    根据窗口获取redis数据,写入neo4j：
-     Args:
-        end_user_id: 终端用户ID
-        memory_config: 内存配置对象
-        langchain_messages：原始数据LIST
-        scope：窗口大小
-    '''
-    scope=scope
+
+async def window_dialogue(end_user_id, langchain_messages, memory_config, scope):
+    """
+    Process dialogue based on window size and write to Neo4j
+
+    Manages conversation data based on a sliding window approach. When the window
+    reaches the specified scope size, it triggers long-term memory storage to Neo4j.
+
+    Args:
+        end_user_id: Terminal user identifier
+        memory_config: Memory configuration object containing settings
+        langchain_messages: Original message data list
+        scope: Window size determining when to trigger long-term storage
+    """
+    scope = scope
     is_end_user_id = count_store.get_sessions_count(end_user_id)
     if is_end_user_id is not False:
         is_end_user_id = count_store.get_sessions_count(end_user_id)[0]
@@ -135,50 +179,72 @@ async def window_dialogue(end_user_id,langchain_messages,memory_config,scope):
     elif int(is_end_user_id) == int(scope):
         logger.info('写入长期记忆NEO4J')
         formatted_messages = (redis_messages)
-        # 获取 config_id（如果 memory_config 是对象，提取 config_id；否则直接使用）
+        # Get config_id (if memory_config is an object, extract config_id; otherwise use directly)
         if hasattr(memory_config, 'config_id'):
             config_id = memory_config.config_id
         else:
             config_id = memory_config
-        
-        await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id,
-                config_id, formatted_messages)
+
+        await write(
+            AgentMemory_Long_Term.STORAGE_NEO4J,
+            end_user_id,
+            "",
+            "",
+            None,
+            end_user_id,
+            config_id,
+            formatted_messages
+        )
         count_store.update_sessions_count(end_user_id, 1, langchain_messages)
     else:
         count_store.save_sessions_count(end_user_id, 1, langchain_messages)
 
 
-"""根据时间"""
-async def memory_long_term_storage(end_user_id,memory_config,time):
-    '''
-    根据时间获取redis数据,写入neo4j：
-     Args:
-        end_user_id: 终端用户ID
-        memory_config: 内存配置对象
-    '''
+"""Time-based memory processing"""
+
+
+async def memory_long_term_storage(end_user_id, memory_config, time):
+    """
+    Process memory storage based on time intervals and write to Neo4j
+
+    Retrieves Redis data based on time intervals and writes it to Neo4j for
+    long-term storage. This function handles time-based memory consolidation.
+
+    Args:
+        end_user_id: Terminal user identifier
+        memory_config: Memory configuration object containing settings
+        time: Time interval for data retrieval
+    """
     long_time_data = write_store.find_user_recent_sessions(end_user_id, time)
-    format_messages = (long_time_data)
-    messages=[]
-    memory_config=memory_config.config_id
+    format_messages = long_time_data
+    messages = []
+    memory_config = memory_config.config_id
     for i in format_messages:
-        message=json.loads(i['Query'])
-        messages+= message
-    if format_messages!=[]:
+        message = json.loads(i['Query'])
+        messages += message
+    if format_messages:
         await write(AgentMemory_Long_Term.STORAGE_NEO4J, end_user_id, "", "", None, end_user_id,
                     memory_config, messages)
-'''聚合判断'''
+
+
 async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config) -> dict:
     """
-    聚合判断函数：判断输入句子和历史消息是否描述同一事件
+    Aggregation judgment function: determine if input sentence and historical messages describe the same event
+
+    Uses LLM-based analysis to determine whether new messages should be aggregated with existing
+    historical data or stored as separate events. This helps optimize memory storage and retrieval.
     
     Args:
-        end_user_id: 终端用户ID
-        ori_messages: 原始消息列表，格式如 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
-        memory_config: 内存配置对象
-    """
+        end_user_id: Terminal user identifier
+        ori_messages: Original message list, format like [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
+        memory_config: Memory configuration object containing LLM settings
 
+    Returns:
+        dict: Aggregation judgment result containing is_same_event flag and processed output
+    """
+    history = None
     try:
-        # 1. 获取历史会话数据（使用新方法）
+        # 1. Get historical session data (using new method)
         result = write_store.get_all_sessions_by_end_user_id(end_user_id)
         history = await format_parsing(result)
         if not result:
@@ -210,7 +276,7 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config
         output_value = structured.output
         if isinstance(output_value, list):
             output_value = [
-                {"role": msg.role, "content": msg.content} 
+                {"role": msg.role, "content": msg.content}
                 for msg in output_value
             ]
 
@@ -223,16 +289,16 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config
             await write("neo4j", end_user_id, "", "", None, end_user_id,
                         memory_config.config_id, output_value)
         return result_dict
-        
+
     except Exception as e:
         print(f"[aggregate_judgment] 发生错误: {e}")
         import traceback
         traceback.print_exc()
-        
+
         return {
             "is_same_event": False,
             "output": ori_messages,
             "messages": ori_messages,
             "history": history if 'history' in locals() else [],
             "error": str(e)
-        }
+        }

api/app/core/memory/agent/langgraph_graph/tools/tool.py

@@ -2,41 +2,53 @@ import asyncio
 import json
 from datetime import datetime, timedelta
 
-
 from langchain.tools import tool
 from pydantic import BaseModel, Field
 
-
 from app.core.memory.src.search import (
     search_by_temporal,
     search_by_keyword_temporal,
 )
 
+
 def extract_tool_message_content(response):
-    """从agent响应中提取ToolMessage内容和工具名称"""
+    """
+    Extract ToolMessage content and tool names from agent response
+
+    Parses agent response messages to extract tool execution results and metadata.
+    Handles JSON parsing and provides structured access to tool output data.
+
+    Args:
+        response: Agent response dictionary containing messages
+
+    Returns:
+        dict: Dictionary containing tool_name and parsed content, or None if no tool message found
+            - tool_name: Name of the executed tool
+            - content: Parsed tool execution result (JSON or raw text)
+    """
     messages = response.get('messages', [])
 
     for message in messages:
         if hasattr(message, 'tool_call_id') and hasattr(message, 'content'):
-            # 这是一个ToolMessage
+            # This is a ToolMessage
             tool_content = message.content
             tool_name = None
 
-            # 尝试获取工具名称
+            # Try to get tool name
             if hasattr(message, 'name'):
                 tool_name = message.name
             elif hasattr(message, 'tool_name'):
                 tool_name = message.tool_name
 
             try:
-                # 解析JSON内容
+                # Parse JSON content
                 parsed_content = json.loads(tool_content)
                 return {
                     'tool_name': tool_name,
                     'content': parsed_content
                 }
             except json.JSONDecodeError:
-                # 如果不是JSON格式，直接返回内容
+                # If not JSON format, return content directly
                 return {
                     'tool_name': tool_name,
                     'content': tool_content
@@ -46,38 +58,61 @@ def extract_tool_message_content(response):
 
 
 class TimeRetrievalInput(BaseModel):
-    """时间检索工具的输入模式"""
+    """
+    Input schema for time retrieval tool
+
+    Defines the expected input parameters for time-based retrieval operations.
+    Used for validation and documentation of tool parameters.
+
+    Attributes:
+        context: User input query content for search
+        end_user_id: Group ID for filtering search results, defaults to test user
+    """
     context: str = Field(description="用户输入的查询内容")
     end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果")
 
+
 def create_time_retrieval_tool(end_user_id: str):
     """
-    创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
+    Create a TimeRetrieval tool with specific end_user_id (synchronous version) for searching statements by time range
+
+    Creates a specialized time-based retrieval tool that searches for statements within
+    specified time ranges. Includes field cleaning functionality to remove unnecessary
+    metadata from search results.
+
+    Args:
+        end_user_id: User identifier for scoping search results
+
+    Returns:
+        function: Configured TimeRetrievalWithGroupId tool function
     """
-    
+
     def clean_temporal_result_fields(data):
         """
-        清理时间搜索结果中不需要的字段，并修改结构
+        Clean unnecessary fields from temporal search results and modify structure
+
+        Removes metadata fields that are not needed for end-user consumption and
+        restructures the response format for better usability.
         
         Args:
-            data: 要清理的数据
+            data: Data to be cleaned (dict, list, or other types)
             
         Returns:
-            清理后的数据
+            Cleaned data with unnecessary fields removed
         """
-        # 需要过滤的字段列表
+        # List of fields to filter out
         fields_to_remove = {
-            'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', 
+            'id', 'apply_id', 'user_id', 'chunk_id', 'created_at',
             'valid_at', 'invalid_at', 'statement_ids'
         }
-        
+
         if isinstance(data, dict):
             cleaned = {}
             for key, value in data.items():
                 if key == 'statements' and isinstance(value, dict) and 'statements' in value:
-                    # 将 statements: {"statements": [...]} 改为 time_search: {"statements": [...]}
+                    # Change statements: {"statements": [...]} to time_search: {"statements": [...]}
                     cleaned_value = clean_temporal_result_fields(value)
-                    # 进一步将内部的 statements 改为 time_search
+                    # Further change internal statements to time_search
                     if 'statements' in cleaned_value:
                         cleaned['results'] = {
                             'time_search': cleaned_value['statements']
@@ -91,26 +126,35 @@ def create_time_retrieval_tool(end_user_id: str):
             return [clean_temporal_result_fields(item) for item in data]
         else:
             return data
-    
+
     @tool
-    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str:
+    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None,
+                                 end_user_id_param: str = None, clean_output: bool = True) -> str:
         """
-        优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段
-        显式接收参数：
-        - context: 查询上下文内容
-        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
-        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - end_user_id_param: 组ID（可选，用于覆盖默认组ID）
-        - clean_output: 是否清理输出中的元数据字段
-        -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
+        Optimized time retrieval tool, combines time range search only (synchronous version), automatically filters unnecessary metadata fields
+
+        Performs time-based search operations with automatic metadata filtering. Supports
+        flexible date range specification and provides clean, user-friendly output.
+
+        Explicit parameters:
+        - context: Query context content
+        - start_date: Start time (optional, format: YYYY-MM-DD)
+        - end_date: End time (optional, format: YYYY-MM-DD)
+        - end_user_id_param: Group ID (optional, overrides default group ID)
+        - clean_output: Whether to clean metadata fields from output
+        - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d")
+
+        Returns:
+            str: JSON formatted search results with temporal data
         """
+
         async def _async_search():
-            # 使用传入的参数或默认值
+            # Use passed parameters or default values
             actual_end_user_id = end_user_id_param or end_user_id
             actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
             actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
             
-            # 基本时间搜索
+            # Basic time search
             results = await search_by_temporal(
                 end_user_id=actual_end_user_id,
                 start_date=actual_start_date,
@@ -118,33 +162,43 @@ def create_time_retrieval_tool(end_user_id: str):
                 limit=10
             )
             
-            # 清理结果中不需要的字段
+            # Clean unnecessary fields from results
             if clean_output:
                 cleaned_results = clean_temporal_result_fields(results)
             else:
                 cleaned_results = results
 
             return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
-        
+
         return asyncio.run(_async_search())
 
     @tool
-    def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, clean_output: bool = True) -> str:
+    def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None,
+                             clean_output: bool = True) -> str:
         """
-        优化的关键词时间检索工具，结合关键词和时间范围搜索（同步版本），自动过滤不需要的元数据字段
-        显式接收参数：
-        - context: 查询内容
-        - days_back: 向前搜索的天数，默认7天
-        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
-        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - clean_output: 是否清理输出中的元数据字段
-        - end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
+        Optimized keyword time retrieval tool, combines keyword and time range search (synchronous version), automatically filters unnecessary metadata fields
+
+        Performs combined keyword and temporal search operations with automatic metadata
+        filtering. Provides more targeted search results by combining content relevance
+        with time-based filtering.
+
+        Explicit parameters:
+        - context: Query content for keyword matching
+        - days_back: Number of days to search backwards, default 7 days
+        - start_date: Start time (optional, format: YYYY-MM-DD)
+        - end_date: End time (optional, format: YYYY-MM-DD)
+        - clean_output: Whether to clean metadata fields from output
+        - end_date needs to be obtained based on user description, output format uses strftime("%Y-%m-%d")
+
+        Returns:
+            str: JSON formatted search results combining keyword and temporal data
         """
+
         async def _async_search():
             actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
             actual_start_date = start_date or (datetime.now() - timedelta(days=days_back)).strftime("%Y-%m-%d")
 
-            # 关键词时间搜索
+            # Keyword time search
             results = await search_by_keyword_temporal(
                 query_text=context,
                 end_user_id=end_user_id,
@@ -153,7 +207,7 @@ def create_time_retrieval_tool(end_user_id: str):
                 limit=15
             )
             
-            # 清理结果中不需要的字段
+            # Clean unnecessary fields from results
             if clean_output:
                 cleaned_results = clean_temporal_result_fields(results)
             else:
@@ -162,51 +216,61 @@ def create_time_retrieval_tool(end_user_id: str):
             return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
 
         return asyncio.run(_async_search())
-    
+
     return TimeRetrievalWithGroupId
 
 
 def create_hybrid_retrieval_tool_async(memory_config, **search_params):
     """
-    创建混合检索工具，使用run_hybrid_search进行混合检索，优化输出格式并过滤不需要的字段
+    Create hybrid retrieval tool using run_hybrid_search for hybrid retrieval, optimize output format and filter unnecessary fields
+
+    Creates an advanced hybrid search tool that combines multiple search strategies
+    (keyword, vector, hybrid) with automatic result cleaning and formatting.
     
     Args:
-        memory_config: 内存配置对象
-        **search_params: 搜索参数，包含end_user_id, limit, include等
+        memory_config: Memory configuration object containing LLM and search settings
+        **search_params: Search parameters including end_user_id, limit, include, etc.
+
+    Returns:
+        function: Configured HybridSearch tool function with async capabilities
     """
-    
+
     def clean_result_fields(data):
         """
-        递归清理结果中不需要的字段
+        Recursively clean unnecessary fields from results
+
+        Removes metadata fields that are not needed for end-user consumption,
+        improving readability and reducing response size.
         
         Args:
-            data: 要清理的数据（可能是字典、列表或其他类型）
+            data: Data to be cleaned (can be dict, list, or other types)
             
         Returns:
-            清理后的数据
+            Cleaned data with unnecessary fields removed
         """
-        # 需要过滤的字段列表
-        # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
+        # List of fields to filter out
+        # TODO: fact_summary functionality temporarily disabled, will be enabled after future development
         fields_to_remove = {
-            'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', 
-            'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', 
-            'user_id', 'statement_ids', 'updated_at',"chunk_ids"  ,"fact_summary"
+            'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids',
+            'expired_at', 'created_at', 'chunk_id', 'apply_id',
+            'user_id', 'statement_ids', 'updated_at', "chunk_ids", "fact_summary"
         }
-        
+        # 注意：'id' 字段保留，community 展开时需要用 community id 查询成员 statements
+
         if isinstance(data, dict):
-            # 对字典进行清理
+            # Clean dictionary
             cleaned = {}
             for key, value in data.items():
                 if key not in fields_to_remove:
-                    cleaned[key] = clean_result_fields(value)  # 递归清理嵌套数据
+                    cleaned[key] = clean_result_fields(value)  # Recursively clean nested data
             return cleaned
         elif isinstance(data, list):
-            # 对列表中的每个元素进行清理
+            # Clean each element in list
             return [clean_result_fields(item) for item in data]
         else:
-            # 其他类型直接返回
+            # Return other types directly
             return data
-    
+
     @tool
     async def HybridSearch(
         context: str, 
@@ -216,57 +280,63 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
         rerank_alpha: float = 0.6,
         use_forgetting_rerank: bool = False,
         use_llm_rerank: bool = False,
-        clean_output: bool = True  # 新增：是否清理输出字段
+        clean_output: bool = True  # New: whether to clean output fields
     ) -> str:
         """
-        优化的混合检索工具，支持关键词、向量和混合搜索，自动过滤不需要的元数据字段
+        Optimized hybrid retrieval tool, supports keyword, vector and hybrid search, automatically filters unnecessary metadata fields
+
+        Provides comprehensive search capabilities combining multiple search strategies
+        with intelligent result ranking and automatic metadata filtering for clean output.
         
         Args:
-            context: 查询内容
-            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
-            limit: 结果数量限制
-            end_user_id: 组ID，用于过滤搜索结果
-            rerank_alpha: 重排序权重参数
-            use_forgetting_rerank: 是否使用遗忘重排序
-            use_llm_rerank: 是否使用LLM重排序
-            clean_output: 是否清理输出中的元数据字段
+            context: Query content for search
+            search_type: Search type ('keyword', 'embedding', 'hybrid')
+            limit: Result quantity limit
+            end_user_id: Group ID for filtering search results
+            rerank_alpha: Reranking weight parameter for result scoring
+            use_forgetting_rerank: Whether to use forgetting-based reranking
+            use_llm_rerank: Whether to use LLM-based reranking
+            clean_output: Whether to clean metadata fields from output
+
+        Returns:
+            str: JSON formatted comprehensive search results
         """
         try:
-            # 导入run_hybrid_search函数
+            # Import run_hybrid_search function
             from app.core.memory.src.search import run_hybrid_search
             
-            # 合并参数，优先使用传入的参数
+            # Merge parameters, prioritize passed parameters
             final_params = {
                 "query_text": context,
                 "search_type": search_type,
                 "end_user_id": end_user_id or search_params.get("end_user_id"),
                 "limit": limit or search_params.get("limit", 10),
-                "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]),
-                "output_path": None,  # 不保存到文件
+                "include": search_params.get("include", ["summaries", "statements", "chunks", "entities", "communities"]),
+                "output_path": None,  # Don't save to file
                 "memory_config": memory_config,
                 "rerank_alpha": rerank_alpha,
                 "use_forgetting_rerank": use_forgetting_rerank,
                 "use_llm_rerank": use_llm_rerank
             }
             
-            # 执行混合检索
+            # Execute hybrid retrieval
             raw_results = await run_hybrid_search(**final_params)
             
-            # 清理结果中不需要的字段
+            # Clean unnecessary fields from results
             if clean_output:
                 cleaned_results = clean_result_fields(raw_results)
             else:
                 cleaned_results = raw_results
             
-            # 格式化返回结果
+            # Format return results
             formatted_results = {
                 "search_query": context,
                 "search_type": search_type,
                 "results": cleaned_results
             }
-            
+
             return json.dumps(formatted_results, ensure_ascii=False, indent=2, default=str)
-            
+
         except Exception as e:
             error_result = {
                 "error": f"混合检索失败: {str(e)}",
@@ -275,38 +345,52 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
                 "timestamp": datetime.now().isoformat()
             }
             return json.dumps(error_result, ensure_ascii=False, indent=2)
-    
+
     return HybridSearch
 
 
 def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
     """
-    创建同步版本的混合检索工具，优化输出格式并过滤不需要的字段
+    Create synchronous version of hybrid retrieval tool, optimize output format and filter unnecessary fields
+
+    Creates a synchronous wrapper around the async hybrid search functionality,
+    making it compatible with synchronous tool execution environments.
     
     Args:
-        memory_config: 内存配置对象
-        **search_params: 搜索参数
+        memory_config: Memory configuration object containing search settings
+        **search_params: Search parameters for configuration
+
+    Returns:
+        function: Configured HybridSearchSync tool function
     """
+
     @tool
     def HybridSearchSync(
-        context: str, 
-        search_type: str = "hybrid",
-        limit: int = 10,
-        end_user_id: str = None,
-        clean_output: bool = True
+            context: str,
+            search_type: str = "hybrid",
+            limit: int = 10,
+            end_user_id: str = None,
+            clean_output: bool = True
     ) -> str:
         """
-        优化的混合检索工具（同步版本），自动过滤不需要的元数据字段
+        Optimized hybrid retrieval tool (synchronous version), automatically filters unnecessary metadata fields
+
+        Provides the same hybrid search capabilities as the async version but in a
+        synchronous execution context. Automatically handles async-to-sync conversion.
         
         Args:
-            context: 查询内容
-            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
-            limit: 结果数量限制
-            end_user_id: 组ID，用于过滤搜索结果
-            clean_output: 是否清理输出中的元数据字段
+            context: Query content for search
+            search_type: Search type ('keyword', 'embedding', 'hybrid')
+            limit: Result quantity limit
+            end_user_id: Group ID for filtering search results
+            clean_output: Whether to clean metadata fields from output
+
+        Returns:
+            str: JSON formatted search results
         """
+
         async def _async_search():
-            # 创建异步工具并执行
+            # Create async tool and execute
             async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params)
             return await async_tool.ainvoke({
                 "context": context,
@@ -315,7 +399,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
                 "end_user_id": end_user_id,
                 "clean_output": clean_output
             })
-        
+
         return asyncio.run(_async_search())
-    
-    return HybridSearchSync
+
+    return HybridSearchSync

api/app/core/memory/agent/langgraph_graph/tools/write_tool.py

@@ -1,20 +1,28 @@
 import json
 
 from langchain_core.messages import HumanMessage, AIMessage
-async def format_parsing(messages: list,type:str='string'):
+
+
+async def format_parsing(messages: list, type: str = 'string'):
     """
-    格式化解析消息列表
+    Format and parse message lists into different output types
+
+    Processes message lists from storage and converts them into either string format
+    or dictionary format based on the specified type parameter. Handles JSON parsing
+    and role-based message organization.
     
     Args:
-        messages: 消息列表
-        type: 返回类型 ('string' 或 'dict')
+        messages: List of message objects from storage containing message data
+        type: Return type specification ('string' for text format, 'dict' for key-value pairs)
         
     Returns:
-        格式化后的消息列表
+        list: Formatted message list in the specified format
+            - 'string': List of formatted text messages with role prefixes
+            - 'dict': List of dictionaries mapping user messages to AI responses
     """
     result = []
-    user=[]
-    ai=[]
+    user = []
+    ai = []
 
     for message in messages:
         hstory_messages = message['messages']
@@ -24,25 +32,38 @@ async def format_parsing(messages: list,type:str='string'):
                 role = content['role']
                 content = content['content']
                 if type == "string":
-                    if role == 'human' or role=="user":
+                    if role == 'human' or role == "user":
                         content = '用户:' + content
                     else:
                         content = 'AI:' + content
                     result.append(content)
-                if type == "dict" :
-                    if role == 'human'  or role=="user":
-                        user.append( content)
+                if type == "dict":
+                    if role == 'human' or role == "user":
+                        user.append(content)
                     else:
                         ai.append(content)
     if type == "dict":
-        for key,values in zip(user,ai):
-            result.append({key:values})
+        for key, values in zip(user, ai):
+            result.append({key: values})
     return result
 
+
 async def messages_parse(messages: list | dict):
-    user=[]
-    ai=[]
-    database=[]
+    """
+    Parse messages from storage format into user-AI conversation pairs
+
+    Extracts and organizes conversation data from stored message format,
+    separating user and AI messages and pairing them for database storage.
+
+    Args:
+        messages: List or dictionary containing stored message data with Query fields
+
+    Returns:
+        list: List of dictionaries containing user-AI message pairs for database storage
+    """
+    user = []
+    ai = []
+    database = []
     for message in messages:
         Query = message['Query']
         Query = json.loads(Query)
@@ -54,10 +75,23 @@ async def messages_parse(messages: list | dict):
                 ai.append(data['content'])
     for key, values in zip(user, ai):
         database.append({key, values})
-    return  database
+    return database
 
 
-async def agent_chat_messages(user_content,ai_content):
+async def agent_chat_messages(user_content, ai_content):
+    """
+    Create structured chat message format for agent conversations
+
+    Formats user and AI content into a standardized message structure suitable
+    for agent processing and storage. Creates role-based message objects.
+
+    Args:
+        user_content: User's message content string
+        ai_content: AI's response content string
+
+    Returns:
+        list: List of structured message dictionaries with role and content fields
+    """
     messages = [
         {
             "role": "user",

api/app/core/memory/agent/langgraph_graph/write_graph.py

@@ -13,7 +13,6 @@ from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
 from app.schemas.memory_agent_schema import AgentMemory_Long_Term
 from app.services.memory_config_service import MemoryConfigService
 
-
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logger = get_agent_logger(__name__)
 
@@ -42,10 +41,26 @@ async def make_write_graph():
 
     yield graph
 
-async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[],memory_config:str='',end_user_id:str='',scope:int=6):
-    from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue,aggregate_judgment
+
+async def long_term_storage(long_term_type: str = "chunk", langchain_messages: list = [], memory_config: str = '',
+                            end_user_id: str = '', scope: int = 6):
+    """
+    Handle long-term memory storage with different strategies
+
+    Supports multiple storage strategies including chunk-based, time-based,
+    and aggregate judgment approaches for long-term memory persistence.
+
+    Args:
+        long_term_type: Storage strategy type ('chunk', 'time', 'aggregate')
+        langchain_messages: List of messages to store
+        memory_config: Memory configuration identifier
+        end_user_id: User group identifier
+        scope: Scope parameter for chunk-based storage (default: 6)
+    """
+    from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue, \
+        aggregate_judgment
     from app.core.memory.agent.utils.redis_tool import write_store
-    write_store.save_session_write(end_user_id,  (langchain_messages))
+    write_store.save_session_write(end_user_id, langchain_messages)
     # 获取数据库会话
     with get_db_context() as db_session:
         config_service = MemoryConfigService(db_session)
@@ -53,26 +68,39 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[
             config_id=memory_config,  # 改为整数
             service_name="MemoryAgentService"
         )
-        if long_term_type=='chunk':
-            '''方案一:对话窗口6轮对话'''
-            await window_dialogue(end_user_id,langchain_messages,memory_config,scope)
-        if long_term_type=='time':
-            """时间"""
-            await memory_long_term_storage(end_user_id, memory_config,5)
-        if  long_term_type=='aggregate':
-            """方案三：聚合判断"""
+        if long_term_type == AgentMemory_Long_Term.STRATEGY_CHUNK:
+            '''Strategy 1: Dialogue window with 6 rounds of conversation'''
+            await window_dialogue(end_user_id, langchain_messages, memory_config, scope)
+        if long_term_type == AgentMemory_Long_Term.STRATEGY_TIME:
+            """Time-based strategy"""
+            await memory_long_term_storage(end_user_id, memory_config, AgentMemory_Long_Term.TIME_SCOPE)
+        if long_term_type == AgentMemory_Long_Term.STRATEGY_AGGREGATE:
+            """Strategy 3: Aggregate judgment"""
             await aggregate_judgment(end_user_id, langchain_messages, memory_config)
 
 
+async def write_long_term(storage_type, end_user_id, message_chat, aimessages, user_rag_memory_id, actual_config_id):
+    """
+    Write long-term memory with different storage types
 
-async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_rag_memory_id,actual_config_id):
+    Handles both RAG-based storage and traditional memory storage approaches.
+    For traditional storage, uses chunk-based strategy with paired user-AI messages.
+
+    Args:
+        storage_type: Type of storage (RAG or traditional)
+        end_user_id: User group identifier
+        message_chat: User message content
+        aimessages: AI response messages
+        user_rag_memory_id: RAG memory identifier
+        actual_config_id: Actual configuration ID
+    """
     from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent
     from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save
-    from app.core.memory.agent.langgraph_graph.tools.write_tool import  agent_chat_messages
+    from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages
     if storage_type == AgentMemory_Long_Term.STORAGE_RAG:
         await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id)
     else:
-        # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
+        # AI reply writing (user messages and AI replies paired, written as complete dialogue at once)
         CHUNK = AgentMemory_Long_Term.STRATEGY_CHUNK
         SCOPE = AgentMemory_Long_Term.DEFAULT_SCOPE
         long_term_messages = await agent_chat_messages(message_chat, aimessages)
@@ -101,4 +129,4 @@ async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_
 #
 # if __name__ == "__main__":
 #     import asyncio
-#     asyncio.run(main())
+#     asyncio.run(main())

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

@@ -13,6 +13,72 @@ from app.core.memory.utils.data.text_utils import escape_lucene_query
 
 logger = get_agent_logger(__name__)
 
+# 需要从展开结果中过滤的字段（含 Neo4j DateTime，不可 JSON 序列化）
+_EXPAND_FIELDS_TO_REMOVE = {
+    'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids',
+    'expired_at', 'created_at', 'chunk_id', 'apply_id',
+    'user_id', 'statement_ids', 'updated_at', 'chunk_ids', 'fact_summary'
+}
+
+
+def _clean_expand_fields(obj):
+    """递归过滤展开结果中不可序列化的字段（DateTime 等）。"""
+    if isinstance(obj, dict):
+        return {k: _clean_expand_fields(v) for k, v in obj.items() if k not in _EXPAND_FIELDS_TO_REMOVE}
+    if isinstance(obj, list):
+        return [_clean_expand_fields(i) for i in obj]
+    return obj
+
+
+async def expand_communities_to_statements(
+    community_results: List[dict],
+    end_user_id: str,
+    existing_content: str = "",
+    limit: int = 10,
+) -> Tuple[List[dict], List[str]]:
+    """
+    社区展开 helper：给定命中的 community 列表，拉取关联 Statement。
+
+    - 对展开结果去重（过滤已在 existing_content 中出现的文本）
+    - 过滤不可序列化字段
+    - 返回 (cleaned_expanded_stmts, new_texts)
+      - cleaned_expanded_stmts: 可直接写回 raw_results 的列表
+      - new_texts: 去重后新增的 statement 文本列表，用于追加到 clean_content
+    """
+    community_ids = [r.get("id") for r in community_results if r.get("id")]
+    if not community_ids or not end_user_id:
+        return [], []
+
+    from app.repositories.neo4j.graph_search import search_graph_community_expand
+    from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+    connector = Neo4jConnector()
+    try:
+        result = await search_graph_community_expand(
+            connector=connector,
+            community_ids=community_ids,
+            end_user_id=end_user_id,
+            limit=limit,
+        )
+    except Exception as e:
+        logger.warning(f"[expand_communities] 社区展开检索失败，跳过: {e}")
+        return [], []
+    finally:
+        await connector.close()
+
+    expanded_stmts = result.get("expanded_statements", [])
+    if not expanded_stmts:
+        return [], []
+
+    existing_lines = set(existing_content.splitlines())
+    new_texts = [
+        s["statement"] for s in expanded_stmts
+        if s.get("statement") and s["statement"] not in existing_lines
+    ]
+    cleaned = _clean_expand_fields(expanded_stmts)
+    logger.info(f"[expand_communities] 展开 {len(expanded_stmts)} 条 statements，新增 {len(new_texts)} 条，community_ids={community_ids}")
+    return cleaned, new_texts
+
 
 class SearchService:
     """Service for executing hybrid search and processing results."""
@@ -21,7 +87,7 @@ class SearchService:
         """Initialize the search service."""
         logger.info("SearchService initialized")
     
-    def extract_content_from_result(self, result: dict) -> str:
+    def extract_content_from_result(self, result: dict, node_type: str = "") -> str:
         """
         Extract only meaningful content from search results, dropping all metadata.
         
@@ -30,9 +96,11 @@ class SearchService:
         - Entities: extract 'name' and 'fact_summary' fields
         - Summaries: extract 'content' field
         - Chunks: extract 'content' field
+        - Communities: extract 'content' field (c.summary), prefixed with community name
         
         Args:
             result: Search result dictionary
+            node_type: Hint for node type ("community", "summary", etc.)
             
         Returns:
             Clean content string without metadata
@@ -46,8 +114,21 @@ class SearchService:
         if 'statement' in result and result['statement']:
             content_parts.append(result['statement'])
         
-        # Summaries/Chunks: extract content field
-        if 'content' in result and result['content']:
+        # Community 节点：有 member_count 或 core_entities 字段，或 node_type 明确指定
+        # 用 "[主题：{name}]" 前缀区分，让 LLM 知道这是主题级摘要
+        is_community = (
+            node_type == "community"
+            or 'member_count' in result
+            or 'core_entities' in result
+        )
+        if is_community:
+            name = result.get('name', '')
+            content = result.get('content', '')
+            if content:
+                prefix = f"[主题：{name}] " if name else ""
+                content_parts.append(f"{prefix}{content}")
+        elif 'content' in result and result['content']:
+            # Summaries / Chunks
             content_parts.append(result['content'])
         
         # Entities: extract name and fact_summary (commented out in original)
@@ -99,7 +180,8 @@ class SearchService:
         rerank_alpha: float = 0.4,
         output_path: str = "search_results.json",
         return_raw_results: bool = False,
-        memory_config = None
+        memory_config = None,
+        expand_communities: bool = True,
     ) -> Tuple[str, str, Optional[dict]]:
         """
         Execute hybrid search and return clean content.
@@ -114,13 +196,15 @@ class SearchService:
             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: Memory configuration object (required)
+            expand_communities: If True, expand community hits to member statements (default: True).
+                                 Set to False for quick-summary paths that only need community-level text.
         
         Returns:
             Tuple of (clean_content, cleaned_query, raw_results)
             raw_results is None if return_raw_results=False
         """
         if include is None:
-            include = ["statements", "chunks", "entities", "summaries"]
+            include = ["statements", "chunks", "entities", "summaries", "communities"]
         
         # Clean query
         cleaned_query = self.clean_query(question)
@@ -146,8 +230,8 @@ class SearchService:
             if search_type == "hybrid":
                 reranked_results = answer.get('reranked_results', {})
                 
-                # Priority order: summaries first (most contextual), then statements, chunks, entities
-                priority_order = ['summaries', 'statements', 'chunks', 'entities']
+                # Priority order: summaries first (most contextual), then communities, statements, chunks, entities
+                priority_order = ['summaries', 'communities', 'statements', 'chunks', 'entities']
                 
                 for category in priority_order:
                     if category in include and category in reranked_results:
@@ -157,19 +241,33 @@ class SearchService:
             else:
                 # For keyword or embedding search, results are directly in answer dict
                 # Apply same priority order
-                priority_order = ['summaries', 'statements', 'chunks', 'entities']
+                priority_order = ['summaries', 'communities', 'statements', 'chunks', 'entities']
                 
                 for category in priority_order:
                     if category in include and category in answer:
                         category_results = answer[category]
                         if isinstance(category_results, list):
                             answer_list.extend(category_results)
+
+            # 对命中的 community 节点展开其成员 statements（路径 "0"/"1" 需要，路径 "2" 不需要）
+            if expand_communities and "communities" in include:
+                community_results = (
+                    answer.get('reranked_results', {}).get('communities', [])
+                    if search_type == "hybrid"
+                    else answer.get('communities', [])
+                )
+                cleaned_stmts, new_texts = await expand_communities_to_statements(
+                    community_results=community_results,
+                    end_user_id=end_user_id,
+                )
+                answer_list.extend(cleaned_stmts)
             
-            # Extract clean content from all results
-            content_list = [
-                self.extract_content_from_result(ans) 
-                for ans in answer_list
-            ]
+            # Extract clean content from all results，按类型传入 node_type 区分 community
+            content_list = []
+            for ans in answer_list:
+                # community 节点有 member_count 或 core_entities 字段
+                ntype = "community" if ('member_count' in ans or 'core_entities' in ans) else ""
+                content_list.append(self.extract_content_from_result(ans, node_type=ntype))
 
             
             # Filter out empty strings and join with newlines

api/app/core/memory/agent/utils/get_dialogs.py

@@ -84,7 +84,7 @@ async def get_chunked_dialogs(
                             pruning_scene=memory_config.pruning_scene or "education",
                             pruning_threshold=memory_config.pruning_threshold,
                             scene_id=str(memory_config.scene_id) if memory_config.scene_id else None,
-                            ontology_classes=memory_config.ontology_classes,
+                            ontology_class_infos=memory_config.ontology_class_infos,
                         )
                         logger.info(f"[剪枝] 加载配置: switch={pruning_config.pruning_switch}, scene={pruning_config.pruning_scene}, threshold={pruning_config.pruning_threshold}")
                         

api/app/core/memory/agent/utils/llm_tools.py

@@ -8,10 +8,11 @@ from langgraph.graph import add_messages
 
 PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3])
 
+
 class WriteState(TypedDict):
-    '''
+    """
     Langgrapg Writing TypedDict
-    '''
+    """
     messages: Annotated[list[AnyMessage], add_messages]
     end_user_id: str
     errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
@@ -20,6 +21,7 @@ class WriteState(TypedDict):
     data: str
     language: str  # 语言类型 ("zh" 中文, "en" 英文)
 
+
 class ReadState(TypedDict):
     """
     LangGraph 工作流状态定义
@@ -43,18 +45,20 @@ class ReadState(TypedDict):
     config_id: str
     data: str  # 新增字段用于传递内容
     spit_data: dict  # 新增字段用于传递问题分解结果
-    problem_extension:dict
+    problem_extension: dict
     storage_type: str
     user_rag_memory_id: str
     llm_id: str
     embedding_id: str
     memory_config: object  # 新增字段用于传递内存配置对象
-    retrieve:dict
+    retrieve: dict
     RetrieveSummary: dict
     InputSummary: dict
     verify: dict
     SummaryFails: dict
     summary: dict
+
+
 class COUNTState:
     """
     工作流对话检索内容计数器
@@ -99,6 +103,7 @@ class COUNTState:
         self.total = 0
         print("[COUNTState] 已重置为 0")
 
+
 def deduplicate_entries(entries):
     seen = set()
     deduped = []
@@ -109,6 +114,7 @@ def deduplicate_entries(entries):
             deduped.append(entry)
     return deduped
 
+
 def merge_to_key_value_pairs(data, query_key, result_key):
     grouped = defaultdict(list)
     for item in data:
@@ -142,4 +148,4 @@ def convert_extended_question_to_question(data):
         return [convert_extended_question_to_question(item) for item in data]
     else:
         # 其他类型直接返回
-        return data
+        return data

api/app/core/memory/agent/utils/write_tools.py

@@ -19,7 +19,7 @@ from app.core.memory.utils.log.logging_utils import log_time
 from app.db import get_db_context
 from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges
 from app.repositories.neo4j.add_nodes import add_memory_summary_nodes
-from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j
+from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j, schedule_clustering_after_write
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import MemoryConfig
 
@@ -165,10 +165,16 @@ async def write(
                 statement_chunk_edges=all_statement_chunk_edges,
                 statement_entity_edges=all_statement_entity_edges,
                 entity_edges=all_entity_entity_edges,
-                connector=neo4j_connector
+                connector=neo4j_connector,
             )
             if success:
                 logger.info("Successfully saved all data to Neo4j")
+                # 写入成功后，异步触发聚类（不阻塞写入响应）
+                schedule_clustering_after_write(
+                    all_entity_nodes,
+                    llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
+                    embedding_model_id=str(memory_config.embedding_model_id) if memory_config.embedding_model_id else None,
+                )
                 break
             else:
                 logger.warning("Failed to save some data to Neo4j")

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

@@ -6,6 +6,7 @@ of the memory system including LLM, chunking, pruning, and search.
 Classes:
     LLMConfig: Configuration for LLM client
     ChunkerConfig: Configuration for dialogue chunking
+    OntologyClassInfo: Single ontology class with name and description
     PruningConfig: Configuration for semantic pruning
     TemporalSearchParams: Parameters for temporal search queries
 """
@@ -50,30 +51,41 @@ class ChunkerConfig(BaseModel):
     min_characters_per_chunk: Optional[int] = Field(24, ge=0, description="The minimum number of characters in each chunk.")
 
 
+class OntologyClassInfo(BaseModel):
+    """本体类型的名称与语义描述，用于剪枝提示词注入。
+
+    Attributes:
+        class_name: 本体类型名称（如"患者"、"课程"）
+        class_description: 本体类型语义描述，告知 LLM 该类型在当前场景下的含义
+    """
+    class_name: str = Field(..., description="本体类型名称")
+    class_description: str = Field(default="", description="本体类型语义描述")
+
+
 class PruningConfig(BaseModel):
     """Configuration for semantic pruning of dialogue content.
 
     Attributes:
         pruning_switch: Enable or disable semantic pruning
-        pruning_scene: Scene name for pruning, either a built-in key
-            ('education', 'online_service', 'outbound') or a custom scene_name
-            from ontology_scene table
+        pruning_scene: Scene name for pruning from ontology_scene table
         pruning_threshold: Pruning ratio (0-0.9, max 0.9 to avoid complete removal)
-        scene_id: Optional ontology scene UUID, used to load custom ontology classes
-        ontology_classes: List of class_name strings from ontology_class table,
-            injected into the prompt when pruning_scene is not a built-in scene
+        scene_id: Optional ontology scene UUID
+        ontology_class_infos: Full ontology class info (name + description) from
+            ontology_class table, injected into the pruning prompt to drive
+            scene-aware preservation decisions
     """
     pruning_switch: bool = Field(False, description="Enable semantic pruning when True.")
     pruning_scene: str = Field(
         "education",
-        description="Scene for pruning: built-in key or custom scene_name from ontology_scene.",
+        description="Scene name from ontology_scene table.",
     )
     pruning_threshold: float = Field(
         0.5, ge=0.0, le=0.9,
         description="Pruning ratio within 0-0.9 (max 0.9 to avoid termination).")
     scene_id: Optional[str] = Field(None, description="Ontology scene UUID (optional).")
-    ontology_classes: Optional[List[str]] = Field(
-        None, description="Class names from ontology_class table for custom scenes."
+    ontology_class_infos: List[OntologyClassInfo] = Field(
+        default_factory=list,
+        description="Full ontology class info (name + description) injected into pruning prompt."
     )
 
 

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

@@ -238,7 +238,7 @@ def rerank_with_activation(
     
     reranked: Dict[str, List[Dict[str, Any]]] = {}
     
-    for category in ["statements", "chunks", "entities", "summaries"]:
+    for category in ["statements", "chunks", "entities", "summaries", "communities"]:
         keyword_items = keyword_results.get(category, [])
         embedding_items = embedding_results.get(category, [])
         
@@ -281,21 +281,23 @@ def rerank_with_activation(
         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)
+                combined_items[item_id]["normalized_activation_value"] = item.get("normalized_activation_value")
         
         # 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)
+            # normalized_activation_value 为 None 表示该节点无激活值，保留 None 语义
+            raw_act_norm = item.get("normalized_activation_value")
+            act_norm = float(raw_act_norm) if raw_act_norm is not None else None
             
             # 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
+            # 存储激活度分数供第二阶段使用（None 表示无激活值，不参与激活值排序）
+            item["activation_score"] = act_norm  # 可能为 None
             item["content_score"] = content_score
             item["base_score"] = base_score
             
@@ -724,6 +726,8 @@ async def run_hybrid_search(
     try:
         keyword_task = None
         embedding_task = None
+        keyword_results: Dict[str, List] = {}
+        embedding_results: Dict[str, List] = {}
 
         if search_type in ["keyword", "hybrid"]:
             # Keyword-based search
@@ -746,35 +750,42 @@ async def run_hybrid_search(
             
             # 从数据库读取嵌入器配置（按 ID）并构建 RedBearModelConfig
             config_load_start = time.time()
-            with get_db_context() as db:
-                config_service = MemoryConfigService(db)
-                embedder_config_dict = config_service.get_embedder_config(str(memory_config.embedding_model_id))
-            rb_config = RedBearModelConfig(
-                model_name=embedder_config_dict["model_name"],
-                provider=embedder_config_dict["provider"],
-                api_key=embedder_config_dict["api_key"],
-                base_url=embedder_config_dict["base_url"],
-                type="llm"
-            )
-            config_load_time = time.time() - config_load_start
-            logger.info(f"[PERF] Config loading took {config_load_time:.4f}s")
-
-            # Init embedder
-            embedder_init_start = time.time()
-            embedder = OpenAIEmbedderClient(model_config=rb_config)
-            embedder_init_time = time.time() - embedder_init_start
-            logger.info(f"[PERF] Embedder init took {embedder_init_time:.4f}s")
-            
-            embedding_task = asyncio.create_task(
-                search_graph_by_embedding(
-                    connector=connector,
-                    embedder_client=embedder,
-                    query_text=query_text,
-                    end_user_id=end_user_id,
-                    limit=limit,
-                    include=include,
+            try:
+                with get_db_context() as db:
+                    config_service = MemoryConfigService(db)
+                    embedder_config_dict = config_service.get_embedder_config(str(memory_config.embedding_model_id))
+                rb_config = RedBearModelConfig(
+                    model_name=embedder_config_dict["model_name"],
+                    provider=embedder_config_dict["provider"],
+                    api_key=embedder_config_dict["api_key"],
+                    base_url=embedder_config_dict["base_url"],
+                    type="llm"
                 )
-            )
+                config_load_time = time.time() - config_load_start
+                logger.info(f"[PERF] Config loading took {config_load_time:.4f}s")
+
+                # Init embedder
+                embedder_init_start = time.time()
+                embedder = OpenAIEmbedderClient(model_config=rb_config)
+                embedder_init_time = time.time() - embedder_init_start
+                logger.info(f"[PERF] Embedder init took {embedder_init_time:.4f}s")
+                
+                embedding_task = asyncio.create_task(
+                    search_graph_by_embedding(
+                        connector=connector,
+                        embedder_client=embedder,
+                        query_text=query_text,
+                        end_user_id=end_user_id,
+                        limit=limit,
+                        include=include,
+                    )
+                )
+            except Exception as emb_init_err:
+                logger.warning(
+                    f"[PERF] Embedding search skipped due to init error "
+                    f"(embedding_model_id={memory_config.embedding_model_id}): {emb_init_err}"
+                )
+                embedding_task = None
 
         if keyword_task:
             keyword_results = await keyword_task

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

@@ -0,0 +1,3 @@
+from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine
+
+__all__ = ["LabelPropagationEngine"]

api/app/core/memory/storage_services/clustering_engine/label_propagation.py

@@ -0,0 +1,572 @@
+"""标签传播聚类引擎
+
+基于 ZEP 论文的动态标签传播算法，对 Neo4j 中的 ExtractedEntity 节点进行社区聚类。
+
+支持两种模式：
+- 全量初始化（full_clustering）：首次运行，对所有实体做完整 LPA 迭代
+- 增量更新（incremental_update）：新实体到达时，只处理新实体及其邻居
+"""
+
+import asyncio
+import logging
+import uuid
+from math import sqrt
+from typing import Dict, List, Optional
+
+from app.repositories.neo4j.community_repository import CommunityRepository
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+logger = logging.getLogger(__name__)
+
+# 全量迭代最大轮数，防止不收敛
+MAX_ITERATIONS = 10
+
+# 社区核心实体取 top-N 数量
+CORE_ENTITY_LIMIT = 10
+
+
+def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float:
+    """计算两个向量的余弦相似度，任一为空则返回 0。"""
+    if not v1 or not v2 or len(v1) != len(v2):
+        return 0.0
+    dot = sum(a * b for a, b in zip(v1, v2))
+    norm1 = sqrt(sum(a * a for a in v1))
+    norm2 = sqrt(sum(b * b for b in v2))
+    if norm1 == 0 or norm2 == 0:
+        return 0.0
+    return dot / (norm1 * norm2)
+
+
+def _weighted_vote(
+    neighbors: List[Dict],
+    self_embedding: Optional[List[float]],
+) -> Optional[str]:
+    """
+    加权多数投票，选出得票最高的社区。
+
+    权重 = 语义相似度（name_embedding 余弦）* activation_value 加成
+    没有 community_id 的邻居不参与投票。
+    """
+    votes: Dict[str, float] = {}
+    for nb in neighbors:
+        cid = nb.get("community_id")
+        if not cid:
+            continue
+        sem = _cosine_similarity(self_embedding, nb.get("name_embedding"))
+        act = nb.get("activation_value") or 0.5
+        # 语义相似度权重 0.6，激活值权重 0.4
+        weight = 0.6 * sem + 0.4 * act
+        votes[cid] = votes.get(cid, 0.0) + weight
+
+    if not votes:
+        return None
+    return max(votes, key=votes.__getitem__)
+
+
+class LabelPropagationEngine:
+    """标签传播聚类引擎"""
+
+    def __init__(
+        self,
+        connector: Neo4jConnector,
+        llm_model_id: Optional[str] = None,
+        embedding_model_id: Optional[str] = None,
+    ):
+        self.connector = connector
+        self.repo = CommunityRepository(connector)
+        self.llm_model_id = llm_model_id
+        self.embedding_model_id = embedding_model_id
+
+    # ──────────────────────────────────────────────────────────────────────────
+    # 公开接口
+    # ──────────────────────────────────────────────────────────────────────────
+
+    async def run(
+        self,
+        end_user_id: str,
+        new_entity_ids: Optional[List[str]] = None,
+    ) -> None:
+        """
+        统一入口：自动判断全量还是增量。
+
+        - 若该用户尚无 Community 节点 → 全量初始化
+        - 否则 → 增量更新（仅处理 new_entity_ids）
+        """
+        has_communities = await self.repo.has_communities(end_user_id)
+        if not has_communities:
+            logger.info(f"[Clustering] 用户 {end_user_id} 首次聚类，执行全量初始化")
+            await self.full_clustering(end_user_id)
+        else:
+            if new_entity_ids:
+                logger.info(
+                    f"[Clustering] 增量更新，新实体数: {len(new_entity_ids)}"
+                )
+                await self.incremental_update(new_entity_ids, end_user_id)
+
+    async def full_clustering(self, end_user_id: str) -> None:
+        """
+        全量标签传播初始化（分批处理，控制内存峰值）。
+
+        策略：
+        - 每次只加载 BATCH_SIZE 个实体及其邻居进内存
+        - labels 字典跨批次共享（只存 id→community_id，内存极小）
+        - 每批独立跑 MAX_ITERATIONS 轮 LPA，批次间通过 labels 传递社区信息
+        - 所有批次完成后统一 flush 和 merge
+        """
+        BATCH_SIZE = 888  # 每批实体数，可按需调整
+
+        # 轻量查询：只获取总数和 ID 列表，不加载 embedding 等大字段
+        total_count = await self.repo.get_entity_count(end_user_id)
+        if not total_count:
+            logger.info(f"[Clustering] 用户 {end_user_id} 无实体，跳过全量聚类")
+            return
+
+        all_entity_ids = await self.repo.get_all_entity_ids(end_user_id)
+        logger.info(f"[Clustering] 用户 {end_user_id} 共 {total_count} 个实体，"
+                    f"分批大小 {BATCH_SIZE}，共 {(total_count + BATCH_SIZE - 1) // BATCH_SIZE} 批")
+
+        # labels 跨批次共享：只存 id→community_id，内存极小
+        labels: Dict[str, str] = {eid: eid for eid in all_entity_ids}
+        del all_entity_ids  # 释放 ID 列表，后续按批次加载完整数据
+
+        for batch_start in range(0, total_count, BATCH_SIZE):
+            batch_entities = await self.repo.get_entities_page(
+                end_user_id, skip=batch_start, limit=BATCH_SIZE
+            )
+            if not batch_entities:
+                break
+
+            batch_ids = [e["id"] for e in batch_entities]
+            batch_embeddings: Dict[str, Optional[List[float]]] = {
+                e["id"]: e.get("name_embedding") for e in batch_entities
+            }
+
+            logger.info(
+                f"[Clustering] 批次 {batch_start // BATCH_SIZE + 1}："
+                f"加载 {len(batch_entities)} 个实体的邻居图..."
+            )
+            neighbors_cache = await self.repo.get_entity_neighbors_for_ids(
+                batch_ids, end_user_id
+            )
+            logger.info(f"[Clustering] 邻居预加载完成，覆盖实体数: {len(neighbors_cache)}")
+
+            for iteration in range(MAX_ITERATIONS):
+                changed = 0
+                for entity in batch_entities:
+                    eid = entity["id"]
+                    neighbors = neighbors_cache.get(eid, [])
+
+                    # 注入跨批次的最新标签（邻居可能在其他批次，labels 里有其最新值）
+                    enriched = []
+                    for nb in neighbors:
+                        nb_copy = dict(nb)
+                        nb_copy["community_id"] = labels.get(nb["id"], nb.get("community_id"))
+                        enriched.append(nb_copy)
+
+                    new_label = _weighted_vote(enriched, batch_embeddings.get(eid))
+                    if new_label and new_label != labels[eid]:
+                        labels[eid] = new_label
+                        changed += 1
+
+                logger.info(
+                    f"[Clustering] 批次 {batch_start // BATCH_SIZE + 1} "
+                    f"迭代 {iteration + 1}/{MAX_ITERATIONS}，标签变化数: {changed}"
+                )
+                if changed == 0:
+                    logger.info("[Clustering] 标签已收敛，提前结束本批迭代")
+                    break
+
+            # 释放本批次的大对象
+            del neighbors_cache, batch_embeddings, batch_entities
+
+        # 所有批次完成，统一写入 Neo4j
+        await self._flush_labels(labels, end_user_id)
+        pre_merge_count = len(set(labels.values()))
+        logger.info(
+            f"[Clustering] 全量迭代完成，共 {pre_merge_count} 个社区，"
+            f"{len(labels)} 个实体，开始后处理合并"
+        )
+
+        all_community_ids = list(set(labels.values()))
+        await self._evaluate_merge(all_community_ids, end_user_id)
+
+        logger.info(
+            f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，"
+            f"{len(labels)} 个实体"
+        )
+
+        # 查询存活社区并生成元数据
+        surviving_communities = await self.repo.get_all_entities(end_user_id)
+        surviving_community_ids = list({
+            e.get("community_id") for e in surviving_communities
+            if e.get("community_id")
+        })
+        logger.info(f"[Clustering] 合并后实际存活社区数: {len(surviving_community_ids)}")
+        await self._generate_community_metadata(surviving_community_ids, end_user_id)
+
+    async def incremental_update(
+        self, new_entity_ids: List[str], end_user_id: str
+    ) -> None:
+        """
+        增量更新：只处理新实体及其邻居，不重跑全图。
+
+        1. 对每个新实体查询邻居
+        2. 加权多数投票决定社区归属
+        3. 若邻居无社区 → 创建新社区
+        4. 若邻居分属多个社区 → 评估是否合并
+        """
+        for entity_id in new_entity_ids:
+            await self._process_single_entity(entity_id, end_user_id)
+
+    # ──────────────────────────────────────────────────────────────────────────
+    # 内部方法
+    # ──────────────────────────────────────────────────────────────────────────
+
+    async def _process_single_entity(
+        self, entity_id: str, end_user_id: str
+    ) -> None:
+        """处理单个新实体的社区分配。"""
+        neighbors = await self.repo.get_entity_neighbors(entity_id, end_user_id)
+
+        # 查询自身 embedding（从邻居查询结果中无法获取，需单独查）
+        self_embedding = await self._get_entity_embedding(entity_id, end_user_id)
+
+        if not neighbors:
+            # 孤立实体：创建单成员社区
+            new_cid = self._new_community_id()
+            await self.repo.upsert_community(new_cid, end_user_id, member_count=1)
+            await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id)
+            logger.debug(f"[Clustering] 孤立实体 {entity_id} → 新社区 {new_cid}")
+            return
+
+        # 统计邻居社区分布
+        community_ids_in_neighbors = set(
+            nb["community_id"] for nb in neighbors if nb.get("community_id")
+        )
+
+        target_cid = _weighted_vote(neighbors, self_embedding)
+
+        if target_cid is None:
+            # 邻居都没有社区，连同新实体一起创建新社区
+            new_cid = self._new_community_id()
+            await self.repo.upsert_community(new_cid, end_user_id)
+            await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id)
+            for nb in neighbors:
+                await self.repo.assign_entity_to_community(
+                    nb["id"], new_cid, end_user_id
+                )
+            await self.repo.refresh_member_count(new_cid, end_user_id)
+            logger.debug(
+                f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}"
+            )
+            await self._generate_community_metadata([new_cid], end_user_id)
+        else:
+            # 加入得票最多的社区
+            await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id)
+            await self.repo.refresh_member_count(target_cid, end_user_id)
+            logger.debug(f"[Clustering] 新实体 {entity_id} → 社区 {target_cid}")
+
+            # 若邻居分属多个社区，评估合并
+            if len(community_ids_in_neighbors) > 1:
+                await self._evaluate_merge(
+                    list(community_ids_in_neighbors), end_user_id
+                )
+            await self._generate_community_metadata([target_cid], end_user_id)
+
+    async def _evaluate_merge(
+        self, community_ids: List[str], end_user_id: str
+    ) -> None:
+        """
+        评估多个社区是否应合并。
+
+        策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。
+        合并时保留成员数最多的社区，其余成员迁移过来。
+
+        全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。
+        """
+        MERGE_THRESHOLD = 0.85
+        BATCH_THRESHOLD = 20  # 超过此数量走批量查询
+
+        community_embeddings: Dict[str, Optional[List[float]]] = {}
+        community_sizes: Dict[str, int] = {}
+
+        if len(community_ids) > BATCH_THRESHOLD:
+            # 批量查询：一次拉取所有社区成员
+            all_members = await self.repo.get_all_community_members_batch(
+                community_ids, end_user_id
+            )
+            for cid in community_ids:
+                members = all_members.get(cid, [])
+                community_sizes[cid] = len(members)
+                valid_embeddings = [
+                    m["name_embedding"] for m in members if m.get("name_embedding")
+                ]
+                if valid_embeddings:
+                    dim = len(valid_embeddings[0])
+                    community_embeddings[cid] = [
+                        sum(e[i] for e in valid_embeddings) / len(valid_embeddings)
+                        for i in range(dim)
+                    ]
+                else:
+                    community_embeddings[cid] = None
+        else:
+            # 增量场景：逐个查询
+            for cid in community_ids:
+                members = await self.repo.get_community_members(cid, end_user_id)
+                community_sizes[cid] = len(members)
+                valid_embeddings = [
+                    m["name_embedding"] for m in members if m.get("name_embedding")
+                ]
+                if valid_embeddings:
+                    dim = len(valid_embeddings[0])
+                    community_embeddings[cid] = [
+                        sum(e[i] for e in valid_embeddings) / len(valid_embeddings)
+                        for i in range(dim)
+                    ]
+                else:
+                    community_embeddings[cid] = None
+
+        # 找出应合并的社区对
+        to_merge: List[tuple] = []
+        cids = list(community_ids)
+        for i in range(len(cids)):
+            for j in range(i + 1, len(cids)):
+                sim = _cosine_similarity(
+                    community_embeddings[cids[i]],
+                    community_embeddings[cids[j]],
+                )
+                if sim > MERGE_THRESHOLD:
+                    to_merge.append((cids[i], cids[j]))
+
+        logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区")
+
+        # 执行合并：逐对处理，每次合并后重新计算合并社区的平均向量
+        # 避免 union-find 链式传递导致语义不相关的社区被间接合并
+        # （A≈B、B≈C 不代表 A≈C，不能因传递性把 A/B/C 全部合并）
+        merged_into: Dict[str, str] = {}  # dissolve → keep 的最终映射
+
+        def get_root(x: str) -> str:
+            """路径压缩，找到 x 当前所属的根社区。"""
+            while x in merged_into:
+                merged_into[x] = merged_into.get(merged_into[x], merged_into[x])
+                x = merged_into[x]
+            return x
+
+        for c1, c2 in to_merge:
+            root1, root2 = get_root(c1), get_root(c2)
+            if root1 == root2:
+                continue
+
+            # 用合并后的最新平均向量重新验证相似度
+            # 防止链式传递：A≈B 合并后 B 的向量已更新，C 必须和新 B 相似才能合并
+            current_sim = _cosine_similarity(
+                community_embeddings.get(root1),
+                community_embeddings.get(root2),
+            )
+            if current_sim <= MERGE_THRESHOLD:
+                # 合并后向量已漂移，不再满足阈值，跳过
+                logger.debug(
+                    f"[Clustering] 跳过合并 {root1} ↔ {root2}，"
+                    f"当前相似度 {current_sim:.3f} ≤ {MERGE_THRESHOLD}"
+                )
+                continue
+
+            keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2
+            dissolve = root2 if keep == root1 else root1
+            merged_into[dissolve] = keep
+
+            members = await self.repo.get_community_members(dissolve, end_user_id)
+            for m in members:
+                await self.repo.assign_entity_to_community(m["id"], keep, end_user_id)
+
+            # 合并后重新计算 keep 的平均向量（加权平均）
+            keep_emb = community_embeddings.get(keep)
+            dissolve_emb = community_embeddings.get(dissolve)
+            keep_size = community_sizes.get(keep, 0)
+            dissolve_size = community_sizes.get(dissolve, 0)
+            total_size = keep_size + dissolve_size
+            if keep_emb and dissolve_emb and total_size > 0:
+                dim = len(keep_emb)
+                community_embeddings[keep] = [
+                    (keep_emb[i] * keep_size + dissolve_emb[i] * dissolve_size) / total_size
+                    for i in range(dim)
+                ]
+            community_embeddings[dissolve] = None
+
+            community_sizes[keep] = total_size
+            community_sizes[dissolve] = 0
+            await self.repo.refresh_member_count(keep, end_user_id)
+            logger.info(
+                f"[Clustering] 社区合并: {dissolve} → {keep}，"
+                f"相似度={current_sim:.3f}，迁移 {len(members)} 个成员"
+            )
+
+    async def _flush_labels(
+        self, labels: Dict[str, str], end_user_id: str
+    ) -> None:
+        """将内存中的标签批量写入 Neo4j。"""
+        # 先创建所有唯一社区节点
+        unique_communities = set(labels.values())
+        for cid in unique_communities:
+            await self.repo.upsert_community(cid, end_user_id)
+
+        # 再批量分配实体
+        for entity_id, community_id in labels.items():
+            await self.repo.assign_entity_to_community(
+                entity_id, community_id, end_user_id
+            )
+
+        # 刷新成员数
+        for cid in unique_communities:
+            await self.repo.refresh_member_count(cid, end_user_id)
+
+    async def _get_entity_embedding(
+        self, entity_id: str, end_user_id: str
+    ) -> Optional[List[float]]:
+        """查询单个实体的 name_embedding。"""
+        try:
+            result = await self.connector.execute_query(
+                "MATCH (e:ExtractedEntity {id: $eid, end_user_id: $uid}) "
+                "RETURN e.name_embedding AS name_embedding",
+                eid=entity_id,
+                uid=end_user_id,
+            )
+            return result[0]["name_embedding"] if result else None
+        except Exception:
+            return None
+
+    @staticmethod
+    def _build_entity_lines(members: List[Dict]) -> List[str]:
+        """将实体列表格式化为 prompt 行，包含 name、aliases、description、example。"""
+        lines = []
+        for m in members:
+            m_name = m.get("name", "")
+            aliases = m.get("aliases") or []
+            description = m.get("description") or ""
+            example = m.get("example") or ""
+            aliases_str = f"（别名：{'、'.join(aliases)}）" if aliases else ""
+            desc_str = f"：{description}" if description else ""
+            example_str = f"（示例：{example}）" if example else ""
+            lines.append(f"- {m_name}{aliases_str}{desc_str}{example_str}")
+        return lines
+
+    async def _generate_community_metadata(
+        self, community_ids: List[str], end_user_id: str
+    ) -> None:
+        """
+        为一个或多个社区生成并写入元数据。
+
+        流程：
+        1. 逐个社区调 LLM 生成 name / summary（串行）
+        2. 收集所有 summary，一次性批量 embed
+        3. 单个社区用 update_community_metadata，多个用 batch_update_community_metadata
+        """
+        if not community_ids:
+            return
+
+        from app.db import get_db_context
+        from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+
+        # --- 阶段1：并发调 LLM 生成每个社区的 name / summary ---
+        async def _build_one(cid: str):
+            members = await self.repo.get_community_members(cid, end_user_id)
+            if not members:
+                return None
+
+            sorted_members = sorted(
+                members,
+                key=lambda m: m.get("activation_value") or 0,
+                reverse=True,
+            )
+            core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")]
+
+            entity_list_str = "\n".join(self._build_entity_lines(members))
+
+            # 方案四：注入社区内实体间关系三元组
+            relationships = await self.repo.get_community_relationships(cid, end_user_id)
+            rel_lines = [
+                f"- {r['subject']} → {r['predicate']} → {r['object']}"
+                for r in relationships
+                if r.get("subject") and r.get("predicate") and r.get("object")
+            ]
+            rel_section = (
+                f"\n实体间关系：\n" + "\n".join(rel_lines)
+                if rel_lines else ""
+            )
+
+            prompt = (
+                f"以下是一组语义相关的实体：\n{entity_list_str}{rel_section}\n\n"
+                f"请为这组实体所代表的主题：\n"
+                f"1. 起一个简洁的中文名称（不超过10个字）\n"
+                f"2. 写一句话摘要（不超过80个字）\n\n"
+                f"严格按以下格式输出，不要有其他内容：\n"
+                f"名称：<名称>\n摘要：<摘要>"
+            )
+            with get_db_context() as db:
+                llm_client = MemoryClientFactory(db).get_llm_client(self.llm_model_id)
+                response = await llm_client.chat([{"role": "user", "content": prompt}])
+                text = response.content if hasattr(response, "content") else str(response)
+
+            name, summary = "", ""
+            for line in text.strip().splitlines():
+                if line.startswith("名称："):
+                    name = line[3:].strip()
+                elif line.startswith("摘要："):
+                    summary = line[3:].strip()
+
+            return {
+                "community_id": cid,
+                "end_user_id": end_user_id,
+                "name": name,
+                "summary": summary,
+                "core_entities": core_entities,
+                "summary_embedding": None,
+            }
+
+        results = await asyncio.gather(
+            *[_build_one(cid) for cid in community_ids],
+            return_exceptions=True,
+        )
+        metadata_list = []
+        for cid, res in zip(community_ids, results):
+            if isinstance(res, Exception):
+                logger.error(f"[Clustering] 社区 {cid} 元数据准备失败: {res}", exc_info=res)
+            elif res is not None:
+                metadata_list.append(res)
+
+        if not metadata_list:
+            return
+
+        # --- 阶段2：批量生成 summary_embedding ---
+        summaries = [m["summary"] for m in metadata_list]
+        with get_db_context() as db:
+            embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
+        embeddings = await embedder.response(summaries)
+        for i, meta in enumerate(metadata_list):
+            meta["summary_embedding"] = embeddings[i] if i < len(embeddings) else None
+
+        # --- 阶段3：写入（单个 or 批量）---
+        if len(metadata_list) == 1:
+            m = metadata_list[0]
+            result = await self.repo.update_community_metadata(
+                community_id=m["community_id"],
+                end_user_id=m["end_user_id"],
+                name=m["name"],
+                summary=m["summary"],
+                core_entities=m["core_entities"],
+                summary_embedding=m["summary_embedding"],
+            )
+            if result:
+                logger.info(f"[Clustering] 社区 {m['community_id']} 元数据写入成功: name={m['name']}, summary={m['summary'][:30]}...")
+            else:
+                logger.warning(f"[Clustering] 社区 {m['community_id']} 元数据写入返回 False")
+        else:
+            ok = await self.repo.batch_update_community_metadata(metadata_list)
+            if ok:
+                logger.info(f"[Clustering] 批量写入 {len(metadata_list)} 个社区元数据成功")
+            else:
+                logger.warning(f"[Clustering] 批量写入社区元数据失败")
+
+    @staticmethod
+    def _new_community_id() -> str:
+        return str(uuid.uuid4())

api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py

@@ -20,7 +20,6 @@ from pydantic import BaseModel, Field
 
 from app.core.memory.models.message_models import DialogData, ConversationMessage, ConversationContext
 from app.core.memory.models.config_models import PruningConfig
-from app.core.memory.utils.config.config_utils import get_pruning_config
 from app.core.memory.utils.prompt.prompt_utils import prompt_env, log_prompt_rendering, log_template_rendering
 from app.core.memory.storage_services.extraction_engine.data_preprocessing.scene_config import (
     SceneConfigRegistry,
@@ -33,6 +32,9 @@ class DialogExtractionResponse(BaseModel):
 
     - is_related：对话与场景的相关性判定。
     - times / ids / amounts / contacts / addresses / keywords：重要信息片段，用来在不相关对话中保留关键消息。
+    - preserve_keywords：情绪/兴趣/爱好/个人观点相关词，包含这些词的消息必须强制保留。
+    - scene_unrelated_snippets：与当前场景无关且无语义关联的消息片段（原文截取），
+      用于高阈值阶段精准删除跨场景内容。
     """
     is_related: bool = Field(...)
     times: List[str] = Field(default_factory=list)
@@ -41,6 +43,8 @@ class DialogExtractionResponse(BaseModel):
     contacts: List[str] = Field(default_factory=list)
     addresses: List[str] = Field(default_factory=list)
     keywords: List[str] = Field(default_factory=list)
+    preserve_keywords: List[str] = Field(default_factory=list, description="情绪/兴趣/爱好/个人观点相关词，包含这些词的消息强制保留")
+    scene_unrelated_snippets: List[str] = Field(default_factory=list,description="与当前场景无关且无语义关联的消息原文片段，高阈值阶段用于精准删除跨场景内容")
 
 
 class MessageImportanceResponse(BaseModel):
@@ -86,26 +90,19 @@ class SemanticPruner:
         self._detailed_prune_logging = True  # 是否启用详细日志
         self._max_debug_msgs_per_dialog = 20  # 每个对话最多记录前N条消息的详细日志
         
-        # 加载场景特定配置（内置场景走专门规则，自定义场景 fallback 到通用规则）
-        self.scene_config: ScenePatterns = SceneConfigRegistry.get_config(
-            self.config.pruning_scene, 
-            fallback_to_generic=True
-        )
+        # 加载统一填充词库
+        self.scene_config: ScenePatterns = SceneConfigRegistry.get_config(self.config.pruning_scene)
         
-        # 判断是否为内置专门场景
-        self._is_builtin_scene = SceneConfigRegistry.is_scene_supported(self.config.pruning_scene)
+        # 本体类型列表：直接使用 ontology_class_infos（name + description）
+        self._ontology_class_infos = getattr(self.config, "ontology_class_infos", None) or []
+        # _ontology_classes 仅用于日志统计
+        self._ontology_classes = [info.class_name for info in self._ontology_class_infos]
         
-        # 自定义场景的本体类型列表（用于注入提示词）
-        self._ontology_classes = getattr(self.config, "ontology_classes", None) or []
-        
-        if self._is_builtin_scene:
-            self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 使用内置专门配置")
+        self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene}")
+        if self._ontology_class_infos:
+            self._log(f"[剪枝-初始化] 注入本体类型({len(self._ontology_class_infos)}个): {self._ontology_classes}")
         else:
-            self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 为自定义场景，使用通用规则 + 本体类型提示词注入")
-            if self._ontology_classes:
-                self._log(f"[剪枝-初始化] 注入本体类型: {self._ontology_classes}")
-            else:
-                self._log(f"[剪枝-初始化] 未找到本体类型，将使用通用提示词")
+            self._log(f"[剪枝-初始化] 未找到本体类型，将使用通用提示词")
         
         # Load Jinja2 template
         self.template = prompt_env.get_template("extracat_Pruning.jinja2")
@@ -117,107 +114,28 @@ class SemanticPruner:
         # 运行日志：收集关键终端输出，便于写入 JSON
         self.run_logs: List[str] = []
 
-    def _is_important_message(self, message: ConversationMessage) -> bool:
-        """基于启发式规则识别重要信息消息，优先保留。
-
-        改进版：使用场景特定的模式进行识别
-        - 根据 pruning_scene 动态加载对应的识别规则
-        - 支持教育、在线服务、外呼三个场景的特定模式
-        """
-        text = message.msg.strip()
-        if not text:
-            return False
-        
-        # 使用场景特定的模式
-        all_patterns = (
-            self.scene_config.high_priority_patterns +
-            self.scene_config.medium_priority_patterns +
-            self.scene_config.low_priority_patterns
-        )
-        
-        for pattern, _ in all_patterns:
-            if re.search(pattern, text, flags=re.IGNORECASE):
-                return True
-        
-        # 检查是否为问句（以问号结尾或包含疑问词）
-        if text.endswith("？") or text.endswith("?"):
-            return True
-        
-        # 检查是否包含问句关键词
-        if any(keyword in text for keyword in self.scene_config.question_keywords):
-            return True
-        
-        # 检查是否包含决策性关键词
-        if any(keyword in text for keyword in self.scene_config.decision_keywords):
-            return True
-            
-        return False
-    
-    def _importance_score(self, message: ConversationMessage) -> int:
-        """为重要消息打分，用于在保留比例内优先保留更关键的内容。
-
-        改进版：使用场景特定的权重体系（0-10分）
-        - 根据场景动态调整不同信息类型的权重
-        - 高优先级模式：4-6分
-        - 中优先级模式：2-3分
-        - 低优先级模式：1分
-        """
-        text = message.msg.strip()
-        score = 0
-        
-        # 使用场景特定的权重
-        for pattern, weight in self.scene_config.high_priority_patterns:
-            if re.search(pattern, text, flags=re.IGNORECASE):
-                score += weight
-        
-        for pattern, weight in self.scene_config.medium_priority_patterns:
-            if re.search(pattern, text, flags=re.IGNORECASE):
-                score += weight
-        
-        for pattern, weight in self.scene_config.low_priority_patterns:
-            if re.search(pattern, text, flags=re.IGNORECASE):
-                score += weight
-        
-        # 问句加分
-        if text.endswith("？") or text.endswith("?"):
-            score += 2
-        
-        # 包含问句关键词加分
-        if any(keyword in text for keyword in self.scene_config.question_keywords):
-            score += 1
-        
-        # 包含决策性关键词加分
-        if any(keyword in text for keyword in self.scene_config.decision_keywords):
-            score += 2
-        
-        # 长度加分（较长的消息通常包含更多信息）
-        if len(text) > 50:
-            score += 1
-        if len(text) > 100:
-            score += 1
-            
-        return min(score, 10)  # 最高10分
+    # _is_important_message 和 _importance_score 已移除：
+    # 重要性判断完全由 extracat_Pruning.jinja2 提示词 + LLM 的 preserve_tokens 机制承担。
+    # LLM 根据注入的本体工程类型语义识别需要保护的内容，无需硬编码正则规则。
 
     def _is_filler_message(self, message: ConversationMessage) -> bool:
         """检测典型寒暄/口头禅/确认类短消息。
 
-        改进版：更严格的填充消息判断，避免误删场景相关内容
-        满足以下之一视为填充消息：
-        - 纯标点或空白
-        - 在场景特定填充词库中（精确匹配）
-        - 纯表情符号
-        - 常见寒暄（精确匹配短语）
-        
-        注意：不再使用长度判断，避免误删短但重要的消息
+        判断顺序：
+        1. 空消息
+        2. 场景特定填充词库精确匹配
+        3. 常见寒暄精确匹配
+        4. 组合寒暄模式（前缀+后缀组合，如"好的谢谢"、"同学你好"、"明白了"）
+        5. 纯表情/标点
         """
         t = message.msg.strip()
         if not t:
             return True
-        
+
         # 检查是否在场景特定填充词库中（精确匹配）
         if t in self.scene_config.filler_phrases:
             return True
-        
+
         # 常见寒暄和问候（精确匹配，避免误删）
         common_greetings = {
             "在吗", "在不在", "在呢", "在的",
@@ -229,25 +147,60 @@ class SemanticPruner:
         }
         if t in common_greetings:
             return True
-        
+
+        # 组合寒暄模式：短消息（≤15字）且完全由寒暄成分构成
+        # 策略：将消息拆分后，每个片段都能在填充词库或常见寒暄中找到，则整体为填充
+        if len(t) <= 15:
+            # 确认+称呼/感谢组合，如"好的谢谢"、"明白了"、"知道了谢谢"
+            _confirm_prefixes = {"好的", "好", "嗯", "嗯嗯", "哦", "明白", "明白了", "知道了", "了解", "收到", "没问题"}
+            _thanks_suffixes = {"谢谢", "谢谢你", "谢谢您", "多谢", "感谢", "谢了"}
+            _greeting_suffixes = {"你好", "您好", "老师好", "同学好", "大家好"}
+            _greeting_prefixes = {"同学", "老师", "您好", "你好"}
+            _close_patterns = {
+                "没有了", "没事了", "没问题了", "好了", "行了", "可以了",
+                "不用了", "不需要了", "就这样", "就这样吧", "那就这样",
+            }
+            _polite_responses = {
+                "不客气", "不用谢", "没关系", "没事", "应该的", "这是我应该做的",
+            }
+
+            # 规则1：确认词 + 感谢词（如"好的谢谢"、"嗯谢谢"）
+            for cp in _confirm_prefixes:
+                for ts in _thanks_suffixes:
+                    if t == cp + ts or t == cp + "，" + ts or t == cp + "," + ts:
+                        return True
+
+            # 规则2：称呼前缀 + 问候（如"同学你好"、"老师好"）
+            for gp in _greeting_prefixes:
+                for gs in _greeting_suffixes:
+                    if t == gp + gs or t.startswith(gp) and t.endswith("好"):
+                        return True
+
+            # 规则3：结束语 + 感谢（如"没有了，谢谢老师"、"没有了谢谢"）
+            for cp in _close_patterns:
+                if t.startswith(cp):
+                    remainder = t[len(cp):].lstrip("，,、 ")
+                    if not remainder or any(remainder.startswith(ts) for ts in _thanks_suffixes):
+                        return True
+
+            # 规则4：礼貌回应（如"不客气，祝你考试顺利"——前缀是礼貌词，后半是祝福套话）
+            for pr in _polite_responses:
+                if t.startswith(pr):
+                    remainder = t[len(pr):].lstrip("，,、 ")
+                    # 后半是祝福/套话（不含实质信息）
+                    if not remainder or re.match(r"^(祝|希望|期待|加油|顺利|好好|保重)", remainder):
+                        return True
+
+            # 规则5：纯确认词加"了"后缀（如"明白了"、"知道了"、"好了"）
+            _confirm_base = {"明白", "知道", "了解", "收到", "好", "行", "可以", "没问题"}
+            for cb in _confirm_base:
+                if t == cb + "了" or t == cb + "了。" or t == cb + "了！":
+                    return True
+
         # 检查是否为纯表情符号（方括号包裹）
         if re.fullmatch(r"(\[[^\]]+\])+", t):
             return True
         
-        # 检查是否为纯emoji（Unicode表情）
-        emoji_pattern = re.compile(
-            "["
-            "\U0001F600-\U0001F64F"  # 表情符号
-            "\U0001F300-\U0001F5FF"  # 符号和象形文字
-            "\U0001F680-\U0001F6FF"  # 交通和地图符号
-            "\U0001F1E0-\U0001F1FF"  # 旗帜
-            "\U00002702-\U000027B0"
-            "\U000024C2-\U0001F251"
-            "]+", flags=re.UNICODE
-        )
-        if emoji_pattern.fullmatch(t):
-            return True
-        
         # 纯标点符号
         if re.fullmatch(r"[。！？,.!?…·\s]+", t):
             return True
@@ -432,15 +385,13 @@ class SemanticPruner:
 
         rendered = self.template.render(
             pruning_scene=self.config.pruning_scene,
-            is_builtin_scene=self._is_builtin_scene,
-            ontology_classes=self._ontology_classes,
+            ontology_class_infos=self._ontology_class_infos,
             dialog_text=dialog_text,
             language=self.language
         )
         log_template_rendering("extracat_Pruning.jinja2", {
             "pruning_scene": self.config.pruning_scene,
-            "is_builtin_scene": self._is_builtin_scene,
-            "ontology_classes_count": len(self._ontology_classes),
+            "ontology_class_infos_count": len(self._ontology_class_infos),
             "language": self.language
         })
         log_prompt_rendering("pruning-extract", rendered)
@@ -480,6 +431,183 @@ class SemanticPruner:
                     )
                     return fallback_response
 
+    def _get_pruning_mode(self) -> str:
+        """根据 pruning_threshold 返回当前剪枝阶段。
+
+        - 低阈值 [0.0, 0.3)：conservative  只删填充，保留所有实质内容
+        - 中阈值 [0.3, 0.6)：semantic      保留场景相关 + 有语义关联的内容，删除无关联内容
+        - 高阈值 [0.6, 0.9]：strict        只保留场景相关内容，跨场景内容可被删除
+        """
+        t = float(self.config.pruning_threshold)
+        if t < 0.3:
+            return "conservative"
+        elif t < 0.6:
+            return "semantic"
+        else:
+            return "strict"
+
+    def _apply_related_dialog_pruning(
+        self,
+        msgs: List[ConversationMessage],
+        extraction: "DialogExtractionResponse",
+        dialog_label: str,
+        pruning_mode: str,
+    ) -> List[ConversationMessage]:
+        """相关对话统一剪枝入口，消除 prune_dialog / prune_dataset 中的重复逻辑。
+
+        - conservative：只删填充
+        - semantic / strict：场景感知剪枝
+        """
+        if pruning_mode == "conservative":
+            preserve_tokens = self._build_preserve_tokens(extraction)
+            return self._prune_fillers_only(msgs, preserve_tokens, dialog_label)
+        else:
+            return self._prune_with_scene_filter(msgs, extraction, dialog_label, pruning_mode)
+
+    def _prune_fillers_only(
+        self,
+        msgs: List[ConversationMessage],
+        preserve_tokens: List[str],
+        dialog_label: str,
+    ) -> List[ConversationMessage]:
+        """相关对话专用：只删填充消息，LLM 保护消息和实质内容一律保留。
+
+        不受 pruning_threshold 约束，删多少算多少（填充有多少删多少）。
+        至少保留 1 条消息。
+        注意：填充检测优先于 preserve_tokens 保护——填充消息本身无信息价值，
+        即使 LLM 误将其关键词放入 preserve_tokens 也应删除。
+        """
+        to_delete_ids: set = set()
+        for m in msgs:
+            # 填充检测优先：先判断是否为填充，再看 LLM 保护
+            if self._is_filler_message(m):
+                to_delete_ids.add(id(m))
+                self._log(f"  [填充] '{m.msg[:40]}' → 删除")
+                continue
+            if self._msg_matches_tokens(m, preserve_tokens):
+                self._log(f"  [保护] '{m.msg[:40]}' → LLM保护，跳过")
+
+        kept = [m for m in msgs if id(m) not in to_delete_ids]
+        if not kept and msgs:
+            kept = [msgs[0]]
+
+        deleted = len(msgs) - len(kept)
+        self._log(
+            f"[剪枝-相关] {dialog_label} 总消息={len(msgs)} "
+            f"填充删除={deleted} 保留={len(kept)}"
+        )
+        return kept
+
+    def _prune_with_scene_filter(
+        self,
+        msgs: List[ConversationMessage],
+        extraction: "DialogExtractionResponse",
+        dialog_label: str,
+        mode: str,
+    ) -> List[ConversationMessage]:
+        """场景感知剪枝，供 semantic / strict 两个阈值档位调用。
+
+        本函数体现剪枝系统的三层递进逻辑：
+
+        第一层（conservative，阈值 < 0.3）：
+            不进入本函数，由 _prune_fillers_only 处理。
+            保留标准：只问"有没有信息量"，填充消息（嗯/好的/哈哈等）删除，其余一律保留。
+
+        第二层（semantic，阈值 [0.3, 0.6)）：
+            保留标准：内容价值优先，场景相关性是参考而非唯一标准。
+            - 填充消息 → 删除（最高优先级）
+            - 场景相关消息 → 保留
+            - 场景无关消息 → 有两次豁免机会：
+                1. 命中 scene_preserve_tokens（LLM 标记的关键词/时间/金额等）→ 保留
+                2. 含情感词（感觉/压力/开心等）→ 保留（情感内容有记忆价值）
+                3. 两次豁免均未命中 → 删除
+
+        第三层（strict，阈值 [0.6, 0.9]）：
+            保留标准：场景相关性优先，无任何豁免。
+            - 填充消息 → 删除（最高优先级）
+            - 场景相关消息 → 保留
+            - 场景无关消息 → 直接删除，preserve_keywords 和情感词在此模式下均不生效
+
+        至少保留 1 条消息（兜底取第一条）。
+        """
+        # strict 模式收窄保护范围：只保护结构化关键信息（时间/编号/金额/联系方式/地址），
+        # 不保护 keywords / preserve_keywords，让场景过滤能删掉更多内容。
+        # semantic 模式完整保护：包含 LLM 抽取的所有重要片段（含 keywords 和 preserve_keywords）。
+        if mode == "strict":
+            scene_preserve_tokens = (
+                extraction.times + extraction.ids + extraction.amounts +
+                extraction.contacts + extraction.addresses
+            )
+        else:
+            scene_preserve_tokens = self._build_preserve_tokens(extraction)
+
+        unrelated_snippets = extraction.scene_unrelated_snippets or []
+
+        to_delete_ids: set = set()
+        for m in msgs:
+            msg_text = m.msg.strip()
+
+            # 第一优先级：填充消息无论模式直接删除，不参与后续场景判断
+            if self._is_filler_message(m):
+                to_delete_ids.add(id(m))
+                self._log(f"  [填充] '{msg_text[:40]}' → 删除")
+                continue
+
+            # 双向包含匹配：处理 LLM 返回片段与原始消息文本长度不完全一致的情况
+            is_scene_unrelated = any(
+                snip and (snip in msg_text or msg_text in snip)
+                for snip in unrelated_snippets
+            )
+
+            if is_scene_unrelated:
+                if mode == "strict":
+                    # strict：场景无关直接删除，不做任何豁免
+                    # 场景相关性是唯一裁决标准，preserve_keywords 在此模式下不生效
+                    to_delete_ids.add(id(m))
+                    self._log(f"  [场景无关-严格] '{msg_text[:40]}' → 删除")
+                elif mode == "semantic":
+                    # semantic：场景无关但有内容价值 → 保留
+                    # 豁免第一层：命中 scene_preserve_tokens（关键词/结构化信息保护）
+                    if self._msg_matches_tokens(m, scene_preserve_tokens):
+                        self._log(f"  [保护] '{msg_text[:40]}' → 场景关键词保护，保留")
+                    else:
+                        # 豁免第二层：含情感词，认为有情境记忆价值，即使场景无关也保留
+                        has_contextual_emotion = any(
+                            word in msg_text
+                            for word in ["感觉", "觉得", "心情", "开心", "难过", "高兴", "沮丧",
+                                         "喜欢", "讨厌", "爱", "恨", "担心", "害怕", "兴奋",
+                                         "压力", "累", "疲惫", "烦", "焦虑", "委屈", "感动"]
+                        )
+                        if not has_contextual_emotion:
+                            to_delete_ids.add(id(m))
+                            self._log(f"  [场景无关-语义] '{msg_text[:40]}' → 删除（无情感关联）")
+                        else:
+                            self._log(f"  [场景关联-保留] '{msg_text[:40]}' → 有情感关联，保留")
+            else:
+                # 不在 scene_unrelated_snippets 中 → 场景相关，直接保留
+                if self._msg_matches_tokens(m, scene_preserve_tokens):
+                    self._log(f"  [保护] '{msg_text[:40]}' → LLM保护，跳过")
+                # else: 普通场景相关消息，保留，不输出日志
+
+        kept = [m for m in msgs if id(m) not in to_delete_ids]
+        if not kept and msgs:
+            kept = [msgs[0]]
+
+        deleted = len(msgs) - len(kept)
+        self._log(
+            f"[剪枝-{mode}] {dialog_label} 总消息={len(msgs)} "
+            f"删除={deleted} 保留={len(kept)}"
+        )
+        return kept
+
+    def _build_preserve_tokens(self, extraction: "DialogExtractionResponse") -> List[str]:
+        """统一构建 preserve_tokens，合并 LLM 抽取的所有重要片段。"""
+        return (
+            extraction.times + extraction.ids + extraction.amounts +
+            extraction.contacts + extraction.addresses + extraction.keywords +
+            extraction.preserve_keywords
+        )
+
     def _msg_matches_tokens(self, message: ConversationMessage, tokens: List[str]) -> bool:
         """判断消息是否包含任意抽取到的重要片段。"""
         if not tokens:
@@ -500,66 +628,62 @@ class SemanticPruner:
 
         proportion = float(self.config.pruning_threshold)
         extraction = await self._extract_dialog_important(dialog.content)
+        pruning_mode = self._get_pruning_mode()
+        self._log(f"[剪枝-模式] 阈值={proportion} → 模式={pruning_mode}")
+
         if extraction.is_related:
-            # 相关对话不剪枝
+            kept = self._apply_related_dialog_pruning(
+                dialog.context.msgs, extraction, f"对话ID={dialog.id}", pruning_mode
+            )
+            dialog.context = ConversationContext(msgs=kept)
             return dialog
 
-        # 在不相关对话中，识别重要/不重要消息
-        tokens = extraction.times + extraction.ids + extraction.amounts + extraction.contacts + extraction.addresses + extraction.keywords
+        # 在不相关对话中，LLM 已通过 preserve_tokens 标记需要保护的内容
+        preserve_tokens = self._build_preserve_tokens(extraction)
         msgs = dialog.context.msgs
-        imp_unrel_msgs: List[ConversationMessage] = []
-        unimp_unrel_msgs: List[ConversationMessage] = []
+
+        # 分类：填充 / 其他可删（LLM保护消息通过不加入任何桶来隐式保护）
+        filler_ids: set = set()
+        deletable: List[ConversationMessage] = []
+
         for m in msgs:
-            if self._msg_matches_tokens(m, tokens) or self._is_important_message(m):
-                imp_unrel_msgs.append(m)
+            if self._msg_matches_tokens(m, preserve_tokens):
+                pass  # 保护消息：不加入任何桶，不会被删除
+            elif self._is_filler_message(m):
+                filler_ids.add(id(m))
             else:
-                unimp_unrel_msgs.append(m)
-        # 计算总删除目标数量
+                deletable.append(m)
+
+        # 计算删除目标
         total_unrel = len(msgs)
         delete_target = int(total_unrel * proportion)
         if proportion > 0 and total_unrel > 0 and delete_target == 0:
             delete_target = 1
-        imp_del_cap = min(int(len(imp_unrel_msgs) * proportion), len(imp_unrel_msgs))
-        unimp_del_cap = len(unimp_unrel_msgs)
-        max_capacity = max(0, len(msgs) - 1)
-        max_deletable = min(imp_del_cap + unimp_del_cap, max_capacity)
+        max_deletable = min(len(filler_ids) + len(deletable), max(0, total_unrel - 1))
         delete_target = min(delete_target, max_deletable)
-        # 删除配额分配
-        del_unimp = min(delete_target, unimp_del_cap)
-        rem = delete_target - del_unimp
-        del_imp = min(rem, imp_del_cap)
 
-        # 选取删除集合
-        unimp_delete_ids = []
-        imp_delete_ids = []
-        if del_unimp > 0:
-            # 按出现顺序选取前 del_unimp 条不重要消息进行删除（确定性、可复现）
-            unimp_delete_ids = [id(m) for m in unimp_unrel_msgs[:del_unimp]]
-        if del_imp > 0:
-            imp_sorted = sorted(imp_unrel_msgs, key=lambda m: self._importance_score(m))
-            imp_delete_ids = [id(m) for m in imp_sorted[:del_imp]]
-
-        # 统计实际删除数量（重要/不重要）
-        actual_unimp_deleted = 0
-        actual_imp_deleted = 0
-        kept_msgs = []
-        delete_targets = set(unimp_delete_ids) | set(imp_delete_ids)
+        # 优先删填充，再删其他可删消息（按出现顺序）
+        to_delete_ids: set = set()
         for m in msgs:
-            mid = id(m)
-            if mid in delete_targets:
-                if mid in set(unimp_delete_ids) and actual_unimp_deleted < del_unimp:
-                    actual_unimp_deleted += 1
-                    continue
-                if mid in set(imp_delete_ids) and actual_imp_deleted < del_imp:
-                    actual_imp_deleted += 1
-                    continue
-            kept_msgs.append(m)
+            if len(to_delete_ids) >= delete_target:
+                break
+            if id(m) in filler_ids:
+                to_delete_ids.add(id(m))
+        for m in deletable:
+            if len(to_delete_ids) >= delete_target:
+                break
+            to_delete_ids.add(id(m))
+
+        kept_msgs = [m for m in msgs if id(m) not in to_delete_ids]
         if not kept_msgs and msgs:
             kept_msgs = [msgs[0]]
 
-        deleted_total = actual_unimp_deleted + actual_imp_deleted
+        deleted_total = len(msgs) - len(kept_msgs)
+        protected_count = len(msgs) - len(filler_ids) - len(deletable)
         self._log(
-            f"[剪枝-对话] 对话ID={dialog.id} 总消息={len(msgs)} 删除目标={delete_target} 实删={deleted_total} 保留={len(kept_msgs)}"
+            f"[剪枝-对话] 对话ID={dialog.id} 总消息={len(msgs)} "
+            f"(保护={protected_count} 填充={len(filler_ids)} 可删={len(deletable)}) "
+            f"删除目标={delete_target} 实删={deleted_total} 保留={len(kept_msgs)}"
         )
 
         dialog.context = ConversationContext(msgs=kept_msgs)
@@ -590,140 +714,192 @@ class SemanticPruner:
         self._log(
             f"[剪枝-数据集] 对话总数={len(dialogs)} 场景={self.config.pruning_scene} 删除比例={proportion} 开关={self.config.pruning_switch} 模式=消息级独立判断"
         )
-        
+
+        pruning_mode = self._get_pruning_mode()
+        self._log(f"[剪枝-数据集] 阈值={proportion} → 剪枝阶段={pruning_mode}")
+
         result: List[DialogData] = []
         total_original_msgs = 0
         total_deleted_msgs = 0
-        
-        for d_idx, dd in enumerate(dialogs):
+
+        # 统计对象：直接收集结构化数据，无需事后正则解析
+        stats = {
+            "scene": self.config.pruning_scene,
+            "dialog_total": len(dialogs),
+            "deletion_ratio": proportion,
+            "enabled": self.config.pruning_switch,
+            "pruning_mode": pruning_mode,
+            "related_count": 0,
+            "unrelated_count": 0,
+            "related_indices": [],
+            "unrelated_indices": [],
+            "total_deleted_messages": 0,
+            "remaining_dialogs": 0,
+            "dialogs": [],
+        }
+
+        # 并发执行所有对话的 LLM 抽取（获取 preserve_keywords 等保护信息）
+        semaphore = asyncio.Semaphore(self.max_concurrent)
+
+        async def extract_with_semaphore(dd: DialogData) -> DialogExtractionResponse:
+            async with semaphore:
+                try:
+                    return await self._extract_dialog_important(dd.content)
+                except Exception as e:
+                    self._log(f"[剪枝-LLM] 对话抽取失败，使用降级策略: {str(e)[:100]}")
+                    return DialogExtractionResponse(is_related=True)
+
+        extraction_tasks = [extract_with_semaphore(dd) for dd in dialogs]
+        extraction_results: List[DialogExtractionResponse] = await asyncio.gather(*extraction_tasks)
+
+        for d_idx, (dd, extraction) in enumerate(zip(dialogs, extraction_results)):
             msgs = dd.context.msgs
             original_count = len(msgs)
             total_original_msgs += original_count
-            
-            # ========== 问答对保护（已注释，暂不启用，留作观察） ==========
-            # qa_pairs = self._identify_qa_pairs(msgs)
-            # protected_indices = self._get_protected_indices(msgs, qa_pairs, window_size=0)
-            # ========================================================
-            
-            # 消息级分类：每条消息独立判断
-            important_msgs = []  # 重要消息（保留）
-            unimportant_msgs = []  # 不重要消息（可删除）
-            filler_msgs = []  # 填充消息（优先删除）
-            
-            # 判断是否需要详细日志（仅对前N条消息记录）
+
+            # 相关对话：根据阶段决定处理力度
+            if extraction.is_related:
+                stats["related_count"] += 1
+                stats["related_indices"].append(d_idx + 1)
+                kept = self._apply_related_dialog_pruning(
+                    msgs, extraction, f"对话 {d_idx+1}", pruning_mode
+                )
+                deleted_count = original_count - len(kept)
+                total_deleted_msgs += deleted_count
+                dd.context.msgs = kept
+                result.append(dd)
+                stats["dialogs"].append({
+                    "index": d_idx + 1,
+                    "is_related": True,
+                    "total_messages": original_count,
+                    "deleted": deleted_count,
+                    "kept": len(kept),
+                })
+                continue
+
+            stats["unrelated_count"] += 1
+            stats["unrelated_indices"].append(d_idx + 1)
+
+            # 从 LLM 抽取结果中获取所有需要保留的 token
+            preserve_tokens = self._build_preserve_tokens(extraction)
+
+            # 判断是否需要详细日志
             should_log_details = self._detailed_prune_logging and original_count <= self._max_debug_msgs_per_dialog
             if self._detailed_prune_logging and original_count > self._max_debug_msgs_per_dialog:
                 self._log(f"  对话[{d_idx}]消息数={original_count}，仅采样前{self._max_debug_msgs_per_dialog}条进行详细日志")
-            
+
+            if extraction.preserve_keywords:
+                self._log(f"  对话[{d_idx}] LLM抽取到情绪/兴趣保护词: {extraction.preserve_keywords}")
+
+            # 消息级分类：LLM保护 / 填充 / 其他可删
+            llm_protected_msgs = []  # LLM 保护消息（preserve_tokens 命中）：绝对不可删除
+            filler_msgs = []         # 填充消息（优先删除）
+            deletable_msgs = []      # 其余消息（按比例删除）
+
             for idx, m in enumerate(msgs):
                 msg_text = m.msg.strip()
-                
-                # ========== 问答对保护判断（已注释） ==========
-                # if idx in protected_indices:
-                #     important_msgs.append((idx, m))
-                #     self._log(f"  [{idx}] '{msg_text[:30]}...' → 重要（问答对保护）")
-                # ==========================================
-                
-                # 填充消息（寒暄、表情等）
-                if self._is_filler_message(m):
+
+                if self._msg_matches_tokens(m, preserve_tokens):
+                    llm_protected_msgs.append((idx, m))
+                    if should_log_details or idx < self._max_debug_msgs_per_dialog:
+                        self._log(f"  [{idx}] '{msg_text[:30]}...' → 保护（LLM，不可删）")
+                elif self._is_filler_message(m):
                     filler_msgs.append((idx, m))
                     if should_log_details or idx < self._max_debug_msgs_per_dialog:
                         self._log(f"  [{idx}] '{msg_text[:30]}...' → 填充")
-                # 重要信息（学号、成绩、时间、金额等）
-                elif self._is_important_message(m):
-                    important_msgs.append((idx, m))
-                    if should_log_details or idx < self._max_debug_msgs_per_dialog:
-                        self._log(f"  [{idx}] '{msg_text[:30]}...' → 重要（场景规则）")
-                # 其他消息
                 else:
-                    unimportant_msgs.append((idx, m))
+                    deletable_msgs.append((idx, m))
                     if should_log_details or idx < self._max_debug_msgs_per_dialog:
-                        self._log(f"  [{idx}] '{msg_text[:30]}...' → 不重要")
-            
+                        self._log(f"  [{idx}] '{msg_text[:30]}...' → 可删")
+
+            # important_msgs 仅用于日志统计
+            important_msgs = llm_protected_msgs
+
             # 计算删除配额
             delete_target = int(original_count * proportion)
             if proportion > 0 and original_count > 0 and delete_target == 0:
                 delete_target = 1
-            
+
             # 确保至少保留1条消息
             max_deletable = max(0, original_count - 1)
             delete_target = min(delete_target, max_deletable)
-            
-            # 删除策略：优先删除填充消息，再删除不重要消息
+
+            # 删除策略：优先删填充消息，再按出现顺序删其余可删消息
             to_delete_indices = set()
-            deleted_details = []  # 记录删除的消息详情
-            
+            deleted_details = []
+
             # 第一步：删除填充消息
-            filler_to_delete = min(len(filler_msgs), delete_target)
-            for i in range(filler_to_delete):
-                idx, msg = filler_msgs[i]
+            for idx, msg in filler_msgs:
+                if len(to_delete_indices) >= delete_target:
+                    break
                 to_delete_indices.add(idx)
                 deleted_details.append(f"[{idx}] 填充: '{msg.msg[:50]}'")
-            
-            # 第二步：如果还需要删除，删除不重要消息
-            remaining_quota = delete_target - len(to_delete_indices)
-            if remaining_quota > 0:
-                unimp_to_delete = min(len(unimportant_msgs), remaining_quota)
-                for i in range(unimp_to_delete):
-                    idx, msg = unimportant_msgs[i]
-                    to_delete_indices.add(idx)
-                    deleted_details.append(f"[{idx}] 不重要: '{msg.msg[:50]}'")
-            
-            # 第三步：如果还需要删除，按重要性分数删除重要消息
-            remaining_quota = delete_target - len(to_delete_indices)
-            if remaining_quota > 0 and important_msgs:
-                # 按重要性分数排序（分数低的优先删除）
-                imp_sorted = sorted(important_msgs, key=lambda x: self._importance_score(x[1]))
-                imp_to_delete = min(len(imp_sorted), remaining_quota)
-                for i in range(imp_to_delete):
-                    idx, msg = imp_sorted[i]
-                    to_delete_indices.add(idx)
-                    score = self._importance_score(msg)
-                    deleted_details.append(f"[{idx}] 重要(分数{score}): '{msg.msg[:50]}'")
-            
+
+            # 第二步：如果还需要删除，按出现顺序删可删消息
+            for idx, msg in deletable_msgs:
+                if len(to_delete_indices) >= delete_target:
+                    break
+                to_delete_indices.add(idx)
+                deleted_details.append(f"[{idx}] 可删: '{msg.msg[:50]}'")
+
             # 执行删除
             kept_msgs = []
             for idx, m in enumerate(msgs):
                 if idx not in to_delete_indices:
                     kept_msgs.append(m)
-            
+
             # 确保至少保留1条
             if not kept_msgs and msgs:
                 kept_msgs = [msgs[0]]
-            
+
             dd.context.msgs = kept_msgs
             deleted_count = original_count - len(kept_msgs)
             total_deleted_msgs += deleted_count
-            
+
             # 输出删除详情
             if deleted_details:
                 self._log(f"[剪枝-删除详情] 对话 {d_idx+1} 删除了以下消息:")
                 for detail in deleted_details:
                     self._log(f"  {detail}")
-            
+
             # ========== 问答对统计（已注释） ==========
             # qa_info = f"，问答对={len(qa_pairs)}" if qa_pairs else ""
             # ========================================
-            
+
             self._log(
                 f"[剪枝-对话] 对话 {d_idx+1} 总消息={original_count} "
-                f"(重要={len(important_msgs)} 不重要={len(unimportant_msgs)} 填充={len(filler_msgs)}) "
+                f"(保护={len(important_msgs)} 填充={len(filler_msgs)} 可删={len(deletable_msgs)}) "
                 f"删除={deleted_count} 保留={len(kept_msgs)}"
             )
-            
-            result.append(dd)
-        
-        self._log(f"[剪枝-数据集] 剩余对话数={len(result)}")
 
-        # 保存日志
+            stats["dialogs"].append({
+                "index": d_idx + 1,
+                "is_related": False,
+                "total_messages": original_count,
+                "protected": len(important_msgs),
+                "fillers": len(filler_msgs),
+                "deletable": len(deletable_msgs),
+                "deleted": deleted_count,
+                "kept": len(kept_msgs),
+            })
+
+            result.append(dd)
+
+        # 补全统计对象
+        stats["total_deleted_messages"] = total_deleted_msgs
+        stats["remaining_dialogs"] = len(result)
+
+        self._log(f"[剪枝-数据集] 剩余对话数={len(result)}")
+        self._log(f"[剪枝-数据集] 相关对话数={stats['related_count']} 不相关对话数={stats['unrelated_count']}")
+        self._log(f"[剪枝-数据集] 总删除 {total_deleted_msgs} 条")
+
+        # 直接序列化统计对象，无需正则解析
         try:
             from app.core.config import settings
             settings.ensure_memory_output_dir()
             log_output_path = settings.get_memory_output_path("pruned_terminal.json")
-            sanitized_logs = [self._sanitize_log_line(l) for l in self.run_logs]
-            payload = self._parse_logs_to_structured(sanitized_logs)
             with open(log_output_path, "w", encoding="utf-8") as f:
-                json.dump(payload, f, ensure_ascii=False, indent=2)
+                json.dump(stats, f, ensure_ascii=False, indent=2)
         except Exception as e:
             self._log(f"[剪枝-数据集] 保存终端输出日志失败：{e}")
 
@@ -731,7 +907,7 @@ class SemanticPruner:
         if not result:
             print("警告: 语义剪枝后数据集为空，已回退为未剪枝数据以避免流程中断")
             return dialogs
-        
+
         return result
 
     def _log(self, msg: str) -> None:
@@ -743,114 +919,4 @@ class SemanticPruner:
             pass
         print(msg)
 
-    def _sanitize_log_line(self, line: str) -> str:
-        """移除行首的方括号标签前缀，例如 [剪枝-数据集] 或 [剪枝-对话]。"""
-        try:
-            return re.sub(r"^\[[^\]]+\]\s*", "", line)
-        except Exception:
-            return line
 
-    def _parse_logs_to_structured(self, logs: List[str]) -> dict:
-        """将已去前缀的日志列表解析为结构化 JSON，便于数据对接。"""
-        summary = {
-            "scene": self.config.pruning_scene,
-            "dialog_total": None,
-            "deletion_ratio": None,
-            "enabled": None,
-            "related_count": None,
-            "unrelated_count": None,
-            "related_indices": [],
-            "unrelated_indices": [],
-            "total_deleted_messages": None,
-            "remaining_dialogs": None,
-        }
-        dialogs = []
-
-        # 解析函数
-        def parse_int(value: str) -> Optional[int]:
-            try:
-                return int(value)
-            except Exception:
-                return None
-
-        def parse_float(value: str) -> Optional[float]:
-            try:
-                return float(value)
-            except Exception:
-                return None
-
-        def parse_indices(s: str) -> List[int]:
-            s = s.strip()
-            if not s:
-                return []
-            parts = [p.strip() for p in s.split(",") if p.strip()]
-            out: List[int] = []
-            for p in parts:
-                try:
-                    out.append(int(p))
-                except Exception:
-                    pass
-            return out
-
-        # 正则
-        re_header = re.compile(r"对话总数=(\d+)\s+场景=([^\s]+)\s+删除比例=([0-9.]+)\s+开关=(True|False)")
-        re_counts = re.compile(r"相关对话数=(\d+)\s+不相关对话数=(\d+)")
-        re_indices = re.compile(r"相关对话：第\[(.*?)\]段；不相关对话：第\[(.*?)\]段")
-        re_dialog = re.compile(r"对话\s+(\d+)\s+总消息=(\d+)\s+分配删除=(\d+)\s+实删=(\d+)\s+保留=(\d+)")
-        re_total_del = re.compile(r"总删除\s+(\d+)\s+条")
-        re_remaining = re.compile(r"剩余对话数=(\d+)")
-
-        for line in logs:
-            # 第一行：总览
-            m = re_header.search(line)
-            if m:
-                summary["dialog_total"] = parse_int(m.group(1))
-                # 顶层 scene 依配置，这里不覆盖，但也可校验 m.group(2)
-                summary["deletion_ratio"] = parse_float(m.group(3))
-                summary["enabled"] = True if m.group(4) == "True" else False
-                continue
-
-            # 第二行：相关/不相关数量
-            m = re_counts.search(line)
-            if m:
-                summary["related_count"] = parse_int(m.group(1))
-                summary["unrelated_count"] = parse_int(m.group(2))
-                continue
-
-            # 第三行：相关/不相关索引
-            m = re_indices.search(line)
-            if m:
-                summary["related_indices"] = parse_indices(m.group(1))
-                summary["unrelated_indices"] = parse_indices(m.group(2))
-                continue
-
-            # 对话级统计
-            m = re_dialog.search(line)
-            if m:
-                dialogs.append({
-                    "index": parse_int(m.group(1)),
-                    "total_messages": parse_int(m.group(2)),
-                    "quota_delete": parse_int(m.group(3)),
-                    "actual_deleted": parse_int(m.group(4)),
-                    "kept": parse_int(m.group(5)),
-                })
-                continue
-
-            # 全局删除总数
-            m = re_total_del.search(line)
-            if m:
-                summary["total_deleted_messages"] = parse_int(m.group(1))
-                continue
-
-            # 剩余对话数
-            m = re_remaining.search(line)
-            if m:
-                summary["remaining_dialogs"] = parse_int(m.group(1))
-                continue
-
-        return {
-            "scene": summary["scene"],
-            "timestamp": datetime.now().isoformat(),
-            "summary": {k: v for k, v in summary.items() if k != "scene"},
-            "dialogs": dialogs,
-        }

api/app/core/memory/storage_services/extraction_engine/data_preprocessing/scene_config.py

@@ -1,66 +1,25 @@
 """
-场景特定配置 - 为不同场景提供定制化的剪枝规则
+场景特定配置 - 统一填充词库
 
-功能：
-- 场景特定的重要信息识别模式
-- 场景特定的重要性评分权重
-- 场景特定的填充词库
-- 场景特定的问答对识别规则
+重要性判断已完全交由 extracat_Pruning.jinja2 提示词 + LLM preserve_tokens 机制承担。
+本模块仅保留统一填充词库（filler_phrases），用于识别无意义寒暄/表情/口头禅。
+所有场景共用同一份词库，场景差异由 LLM 语义判断处理。
 """
 
-from typing import Dict, List, Set, Tuple
+from typing import List, Set
 from dataclasses import dataclass, field
 
 
 @dataclass
 class ScenePatterns:
-    """场景特定的识别模式"""
-    
-    # 重要信息的正则模式（优先级从高到低）
-    high_priority_patterns: List[Tuple[str, int]] = field(default_factory=list)  # (pattern, weight)
-    medium_priority_patterns: List[Tuple[str, int]] = field(default_factory=list)
-    low_priority_patterns: List[Tuple[str, int]] = field(default_factory=list)
-    
-    # 填充词库（无意义对话）
+    """场景特定的识别模式（仅保留填充词库）"""
     filler_phrases: Set[str] = field(default_factory=set)
-    
-    # 问句关键词（用于识别问答对）
-    question_keywords: Set[str] = field(default_factory=set)
-    
-    # 决策性/承诺性关键词
-    decision_keywords: Set[str] = field(default_factory=set)
 
 
 class SceneConfigRegistry:
-    """场景配置注册表 - 管理所有场景的特定配置"""
-    
-    # 基础通用模式（所有场景共享）
-    BASE_HIGH_PRIORITY = [
-        (r"订单号|工单|申请号|编号|ID|账号|账户", 5),
-        (r"金额|费用|价格|¥|￥|\d+元", 5),
-        (r"\d{11}", 4),  # 手机号
-        (r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", 4),  # 邮箱
-    ]
-    
-    BASE_MEDIUM_PRIORITY = [
-        (r"\d{4}-\d{1,2}-\d{1,2}", 3),  # 日期
-        (r"\d{4}年\d{1,2}月\d{1,2}日", 3),
-        (r"电话|手机号|微信|QQ|联系方式", 3),
-        (r"地址|地点|位置", 2),
-        (r"时间|日期|有效期|截止", 2),
-        (r"今天|明天|后天|昨天|前天", 3),  # 相对时间（提高权重）
-        (r"下周|下月|下年|上周|上月|上年|本周|本月|本年", 3),
-        (r"今年|去年|明年", 3),
-    ]
-    
-    BASE_LOW_PRIORITY = [
-        (r"\d{1,2}:\d{2}", 2),  # 时间点 HH:MM
-        (r"\d{1,2}点\d{0,2}分?", 2),  # 时间点 X点Y分 或 X点
-        (r"上午|下午|中午|晚上|早上|傍晚|凌晨", 2),  # 时段（提高权重并扩充）
-        (r"AM|PM|am|pm", 1),
-    ]
-    
-    BASE_FILLERS = {
+    """场景配置注册表 - 所有场景共用统一填充词库"""
+
+    BASE_FILLERS: Set[str] = {
         # 基础寒暄
         "你好", "您好", "在吗", "在的", "在呢", "嗯", "嗯嗯", "哦", "哦哦",
         "好的", "好", "行", "可以", "不可以", "谢谢", "多谢", "感谢",
@@ -69,7 +28,26 @@ class SceneConfigRegistry:
         "哈哈", "呵呵", "哈哈哈", "嘿嘿", "嘻嘻", "hiahia",
         "额", "呃", "啊", "诶", "唉", "哎", "嗯哼",
         # 确认词
-        "是的", "对", "对的", "没错", "嗯嗯", "好嘞", "收到", "明白", "了解", "知道了",
+        "是的", "对", "对的", "没错", "好嘞", "收到", "明白", "了解", "知道了",
+        # 服务类套话
+        "请问", "请稍等", "稍等", "马上", "立即",
+        "正在查询", "正在处理", "正在为您", "帮您查一下",
+        "还有其他问题吗", "还需要什么帮助", "很高兴为您服务",
+        "感谢您的耐心等待", "抱歉让您久等了",
+        "已记录", "已反馈", "已转接", "已升级",
+        "祝您生活愉快", "欢迎下次咨询",
+        # 外呼套话
+        "喂", "hello", "打扰了", "不好意思",
+        "方便接电话吗", "现在方便吗", "占用您一点时间",
+        "我是", "我们是", "我们公司", "我们这边",
+        "了解一下", "介绍一下", "简单说一下",
+        "考虑考虑", "想一想", "再说", "再看看",
+        "不需要", "不感兴趣", "没兴趣", "不用了",
+        "没问题", "那就这样", "再联系", "回头聊", "有需要再说",
+        # 教育场景套话
+        "老师好", "同学们好", "上课", "下课", "起立", "坐下",
+        "举手", "请坐", "很好", "不错", "继续",
+        "下一个", "下一题", "下一位", "还有吗", "还有问题吗",
         # 标点和符号
         "。。。", "...", "???", "？？？", "!!!", "！！！",
         # 表情符号
@@ -81,246 +59,8 @@ class SceneConfigRegistry:
         "hhh", "hhhh", "2333", "666", "gg", "ok", "OK", "okok",
         "emmm", "emm", "em", "mmp", "wtf", "omg",
     }
-    
-    BASE_QUESTION_KEYWORDS = {
-        "什么", "为什么", "怎么", "如何", "哪里", "哪个", "谁", "多少", "几点", "何时", "吗"
-    }
-    
-    BASE_DECISION_KEYWORDS = {
-        "必须", "一定", "务必", "需要", "要求", "规定", "应该",
-        "承诺", "保证", "确保", "负责", "同意", "答应"
-    }
-    
+
     @classmethod
-    def get_education_config(cls) -> ScenePatterns:
-        """教育场景配置"""
-        return ScenePatterns(
-            high_priority_patterns=cls.BASE_HIGH_PRIORITY + [
-                # 成绩相关（最高优先级）
-                (r"成绩|分数|得分|满分|及格|不及格", 6),
-                (r"GPA|绩点|学分|平均分", 6),
-                (r"\d+分|\d+\.?\d*分", 5),  # 具体分数
-                (r"排名|名次|第.{1,3}名", 5),  # 支持"第三名"、"第1名"等
-                
-                # 学籍信息
-                (r"学号|学生证|教师工号|工号", 5),
-                (r"班级|年级|专业|院系", 4),
-                
-                # 课程相关
-                (r"课程|科目|学科|必修|选修", 4),
-                (r"教材|课本|教科书|参考书", 4),
-                (r"章节|第.{1,3}章|第.{1,3}节", 3),  # 支持"第三章"、"第1章"等
-                
-                # 学科内容（新增）
-                (r"微积分|导数|积分|函数|极限|微分", 4),
-                (r"代数|几何|三角|概率|统计", 4),
-                (r"物理|化学|生物|历史|地理", 4),
-                (r"英语|语文|数学|政治|哲学", 4),
-                (r"定义|定理|公式|概念|原理|法则", 3),
-                (r"例题|解题|证明|推导|计算", 3),
-            ],
-            medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY + [
-                # 教学活动
-                (r"作业|练习|习题|题目", 3),
-                (r"考试|测验|测试|考核|期中|期末", 3),
-                (r"上课|下课|课堂|讲课", 2),
-                (r"提问|回答|发言|讨论", 2),
-                (r"问一下|请教|咨询|询问", 2),  # 新增：问询相关
-                (r"理解|明白|懂|掌握|学会", 2),  # 新增：学习状态
-                
-                # 时间安排
-                (r"课表|课程表|时间表", 3),
-                (r"第.{1,3}节课|第.{1,3}周", 2),  # 支持"第三节课"、"第1周"等
-            ],
-            low_priority_patterns=cls.BASE_LOW_PRIORITY + [
-                (r"老师|教师|同学|学生", 1),
-                (r"教室|实验室|图书馆", 1),
-            ],
-            filler_phrases=cls.BASE_FILLERS | {
-                # 教育场景特有填充词（移除了"明白了"、"懂了"、"不懂"等，这些在教育场景中有意义）
-                "老师好", "同学们好", "上课", "下课", "起立", "坐下",
-                "举手", "请坐", "很好", "不错", "继续",
-                "下一个", "下一题", "下一位", "还有吗", "还有问题吗",
-            },
-            question_keywords=cls.BASE_QUESTION_KEYWORDS | {
-                "为啥", "咋", "咋办", "怎样", "如何做",
-                "能不能", "可不可以", "行不行", "对不对", "是不是",
-            },
-            decision_keywords=cls.BASE_DECISION_KEYWORDS | {
-                "必考", "重点", "考点", "难点", "关键",
-                "记住", "背诵", "掌握", "理解", "复习",
-            }
-        )
-    
-    @classmethod
-    def get_online_service_config(cls) -> ScenePatterns:
-        """在线服务场景配置"""
-        return ScenePatterns(
-            high_priority_patterns=cls.BASE_HIGH_PRIORITY + [
-                # 工单相关（最高优先级）
-                (r"工单号|工单编号|ticket|TK\d+", 6),
-                (r"工单状态|处理中|已解决|已关闭|待处理", 5),
-                (r"优先级|紧急|高优先级|P0|P1|P2", 5),
-                
-                # 产品信息
-                (r"产品型号|型号|SKU|产品编号", 5),
-                (r"序列号|SN|设备号", 5),
-                (r"版本号|软件版本|固件版本", 4),
-                
-                # 问题描述
-                (r"故障|错误|异常|bug|问题", 4),
-                (r"错误代码|故障代码|error code", 5),
-                (r"无法|不能|失败|报错", 3),
-            ],
-            medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY + [
-                # 服务相关
-                (r"退款|退货|换货|补发", 4),
-                (r"发票|收据|凭证", 3),
-                (r"物流|快递|运单号", 3),
-                (r"保修|质保|售后", 3),
-                
-                # 时效相关
-                (r"SLA|响应时间|处理时长", 4),
-                (r"超时|延迟|等待", 2),
-            ],
-            low_priority_patterns=cls.BASE_LOW_PRIORITY + [
-                (r"客服|工程师|技术支持", 1),
-                (r"用户|客户|会员", 1),
-            ],
-            filler_phrases=cls.BASE_FILLERS | {
-                # 在线服务特有填充词
-                "您好", "请问", "请稍等", "稍等", "马上", "立即",
-                "正在查询", "正在处理", "正在为您", "帮您查一下",
-                "还有其他问题吗", "还需要什么帮助", "很高兴为您服务",
-                "感谢您的耐心等待", "抱歉让您久等了",
-                "已记录", "已反馈", "已转接", "已升级",
-                "祝您生活愉快", "再见", "欢迎下次咨询",
-            },
-            question_keywords=cls.BASE_QUESTION_KEYWORDS | {
-                "能否", "可否", "是否", "有没有", "能不能",
-                "怎么办", "如何处理", "怎么解决",
-            },
-            decision_keywords=cls.BASE_DECISION_KEYWORDS | {
-                "立即处理", "马上解决", "尽快", "优先",
-                "升级", "转接", "派单", "跟进",
-                "补偿", "赔偿", "退款", "换货",
-            }
-        )
-    
-    @classmethod
-    def get_outbound_config(cls) -> ScenePatterns:
-        """外呼场景配置"""
-        return ScenePatterns(
-            high_priority_patterns=cls.BASE_HIGH_PRIORITY + [
-                # 意向相关（最高优先级）
-                (r"意向|意愿|兴趣|感兴趣", 6),
-                (r"A类|B类|C类|D类|高意向|低意向", 6),
-                (r"成交|签约|下单|购买|确认", 6),
-                
-                # 联系信息（外呼场景中更重要）
-                (r"预约|约定|安排|确定时间", 5),
-                (r"下次联系|回访|跟进", 5),
-                (r"方便|有空|可以|时间", 4),
-                
-                # 通话状态
-                (r"接通|未接通|占线|关机|停机", 4),
-                (r"通话时长|通话时间", 3),
-            ],
-            medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY + [
-                # 客户信息
-                (r"姓名|称呼|先生|女士", 3),
-                (r"公司|单位|职位|职务", 3),
-                (r"需求|要求|期望", 3),
-                
-                # 跟进状态
-                (r"跟进状态|进展|进度", 3),
-                (r"已联系|待联系|联系中", 2),
-                (r"拒绝|不感兴趣|考虑|再说", 3),
-            ],
-            low_priority_patterns=cls.BASE_LOW_PRIORITY + [
-                (r"销售|客户经理|业务员", 1),
-                (r"产品|服务|方案", 1),
-            ],
-            filler_phrases=cls.BASE_FILLERS | {
-                # 外呼场景特有填充词
-                "您好", "喂", "hello", "打扰了", "不好意思",
-                "方便接电话吗", "现在方便吗", "占用您一点时间",
-                "我是", "我们是", "我们公司", "我们这边",
-                "了解一下", "介绍一下", "简单说一下",
-                "考虑考虑", "想一想", "再说", "再看看",
-                "不需要", "不感兴趣", "没兴趣", "不用了",
-                "好的", "行", "可以", "没问题", "那就这样",
-                "再联系", "回头聊", "有需要再说",
-            },
-            question_keywords=cls.BASE_QUESTION_KEYWORDS | {
-                "有没有", "需不需要", "要不要", "考虑不考虑",
-                "了解吗", "知道吗", "听说过吗",
-                "方便吗", "有空吗", "在吗",
-            },
-            decision_keywords=cls.BASE_DECISION_KEYWORDS | {
-                "确定", "决定", "选择", "购买", "下单",
-                "预约", "安排", "约定", "确认",
-                "跟进", "回访", "联系", "沟通",
-            }
-        )
-    
-    @classmethod
-    def get_config(cls, scene: str, fallback_to_generic: bool = True) -> ScenePatterns:
-        """根据场景名称获取配置
-        
-        Args:
-            scene: 场景名称 ('education', 'online_service', 'outbound' 或其他)
-            fallback_to_generic: 如果场景不存在，是否降级到通用配置
-            
-        Returns:
-            对应场景的配置，如果场景不存在：
-            - fallback_to_generic=True: 返回通用配置（仅基础规则）
-            - fallback_to_generic=False: 抛出异常
-        """
-        scene_map = {
-            'education': cls.get_education_config,
-            'online_service': cls.get_online_service_config,
-            'outbound': cls.get_outbound_config,
-        }
-        
-        if scene in scene_map:
-            return scene_map[scene]()
-        
-        if fallback_to_generic:
-            # 返回通用配置（仅包含基础规则，不包含场景特定规则）
-            return cls.get_generic_config()
-        else:
-            raise ValueError(f"不支持的场景: {scene}，支持的场景: {list(scene_map.keys())}")
-    
-    @classmethod
-    def get_generic_config(cls) -> ScenePatterns:
-        """通用场景配置 - 仅包含基础规则，适用于未定义的场景
-        
-        这是一个保守的配置，只使用最通用的规则，避免误删重要信息
-        """
-        return ScenePatterns(
-            high_priority_patterns=cls.BASE_HIGH_PRIORITY,
-            medium_priority_patterns=cls.BASE_MEDIUM_PRIORITY,
-            low_priority_patterns=cls.BASE_LOW_PRIORITY,
-            filler_phrases=cls.BASE_FILLERS,
-            question_keywords=cls.BASE_QUESTION_KEYWORDS,
-            decision_keywords=cls.BASE_DECISION_KEYWORDS
-        )
-    
-    @classmethod
-    def get_all_scenes(cls) -> List[str]:
-        """获取所有预定义场景的列表"""
-        return ['education', 'online_service', 'outbound']
-    
-    @classmethod
-    def is_scene_supported(cls, scene: str) -> bool:
-        """检查场景是否有专门的配置支持
-        
-        Args:
-            scene: 场景名称
-            
-        Returns:
-            True: 有专门配置
-            False: 将使用通用配置
-        """
-        return scene in cls.get_all_scenes()
+    def get_config(cls, scene: str = "") -> ScenePatterns:
+        """所有场景统一返回同一份填充词库"""
+        return ScenePatterns(filler_phrases=cls.BASE_FILLERS)

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

@@ -384,6 +384,14 @@ class ExtractionOrchestrator:
         
         logger.info(f"陈述句提取完成，共提取 {len(all_statements)} 条陈述句")
 
+        # 试运行模式下，所有分块提取完成后发送完成事件
+        if self.progress_callback and self.is_pilot_run:
+            await self.progress_callback(
+                "knowledge_extraction_complete",
+                f"陈述句提取完成，共提取 {len(all_statements)} 条",
+                {"total_statements": len(all_statements), "total_chunks": total_chunks}
+            )
+
         return dialog_data_list
 
     async def _extract_triplets(

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

@@ -5,7 +5,7 @@ from typing import List, Dict, Optional
 from app.core.logging_config import get_memory_logger
 from app.core.memory.llm_tools.openai_client import OpenAIClient
 from app.core.memory.utils.prompt.prompt_utils import render_triplet_extraction_prompt
-from app.core.memory.utils.data.ontology import PREDICATE_DEFINITIONS, Predicate # 引入枚举 Predicate 白名单过滤
+from app.core.memory.utils.data.ontology import PREDICATE_DEFINITIONS, Predicate  # 引入枚举 Predicate 白名单过滤
 from app.core.memory.models.triplet_models import TripletExtractionResponse
 from app.core.memory.models.message_models import DialogData, Statement
 from app.core.memory.models.ontology_extraction_models import OntologyTypeList
@@ -14,15 +14,15 @@ from app.core.memory.utils.log.logging_utils import prompt_logger
 logger = get_memory_logger(__name__)
 
 
-
 class TripletExtractor:
     """Extracts knowledge triplets and entities from statements using LLM"""
 
     def __init__(
-        self,
-        llm_client: OpenAIClient,
-        ontology_types: Optional[OntologyTypeList] = None,
-        language: str = "zh"):
+            self,
+            llm_client: OpenAIClient,
+            ontology_types: Optional[OntologyTypeList] = None,
+            language: str = "zh"
+    ):
         """Initialize the TripletExtractor with an LLM client
 
         Args:
@@ -65,7 +65,8 @@ class TripletExtractor:
 
         # Create messages for LLM
         messages = [
-            {"role": "system", "content": "You are an expert at extracting knowledge triplets and entities from text. Follow the provided instructions carefully and return valid JSON."},
+            {"role": "system",
+             "content": "You are an expert at extracting knowledge triplets and entities from text. Follow the provided instructions carefully and return valid JSON."},
             {"role": "user", "content": prompt_content}
         ]
 
@@ -116,7 +117,8 @@ class TripletExtractor:
             logger.error(f"Error processing statement: {e}", exc_info=True)
             return TripletExtractionResponse(triplets=[], entities=[])
 
-    async def extract_triplets_from_statements(self, dialog_data: DialogData, limit_chunks: int = None) -> Dict[str, TripletExtractionResponse]:
+    async def extract_triplets_from_statements(self, dialog_data: DialogData, limit_chunks: int = None) -> Dict[
+        str, TripletExtractionResponse]:
         """Extract triplets and entities from statements
 
         Args:

api/app/core/memory/storage_services/reflection_engine/self_reflexion.py

@@ -1,11 +1,11 @@
 """
-自我反思引擎实现
+Self-Reflection Engine Implementation
 
-该模块实现了记忆系统的自我反思功能，包括：
-1. 基于时间的反思 - 根据时间周期触发反思
-2. 基于事实的反思 - 检测记忆冲突并解决
-3. 综合反思 - 整合多种反思策略
-4. 反思结果应用 - 更新记忆库
+This module implements the self-reflection functionality of the memory system, including:
+1. Time-based reflection - Triggers reflection based on time cycles
+2. Fact-based reflection - Detects and resolves memory conflicts
+3. Comprehensive reflection - Integrates multiple reflection strategies
+4. Reflection result application - Updates memory database
 """
 
 import asyncio
@@ -38,7 +38,7 @@ from app.schemas.memory_storage_schema import (
 )
 from pydantic import BaseModel
 
-# 配置日志
+# Configure logging
 _root_logger = logging.getLogger()
 if not _root_logger.handlers:
     logging.basicConfig(
@@ -49,35 +49,62 @@ else:
     _root_logger.setLevel(logging.INFO)
 
 class TranslationResponse(BaseModel):
-    """翻译响应模型"""
+    """Translation response model for language conversion"""
     data: str
+    
 class ReflectionRange(str, Enum):
-    """反思范围枚举"""
-    PARTIAL = "partial"  # 从检索结果中反思
-    ALL = "all"  # 从整个数据库中反思
+    """
+    Reflection range enumeration
+    
+    Defines the scope of data to be included in reflection operations.
+    """
+    PARTIAL = "partial"  # Reflect from retrieval results
+    ALL = "all"  # Reflect from entire database
 
 
 class ReflectionBaseline(str, Enum):
-    """反思基线枚举"""
-    TIME = "TIME"  # 基于时间的反思
-    FACT = "FACT"  # 基于事实的反思
-    HYBRID = "HYBRID"  # 混合反思
+    """
+    Reflection baseline enumeration
+    
+    Defines the strategy or approach used for reflection operations.
+    """
+    TIME = "TIME"  # Time-based reflection
+    FACT = "FACT"  # Fact-based reflection
+    HYBRID = "HYBRID"  # Hybrid reflection combining multiple strategies
 
 
 class ReflectionConfig(BaseModel):
-    """反思引擎配置"""
+    """
+    Reflection engine configuration
+    
+    Defines all configuration parameters for the reflection engine including
+    operation modes, model settings, and evaluation criteria.
+    
+    Attributes:
+        enabled: Whether reflection engine is enabled
+        iteration_period: Reflection cycle period (e.g., "3" hours)
+        reflexion_range: Scope of reflection (PARTIAL or ALL)
+        baseline: Reflection strategy (TIME, FACT, or HYBRID)
+        model_id: LLM model identifier for reflection operations
+        end_user_id: User identifier for scoped operations
+        output_example: Example output format for guidance
+        memory_verify: Enable memory verification checks
+        quality_assessment: Enable quality assessment evaluation
+        violation_handling_strategy: Strategy for handling violations
+        language_type: Language type for output ("zh" or "en")
+    """
     enabled: bool = False
-    iteration_period: str = "3"  # 反思周期
+    iteration_period: str = "3"  # Reflection cycle period
     reflexion_range: ReflectionRange = ReflectionRange.PARTIAL
     baseline: ReflectionBaseline = ReflectionBaseline.TIME
-    model_id: Optional[str] = None  # 模型ID
+    model_id: Optional[str] = None  # Model ID
     end_user_id: Optional[str] = None
-    output_example: Optional[str] = None  # 输出示例
+    output_example: Optional[str] = None  # Output example
 
-    # 评估相关字段
-    memory_verify: bool = True  # 记忆验证
-    quality_assessment: bool = True  # 质量评估
-    violation_handling_strategy: str = "warn"  # 违规处理策略
+    # Evaluation related fields
+    memory_verify: bool = True  # Memory verification
+    quality_assessment: bool = True  # Quality assessment
+    violation_handling_strategy: str = "warn"  # Violation handling strategy
     language_type: str = "zh"
 
     class Config:
@@ -85,7 +112,21 @@ class ReflectionConfig(BaseModel):
 
 
 class ReflectionResult(BaseModel):
-    """反思结果"""
+    """
+    Reflection operation result
+    
+    Contains comprehensive information about the outcome of a reflection operation
+    including success status, metrics, and execution details.
+    
+    Attributes:
+        success: Whether the reflection operation succeeded
+        message: Descriptive message about the operation result
+        conflicts_found: Number of conflicts detected during reflection
+        conflicts_resolved: Number of conflicts successfully resolved
+        memories_updated: Number of memory entries updated in database
+        execution_time: Total time taken for the reflection operation
+        details: Additional details about the operation (optional)
+    """
     success: bool
     message: str
     conflicts_found: int = 0
@@ -97,9 +138,22 @@ class ReflectionResult(BaseModel):
 
 class ReflectionEngine:
     """
-    自我反思引擎
-
-    负责执行记忆系统的自我反思，包括冲突检测、冲突解决和记忆更新。
+    Self-Reflection Engine
+    
+    Responsible for executing memory system self-reflection operations including
+    conflict detection, conflict resolution, and memory updates. Supports multiple
+    reflection strategies and provides comprehensive result tracking.
+    
+    The engine can operate in different modes:
+    - Time-based: Reflects on memories within specific time periods
+    - Fact-based: Detects and resolves factual conflicts in memories
+    - Hybrid: Combines multiple reflection strategies
+    
+    Attributes:
+        config: Reflection engine configuration
+        neo4j_connector: Neo4j database connector
+        llm_client: Language model client for analysis
+        Various function handlers for data processing and prompt rendering
     """
 
     def __init__(
@@ -115,18 +169,21 @@ class ReflectionEngine:
             update_query: Optional[str] = None
     ):
         """
-        初始化反思引擎
+        Initialize reflection engine
+        
+        Sets up the reflection engine with configuration and optional dependencies.
+        Uses lazy initialization to avoid circular imports and optimize startup time.
 
         Args:
-            config: 反思引擎配置
-            neo4j_connector: Neo4j 连接器（可选）
-            llm_client: LLM 客户端（可选）
-            get_data_func: 获取数据的函数（可选）
-            render_evaluate_prompt_func: 渲染评估提示词的函数（可选）
-            render_reflexion_prompt_func: 渲染反思提示词的函数（可选）
-            conflict_schema: 冲突结果 Schema（可选）
-            reflexion_schema: 反思结果 Schema（可选）
-            update_query: 更新查询语句（可选）
+            config: Reflection engine configuration object
+            neo4j_connector: Neo4j connector instance (optional, will be created if not provided)
+            llm_client: LLM client instance (optional, will be created if not provided)
+            get_data_func: Function for retrieving data (optional, uses default if not provided)
+            render_evaluate_prompt_func: Function for rendering evaluation prompts (optional)
+            render_reflexion_prompt_func: Function for rendering reflection prompts (optional)
+            conflict_schema: Schema for conflict result validation (optional)
+            reflexion_schema: Schema for reflection result validation (optional)
+            update_query: Query string for database updates (optional)
         """
         self.config = config
         self.neo4j_connector = neo4j_connector
@@ -137,14 +194,20 @@ class ReflectionEngine:
         self.conflict_schema = conflict_schema
         self.reflexion_schema = reflexion_schema
         self.update_query = update_query
-        self._semaphore = asyncio.Semaphore(5)  # 默认并发数为5
+        self._semaphore = asyncio.Semaphore(5)  # Default concurrency limit of 5
 
 
-        # 延迟导入以避免循环依赖
+        # Lazy import to avoid circular dependencies
         self._lazy_init_done = False
 
     def _lazy_init(self):
-        """延迟初始化，避免循环导入"""
+        """
+        Lazy initialization to avoid circular imports
+        
+        Initializes dependencies only when needed, preventing circular import issues
+        and optimizing startup performance. Sets up default implementations for
+        any components not provided during construction.
+        """
         if self._lazy_init_done:
             return
 
@@ -158,7 +221,7 @@ class ReflectionEngine:
                 factory = MemoryClientFactory(db)
                 self.llm_client = factory.get_llm_client(self.config.model_id)
         elif isinstance(self.llm_client, str):
-            # 如果 llm_client 是字符串（model_id），则用它初始化客户端
+            # If llm_client is a string (model_id), use it to initialize the client
             from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
             from app.db import get_db_context
             from app.services.memory_config_service import MemoryConfigService
@@ -172,10 +235,10 @@ class ReflectionEngine:
                 model_config = config_service.get_model_config(model_id)
                 
             extra_params={
-                    "temperature": 0.2,  # 降低温度提高响应速度和一致性
-                    "max_tokens": 600,  # 限制最大token数
-                    "top_p": 0.8,  # 优化采样参数
-                    "stream": False,  # 确保非流式输出以获得最快响应
+                    "temperature": 0.2,  # Lower temperature for faster response and consistency
+                    "max_tokens": 600,  # Limit maximum token count
+                    "top_p": 0.8,  # Optimize sampling parameters
+                    "stream": False,  # Ensure non-streaming output for fastest response
                 }
 
             self.llm_client  = OpenAIClient(RedBearModelConfig(
@@ -191,7 +254,7 @@ class ReflectionEngine:
         if self.get_data_func is None:
             self.get_data_func = get_data
 
-        # 导入get_data_statement函数
+        # Import get_data_statement function
         if not hasattr(self, 'get_data_statement'):
             self.get_data_statement = get_data_statement
 
@@ -223,13 +286,20 @@ class ReflectionEngine:
 
     async def execute_reflection(self, host_id) -> ReflectionResult:
         """
-        执行完整的反思流程
+        Execute complete reflection workflow
+        
+        Performs the full reflection process including data retrieval, conflict detection,
+        conflict resolution, and memory updates. This is the main entry point for
+        reflection operations.
+        
         Args:
-            host_id: 主机ID
+            host_id: Host identifier for scoping reflection operations
+            
         Returns:
-            ReflectionResult: 反思结果
+            ReflectionResult: Comprehensive result of the reflection operation including
+                            success status, conflict metrics, and execution time
         """
-        # 延迟初始化
+        # Lazy initialization
         self._lazy_init()
 
         if not self.config.enabled:
@@ -243,7 +313,7 @@ class ReflectionEngine:
 
         print(self.config.baseline, self.config.memory_verify, self.config.quality_assessment)
         try:
-            # 1. 获取反思数据
+            # 1. Get reflection data
             reflexion_data, statement_databasets = await self._get_reflexion_data(host_id)
             if not reflexion_data:
                 return ReflectionResult(
@@ -252,7 +322,7 @@ class ReflectionEngine:
                     execution_time=asyncio.get_event_loop().time() - start_time
                 )
 
-            # 2. 检测冲突（基于事实的反思）
+            # 2. Detect conflicts (fact-based reflection)
             conflict_data = await self._detect_conflicts(reflexion_data, statement_databasets)
             conflict_list=[]
             for i  in conflict_data:
@@ -261,7 +331,7 @@ class ReflectionEngine:
 
 
             conflicts_found=0
-            # 3. 解决冲突
+            # 3. Resolve conflicts
             solved_data = await self._resolve_conflicts(conflict_list, statement_databasets)
 
             if not solved_data:
@@ -276,7 +346,7 @@ class ReflectionEngine:
             logging.info(f"解决了 {conflicts_resolved} 个冲突")
 
 
-            # 4. 应用反思结果（更新记忆库）
+            # 4. Apply reflection results (update memory database)
             memories_updated=await self._apply_reflection_results(solved_data)
 
             execution_time = asyncio.get_event_loop().time() - start_time
@@ -302,7 +372,19 @@ class ReflectionEngine:
             )
 
     async def Translate(self, text):
-        # 翻译中文为英文
+        """
+        Translate Chinese text to English
+        
+        Uses the configured LLM to translate Chinese text to English with structured output.
+        Provides consistent translation format for reflection results.
+        
+        Args:
+            text: Chinese text to be translated
+            
+        Returns:
+            str: Translated English text
+        """
+        # Translate Chinese to English
         translation_messages = [
             {
                 "role": "user",
@@ -316,6 +398,19 @@ class ReflectionEngine:
         )
         return response.data
     async def extract_translation(self,data):
+        """
+        Extract and translate reflection data to English
+        
+        Processes reflection data structure and translates all Chinese content to English.
+        Handles nested data structures including memory verifications, quality assessments,
+        and reflection data while preserving the original structure.
+        
+        Args:
+            data: Dictionary containing reflection data with Chinese content
+            
+        Returns:
+            dict: Translated data structure with English content
+        """
         end_datas={}
         end_datas['source_data']=await self.Translate(data['source_data'])
         quality_assessments = []
@@ -350,6 +445,18 @@ class ReflectionEngine:
         return end_datas
 
     async def reflection_run(self):
+        """
+        Execute reflection workflow with comprehensive data processing
+        
+        Performs a complete reflection operation including conflict detection, resolution,
+        and result formatting. Supports both Chinese and English output based on
+        configuration settings.
+        
+        Returns:
+            dict: Comprehensive reflection results including source data, memory verifications,
+                 quality assessments, and reflection data. Results are translated to English
+                 if language_type is set to 'en'.
+        """
         self._lazy_init()
         start_time = time.time()
         memory_verifies_flag = self.config.memory_verify
@@ -367,7 +474,7 @@ class ReflectionEngine:
         result_data['source_data'] = "我是 2023 年春天去北京工作的，后来基本一直都在北京上班，也没怎么换过城市。不过后来公司调整，2024 年上半年我被调到上海待了差不多半年，那段时间每天都是在上海办公室打卡。当时入职资料用的还是我之前的身份信息，身份证号是 11010119950308123X，银行卡是 6222023847595898，这些一直没变。对了，其实我 从 2023 年开始就一直在北京生活，从来没有长期离开过北京，上海那段更多算是远程配合"
         # 2. 检测冲突（基于事实的反思）
         conflict_data = await self._detect_conflicts(databasets, source_data)
-        # 遍历数据提取字段
+        # Traverse data to extract fields
         quality_assessments = []
         memory_verifies = []
         for item in conflict_data:
@@ -375,9 +482,9 @@ class ReflectionEngine:
             memory_verifies.append(item['memory_verify'])
         result_data['memory_verifies'] = memory_verifies
         result_data['quality_assessments'] = quality_assessments
-        conflicts_found = 0  # 初始化为整数0而不是空字符串
+        conflicts_found = 0  # Initialize as integer 0 instead of empty string
         REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"}
-        # Clearn conflict_data，And memory_verify和quality_assessment
+        # Clean conflict_data, and memory_verify and quality_assessment
         cleaned_conflict_data = []
         for item in conflict_data:
             cleaned_item = {
@@ -389,7 +496,7 @@ class ReflectionEngine:
         for item in conflict_data:
             cleaned_data = []
             for row in item.get("data", []):
-                # 删除 created_at / expired_at
+                # Remove created_at / expired_at
                 cleaned_row = {
                     k: v
                     for k, v in row.items()
@@ -402,7 +509,7 @@ class ReflectionEngine:
             }
             cleaned_conflict_data_.append(cleaned_item)
         print(cleaned_conflict_data_)
-        # 3. 解决冲突
+        # 3. Resolve conflicts
         solved_data = await self._resolve_conflicts(cleaned_conflict_data_, source_data)
         if not solved_data:
             return ReflectionResult(
@@ -413,7 +520,7 @@ class ReflectionEngine:
             )
         reflexion_data = []
 
-        # 遍历数据提取reflexion字段
+        # Traverse data to extract reflexion fields
         for item in solved_data:
             if 'results' in item:
                 for result in item['results']:
@@ -431,15 +538,24 @@ class ReflectionEngine:
 
 
     async def extract_fields_from_json(self):
-        """从example.json中提取source_data和databasets字段"""
+        """
+        Extract source_data and databasets fields from example.json
+        
+        Reads reflection example data from the example.json file and extracts
+        the source data and database statements for testing and demonstration purposes.
+        
+        Returns:
+            tuple: (source_data, databasets) extracted from the example file
+                  Returns empty lists if file reading fails
+        """
 
         prompt_dir = os.path.join(os.path.dirname(__file__), "example")
         try:
-            # 读取JSON文件
+            # Read JSON file
             with open(prompt_dir + '/example.json', 'r', encoding='utf-8') as f:
                 data = json.loads(f.read())
 
-            # 提取memory_verify下的字段
+            # Extract fields under memory_verify
             memory_verify = data.get("memory_verify", {})
             source_data = memory_verify.get("source_data", [])
             databasets = memory_verify.get("databasets", [])
@@ -451,15 +567,17 @@ class ReflectionEngine:
 
     async def _get_reflexion_data(self, host_id: uuid.UUID) -> List[Any]:
         """
-        获取反思数据
-
-        根据配置的反思范围获取需要反思的记忆数据。
+        Get reflection data from database
+        
+        Retrieves memory data for reflection based on the configured reflection range.
+        Supports both partial (from retrieval results) and full (entire database) modes.
 
         Args:
-            host_id: 主机ID
+            host_id: Host UUID identifier for scoping data retrieval
 
         Returns:
-            List[Any]: 反思数据列表
+            tuple: (reflexion_data, statement_data) containing memory data for reflection
+                  Returns empty lists if query fails
         """
 
         print("=== 获取反思数据 ===")
@@ -484,26 +602,29 @@ class ReflectionEngine:
 
     async def _detect_conflicts(self, data: List[Any], statement_databasets: List[Any]) -> List[Any]:
         """
-        检测冲突（基于事实的反思）
-
-        使用 LLM 分析记忆数据，检测其中的冲突。
+        Detect conflicts (fact-based reflection)
+        
+        Uses LLM to analyze memory data and detect conflicts within the memories.
+        Performs comprehensive conflict detection including memory verification and
+        quality assessment based on configuration settings.
 
         Args:
-            data: 待检测的记忆数据
+            data: Memory data to be analyzed for conflicts
+            statement_databasets: Statement database records for context
 
         Returns:
-            List[Any]: 冲突记忆列表
+            List[Any]: List of detected conflicts with detailed analysis
         """
         if not data:
             return []
 
-        # 数据预处理：如果数据量太少，直接返回无冲突
+        # Data preprocessing: if data is too small, return no conflicts directly
         if len(data) < 2:
             logging.info("数据量不足，无需检测冲突")
             return []
 
-        # 使用转换后的数据
-        # print("转换后的数据:", data[:2] if len(data) > 2 else data)  # 只打印前2条避免日志过长
+        # Use converted data
+        # print("Converted data:", data[:2] if len(data) > 2 else data)  # Only print first 2 to avoid long logs
         memory_verify = self.config.memory_verify
 
         logging.info("====== 冲突检测开始 ======")
@@ -512,7 +633,7 @@ class ReflectionEngine:
         language_type=self.config.language_type
 
         try:
-            # 渲染冲突检测提示词
+            # Render conflict detection prompt
             rendered_prompt = await self.render_evaluate_prompt_func(
                 data,
                 self.conflict_schema,
@@ -526,7 +647,7 @@ class ReflectionEngine:
             messages = [{"role": "user", "content": rendered_prompt}]
             logging.info(f"提示词长度: {len(rendered_prompt)}")
 
-            # 调用 LLM 进行冲突检测
+            # Call LLM for conflict detection
             response = await self.llm_client.response_structured(
                 messages,
                 self.conflict_schema
@@ -539,7 +660,7 @@ class ReflectionEngine:
                 logging.error("LLM 冲突检测输出解析失败")
                 return []
 
-            # 标准化返回格式
+            # Standardize return format
             if isinstance(response, BaseModel):
                 return [response.model_dump()]
             elif hasattr(response, 'dict'):
@@ -553,15 +674,17 @@ class ReflectionEngine:
 
     async def _resolve_conflicts(self, conflicts: List[Any], statement_databasets: List[Any]) -> List[Any]:
         """
-        解决冲突
-
-        使用 LLM 对检测到的冲突进行反思和解决。
+        Resolve detected conflicts
+        
+        Uses LLM to perform reflection and resolution on detected conflicts.
+        Processes conflicts in parallel for efficiency while respecting concurrency limits.
 
         Args:
-            conflicts: 冲突列表
+            conflicts: List of conflicts to be resolved
+            statement_databasets: Statement database records for context
 
         Returns:
-            List[Any]: 解决方案列表
+            List[Any]: List of resolution solutions with reflection results
         """
         if not conflicts:
             return []
@@ -570,12 +693,12 @@ class ReflectionEngine:
         baseline = self.config.baseline
         memory_verify = self.config.memory_verify
 
-        # 并行处理每个冲突
+        # Process each conflict in parallel
         async def _resolve_one(conflict: Any) -> Optional[Dict[str, Any]]:
-            """解决单个冲突"""
+            """Resolve a single conflict"""
             async with self._semaphore:
                 try:
-                    # 渲染反思提示词
+                    # Render reflection prompt
                     rendered_prompt = await self.render_reflexion_prompt_func(
                         [conflict],
                         self.reflexion_schema,
@@ -587,7 +710,7 @@ class ReflectionEngine:
 
                     messages = [{"role": "user", "content": rendered_prompt}]
 
-                    # 调用 LLM 进行反思
+                    # Call LLM for reflection
                     response = await self.llm_client.response_structured(
                         messages,
                         self.reflexion_schema
@@ -596,7 +719,7 @@ class ReflectionEngine:
                     if not response:
                         return None
 
-                    # 标准化返回格式
+                    # Standardize return format
                     if isinstance(response, BaseModel):
                         return response.model_dump()
                     elif hasattr(response, 'dict'):
@@ -610,11 +733,11 @@ class ReflectionEngine:
                     logging.warning(f"解决单个冲突失败: {e}")
                     return None
 
-        # 并发执行所有冲突解决任务
+        # Execute all conflict resolution tasks concurrently
         tasks = [_resolve_one(conflict) for conflict in conflicts]
         results = await asyncio.gather(*tasks, return_exceptions=False)
 
-        # 过滤掉失败的结果
+        # Filter out failed results
         solved = [r for r in results if r is not None]
 
         logging.info(f"成功解决 {len(solved)}/{len(conflicts)} 个冲突")
@@ -626,15 +749,16 @@ class ReflectionEngine:
             solved_data: List[Dict[str, Any]]
     ) -> int:
         """
-        应用反思结果（更新记忆库）
-
-        将解决冲突后的记忆更新到 Neo4j 数据库中。
+        Apply reflection results (update memory database)
+        
+        Updates the Neo4j database with resolved conflicts and reflection results.
+        Processes the solved data and applies changes to the memory storage system.
 
         Args:
-            solved_data: 解决方案列表
+            solved_data: List of resolved conflict solutions with reflection data
 
         Returns:
-            int: 成功更新的记忆数量
+            int: Number of successfully updated memory entries
         """
         changes = extract_and_process_changes(solved_data)
         success_count = await neo4j_data(changes)
@@ -642,80 +766,86 @@ class ReflectionEngine:
 
 
 
-    # 基于时间的反思方法
+    # Time-based reflection methods
     async def time_based_reflection(
             self,
             host_id: uuid.UUID,
             time_period: Optional[str] = None
     ) -> ReflectionResult:
         """
-        基于时间的反思
-
-        根据时间周期触发反思，检查在指定时间段内的记忆。
+        Time-based reflection
+        
+        Triggers reflection based on time cycles, checking memories within
+        specified time periods. Uses the configured iteration period if
+        no specific time period is provided.
 
         Args:
-            host_id: 主机ID
-            time_period: 时间周期（如"三小时"），如果不提供则使用配置中的值
+            host_id: Host UUID identifier for scoping reflection
+            time_period: Time period (e.g., "three hours"), uses config value if not provided
 
         Returns:
-            ReflectionResult: 反思结果
+            ReflectionResult: Comprehensive reflection operation result
         """
         period = time_period or self.config.iteration_period
         logging.info(f"执行基于时间的反思，周期: {period}")
 
-        # 使用标准反思流程
+        # Use standard reflection workflow
         return await self.execute_reflection(host_id)
 
-    # 基于事实的反思方法
+    # Fact-based reflection methods
     async def fact_based_reflection(
             self,
             host_id: uuid.UUID
     ) -> ReflectionResult:
         """
-        基于事实的反思
-
-        检测记忆中的事实冲突并解决。
+        Fact-based reflection
+        
+        Detects and resolves factual conflicts within memories. Analyzes
+        memory data for inconsistencies and contradictions that need resolution.
 
         Args:
-            host_id: 主机ID
+            host_id: Host UUID identifier for scoping reflection
 
         Returns:
-            ReflectionResult: 反思结果
+            ReflectionResult: Comprehensive reflection operation result
         """
         logging.info("执行基于事实的反思")
 
-        # 使用标准反思流程
+        # Use standard reflection workflow
         return await self.execute_reflection(host_id)
 
-    # 综合反思方法
+    # Comprehensive reflection methods
     async def comprehensive_reflection(
             self,
             host_id: uuid.UUID
     ) -> ReflectionResult:
         """
-        综合反思
-
-        整合基于时间和基于事实的反思策略。
+        Comprehensive reflection
+        
+        Integrates time-based and fact-based reflection strategies based on
+        the configured baseline. Supports hybrid approaches that combine
+        multiple reflection methodologies.
 
         Args:
-            host_id: 主机ID
+            host_id: Host UUID identifier for scoping reflection
 
         Returns:
-            ReflectionResult: 反思结果
+            ReflectionResult: Comprehensive reflection operation result combining
+                            multiple strategies if using hybrid baseline
         """
         logging.info("执行综合反思")
 
-        # 根据配置的基线选择反思策略
+        # Choose reflection strategy based on configured baseline
         if self.config.baseline == ReflectionBaseline.TIME:
             return await self.time_based_reflection(host_id)
         elif self.config.baseline == ReflectionBaseline.FACT:
             return await self.fact_based_reflection(host_id)
         elif self.config.baseline == ReflectionBaseline.HYBRID:
-            # 混合策略：先执行基于时间的反思，再执行基于事实的反思
+            # Hybrid strategy: execute time-based reflection first, then fact-based reflection
             time_result = await self.time_based_reflection(host_id)
             fact_result = await self.fact_based_reflection(host_id)
 
-            # 合并结果
+            # Merge results
             return ReflectionResult(
                 success=time_result.success and fact_result.success,
                 message=f"时间反思: {time_result.message}; 事实反思: {fact_result.message}",

api/app/core/memory/utils/data/text_utils.py

@@ -2,9 +2,17 @@ import json
 
 
 def escape_lucene_query(query: str) -> str:
-    """Escape Lucene special characters in a free-text query.
-
-    This prevents ParseException when using Neo4j full-text procedures.
+    """
+    Escape special characters in Lucene queries
+    
+    Prevents ParseException when using Neo4j full-text search procedures.
+    Escapes all Lucene reserved special characters and operators.
+    
+    Args:
+        query: Original query string
+        
+    Returns:
+        str: Escaped query string safe for Lucene search
     """
     if query is None:
         return ""
@@ -22,11 +30,21 @@ def escape_lucene_query(query: str) -> str:
     return s
 
 def extract_plain_query(query_input: str) -> str:
-    """Extract clean, plain-text query from various input forms.
-
+    """
+    Extract clean plain-text query from various input forms
+    
+    Handles the following cases:
     - Strips surrounding quotes and whitespace
     - If input looks like JSON, prefers the 'original' field
-    - Fallbacks to the raw string when parsing fails
+    - Falls back to raw string when parsing fails
+    - Handles dictionary-type input
+    - Best-effort unescape common escape characters
+    
+    Args:
+        query_input: Query input in various forms (string, dict, etc.)
+        
+    Returns:
+        str: Extracted plain-text query string
     """
     if query_input is None:
         return ""

api/app/core/memory/utils/data/time_utils.py

@@ -4,7 +4,13 @@ from datetime import datetime
 
 def validate_date_format(date_str: str) -> bool:
     """
-    Validate if the date string is in the format YYYY-MM-DD.
+    Validate if date string conforms to YYYY-MM-DD format
+    
+    Args:
+        date_str: Date string to validate
+        
+    Returns:
+        bool: True if format is correct, False otherwise
     """
     pattern = r"^\d{4}-\d{1,2}-\d{1,2}$"
     return bool(re.match(pattern, date_str))
@@ -41,7 +47,20 @@ def normalize_date(date_str: str) -> str:
 
 
 def preprocess_date_string(date_str: str) -> str:
-    """预处理日期字符串，处理特殊格式"""
+    """
+    预处理日期字符串，处理特殊格式
+    
+    处理以下特殊格式：
+    - 年份后直接跟月份没有分隔符的格式（如 "20259/28"）
+    - 无分隔符的纯数字格式（如 "20251028", "251028"）
+    - 混合分隔符，统一为 "-"
+    
+    Args:
+        date_str: 原始日期字符串
+        
+    Returns:
+        str: 预处理后的日期字符串，格式为 "YYYY-MM-DD" 或 "YYYY-MM"
+    """
 
     # 处理类似 "20259/28" 的格式（年份后直接跟月份没有分隔）
     match = re.match(r'^(\d{4,5})[/\.\-_]?(\d{1,2})[/\.\-_]?(\d{1,2})$', date_str)
@@ -78,7 +97,23 @@ def preprocess_date_string(date_str: str) -> str:
 
 
 def fallback_parse(date_str: str) -> str:
-    """备选解析方案"""
+    """
+    备选日期解析方案
+    
+    当智能解析失败时，尝试使用预定义的日期格式进行解析。
+    支持多种常见的日期格式，包括：
+    - YYYY-MM-DD, YYYY/MM/DD, YYYY.MM.DD
+    - YYYYMMDD, YYMMDD
+    - MM-DD-YYYY, MM/DD/YYYY, MM.DD.YYYY
+    - DD-MM-YYYY, DD/MM/YYYY, DD.MM.YYYY
+    - YYYY-MM, YYYY/MM, YYYY.MM
+    
+    Args:
+        date_str: 待解析的日期字符串
+        
+    Returns:
+        str: 标准化后的日期字符串（YYYY-MM-DD格式），解析失败时返回原字符串
+    """
 
     # 尝试常见的日期格式[citation:4][citation:5]
     formats_to_try = [

api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2

@@ -1,6 +1,7 @@
 {#
   对话级抽取与相关性判定模板（用于剪枝加速）
-  输入：pruning_scene, is_builtin_scene, ontology_classes, dialog_text, language
+  输入：pruning_scene, ontology_class_infos, dialog_text, language
+    - ontology_class_infos: List[{class_name: str, class_description: str}]
   输出：严格 JSON（不要包含任何多余文本），字段：
     - is_related: bool，是否与所选场景相关
     - times: [string]，从对话中抽取的时间相关文本（日期、时间、时间段、有效期等）
@@ -9,64 +10,103 @@
     - contacts: [string]，联系方式（电话/手机号/邮箱/微信/QQ等）
     - addresses: [string]，地址/地点相关文本
     - keywords: [string]，其它有助于保留的重要关键词（与场景强相关的术语）
+    - preserve_keywords: [string]，必须保留的情绪/兴趣/爱好/个人偏好相关词或短语片段
 
   要求：
   - 必须只输出上述 JSON，且键名一致；不得输出解释、前后缀；不得包含注释。
-  - times/ids/amounts/contacts/addresses/keywords 仅抽取原文片段或规范化后的简单字符串。
+  - times/ids/amounts/contacts/addresses/keywords/preserve_keywords 仅抽取原文片段或规范化后的简单字符串。
   - 仅输出上述键；避免多余解释或字段。
 #}
 
-{# ── 内置场景的固定说明 ── #}
-{% set builtin_scene_instructions = {
-  'education': {
-    'zh': '教育场景：教学、课程、考试、作业、老师/学生互动、学习资源、学校管理等。',
-    'en': 'Education Scenario: Teaching, courses, exams, homework, teacher/student interaction, learning resources, school management, etc.'
-  },
-  'online_service': {
-    'zh': '在线客服场景：客户咨询、问题排查、服务工单、售后支持、订单/退款、工单升级等。',
-    'en': 'Online Service Scenario: Customer inquiries, troubleshooting, service tickets, after-sales support, orders/refunds, ticket escalation, etc.'
-  },
-  'outbound': {
-    'zh': '外呼场景：电话外呼、邀约、调研问卷、线索跟进、对话脚本、回访记录等。',
-    'en': 'Outbound Scenario: Outbound calls, invitations, survey questionnaires, lead follow-up, call scripts, follow-up records, etc.'
-  }
-} %}
-
-{# ── 确定最终使用的场景说明 ── #}
-{% if is_builtin_scene %}
-  {# 内置专门场景：使用固定说明 #}
-  {% set scene_key = pruning_scene %}
-  {% if scene_key not in builtin_scene_instructions %}{% set scene_key = 'education' %}{% endif %}
-  {% set instruction = builtin_scene_instructions[scene_key][language] if language in ['zh', 'en'] else builtin_scene_instructions[scene_key]['zh'] %}
-  {% set custom_types_str = '' %}
-{% else %}
-  {# 自定义场景：使用场景名称 + 本体类型列表构建说明 #}
-  {% if ontology_classes and ontology_classes | length > 0 %}
-    {% if language == 'en' %}
-      {% set custom_types_str = ontology_classes | join(', ') %}
-      {% set instruction = 'Custom scene "' ~ pruning_scene ~ '": The dialogue is related to this scene if it involves any of the following entity types: ' ~ custom_types_str ~ '.' %}
-    {% else %}
-      {% set custom_types_str = ontology_classes | join('、') %}
-      {% set instruction = '自定义场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关：' ~ custom_types_str ~ '。' %}
-    {% endif %}
+{# ── 确定场景说明 ── #}
+{% if ontology_class_infos and ontology_class_infos | length > 0 %}
+  {% if language == 'en' %}
+    {% set instruction = 'Scene "' ~ pruning_scene ~ '": The dialogue is relevant if it involves any of the following entity types.' %}
   {% else %}
-    {# 无本体类型时退化为通用说明 #}
-    {% if language == 'en' %}
-      {% set instruction = 'Custom scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %}
-    {% else %}
-      {% set instruction = '自定义场景「' ~ pruning_scene ~ '」：根据对话整体内容判断是否与该场景相关。' %}
-    {% endif %}
-    {% set custom_types_str = '' %}
+    {% set instruction = '场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关。' %}
+  {% endif %}
+{% else %}
+  {% if language == 'en' %}
+    {% set instruction = 'Scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %}
+  {% else %}
+    {% set instruction = '场景「' ~ pruning_scene ~ '」：根据对话整体内容判断是否与该场景相关。' %}
   {% endif %}
 {% endif %}
 
 {% if language == "zh" %}
-请在下方对话全文基础上，按该场景进行一次性抽取并判定相关性：
+你是一个对话内容分析助手。请对下方对话全文进行一次性分析，完成两项任务：
+1. 判断对话是否与指定场景相关；
+2. 从对话中抽取所有需要保留的重要信息片段。
+
 场景说明：{{ instruction }}
-{% if not is_builtin_scene and custom_types_str %}
-重要提示：只要对话中出现与上述实体类型（{{ custom_types_str }}）相关的内容，即判定为相关（is_related=true）。
+
+{% if ontology_class_infos and ontology_class_infos | length > 0 %}
+【本场景实体类型定义】
+以下实体类型定义了本场景中哪些内容是重要的。
+凡是与以下任意类型相关的内容，都必须保留，并将关键词/短语提取到 keywords 字段：
+
+{% for info in ontology_class_infos %}
+- {{ info.class_name }}：{{ info.class_description }}
+{% endfor %}
+
+重要提示：只要对话中出现与上述任意实体类型相关的内容，即判定为相关（is_related=true）。
 {% endif %}
 
+---
+【必须保留的内容（不可删除）】
+以下类型的内容无论是否与场景直接相关，都必须保留，请将其关键词/短语抽取到对应字段：
+- 时间信息：日期、时间点、时间段、有效期 → times 字段
+- 编号信息：学号、工号、订单号、申请号、账号、ID → ids 字段
+- 金额信息：价格、费用、金额（含货币符号或单位，如"100元"、"¥200"）→ amounts 字段（注意：考试分数、成绩分数不属于金额，不要放入此字段）
+- 联系方式：电话、手机号、邮箱、微信、QQ → contacts 字段
+- 地址信息：地点、地址、位置 → addresses 字段
+- 场景关键词：与**当前场景**强相关的专业术语、事件名称 → keywords 字段（注意：只放与当前场景直接相关的词，跨场景的内容不要放入此字段）
+- **情绪与情感**：喜悦、悲伤、愤怒、焦虑、开心、难过、委屈、兴奋、害怕、担心、压力、感动等情绪表达 → preserve_keywords 字段
+- **兴趣与爱好**：喜欢、热爱、爱好、擅长、享受、沉迷、着迷、讨厌某事物等个人偏好表达 → preserve_keywords 字段
+- **个人情感态度**：对人际关系、情感状态的明确表达（如"我跟室友闹矛盾了"、"我都快抑郁了"）→ preserve_keywords 字段
+- 注意：学业目标（如"我想考研"）、成绩（如"87分"）、学科偏好（如"喜欢数学"）属于学业信息，不属于情绪/情感，不要放入 preserve_keywords 字段
+
+【场景无关内容标记】
+请从对话中识别出与当前场景（{{ pruning_scene }}）**既不相关、也无语义关联**的消息片段，将其原文（或关键片段）提取到 scene_unrelated_snippets 字段。
+判断标准：
+- 与场景实体类型完全无关
+- 与场景话题没有因果/时间/情境上的关联（例如：不是"因为上课所以累"这种关联）
+- 纯粹是另一个话题的内容（如在教育场景中讨论购物、娱乐等）
+注意：有情绪/感受表达的消息即使话题不同，也可能有语义关联，请谨慎标记。
+
+**重要：scene_unrelated_snippets 必须认真填写，不能为空数组。**
+如果对话中存在与场景无关的内容，必须将其原文片段提取出来。
+
+示例（场景=在线教育）：
+- "我最近心情很差，跟室友闹矛盾了" → 与教育场景无关，加入 scene_unrelated_snippets
+- "她总是很晚回来吵到我睡觉" → 与教育场景无关，加入 scene_unrelated_snippets
+- "对，我都快抑郁了" → 与教育场景无关，加入 scene_unrelated_snippets
+- "期末考试12月25日" → 与教育场景相关，不加入 scene_unrelated_snippets
+- "我上次高数作业87分" → 与教育场景相关，不加入 scene_unrelated_snippets
+- "我的目标是考研" → 与教育场景相关，不加入 scene_unrelated_snippets
+
+示例（场景=情感陪伴）：
+- "我最近心情很差，跟室友闹矛盾了" → 与情感陪伴场景相关（情绪+关系），不加入 scene_unrelated_snippets
+- "对，我都快抑郁了" → 与情感陪伴场景相关（情绪），不加入 scene_unrelated_snippets
+- "期末考试12月25日，3号教学楼201室" → 与情感陪伴场景无关（教育信息），加入 scene_unrelated_snippets
+- "我上次高数作业87分，这次能考好吗" → 与情感陪伴场景无关（学业信息），加入 scene_unrelated_snippets
+- "我的目标是考研，想读应用数学" → 与情感陪伴场景无关（学业目标），加入 scene_unrelated_snippets
+
+【可以删除的内容】
+以下类型的内容属于低价值信息，可以在剪枝时删除：
+- 纯寒暄问候：如"你好"、"在吗"、"拜拜"、"嗯"、"好的"、"哦"等无实质内容的短语
+- 纯表情/符号：如"[微笑]"、"😊"、"哈哈"等
+- 重复确认：如"对对对"、"是的是的"、"嗯嗯嗯"等无新增信息的重复
+- 无意义填充：如"啊"、"呢"、"嘛"等语气词单独成句
+
+**注意：即使消息很短，只要包含情绪、兴趣、爱好、个人观点等有价值信息，就必须保留，不得删除。**
+例如：
+- "我好开心呀" → 包含情绪（开心），必须保留，preserve_keywords 中加入"开心"
+- "好喜欢打羽毛球呀" → 包含兴趣爱好（喜欢打羽毛球），必须保留，preserve_keywords 中加入"喜欢打羽毛球"
+- "我好难过" → 包含情绪（难过），必须保留，preserve_keywords 中加入"难过"
+- "太好啦！看到你开心，我也跟着心情亮起来" → 包含情绪，必须保留，preserve_keywords 中加入"开心"
+
+---
 对话全文：
 """
 {{ dialog_text }}
@@ -80,15 +120,65 @@
   "amounts": [<string>...],
   "contacts": [<string>...],
   "addresses": [<string>...],
-  "keywords": [<string>...]
+  "keywords": [<string>...],
+  "preserve_keywords": [<string>...],
+  "scene_unrelated_snippets": [<string>...]
 }
 {% else %}
-Based on the full dialogue below, perform one-time extraction and relevance determination according to this scenario:
+You are a dialogue content analysis assistant. Please analyze the full dialogue below in one pass and complete two tasks:
+1. Determine whether the dialogue is relevant to the specified scene;
+2. Extract all important information fragments that must be preserved.
+
 Scenario Description: {{ instruction }}
-{% if not is_builtin_scene and custom_types_str %}
-Important: If the dialogue contains content related to any of the entity types above ({{ custom_types_str }}), mark it as relevant (is_related=true).
+
+{% if ontology_class_infos and ontology_class_infos | length > 0 %}
+[Scene Entity Type Definitions]
+The following entity types define what content is important in this scene.
+Content related to ANY of these types must be preserved and extracted into the keywords field:
+
+{% for info in ontology_class_infos %}
+- {{ info.class_name }}: {{ info.class_description }}
+{% endfor %}
+
+Important: If the dialogue contains content related to any of the entity types above, mark it as relevant (is_related=true).
 {% endif %}
 
+---
+[MUST PRESERVE (cannot be deleted)]
+The following types of content must always be preserved regardless of scene relevance. Extract their keywords/phrases into the corresponding fields:
+- Time information: dates, time points, durations, expiry dates → times field
+- ID information: student IDs, employee IDs, order numbers, application numbers, account IDs → ids field
+- Amount information: prices, fees, amounts (with currency symbols or units, e.g., "$100", "¥200") → amounts field (Note: exam scores and grades are NOT amounts, do not put them here)
+- Contact information: phone numbers, emails, WeChat, QQ → contacts field
+- Address information: locations, addresses, places → addresses field
+- Scene keywords: professional terms and event names strongly related to **the current scene** → keywords field (Note: only put terms directly related to the current scene; cross-scene content should not be placed here)
+- **Emotions and feelings**: joy, sadness, anger, anxiety, happiness, sadness, excitement, fear, worry, stress, being moved, etc. → preserve_keywords field
+- **Interests and hobbies**: likes, loves, hobbies, good at, enjoys, obsessed with, hates something, personal preferences → preserve_keywords field
+- **Personal emotional attitudes**: clear expressions about interpersonal relationships or emotional states (e.g., "I had a fight with my roommate", "I'm almost depressed") → preserve_keywords field
+- Note: Academic goals (e.g., "I want to pursue a master's degree"), grades (e.g., "87 points"), and subject preferences (e.g., "I like math") are academic information, NOT emotions/feelings — do not put them in preserve_keywords
+
+[Scene-Unrelated Content Marking]
+Please identify message snippets in the dialogue that are **neither relevant to nor semantically associated with** the current scene ({{ pruning_scene }}), and extract their original text (or key fragments) into the scene_unrelated_snippets field.
+Criteria:
+- Completely unrelated to the scene's entity types
+- No causal/temporal/contextual association with the scene topic (e.g., "feeling tired because of class" IS associated)
+- Purely belongs to a different topic (e.g., discussing shopping or entertainment in an education scene)
+Note: Messages with emotional/feeling expressions may still have semantic association even if the topic differs — mark carefully.
+
+[CAN BE DELETED]
+The following types of content are low-value and can be removed during pruning:
+- Pure greetings: e.g., "hello", "are you there", "bye", "ok", "yeah" — short phrases with no substantive content
+- Pure emojis/symbols: e.g., "[smile]", "😊", "haha"
+- Repetitive confirmations: e.g., "yes yes yes", "right right", "uh huh" — repetitions with no new information
+- Meaningless fillers: standalone interjections like "ah", "well", "hmm"
+
+**Note: Even if a message is short, if it contains emotions, interests, hobbies, or personal opinions, it MUST be preserved.**
+Examples:
+- "I'm so happy!" → contains emotion (happy), must preserve; add "happy" to preserve_keywords
+- "I love playing badminton!" → contains interest (love playing badminton), must preserve; add "love playing badminton" to preserve_keywords
+- "I feel so sad" → contains emotion (sad), must preserve; add "sad" to preserve_keywords
+
+---
 Full Dialogue:
 """
 {{ dialog_text }}
@@ -102,6 +192,8 @@ Output strict JSON only (fixed keys, order doesn't matter):
   "amounts": [<string>...],
   "contacts": [<string>...],
   "addresses": [<string>...],
-  "keywords": [<string>...]
+  "keywords": [<string>...],
+  "preserve_keywords": [<string>...],
+  "scene_unrelated_snippets": [<string>...]
 }
 {% endif %}

api/app/core/memory/utils/prompt/template_render.py

@@ -2,15 +2,15 @@ import os
 from jinja2 import Environment, FileSystemLoader
 from typing import List, Dict, Any
 
-
 # Setup Jinja2 environment
 prompt_dir = os.path.join(os.path.dirname(__file__), "prompts")
 prompt_env = Environment(loader=FileSystemLoader(prompt_dir))
 
+
 async def render_evaluate_prompt(evaluate_data: List[Any], schema: Any,
                                  baseline: str = "TIME",
-                                 memory_verify: bool = False,quality_assessment:bool = False,
-                                 statement_databasets: List[str] = [],language_type:str = "zh") -> str:
+                                 memory_verify: bool = False, quality_assessment: bool = False,
+                                 statement_databasets=None, language_type: str = "zh") -> str:
     """
     Renders the evaluate prompt using the evaluate_optimized.jinja2 template.
 
@@ -23,6 +23,8 @@ async def render_evaluate_prompt(evaluate_data: List[Any], schema: Any,
     Returns:
         Rendered prompt content as string
     """
+    if statement_databasets is None:
+        statement_databasets = []
     template = prompt_env.get_template("evaluate.jinja2")
 
     # Convert Pydantic model to JSON schema if needed
@@ -46,7 +48,7 @@ async def render_evaluate_prompt(evaluate_data: List[Any], schema: Any,
 
 
 async def render_reflexion_prompt(data: Dict[str, Any], schema: Any, baseline: str, memory_verify: bool = False,
-                                  statement_databasets: List[str] = [],language_type:str = "zh") -> str:
+                                  statement_databasets=None, language_type: str = "zh") -> str:
     """
     Renders the reflexion prompt using the reflexion_optimized.jinja2 template.
 
@@ -58,6 +60,8 @@ async def render_reflexion_prompt(data: Dict[str, Any], schema: Any, baseline: s
     Returns:
         Rendered prompt content as a string.
     """
+    if statement_databasets is None:
+        statement_databasets = []
     template = prompt_env.get_template("reflexion.jinja2")
 
     # Convert Pydantic model to JSON schema if needed
@@ -69,7 +73,7 @@ async def render_reflexion_prompt(data: Dict[str, Any], schema: Any, baseline: s
         json_schema = schema
 
     rendered_prompt = template.render(data=data, json_schema=json_schema,
-                                      baseline=baseline,memory_verify=memory_verify,
-                                      statement_databasets=statement_databasets,language_type=language_type)
+                                      baseline=baseline, memory_verify=memory_verify,
+                                      statement_databasets=statement_databasets, language_type=language_type)
 
     return rendered_prompt

api/app/core/models/base.py

@@ -1,23 +1,19 @@
 from __future__ import annotations
 
-import asyncio
 import os
-import time
-from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Optional, TypeVar
+from typing import Any, Dict, Optional, TypeVar
+
+from langchain_aws import ChatBedrock
+from langchain_community.chat_models import ChatTongyi
+from langchain_core.embeddings import Embeddings
+from langchain_core.language_models import BaseLLM
+from langchain_ollama import OllamaLLM
+from langchain_openai import ChatOpenAI, OpenAI
+from pydantic import BaseModel, Field
 
-import httpx
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.models.models_model import ModelProvider, ModelType
-from langchain_community.document_compressors import JinaRerank
-from langchain_core.callbacks import CallbackManagerForLLMRun
-from langchain_core.embeddings import Embeddings
-from langchain_core.language_models import BaseLanguageModel, BaseLLM
-from langchain_core.outputs import Generation, LLMResult
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.runnables import RunnableSerializable
-from pydantic import BaseModel, Field
 
 T = TypeVar("T")
 
@@ -163,25 +159,17 @@ def get_provider_llm_class(config: RedBearModelConfig, type: ModelType = ModelTy
 
     # dashscope 的 omni 模型使用 OpenAI 兼容模式
     if provider == ModelProvider.DASHSCOPE and config.is_omni:
-        from langchain_openai import ChatOpenAI
         return ChatOpenAI
-
-    if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK] :
+    if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
         if type == ModelType.LLM:
-            from langchain_openai import OpenAI
             return OpenAI
         elif type == ModelType.CHAT:
-            from langchain_openai import ChatOpenAI
             return ChatOpenAI
     elif provider == ModelProvider.DASHSCOPE:
-        from langchain_community.chat_models import ChatTongyi
         return ChatTongyi
     elif provider == ModelProvider.OLLAMA:
-        from langchain_ollama import OllamaLLM
         return OllamaLLM
     elif provider == ModelProvider.BEDROCK:
-        from langchain_aws import ChatBedrock, ChatBedrockConverse
-
         return ChatBedrock
     else:
         raise BusinessException(f"不支持的模型提供商: {provider}", code=BizCode.PROVIDER_NOT_SUPPORTED)

api/app/core/rag/nlp/search.py

@@ -94,72 +94,16 @@ def knowledge_retrieval(
                 db_knowledge = knowledge_repository.get_knowledge_by_id(db, knowledge_id=kb_id)
                 if db_knowledge and db_knowledge.chunk_num > 0 and db_knowledge.status == 1:
                     # Process shared knowledge base
-                    if db_knowledge.permission_id.lower() == knowledge_model.PermissionType.Share:
-                        knowledgeshare = knowledgeshare_repository.get_knowledgeshare_by_id(db=db,
-                                                                                            knowledgeshare_id=db_knowledge.id)
-                        if knowledgeshare:
-                            db_knowledge = knowledge_repository.get_knowledge_by_id(db,
-                                                                                    knowledge_id=knowledgeshare.source_kb_id)
-                            if not (db_knowledge and db_knowledge.chunk_num > 0 and db_knowledge.status == 1):
-                                continue
-                        else:
-                            continue
-
-                    if str(db_knowledge.id) not in kb_ids:
-                        kb_ids.append(str(db_knowledge.id))
-                    if str(db_knowledge.workspace_id) not in workspace_ids:
-                        workspace_ids.append(str(db_knowledge.workspace_id))
-                    if not chat_model:
-                        chat_model = Base(
-                            key=db_knowledge.llm.api_keys[0].api_key,
-                            model_name=db_knowledge.llm.api_keys[0].model_name,
-                            base_url=db_knowledge.llm.api_keys[0].api_base
-                        )
-                    if not embedding_model:
-                        embedding_model = OpenAIEmbed(
-                            key=db_knowledge.embedding.api_keys[0].api_key,
-                            model_name=db_knowledge.embedding.api_keys[0].model_name,
-                            base_url=db_knowledge.embedding.api_keys[0].api_base
-                        )
-                    vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
-                    # Retrieve according to the configured retrieval type
-                    match kb_config["retrieve_type"]:
-                        case "participle":
-                            rs = vector_service.search_by_full_text(
-                                query=query,
-                                top_k=kb_config["top_k"],
-                                score_threshold=kb_config["similarity_threshold"],
-                                file_names_filter=file_names_filter
-                            )
-                        case "semantic":
-                            rs = vector_service.search_by_vector(
-                                query=query,
-                                top_k=kb_config["top_k"],
-                                score_threshold=kb_config["vector_similarity_weight"],
-                                file_names_filter=file_names_filter
-                            )
-                        case _:  # hybrid
-                            rs1 = vector_service.search_by_vector(
-                                query=query,
-                                top_k=kb_config["top_k"],
-                                score_threshold=kb_config["vector_similarity_weight"],
-                                file_names_filter=file_names_filter
-                            )
-                            rs2 = vector_service.search_by_full_text(
-                                query=query,
-                                top_k=kb_config["top_k"],
-                                score_threshold=kb_config["similarity_threshold"],
-                                file_names_filter=file_names_filter
-                            )
-
-                            # Deduplication of merge results
-                            seen_ids = set()
-                            unique_rs = []
-                            for doc in rs1 + rs2:
-                                if doc.metadata["doc_id"] not in seen_ids:
-                                    seen_ids.add(doc.metadata["doc_id"])
-                                    unique_rs.append(doc)
-                            rs = unique_rs
+                    rs, chat_model, embedding_model = _retrieve_for_knowledge(
+                        db=db,
+                        db_knowledge=db_knowledge,
+                        kb_config={**kb_config, "query": query},  # 或改为单独参数
+                        file_names_filter=file_names_filter,
+                        chat_model=chat_model,
+                        embedding_model=embedding_model,
+                        kb_ids=kb_ids,
+                        workspace_ids=workspace_ids,
+                    )
 
                     all_results.extend(rs)
             except Exception as e:
@@ -199,6 +143,115 @@ def knowledge_retrieval(
     finally:
         db.close()
 
+def _retrieve_for_knowledge(
+    db: Session,
+    db_knowledge,
+    kb_config: Dict[str, Any],
+    file_names_filter: list[str],
+    chat_model: Base | None,
+    embedding_model: OpenAIEmbed | None,
+    kb_ids: list[str],
+    workspace_ids: list[str],
+) -> tuple[list[DocumentChunk], Base | None, OpenAIEmbed | None]:
+    """
+    对单个知识库进行检索。
+    - 处理共享知识库
+    - 如果是 Folder，则递归检索其子知识库
+    - 返回本知识库(含子库)的检索结果和可能更新后的 chat_model/embedding_model
+    """
+    results: list[DocumentChunk] = []
+
+    # 处理共享知识库
+    if db_knowledge.permission_id.lower() == knowledge_model.PermissionType.Share:
+        knowledgeshare = knowledgeshare_repository.get_knowledgeshare_by_id(db=db, knowledgeshare_id=db_knowledge.id)
+        if not knowledgeshare:
+            return results, chat_model, embedding_model
+
+        db_knowledge = knowledge_repository.get_knowledge_by_id(db, knowledge_id=knowledgeshare.source_kb_id)
+        if not (db_knowledge and db_knowledge.chunk_num > 0 and db_knowledge.status == 1):
+            return results, chat_model, embedding_model
+
+    # Folder 类型：递归处理子知识库
+    if db_knowledge.type == knowledge_model.KnowledgeType.FOLDER:
+        children = knowledge_repository.get_knowledges_by_parent_id(db=db, parent_id=db_knowledge.id)
+        for child in children:
+            if not (child and child.chunk_num > 0 and child.status == 1):
+                continue
+            # 递归处理子知识库（子库如果还是 Folder，会继续往下）
+            child_results, chat_model, embedding_model = _retrieve_for_knowledge(
+                db=db,
+                db_knowledge=child,
+                kb_config=kb_config,
+                file_names_filter=file_names_filter,
+                chat_model=chat_model,
+                embedding_model=embedding_model,
+                kb_ids=kb_ids,
+                workspace_ids=workspace_ids,
+            )
+            results.extend(child_results)
+        return results, chat_model, embedding_model
+
+    # 普通知识库，执行一次检索
+    if str(db_knowledge.id) not in kb_ids:
+        kb_ids.append(str(db_knowledge.id))
+    if str(db_knowledge.workspace_id) not in workspace_ids:
+        workspace_ids.append(str(db_knowledge.workspace_id))
+
+    if not chat_model:
+        chat_model = Base(
+            key=db_knowledge.llm.api_keys[0].api_key,
+            model_name=db_knowledge.llm.api_keys[0].model_name,
+            base_url=db_knowledge.llm.api_keys[0].api_base,
+        )
+    if not embedding_model:
+        embedding_model = OpenAIEmbed(
+            key=db_knowledge.embedding.api_keys[0].api_key,
+            model_name=db_knowledge.embedding.api_keys[0].model_name,
+            base_url=db_knowledge.embedding.api_keys[0].api_base,
+        )
+
+    vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+
+    match kb_config["retrieve_type"]:
+        case "participle":
+            rs = vector_service.search_by_full_text(
+                query=kb_config["query"],  # 或者直接把 query 作为额外参数传进来
+                top_k=kb_config["top_k"],
+                score_threshold=kb_config["similarity_threshold"],
+                file_names_filter=file_names_filter,
+            )
+        case "semantic":
+            rs = vector_service.search_by_vector(
+                query=kb_config["query"],
+                top_k=kb_config["top_k"],
+                score_threshold=kb_config["vector_similarity_weight"],
+                file_names_filter=file_names_filter,
+            )
+        case _:
+            rs1 = vector_service.search_by_vector(
+                query=kb_config["query"],
+                top_k=kb_config["top_k"],
+                score_threshold=kb_config["vector_similarity_weight"],
+                file_names_filter=file_names_filter,
+            )
+            rs2 = vector_service.search_by_full_text(
+                query=kb_config["query"],
+                top_k=kb_config["top_k"],
+                score_threshold=kb_config["similarity_threshold"],
+                file_names_filter=file_names_filter,
+            )
+            # 合并去重
+            seen_ids = set()
+            unique_rs = []
+            for doc in rs1 + rs2:
+                if doc.metadata["doc_id"] not in seen_ids:
+                    seen_ids.add(doc.metadata["doc_id"])
+                    unique_rs.append(doc)
+            rs = unique_rs
+
+    results.extend(rs)
+    return results, chat_model, embedding_model
+
 
 def rerank(db: Session, reranker_id: uuid, query: str, docs: list[DocumentChunk], top_k: int) -> list[DocumentChunk]:
     """

api/app/core/rag_utils/__init__.py

@@ -4,11 +4,12 @@ RAG chunk analysis utilities.
 
 from .chunk_summary import generate_chunk_summary
 from .chunk_tags import extract_chunk_tags, extract_chunk_persona
-from .chunk_insight import generate_chunk_insight
+from .chunk_insight import generate_chunk_insight, generate_chunk_insight_sections
 
 __all__ = [
     "generate_chunk_summary",
     "extract_chunk_tags",
     "extract_chunk_persona",
     "generate_chunk_insight",
+    "generate_chunk_insight_sections",
 ]

api/app/core/rag_utils/chunk_insight.py

@@ -1,213 +1,207 @@
 """
-Generate insights from RAG chunks.
+Generate memory insight report for RAG chunks using memory_insight.jinja2 prompt template.
 
-This module provides functionality to analyze chunk content and generate insights using LLM.
+The memory_insight.jinja2 template produces a four-section report:
+  【总体概述】 → memory_insight
+  【行为模式】 → behavior_pattern
+  【关键发现】 → key_findings
+  【成长轨迹】 → growth_trajectory
+
+generate_chunk_insight() returns the full raw text (stored in end_user.memory_insight).
+generate_chunk_insight_sections() returns a dict with all four fields for richer storage.
 """
 
 import asyncio
+import os
+import re
 from collections import Counter
-from typing import Any, Dict, List
+from typing import Dict, List, Optional
 
 from app.core.logging_config import get_business_logger
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
-from pydantic import BaseModel, Field
 
 business_logger = get_business_logger()
 
+DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus")
 
-def _get_llm_client():
-    """Get LLM client using db context."""
+
+# ── LLM client helper ────────────────────────────────────────────────────────
+
+def _get_llm_client(end_user_id: Optional[str] = None):
+    """Get LLM client, preferring user-connected config with fallback to default."""
     with get_db_context() as db:
+        try:
+            if end_user_id:
+                from app.services.memory_agent_service import get_end_user_connected_config
+                from app.services.memory_config_service import MemoryConfigService
+                connected_config = get_end_user_connected_config(end_user_id, db)
+                config_id = connected_config.get("memory_config_id")
+                workspace_id = connected_config.get("workspace_id")
+                if config_id or workspace_id:
+                    config_service = MemoryConfigService(db)
+                    memory_config = config_service.load_memory_config(
+                        config_id=config_id,
+                        workspace_id=workspace_id
+                    )
+                    factory = MemoryClientFactory(db)
+                    return factory.get_llm_client(memory_config.llm_model_id)
+        except Exception as e:
+            business_logger.warning(f"Failed to get user connected config, using default LLM: {e}")
         factory = MemoryClientFactory(db)
-        return factory.get_llm_client(None)  # Uses default LLM
+        return factory.get_llm_client(DEFAULT_LLM_ID)
 
 
-class ChunkInsight(BaseModel):
-    """Pydantic model for chunk insight."""
-    insight: str = Field(..., description="对chunk内容的深度洞察分析")
+# ── Domain analysis helpers (kept for building prompt inputs) ─────────────────
 
+async def _classify_domain(chunk: str, llm_client) -> str:
+    """Classify a single chunk into a domain category."""
+    from pydantic import BaseModel, Field
 
-class DomainClassification(BaseModel):
-    """Pydantic model for domain classification."""
-    domain: str = Field(
-        ...,
-        description="内容所属的领域分类",
-        examples=["技术", "商业", "教育", "生活", "娱乐", "健康", "其他"]
-    )
+    class _Domain(BaseModel):
+        domain: str = Field(..., description="领域分类")
 
-
-async def classify_chunk_domain(chunk: str) -> str:
-    """
-    Classify a chunk into a specific domain.
-    
-    Args:
-        chunk: Chunk content string
-    
-    Returns:
-        Domain name
-    """
     try:
-        llm_client = _get_llm_client()
-        
-        prompt = f"""请将以下文本内容归类到最合适的领域中。
-
-可选领域及其关键词：
-- 技术：编程、软件、硬件、算法、数据、网络、系统、开发、工程等
-- 商业：市场、销售、管理、财务、投资、创业、营销、战略等
-- 教育：学习、课程、培训、教学、知识、技能、考试、研究等
-- 生活：日常、家庭、饮食、购物、旅行、休闲、娱乐等
-- 娱乐：游戏、电影、音乐、体育、艺术、文化等
-- 健康：医疗、养生、运动、心理、保健、疾病等
-- 其他：无法归入以上类别的内容
-
-文本内容: {chunk[:500]}...
-
-请直接返回最合适的领域名称。"""
-        
-        messages = [
-            {"role": "system", "content": "你是一个专业的文本分类助手。请仔细分析文本内容，选择最合适的领域分类。"},
-            {"role": "user", "content": prompt}
-        ]
-        
-        classification = await llm_client.response_structured(
-            messages=messages,
-            response_model=DomainClassification
+        prompt = (
+            "请将以下文本归类到最合适的领域（技术/商业/教育/生活/娱乐/健康/其他）。\n\n"
+            f"文本: {chunk[:500]}\n\n直接返回领域名称。"
         )
-        
-        return classification.domain if classification else "其他"
-        
-    except Exception as e:
-        business_logger.error(f"分类chunk领域失败: {str(e)}")
+        result = await llm_client.response_structured(
+            messages=[{"role": "user", "content": prompt}],
+            response_model=_Domain,
+        )
+        return result.domain if result else "其他"
+    except Exception:
         return "其他"
 
 
-async def analyze_domain_distribution(chunks: List[str], max_chunks: int = 20) -> Dict[str, float]:
+async def _build_insight_inputs(
+    chunks: List[str],
+    max_chunks: int,
+    end_user_id: Optional[str],
+) -> Dict[str, Optional[str]]:
     """
-    Analyze the domain distribution of chunks.
-    
-    Args:
-        chunks: List of chunk content strings
-        max_chunks: Maximum number of chunks to analyze
-    
-    Returns:
-        Dictionary of domain -> percentage
+    Derive domain_distribution, active_periods, social_connections strings
+    to feed into the memory_insight.jinja2 template.
     """
-    if not chunks:
-        return {}
-    
-    try:
-        # 限制分析的chunk数量
-        chunks_to_analyze = chunks[:max_chunks]
-        
-        # 为每个chunk分类
-        domain_counts = Counter()
-        for chunk in chunks_to_analyze:
-            domain = await classify_chunk_domain(chunk)
-            domain_counts[domain] += 1
-        
-        # 计算百分比
-        total = sum(domain_counts.values())
-        domain_distribution = {
-            domain: count / total
-            for domain, count in domain_counts.items()
-        }
-        
-        # 按百分比降序排序
-        return dict(sorted(domain_distribution.items(), key=lambda x: x[1], reverse=True))
-        
-    except Exception as e:
-        business_logger.error(f"分析领域分布失败: {str(e)}")
-        return {}
+    llm_client = _get_llm_client(end_user_id)
+    chunks_sample = chunks[:max_chunks]
+
+    # Domain distribution
+    domain_counts: Counter = Counter()
+    for chunk in chunks_sample:
+        domain = await _classify_domain(chunk, llm_client)
+        domain_counts[domain] += 1
+
+    total = sum(domain_counts.values()) or 1
+    domain_distribution = ", ".join(
+        f"{d}({c / total:.0%})" for d, c in domain_counts.most_common(3)
+    )
+
+    return {
+        "domain_distribution": domain_distribution,
+        "active_periods": None,      # RAG模式暂无时间维度数据
+        "social_connections": None,  # RAG模式暂无社交关联数据
+    }
 
 
-async def generate_chunk_insight(chunks: List[str], max_chunks: int = 15) -> str:
+# ── Section parser ────────────────────────────────────────────────────────────
+
+_ZH_SECTIONS = {
+    "memory_insight": r"【总体概述】(.*?)(?=【|$)",
+    "behavior_pattern": r"【行为模式】(.*?)(?=【|$)",
+    "key_findings": r"【关键发现】(.*?)(?=【|$)",
+    "growth_trajectory": r"【成长轨迹】(.*?)(?=【|$)",
+}
+
+_EN_SECTIONS = {
+    "memory_insight": r"【Overview】(.*?)(?=【|$)",
+    "behavior_pattern": r"【Behavior Pattern】(.*?)(?=【|$)",
+    "key_findings": r"【Key Findings】(.*?)(?=【|$)",
+    "growth_trajectory": r"【Growth Trajectory】(.*?)(?=【|$)",
+}
+
+
+def _parse_sections(text: str, language: str = "zh") -> Dict[str, str]:
+    """Extract the four sections from the LLM output."""
+    patterns = _ZH_SECTIONS if language == "zh" else _EN_SECTIONS
+    result = {}
+    for key, pattern in patterns.items():
+        match = re.search(pattern, text, re.DOTALL)
+        result[key] = match.group(1).strip() if match else ""
+    return result
+
+
+# ── Public API ────────────────────────────────────────────────────────────────
+
+async def generate_chunk_insight(
+    chunks: List[str],
+    max_chunks: int = 15,
+    end_user_id: Optional[str] = None,
+    language: str = "zh",
+) -> str:
     """
-    Generate insights from the given chunks.
-    
-    Args:
-        chunks: List of chunk content strings
-        max_chunks: Maximum number of chunks to analyze
-    
-    Returns:
-        A comprehensive insight report
+    Generate a memory insight report from RAG chunks.
+
+    Returns the full raw report text (suitable for end_user.memory_insight).
+    Use generate_chunk_insight_sections() when you need all four dimensions.
+    """
+    sections = await generate_chunk_insight_sections(
+        chunks=chunks,
+        max_chunks=max_chunks,
+        end_user_id=end_user_id,
+        language=language,
+    )
+    return sections.get("memory_insight") or sections.get("_raw", "洞察生成失败")
+
+
+async def generate_chunk_insight_sections(
+    chunks: List[str],
+    max_chunks: int = 15,
+    end_user_id: Optional[str] = None,
+    language: str = "zh",
+) -> Dict[str, str]:
+    """
+    Generate a four-section memory insight report from RAG chunks.
+
+    Returns a dict with keys:
+        memory_insight, behavior_pattern, key_findings, growth_trajectory
+    (plus '_raw' containing the full LLM output for debugging)
     """
     if not chunks:
         business_logger.warning("没有提供chunk内容用于生成洞察")
-        return "暂无足够数据生成洞察报告"
-    
+        empty = {k: "" for k in ("memory_insight", "behavior_pattern", "key_findings", "growth_trajectory")}
+        empty["_raw"] = "暂无足够数据生成洞察报告"
+        return empty
+
     try:
-        # 1. 分析领域分布
-        domain_dist = await analyze_domain_distribution(chunks, max_chunks=max_chunks)
-        
-        # 2. 统计基本信息
-        total_chunks = len(chunks)
-        avg_length = sum(len(chunk) for chunk in chunks) / total_chunks if total_chunks > 0 else 0
-        
-        # 3. 构建洞察prompt
-        prompt_parts = []
-        
-        if domain_dist:
-            top_domains = ", ".join([f"{k}({v:.0%})" for k, v in list(domain_dist.items())[:3]])
-            prompt_parts.append(f"- 内容领域分布: {top_domains}")
-        
-        prompt_parts.append(f"- 内容规模: 共{total_chunks}个知识片段，平均长度{avg_length:.0f}字")
-        
-        # 添加部分chunk内容作为参考
-        sample_chunks = chunks[:5]
-        sample_content = "\n".join([f"示例{i+1}: {chunk[:200]}..." for i, chunk in enumerate(sample_chunks)])
-        prompt_parts.append(f"\n内容示例:\n{sample_content}")
-        
-        system_prompt = """你是一位专业的知识内容分析师。你的任务是根据提供的信息，生成一段简洁、有洞察力的分析报告。
+        from app.core.memory.utils.prompt.prompt_utils import render_memory_insight_prompt
 
-重要规则：
-1. 报告需要将所有要点流畅地串联成一个段落
-2. 语言风格要专业、客观，同时易于理解
-3. 不要添加任何额外的解释或标题，直接输出报告内容
-4. 基于提供的数据和示例内容进行分析，不要编造信息
-5. 重点关注内容的主题、特点和价值
-6. 报告长度控制在150-200字
+        # Build template inputs from chunk analysis
+        inputs = await _build_insight_inputs(chunks, max_chunks, end_user_id)
 
-例如，如果输入是：
-- 内容领域分布: 技术(60%), 商业(25%), 教育(15%)
-- 内容规模: 共50个知识片段，平均长度320字
-内容示例: [示例内容...]
+        rendered_prompt = await render_memory_insight_prompt(
+            domain_distribution=inputs["domain_distribution"],
+            active_periods=inputs["active_periods"],
+            social_connections=inputs["social_connections"],
+            language=language,
+        )
 
-你的输出应该类似：
-"该知识库主要聚焦于技术领域(60%)，涵盖商业(25%)和教育(15%)相关内容。共包含50个知识片段，平均每个片段约320字，内容详实。从示例来看，内容涉及[具体主题]，体现了[特点]，对[目标用户]具有较高的参考价值。"
-"""
-        
-        user_prompt = "\n".join(prompt_parts)
-        
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt}
-        ]
-        
-        # 调用LLM生成洞察
-        llm_client = _get_llm_client()
+        messages = [{"role": "user", "content": rendered_prompt}]
+        llm_client = _get_llm_client(end_user_id)
         response = await llm_client.chat(messages=messages)
-        
-        insight = response.content.strip()
-        business_logger.info(f"成功生成chunk洞察，分析了 {min(len(chunks), max_chunks)} 个片段")
-        
-        return insight
-        
+        raw_text = response.content.strip() if response and response.content else ""
+
+        sections = _parse_sections(raw_text, language=language)
+        sections["_raw"] = raw_text
+
+        business_logger.info(
+            f"成功生成chunk洞察四维度，分析了 {min(len(chunks), max_chunks)} 个片段"
+        )
+        return sections
+
     except Exception as e:
         business_logger.error(f"生成chunk洞察失败: {str(e)}")
-        return "洞察生成失败"
-
-
-if __name__ == "__main__":
-    # 测试代码
-    test_chunks = [
-        "Python是一种高级编程语言，以其简洁的语法和强大的功能而闻名。它广泛应用于Web开发、数据分析、人工智能等领域。",
-        "机器学习算法可以从数据中自动学习模式，无需显式编程。常见的算法包括决策树、随机森林、神经网络等。",
-        "深度学习是机器学习的一个分支，使用多层神经网络来学习数据的层次化表示。它在图像识别、语音识别等任务中表现出色。",
-        "自然语言处理技术使计算机能够理解和生成人类语言。应用包括机器翻译、情感分析、文本摘要等。",
-        "数据科学结合了统计学、计算机科学和领域知识，用于从数据中提取有价值的洞察。"
-    ]
-    
-    print("开始生成chunk洞察...")
-    insight = asyncio.run(generate_chunk_insight(test_chunks))
-    print(f"\n生成的洞察：\n{insight}")
+        empty = {k: "" for k in ("memory_insight", "behavior_pattern", "key_findings", "growth_trajectory")}
+        empty["_raw"] = "洞察生成失败"
+        return empty

api/app/core/rag_utils/chunk_summary.py

@@ -1,11 +1,10 @@
 """
-Generate summary for RAG chunks.
-
-This module provides functionality to summarize chunk content using LLM.
+Generate summary for RAG chunks using memory_summary.jinja2 prompt template.
 """
 
 import asyncio
-from typing import Any, Dict, List
+import os
+from typing import List, Optional
 
 from app.core.logging_config import get_business_logger
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -14,94 +13,135 @@ from pydantic import BaseModel, Field
 
 business_logger = get_business_logger()
 
-
-def _get_llm_client():
-    """Get LLM client using db context."""
-    with get_db_context() as db:
-        factory = MemoryClientFactory(db)
-        return factory.get_llm_client(None)  # Uses default LLM
+DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus")
 
 
-class ChunkSummary(BaseModel):
-    """Pydantic model for chunk summary."""
-    summary: str = Field(..., description="简洁的chunk内容摘要")
+# ── Schema ──────────────────────────────────────────────────────────────────
+
+class MemorySummaryStatement(BaseModel):
+    """Single labelled statement extracted by memory_summary.jinja2."""
+    statement: str = Field(..., description="提取的陈述内容")
+    label: Optional[str] = Field(None, description="陈述标签")
 
 
-async def generate_chunk_summary(chunks: List[str], max_chunks: int = 10) -> str:
+class MemorySummaryResponse(BaseModel):
     """
-    Generate a summary for the given chunks.
-    
+    Structured output expected from memory_summary.jinja2.
+    The template asks for a JSON array of labelled statements;
+    we wrap it in an object so response_structured can parse it.
+    """
+    statements: List[MemorySummaryStatement] = Field(
+        default_factory=list,
+        description="从chunk中提取的陈述列表"
+    )
+    summary: Optional[str] = Field(None, description="整体摘要文本（可选）")
+
+
+# ── LLM client helper ────────────────────────────────────────────────────────
+
+def _get_llm_client(end_user_id: Optional[str] = None):
+    """Get LLM client, preferring user-connected config with fallback to default."""
+    with get_db_context() as db:
+        try:
+            if end_user_id:
+                from app.services.memory_agent_service import get_end_user_connected_config
+                from app.services.memory_config_service import MemoryConfigService
+                connected_config = get_end_user_connected_config(end_user_id, db)
+                config_id = connected_config.get("memory_config_id")
+                workspace_id = connected_config.get("workspace_id")
+                if config_id or workspace_id:
+                    config_service = MemoryConfigService(db)
+                    memory_config = config_service.load_memory_config(
+                        config_id=config_id,
+                        workspace_id=workspace_id
+                    )
+                    factory = MemoryClientFactory(db)
+                    return factory.get_llm_client(memory_config.llm_model_id)
+        except Exception as e:
+            business_logger.warning(f"Failed to get user connected config, using default LLM: {e}")
+        factory = MemoryClientFactory(db)
+        return factory.get_llm_client(DEFAULT_LLM_ID)
+
+
+# ── Core function ─────────────────────────────────────────────────────────────
+
+async def generate_chunk_summary(
+    chunks: List[str],
+    max_chunks: int = 10,
+    end_user_id: Optional[str] = None,
+    language: str = "zh",
+) -> str:
+    """
+    Generate a user summary from RAG chunks using the memory_summary.jinja2 template.
+
+    The template extracts labelled statements from the chunks; we then join them
+    into a coherent summary string that can be stored in end_user.user_summary.
+
     Args:
         chunks: List of chunk content strings
-        max_chunks: Maximum number of chunks to process (default: 10)
-    
+        max_chunks: Maximum number of chunks to process
+        end_user_id: Optional end-user ID for model selection
+        language: Output language ("zh" or "en")
+
     Returns:
-        A concise summary of the chunks
+        Summary string (joined statements or fallback text)
     """
     if not chunks:
         business_logger.warning("没有提供chunk内容用于生成摘要")
         return "暂无内容"
-    
+
     try:
-        # 限制处理的chunk数量，避免token过多
+        from app.core.memory.utils.prompt.prompt_utils import render_memory_summary_prompt
+
         chunks_to_process = chunks[:max_chunks]
-        
-        # 合并chunk内容
-        combined_content = "\n\n".join([f"片段{i+1}: {chunk}" for i, chunk in enumerate(chunks_to_process)])
-        
-        # 构建prompt
-        system_prompt = (
-            "你是一位专业的文本摘要助手。请基于提供的文本片段，生成简洁的摘要。要求：\n"
-            "- 摘要长度控制在100-150字；\n"
-            "- 提取核心信息和关键要点；\n"
-            "- 使用客观、清晰的语言；\n"
-            "- 避免冗余和重复；\n"
-            "- 如果内容涉及多个主题，按重要性排序呈现。"
+        chunk_texts = "\n\n".join(
+            [f"片段{i + 1}: {chunk}" for i, chunk in enumerate(chunks_to_process)]
+        )
+
+        json_schema = MemorySummaryResponse.model_json_schema()
+
+        rendered_prompt = await render_memory_summary_prompt(
+            chunk_texts=chunk_texts,
+            json_schema=json_schema,
+            max_words=200,
+            language=language,
+        )
+
+        messages = [{"role": "user", "content": rendered_prompt}]
+
+        llm_client = _get_llm_client(end_user_id)
+
+        # Try structured output; fall back to plain chat only for LLMClientException
+        # (indicates the model/provider doesn't support structured output).
+        # All other exceptions are re-raised so config/schema errors stay visible.
+        try:
+            response: MemorySummaryResponse = await llm_client.response_structured(
+                messages=messages,
+                response_model=MemorySummaryResponse,
+            )
+            if response.summary:
+                summary = response.summary.strip()
+            elif response.statements:
+                summary = "；".join(s.statement for s in response.statements)
+            else:
+                summary = "暂无内容"
+        except Exception as e:
+            from app.core.memory.llm_tools.llm_client import LLMClientException
+            if isinstance(e, LLMClientException):
+                business_logger.warning(
+                    f"结构化输出不可用，降级为普通对话: end_user_id={end_user_id}, reason={e}"
+                )
+                raw = await llm_client.chat(messages=messages)
+                summary = raw.content.strip() if raw and raw.content else "暂无内容"
+            else:
+                business_logger.error(f"生成摘要时发生非预期异常: {e}")
+                raise
+
+        business_logger.info(
+            f"成功生成chunk摘要，处理了 {len(chunks_to_process)} 个片段"
         )
-        
-        user_prompt = f"请为以下文本片段生成摘要：\n\n{combined_content}"
-        
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
-        ]
-        
-        # 调用LLM生成摘要
-        llm_client = _get_llm_client()
-        response = await llm_client.chat(messages=messages)
-        
-        summary = response.content.strip()
-        business_logger.info(f"成功生成chunk摘要，处理了 {len(chunks_to_process)} 个片段")
-        
         return summary
-        
+
     except Exception as e:
         business_logger.error(f"生成chunk摘要失败: {str(e)}")
         return "摘要生成失败"
-
-
-async def generate_chunk_summary_batch(chunks_list: List[List[str]]) -> List[str]:
-    """
-    Generate summaries for multiple chunk lists in batch.
-    
-    Args:
-        chunks_list: List of chunk lists
-    
-    Returns:
-        List of summaries
-    """
-    tasks = [generate_chunk_summary(chunks) for chunks in chunks_list]
-    return await asyncio.gather(*tasks)
-
-
-if __name__ == "__main__":
-    # 测试代码
-    test_chunks = [
-        "这是第一段测试内容，讲述了关于机器学习的基础知识。",
-        "第二段内容介绍了深度学习的应用场景和发展历史。",
-        "第三段讨论了自然语言处理技术的最新进展。"
-    ]
-    
-    print("开始生成chunk摘要...")
-    summary = asyncio.run(generate_chunk_summary(test_chunks))
-    print(f"\n生成的摘要：\n{summary}")

api/app/core/rag_utils/chunk_tags.py

@@ -5,8 +5,9 @@ This module provides functionality to extract meaningful tags from chunk content
 """
 
 import asyncio
+import os
 from collections import Counter
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 from app.core.logging_config import get_business_logger
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -15,12 +16,31 @@ from pydantic import BaseModel, Field
 
 business_logger = get_business_logger()
 
+DEFAULT_LLM_ID = os.getenv("SELECTED_LLM_ID", "openai/qwen-plus")
 
-def _get_llm_client():
-    """Get LLM client using db context."""
+
+def _get_llm_client(end_user_id: Optional[str] = None):
+    """Get LLM client, preferring user-connected config with fallback to default."""
     with get_db_context() as db:
+        try:
+            if end_user_id:
+                from app.services.memory_agent_service import get_end_user_connected_config
+                from app.services.memory_config_service import MemoryConfigService
+                connected_config = get_end_user_connected_config(end_user_id, db)
+                config_id = connected_config.get("memory_config_id")
+                workspace_id = connected_config.get("workspace_id")
+                if config_id or workspace_id:
+                    config_service = MemoryConfigService(db)
+                    memory_config = config_service.load_memory_config(
+                        config_id=config_id,
+                        workspace_id=workspace_id
+                    )
+                    factory = MemoryClientFactory(db)
+                    return factory.get_llm_client(memory_config.llm_model_id)
+        except Exception as e:
+            business_logger.warning(f"Failed to get user connected config, using default LLM: {e}")
         factory = MemoryClientFactory(db)
-        return factory.get_llm_client(None)  # Uses default LLM
+        return factory.get_llm_client(DEFAULT_LLM_ID)
 
 
 class ExtractedTags(BaseModel):
@@ -33,7 +53,7 @@ class ExtractedPersona(BaseModel):
     personas: List[str] = Field(..., description="从文本中提取的人物形象列表，如'产品设计师'、'旅行爱好者'等")
 
 
-async def extract_chunk_tags(chunks: List[str], max_tags: int = 10, max_chunks: int = 10) -> List[Tuple[str, int]]:
+async def extract_chunk_tags(chunks: List[str], max_tags: int = 10, max_chunks: int = 10, end_user_id: Optional[str] = None) -> List[Tuple[str, int]]:
     """
     Extract meaningful tags from the given chunks.
     
@@ -64,7 +84,7 @@ async def extract_chunk_tags(chunks: List[str], max_tags: int = 10, max_chunks:
             "标签应该是名词或名词短语，能够准确概括文本的核心内容。"
         )
         
-        llm_client = _get_llm_client()
+        llm_client = _get_llm_client(end_user_id)
         
         # 为每个chunk单独提取标签，然后统计频率
         all_tags = []
@@ -116,7 +136,7 @@ async def extract_chunk_tags_with_frequency(chunks: List[str], max_tags: int = 1
     return await extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=len(chunks))
 
 
-async def extract_chunk_persona(chunks: List[str], max_personas: int = 5, max_chunks: int = 20) -> List[str]:
+async def extract_chunk_persona(chunks: List[str], max_personas: int = 5, max_chunks: int = 20, end_user_id: Optional[str] = None) -> List[str]:
     """
     Extract persona (人物形象) from the given chunks.
     
@@ -159,7 +179,7 @@ async def extract_chunk_persona(chunks: List[str], max_personas: int = 5, max_ch
         ]
         
         # 调用LLM提取人物形象
-        llm_client = _get_llm_client()
+        llm_client = _get_llm_client(end_user_id)
         structured_response = await llm_client.response_structured(
             messages=messages,
             response_model=ExtractedPersona

api/app/core/storage/base.py

@@ -7,7 +7,7 @@ file operations across different storage backends.
 """
 
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import AsyncIterator, Optional
 
 
 class StorageBackend(ABC):
@@ -42,6 +42,26 @@ class StorageBackend(ABC):
         """
         pass
 
+    @abstractmethod
+    async def upload_stream(
+        self,
+        file_key: str,
+        stream: AsyncIterator[bytes],
+        content_type: Optional[str] = None,
+    ) -> int:
+        """
+        Upload a file from an async byte stream.
+
+        Args:
+            file_key: Unique identifier for the file.
+            stream: Async iterator yielding bytes chunks.
+            content_type: Optional MIME type of the file.
+
+        Returns:
+            Total bytes written.
+        """
+        pass
+
     @abstractmethod
     async def download(self, file_key: str) -> bytes:
         """

api/app/core/storage/factory.py

@@ -85,6 +85,7 @@ class StorageFactory:
                 access_key_id=settings.S3_ACCESS_KEY_ID,
                 secret_access_key=settings.S3_SECRET_ACCESS_KEY,
                 bucket_name=settings.S3_BUCKET_NAME,
+                endpoint_url=settings.S3_ENDPOINT_URL,
             )
 
         else:

api/app/core/storage/local.py

@@ -11,6 +11,7 @@ from typing import Optional
 
 import aiofiles
 import aiofiles.os
+from typing import AsyncIterator
 
 from app.core.storage.base import StorageBackend
 from app.core.storage_exceptions import (
@@ -179,6 +180,36 @@ class LocalStorage(StorageBackend):
         full_path = self._get_full_path(file_key)
         return full_path.exists()
 
+    async def upload_stream(
+        self,
+        file_key: str,
+        stream: AsyncIterator[bytes],
+        content_type: Optional[str] = None,
+    ) -> int:
+        """
+        Upload a file from an async byte stream to the local file system.
+
+        Returns:
+            Total bytes written.
+        """
+        full_path = self._get_full_path(file_key)
+        try:
+            full_path.parent.mkdir(parents=True, exist_ok=True)
+            total = 0
+            async with aiofiles.open(full_path, "wb") as f:
+                async for chunk in stream:
+                    await f.write(chunk)
+                    total += len(chunk)
+            logger.info(f"File stream uploaded successfully: {file_key}")
+            return total
+        except Exception as e:
+            logger.error(f"Failed to stream upload file {file_key}: {e}")
+            raise StorageUploadError(
+                message=f"Failed to stream upload file: {e}",
+                file_key=file_key,
+                cause=e,
+            )
+
     async def get_url(self, file_key: str, expires: int = 3600) -> str:
         """
         Get an access URL for the file.

api/app/core/storage/oss.py

@@ -5,8 +5,9 @@ This module provides a storage backend that stores files on Aliyun Object
 Storage Service (OSS) using the oss2 SDK.
 """
 
+import io
 import logging
-from typing import Optional
+from typing import AsyncIterator, Optional
 
 import oss2
 from oss2.exceptions import NoSuchKey, OssError
@@ -125,10 +126,39 @@ class OSSStorage(StorageBackend):
                 cause=e,
             )
 
+    async def upload_stream(
+        self,
+        file_key: str,
+        stream: AsyncIterator[bytes],
+        content_type: Optional[str] = None,
+    ) -> int:
+        """Upload from async stream to OSS. Returns total bytes written."""
+        buf = io.BytesIO()
+        try:
+            async for chunk in stream:
+                buf.write(chunk)
+            content = buf.getvalue()
+            headers = {"Content-Type": content_type} if content_type else None
+            self.bucket.put_object(file_key, content, headers=headers)
+            logger.info(f"File stream uploaded to OSS successfully: {file_key}")
+            return len(content)
+        except OssError as e:
+            logger.error(f"OSS error stream uploading file {file_key}: {e}")
+            raise StorageUploadError(
+                message=f"Failed to stream upload file to OSS: {e.message}",
+                file_key=file_key,
+                cause=e,
+            )
+        except Exception as e:
+            logger.error(f"Failed to stream upload file to OSS {file_key}: {e}")
+            raise StorageUploadError(
+                message=f"Failed to stream upload file to OSS: {e}",
+                file_key=file_key,
+                cause=e,
+            )
+
     async def download(self, file_key: str) -> bytes:
         """
-        Download a file from OSS.
-
         Args:
             file_key: Unique identifier for the file in the storage system.
 

api/app/core/storage/s3.py

@@ -5,8 +5,9 @@ This module provides a storage backend that stores files on AWS S3
 using the boto3 SDK.
 """
 
+import io
 import logging
-from typing import Optional
+from typing import AsyncIterator, Optional
 
 import boto3
 from botocore.exceptions import ClientError, NoCredentialsError, BotoCoreError
@@ -35,6 +36,19 @@ class S3Storage(StorageBackend):
         bucket_name: The name of the S3 bucket.
         region: The AWS region.
     """
+    AMAZON_S3_ENDPOINT_MAP = {
+        "us-east-1": "https://s3.us-east-1.amazonaws.com",  # 特殊：无地域后缀
+        "us-east-2": "https://s3.us-east-2.amazonaws.com",
+        "us-west-1": "https://s3.us-west-1.amazonaws.com",
+        "us-west-2": "https://s3.us-west-2.amazonaws.com",
+        "ap-east-1": "https://s3.ap-east-1.amazonaws.com",  # 香港
+        "ap-southeast-1": "https://s3.ap-southeast-1.amazonaws.com",  # 新加坡
+        "ap-southeast-2": "https://s3.ap-southeast-2.amazonaws.com",  # 悉尼
+        "ap-northeast-1": "https://s3.ap-northeast-1.amazonaws.com",  # 东京
+        "eu-central-1": "https://s3.eu-central-1.amazonaws.com",  # 法兰克福
+        "eu-west-1": "https://s3.eu-west-1.amazonaws.com",  # 爱尔兰
+        # 可根据需要扩展其他地域
+    }
 
     def __init__(
         self,
@@ -42,6 +56,7 @@ class S3Storage(StorageBackend):
         access_key_id: str,
         secret_access_key: str,
         bucket_name: str,
+        endpoint_url: Optional[str] = None
     ):
         """
         Initialize the S3Storage backend.
@@ -51,6 +66,7 @@ class S3Storage(StorageBackend):
             access_key_id: The AWS access key ID.
             secret_access_key: The AWS secret access key.
             bucket_name: The name of the S3 bucket.
+            endpoint_url: The complete URL to use for the constructed client.
 
         Raises:
             StorageConfigError: If any required configuration is missing.
@@ -69,10 +85,19 @@ class S3Storage(StorageBackend):
         self.region = region
         self.bucket_name = bucket_name
 
+        if not endpoint_url:
+            # 优先匹配内置映射表（解决特殊地域）
+            if region in self.AMAZON_S3_ENDPOINT_MAP:
+                endpoint_url = self.AMAZON_S3_ENDPOINT_MAP[region]
+            # 兜底：通用拼接（适配未配置的新地域）
+            else:
+                endpoint_url = f"https://s3.{region}.amazonaws.com"
+
         try:
             self.client = boto3.client(
                 "s3",
                 region_name=region,
+                endpoint_url=endpoint_url,
                 aws_access_key_id=access_key_id,
                 aws_secret_access_key=secret_access_key,
             )
@@ -150,6 +175,62 @@ class S3Storage(StorageBackend):
                 cause=e,
             )
 
+    async def upload_stream(
+        self,
+        file_key: str,
+        stream: AsyncIterator[bytes],
+        content_type: Optional[str] = None,
+    ) -> int:
+        """Upload from async stream to S3 via multipart upload. Returns total bytes written."""
+        extra_args = {"ContentType": content_type} if content_type else {}
+        mpu = self.client.create_multipart_upload(
+            Bucket=self.bucket_name, Key=file_key, **extra_args
+        )
+        upload_id = mpu["UploadId"]
+        parts = []
+        part_number = 1
+        buf = io.BytesIO()
+        total = 0
+        min_part_size = 5 * 1024 * 1024  # S3 最小分片 5MB
+        try:
+            async for chunk in stream:
+                buf.write(chunk)
+                total += len(chunk)
+                if buf.tell() >= min_part_size:
+                    buf.seek(0)
+                    resp = self.client.upload_part(
+                        Bucket=self.bucket_name, Key=file_key,
+                        UploadId=upload_id, PartNumber=part_number, Body=buf.read()
+                    )
+                    parts.append({"PartNumber": part_number, "ETag": resp["ETag"]})
+                    part_number += 1
+                    buf = io.BytesIO()
+            # 上传剩余数据（最后一片可小于 5MB）
+            remaining = buf.getvalue()
+            if remaining:
+                resp = self.client.upload_part(
+                    Bucket=self.bucket_name, Key=file_key,
+                    UploadId=upload_id, PartNumber=part_number, Body=remaining
+                )
+                parts.append({"PartNumber": part_number, "ETag": resp["ETag"]})
+            self.client.complete_multipart_upload(
+                Bucket=self.bucket_name, Key=file_key,
+                UploadId=upload_id,
+                MultipartUpload={"Parts": parts}
+            )
+            logger.info(f"File stream uploaded to S3 successfully: {file_key}")
+            return total
+        except Exception as e:
+            self.client.abort_multipart_upload(
+                Bucket=self.bucket_name, Key=file_key, UploadId=upload_id
+            )
+            logger.error(f"Failed to stream upload file to S3 {file_key}: {e}")
+            raise StorageUploadError(
+                message=f"Failed to stream upload file to S3: {e}",
+                file_key=file_key,
+                cause=e,
+            )
+
     async def download(self, file_key: str) -> bytes:
         """
         Download a file from S3.

api/app/core/tools/mcp/base.py

@@ -195,6 +195,6 @@ class MCPToolManager:
         except Exception as e:
             return {
                 "success": False,
-                "error": str(e),
-                "message": "连接失败"
+                "error": "连接失败",
+                "message": str(e)
             }

api/app/core/tools/mcp/client.py

@@ -23,7 +23,7 @@ class SimpleMCPClient:
     def __init__(self, server_url: str, connection_config: Dict[str, Any] = None):
         self.server_url = server_url
         self.connection_config = connection_config or {}
-        self.timeout = self.connection_config.get("timeout", 30)
+        self.timeout = self.connection_config.get("timeout", 10)
         
         # 确定连接类型
         self.is_websocket = server_url.startswith(("ws://", "wss://"))
@@ -53,6 +53,7 @@ class SimpleMCPClient:
             else:
                 await self._connect_http()
         except Exception as e:
+            await self.disconnect()
             logger.error(f"MCP连接失败: {self.server_url}, 错误: {e}")
             raise MCPConnectionError(f"连接失败: {e}")
     

api/app/core/workflow/adapters/dify/converter.py

@@ -8,34 +8,60 @@ from typing import Any
 from urllib.parse import quote
 
 from app.core.workflow.adapters.base_converter import BaseConverter
-from app.core.workflow.adapters.errors import UnsupportVariableType, UnknowModelWarning, ExceptionDefineition, \
+from app.core.workflow.adapters.errors import (
+    UnsupportVariableType,
+    UnknowModelWarning,
+    ExceptionDefineition,
     ExceptionType
-from app.core.workflow.nodes.assigner import AssignerNodeConfig
+)
 from app.core.workflow.nodes.assigner.config import AssignmentItem
 from app.core.workflow.nodes.base_config import VariableDefinition, BaseNodeConfig
-from app.core.workflow.nodes.code import CodeNodeConfig
 from app.core.workflow.nodes.code.config import InputVariable, OutputVariable
-from app.core.workflow.nodes.configs import StartNodeConfig, LLMNodeConfig
-from app.core.workflow.nodes.cycle_graph import LoopNodeConfig, IterationNodeConfig
-from app.core.workflow.nodes.cycle_graph.config import ConditionDetail as LoopConditionDetail, ConditionsConfig, \
+from app.core.workflow.nodes.configs import (
+    StartNodeConfig,
+    LLMNodeConfig,
+    AssignerNodeConfig,
+    CodeNodeConfig,
+    LoopNodeConfig,
+    IterationNodeConfig,
+    EndNodeConfig,
+    HttpRequestNodeConfig,
+    IfElseNodeConfig,
+    JinjaRenderNodeConfig,
+    KnowledgeRetrievalNodeConfig,
+    NoteNodeConfig,
+    ParameterExtractorNodeConfig,
+    QuestionClassifierNodeConfig,
+    VariableAggregatorNodeConfig
+)
+from app.core.workflow.nodes.cycle_graph.config import (
+    ConditionDetail as LoopConditionDetail,
+    ConditionsConfig,
     CycleVariable
-from app.core.workflow.nodes.end import EndNodeConfig
-from app.core.workflow.nodes.enums import ValueInputType, ComparisonOperator, AssignmentOperator, HttpAuthType, \
-    HttpContentType, HttpErrorHandle
-from app.core.workflow.nodes.http_request import HttpRequestNodeConfig
-from app.core.workflow.nodes.http_request.config import HttpAuthConfig, HttpContentTypeConfig, HttpFormData, \
-    HttpTimeOutConfig, HttpRetryConfig, HttpErrorDefaultTamplete, HttpErrorHandleConfig
-from app.core.workflow.nodes.if_else import IfElseNodeConfig
+)
+from app.core.workflow.nodes.enums import (
+    ValueInputType,
+    ComparisonOperator,
+    AssignmentOperator,
+    HttpAuthType,
+    HttpContentType,
+    HttpErrorHandle,
+    NodeType
+)
+from app.core.workflow.nodes.http_request.config import (
+    HttpAuthConfig,
+    HttpContentTypeConfig,
+    HttpFormData,
+    HttpTimeOutConfig,
+    HttpRetryConfig,
+    HttpErrorDefaultTamplete,
+    HttpErrorHandleConfig
+)
 from app.core.workflow.nodes.if_else.config import ConditionDetail, ConditionBranchConfig
-from app.core.workflow.nodes.jinja_render import JinjaRenderNodeConfig
 from app.core.workflow.nodes.jinja_render.config import VariablesMappingConfig
-from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNodeConfig
 from app.core.workflow.nodes.llm.config import MemoryWindowSetting, MessageConfig
-from app.core.workflow.nodes.parameter_extractor import ParameterExtractorNodeConfig
 from app.core.workflow.nodes.parameter_extractor.config import ParamsConfig
-from app.core.workflow.nodes.question_classifier import QuestionClassifierNodeConfig
 from app.core.workflow.nodes.question_classifier.config import ClassifierConfig
-from app.core.workflow.nodes.variable_aggregator import VariableAggregatorNodeConfig
 from app.core.workflow.variable.base_variable import VariableType, DEFAULT_VALUE
 
 
@@ -48,24 +74,24 @@ class DifyConverter(BaseConverter):
 
     def __init__(self):
         self.CONFIG_CONVERT_MAP = {
-            "start": self.convert_start_node_config,
-            "llm": self.convert_llm_node_config,
-            "answer": self.convert_end_node_config,
-            "if-else": self.convert_if_else_node_config,
-            "loop": self.convert_loop_node_config,
-            "iteration": self.convert_iteration_node_config,
-            "assigner": self.convert_assigner_node_config,
-            "code": self.convert_code_node_config,
-            "http-request": self.convert_http_node_config,
-            "template-transform": self.convert_jinja_render_node_config,
-            "knowledge-retrieval": self.convert_knowledge_node_config,
-            "parameter-extractor": self.convert_parameter_extractor_node_config,
-            "question-classifier": self.convert_question_classifier_node_config,
-            "variable-aggregator": self.convert_variable_aggregator_node_config,
-            "tool": self.convert_tool_node_config,
-            "loop-start": lambda x: {},
-            "iteration-start": lambda x: {},
-            "loop-end": lambda x: {},
+            NodeType.START: self.convert_start_node_config,
+            NodeType.LLM: self.convert_llm_node_config,
+            NodeType.END: self.convert_end_node_config,
+            NodeType.IF_ELSE: self.convert_if_else_node_config,
+            NodeType.LOOP: self.convert_loop_node_config,
+            NodeType.ITERATION: self.convert_iteration_node_config,
+            NodeType.ASSIGNER: self.convert_assigner_node_config,
+            NodeType.CODE: self.convert_code_node_config,
+            NodeType.HTTP_REQUEST: self.convert_http_node_config,
+            NodeType.JINJARENDER: self.convert_jinja_render_node_config,
+            NodeType.KNOWLEDGE_RETRIEVAL: self.convert_knowledge_node_config,
+            NodeType.PARAMETER_EXTRACTOR: self.convert_parameter_extractor_node_config,
+            NodeType.QUESTION_CLASSIFIER: self.convert_question_classifier_node_config,
+            NodeType.VAR_AGGREGATOR: self.convert_variable_aggregator_node_config,
+            NodeType.TOOL: self.convert_tool_node_config,
+            NodeType.NOTES: self.convert_notes_config,
+            NodeType.CYCLE_START: lambda x: {},
+            NodeType.BREAK: lambda x: {},
         }
 
     def get_node_convert(self, node_type):
@@ -732,3 +758,16 @@ class DifyConverter(BaseConverter):
             detail=f"Please reconfigure the tool node.",
         ))
         return {}
+
+    @staticmethod
+    def convert_notes_config(node: dict):
+        node_data = node["data"]
+        result = NoteNodeConfig.model_construct(
+            author=node_data.get("author", ""),
+            text=node_data.get("text", ""),
+            width=node_data.get("width", 80),
+            height=node_data.get("height", 80),
+            theme=node_data.get("theme", "blue"),
+            show_author=node_data.get("showAuthor", True)
+        ).model_dump()
+        return result

api/app/core/workflow/adapters/dify/dify_adapter.py

@@ -50,7 +50,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
 
     def __init__(self, config: dict[str, Any]):
         DifyConverter.__init__(self)
-        BasePlatformAdapter.__init__(self,  config)
+        BasePlatformAdapter.__init__(self, config)
 
     def get_metadata(self) -> PlatformMetadata:
         return PlatformMetadata(
@@ -59,7 +59,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
             support_node_types=list(self.NODE_TYPE_MAPPING.keys())
         )
 
-    def map_node_type(self, platform_node_type) -> str:
+    def map_node_type(self, platform_node_type) -> NodeType:
         return self.NODE_TYPE_MAPPING.get(platform_node_type, NodeType.UNKNOWN)
 
     @property
@@ -84,7 +84,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
         require_fields = frozenset({'app', 'kind', 'version', 'workflow'})
         if not all(field in self.config for field in require_fields):
             return False
-        if self.config.get("app",{}).get("mode") == "workflow":
+        if self.config.get("app", {}).get("mode") == "workflow":
             self.errors.append(ExceptionDefineition(
                 type=ExceptionType.PLATFORM,
                 detail="workflow mode is not supported"
@@ -163,13 +163,14 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
     def _convert_node(self, node: dict[str, Any]) -> NodeDefinition | None:
         node_data = node["data"]
         try:
+            node_type = self.map_node_type(node_data["type"])
             return NodeDefinition(
                 id=node["id"],
-                type=self.map_node_type(node_data["type"]),
+                type=node_type,
                 name=node_data.get("title") or "notes",
                 cycle=node.get("parentId"),
                 description=None,
-                config=self._convert_node_config(node),
+                config=self._convert_node_config(node_type, node),
                 position={
                     "x": node["position"]["x"],
                     "y": node["position"]["y"]
@@ -183,17 +184,16 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
         except Exception as e:
             logger.debug(f"convert node error - {e}", exc_info=True)
 
-    def _convert_node_config(self, node: dict):
-        node_data = node["data"]
-        node_type = node_data["type"]
+    def _convert_node_config(self, node_type: NodeType, node: dict):
         try:
+            node_data = node["data"]
             converter = self.get_node_convert(node_type)
-            if node_type not in self.CONFIG_CONVERT_MAP:
+            if node_type == NodeType.UNKNOWN:
                 self.errors.append(ExceptionDefineition(
                     type=ExceptionType.NODE,
                     node_id=node["id"],
                     node_name=node["data"]["title"],
-                    detail=f"node type {node_type if node_type else 'notes'} is unsupported",
+                    detail=f"node type {node_data.get('type')} is unsupported",
                 ))
             return converter(node)
         except Exception as e:
@@ -214,7 +214,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
             if source in self.branch_node_cache:
                 case_id = edge["sourceHandle"]
                 if case_id == "false":
-                    label = f'CASE{len(self.branch_node_cache[source])+1}'
+                    label = f'CASE{len(self.branch_node_cache[source]) + 1}'
                 else:
                     label = f'CASE{self.branch_node_cache[source].index(case_id) + 1}'
             if source in self.error_branch_node_cache:
@@ -257,5 +257,3 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
 
     def _convert_execution(self, execution: dict[str, Any]) -> ExecutionConfig:
         return ExecutionConfig()
-
-

api/app/core/workflow/adapters/memory_bear/memory_bear_adapter.py

@@ -4,65 +4,145 @@
 # @Time : 2026/2/25 14:11
 from typing import Any
 
+from app.core.logging_config import get_logger
 from app.core.workflow.adapters.base_adapter import (
     PlatformMetadata,
     PlatformType,
     BasePlatformAdapter,
     WorkflowParserResult
 )
-from app.schemas.workflow_schema import ExecutionConfig
+from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType, UnsupportNodeType
+from app.core.workflow.adapters.memory_bear.memory_bear_converter import MemoryBearConverter
+from app.core.workflow.nodes.enums import NodeType
+from app.schemas.workflow_schema import ExecutionConfig, NodeDefinition, EdgeDefinition, VariableDefinition
+
+logger = get_logger()
+
+VALID_NODE_TYPES = frozenset(t.value for t in NodeType if t != NodeType.UNKNOWN)
 
 
-class MemoryBearAdapter(BasePlatformAdapter):
-    NODE_TYPE_MAPPING = {}
+class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter):
+    NODE_TYPE_MAPPING = {t.value: t for t in NodeType}
+
+    def __init__(self, config: dict[str, Any]):
+        MemoryBearConverter.__init__(self)
+        BasePlatformAdapter.__init__(self, config)
 
     @property
     def origin_nodes(self):
-        return self.config.get("workflow").get("nodes")
+        return self.config.get("workflow").get("nodes") or []
 
     @property
     def origin_edges(self):
-        return self.config.get("workflow").get("edges")
+        return self.config.get("workflow").get("edges") or []
 
     @property
     def origin_variables(self):
-        return self.config.get("workflow").get("variables")
+        return self.config.get("workflow").get("variables") or []
 
     def get_metadata(self) -> PlatformMetadata:
         return PlatformMetadata(
             platform_name=PlatformType.MEMORY_BEAR,
             version="0.2.5",
-            support_node_types=list(self.NODE_TYPE_MAPPING.keys())
+            support_node_types=list(VALID_NODE_TYPES)
         )
 
-    def map_node_type(self, platform_node_type) -> str:
-        return platform_node_type
+    def map_node_type(self, platform_node_type: str) -> NodeType:
+        return self.NODE_TYPE_MAPPING.get(platform_node_type, NodeType.UNKNOWN)
 
     @staticmethod
-    def _valid_nodes(node: dict[str, Any]):
-        if "type" not in node["data"]:
-            return False
+    def _valid_node(node: dict[str, Any]) -> bool:
         if "id" not in node or "type" not in node:
             return False
+        if not isinstance(node.get("config"), dict):
+            return False
         return True
 
     def validate_config(self) -> bool:
         require_fields = frozenset({'app', 'workflow'})
         if not all(field in self.config for field in require_fields):
             return False
-
         for node in self.origin_nodes:
-            if not self._valid_nodes(node):
+            if not self._valid_node(node):
                 return False
         return True
 
+    def _convert_node(self, node: dict[str, Any]) -> NodeDefinition | None:
+        node_id = node.get("id")
+        node_name = node.get("name")
+        try:
+            node_type = self.map_node_type(node["type"])
+            if node_type == NodeType.UNKNOWN:
+                self.errors.append(UnsupportNodeType(
+                    node_id=node_id,
+                    node_type=node["type"]
+                ))
+                return None
+
+            config = node.get("config") or {}
+            converter = self.get_node_convert(node_type)
+            converter(node_id, node_name, config)  # validates and appends errors if invalid
+
+            return NodeDefinition(**node)
+        except Exception as e:
+            self.errors.append(ExceptionDefineition(
+                type=ExceptionType.NODE,
+                node_id=node_id,
+                node_name=node_name,
+                detail=f"convert node error - {e}"
+            ))
+            logger.debug(f"MemoryBear convert node error - {e}", exc_info=True)
+            return None
+
+    def _convert_edge(self, edge: dict[str, Any], valid_node_ids: set) -> EdgeDefinition | None:
+        try:
+            if edge.get("source") not in valid_node_ids or edge.get("target") not in valid_node_ids:
+                self.warnings.append(ExceptionDefineition(
+                    type=ExceptionType.EDGE,
+                    detail=f"edge {edge.get('id')} skipped: source or target node not found"
+                ))
+                return None
+            return EdgeDefinition(**edge)
+        except Exception as e:
+            self.errors.append(ExceptionDefineition(
+                type=ExceptionType.EDGE,
+                detail=f"convert edge error - {e}"
+            ))
+            logger.debug(f"MemoryBear convert edge error - {e}", exc_info=True)
+            return None
+
+    def _convert_variable(self, variable: dict[str, Any]) -> VariableDefinition | None:
+        try:
+            return VariableDefinition(**variable)
+        except Exception as e:
+            self.warnings.append(ExceptionDefineition(
+                type=ExceptionType.VARIABLE,
+                name=variable.get("name"),
+                detail=f"convert variable error - {e}"
+            ))
+            logger.debug(f"MemoryBear convert variable error - {e}", exc_info=True)
+            return None
+
     def parse_workflow(self) -> WorkflowParserResult:
-        self.nodes = self.origin_nodes
-        self.edges = self.origin_edges
-        self.conv_variables = self.origin_variables
+        for node in self.origin_nodes:
+            converted = self._convert_node(node)
+            if converted:
+                self.nodes.append(converted)
+
+        valid_node_ids = {n.id for n in self.nodes}
+
+        for edge in self.origin_edges:
+            converted = self._convert_edge(edge, valid_node_ids)
+            if converted:
+                self.edges.append(converted)
+
+        for variable in self.origin_variables:
+            converted = self._convert_variable(variable)
+            if converted:
+                self.conv_variables.append(converted)
 
         return WorkflowParserResult(
-            success=True,
+            success=not self.errors and not self.warnings,
             platform=self.get_metadata(),
             execution_config=ExecutionConfig(),
             origin_config=self.config,
@@ -72,5 +152,4 @@ class MemoryBearAdapter(BasePlatformAdapter):
             variables=self.conv_variables,
             warnings=self.warnings,
             errors=self.errors,
-
         )

api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py

@@ -0,0 +1,85 @@
+# -*- coding: UTF-8 -*-
+from app.core.workflow.adapters.base_converter import BaseConverter
+from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType
+from app.core.workflow.nodes.base_config import BaseNodeConfig
+from app.core.workflow.nodes.configs import (
+    StartNodeConfig,
+    EndNodeConfig,
+    LLMNodeConfig,
+    AgentNodeConfig,
+    IfElseNodeConfig,
+    KnowledgeRetrievalNodeConfig,
+    AssignerNodeConfig,
+    CodeNodeConfig,
+    HttpRequestNodeConfig,
+    JinjaRenderNodeConfig,
+    VariableAggregatorNodeConfig,
+    ParameterExtractorNodeConfig,
+    LoopNodeConfig,
+    IterationNodeConfig,
+    QuestionClassifierNodeConfig,
+    ToolNodeConfig,
+    MemoryReadNodeConfig,
+    MemoryWriteNodeConfig,
+    NoteNodeConfig,
+)
+from app.core.workflow.nodes.enums import NodeType
+
+
+class MemoryBearConverter(BaseConverter):
+    errors: list
+    warnings: list
+
+    CONFIG_CLASS_MAP: dict[NodeType, type[BaseNodeConfig]] = {
+        NodeType.START: StartNodeConfig,
+        NodeType.END: EndNodeConfig,
+        NodeType.ANSWER: EndNodeConfig,
+        NodeType.LLM: LLMNodeConfig,
+        NodeType.AGENT: AgentNodeConfig,
+        NodeType.IF_ELSE: IfElseNodeConfig,
+        NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNodeConfig,
+        NodeType.ASSIGNER: AssignerNodeConfig,
+        NodeType.CODE: CodeNodeConfig,
+        NodeType.HTTP_REQUEST: HttpRequestNodeConfig,
+        NodeType.JINJARENDER: JinjaRenderNodeConfig,
+        NodeType.VAR_AGGREGATOR: VariableAggregatorNodeConfig,
+        NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNodeConfig,
+        NodeType.LOOP: LoopNodeConfig,
+        NodeType.ITERATION: IterationNodeConfig,
+        NodeType.QUESTION_CLASSIFIER: QuestionClassifierNodeConfig,
+        NodeType.TOOL: ToolNodeConfig,
+        NodeType.MEMORY_READ: MemoryReadNodeConfig,
+        NodeType.MEMORY_WRITE: MemoryWriteNodeConfig,
+        NodeType.NOTES: NoteNodeConfig,
+    }
+
+    @staticmethod
+    def _convert_file(var):
+        return None
+
+    @staticmethod
+    def _convert_array_file(var):
+        return []
+
+    def config_validate(self, node_id: str, node_name: str, config_cls: type[BaseNodeConfig], value: dict):
+        try:
+            return config_cls.model_validate(value)
+        except Exception as e:
+            self.errors.append(ExceptionDefineition(
+                type=ExceptionType.CONFIG,
+                node_id=node_id,
+                node_name=node_name,
+                detail=str(e)
+            ))
+            return None
+
+    def get_node_convert(self, node_type: NodeType):
+        config_cls = self.CONFIG_CLASS_MAP.get(node_type)
+        if not config_cls:
+            return lambda node_id, node_name, config: config
+
+        def validate(node_id: str, node_name: str, config: dict):
+            self.config_validate(node_id, node_name, config_cls, config)
+            return config
+
+        return validate

api/app/core/workflow/engine/graph_builder.py

@@ -20,9 +20,21 @@ from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes import NodeFactory
 from app.core.workflow.nodes.enums import NodeType, BRANCH_NODES
 from app.core.workflow.utils.expression_evaluator import evaluate_condition
+from app.core.workflow.validator import WorkflowValidator
 
 logger = logging.getLogger(__name__)
 
+# Regex to split output into:
+#    - variable placeholders: {{ ... }}
+#    - normal literal text
+#
+# Example:
+#   "Hello {{user.name}}!" ->
+#   ["Hello ", "{{user.name}}", "!"]
+_OUTPUT_PATTERN = re.compile(r'\{\{.*?}}|[^{}]+')
+# Strict variable format: {{ node_id.field_name }}
+_VARIABLE_PATTERN = re.compile(r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+\s*}}')
+
 
 class GraphBuilder:
     def __init__(
@@ -37,13 +49,13 @@ class GraphBuilder:
         self.stream = stream
         self.subgraph = subgraph
 
-        self.start_node_id = None
-        self.end_node_ids = []
+        self.start_node_id: str | None = None
+
         self.node_map = {node["id"]: node for node in self.nodes}
         self.end_node_map: dict[str, StreamOutputConfig] = {}
-        self._find_upstream_branch_node = lru_cache(
+        self._find_upstream_activation_dep = lru_cache(
             maxsize=len(self.nodes) * 2
-        )(self._find_upstream_branch_node)
+        )(self._find_upstream_activation_dep)
         if variable_pool:
             self.variable_pool = variable_pool
         else:
@@ -51,10 +63,19 @@ class GraphBuilder:
 
         self.graph = StateGraph(WorkflowState)
         self.add_nodes()
+        self.reachable_nodes = WorkflowValidator.get_reachable_nodes(self.start_node_id, self.edges)
+        self.end_nodes = [
+            node
+            for node in self.nodes
+            if node.get("type") == "end" and node.get("id") in self.reachable_nodes
+        ]
         self.add_edges()
-        self._analyze_end_node_output()
         # EDGES MUST BE ADDED AFTER NODES ARE ADDED.
 
+        self._reverse_adj: dict[str, list[dict]] = defaultdict(list)
+        self._build_reverse_adj()
+        self._analyze_end_node_output()
+
     @property
     def nodes(self) -> list[dict[str, Any]]:
         return self.workflow_config.get("nodes", [])
@@ -87,60 +108,50 @@ class GraphBuilder:
             result[node[0]].append(node[1])
         return result
 
-    def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[tuple[str, str]]]:
-        """
-        Recursively find all upstream branch (control) nodes that influence the execution
-        of the given target node.
+    def _build_reverse_adj(self):
+        for edge in self.edges:
+            if edge["source"] not in self.reachable_nodes:
+                continue
+            self._reverse_adj[edge.get("target")].append({
+                "id": edge["source"], "branch": edge.get("label")
+            })
 
-        This method walks upstream along the workflow graph starting from `target_node`.
-        It distinguishes between:
-          - branch nodes (node types listed in `BRANCH_NODES`)
-          - non-branch nodes (ordinary processing nodes)
+    def _find_upstream_activation_dep(
+            self,
+            target_node: str
+    ) -> tuple[tuple[tuple[str, str]], tuple[str]]:
+        """Find upstream dependencies that affect the activation of a target node.
 
-        Traversal rules:
-        1. For each immediate upstream node:
-           - If it is a branch node, it is recorded as an affecting control node.
-           - If it is a non-branch node, the traversal continues recursively upstream.
-        2. If ANY upstream path reaches a START / CYCLE_START node without encountering
-           a branch node, the traversal is considered invalid:
-           - `has_branch` will be False
-           - no branch nodes are returned.
-        3. Only when ALL upstream non-branch paths eventually lead to at least one
-           branch node will `has_branch` be True.
+        Walks upstream along the workflow graph from the target node, collecting
+        two types of dependencies:
+            - Branch control nodes: upstream branch nodes (e.g. if-else) whose
+              routing outcome determines whether the target node executes.
+            - Output nodes: upstream END nodes that must complete their output
+              before the target node can activate.
 
-        Special case:
-        - If `target_node` has no upstream nodes AND its type is START or CYCLE_START,
-          it is considered directly reachable from the workflow entry, and therefore
-          has no controlling branch nodes.
+        The traversal terminates early and returns empty tuples if any upstream
+        path reaches START/CYCLE_START without encountering a branch or output
+        node, indicating the target node is directly reachable and should be
+        activated immediately.
 
         Args:
-            target_node (str):
-                The identifier of the node whose upstream control branches
-                are to be resolved.
+            target_node: The ID of the node whose upstream activation
+                dependencies are to be resolved.
 
         Returns:
-            tuple[bool, tuple[tuple[str, str]]]:
-                - has_branch (bool):
-                    True if every upstream path from `target_node` encounters
-                    at least one branch node.
-                    False if any path reaches a start node without a branch.
-                - branch_nodes (tuple[tuple[str, str]]):
-                    A deduplicated tuple of `(branch_node_id, branch_label)` pairs
-                    representing all branch nodes that can influence `target_node`.
-                    Returns an empty tuple if `has_branch` is False.
+            A tuple of two elements:
+                - A deduplicated tuple of (branch_node_id, branch_label) pairs
+                  representing upstream branch control dependencies. Empty if
+                  any clean path to START exists.
+                - A deduplicated tuple of upstream output node IDs that must
+                  complete before this node activates.
         """
-        source_nodes = [
-            {
-                "id": edge.get("source"),
-                "branch": edge.get("label")
-            }
-            for edge in self.edges
-            if edge.get("target") == target_node
-        ]
+        source_nodes = self._reverse_adj[target_node]
         if not source_nodes and self.get_node_type(target_node) in [NodeType.START, NodeType.CYCLE_START]:
-            return False, tuple()
+            return tuple(), tuple()
 
         branch_nodes = []
+        output_nodes = []
         non_branch_nodes = []
 
         for node_info in source_nodes:
@@ -149,19 +160,23 @@ class GraphBuilder:
                     (node_info["id"], node_info["branch"])
                 )
             else:
+                if self.get_node_type(node_info["id"]) == NodeType.END:
+                    output_nodes.append(node_info["id"])
                 non_branch_nodes.append(node_info["id"])
 
         has_branch = True
         for node_id in non_branch_nodes:
-            node_has_branch, nodes = self._find_upstream_branch_node(node_id)
-            has_branch = has_branch and node_has_branch
-            if not has_branch:
-                break
-            branch_nodes.extend(nodes)
-        if not has_branch:
-            branch_nodes = []
+            upstream_control_nodes, upstream_output_nodes = self._find_upstream_activation_dep(node_id)
+            if not upstream_control_nodes:
+                if not upstream_output_nodes and node_id not in output_nodes:
+                    return tuple(), tuple()
+                branch_nodes = []
+                has_branch = False
+            if has_branch:
+                branch_nodes.extend(upstream_control_nodes)
+            output_nodes.extend(upstream_output_nodes)
 
-        return has_branch, tuple(set(branch_nodes))
+        return tuple(set(branch_nodes)), tuple(set(output_nodes))
 
     def _analyze_end_node_output(self):
         """
@@ -182,11 +197,10 @@ class GraphBuilder:
         """
 
         # Collect all End nodes in the workflow
-        end_nodes = [node for node in self.nodes if node.get("type") == "end"]
-        logger.info(f"[Prefix Analysis] Found {len(end_nodes)} End nodes")
+        logger.info(f"[Prefix Analysis] Found {len(self.end_nodes)} End nodes")
 
         # Iterate through each End node to analyze its output
-        for end_node in end_nodes:
+        for end_node in self.end_nodes:
             end_node_id = end_node.get("id")
             config = end_node.get("config", {})
             output = config.get("output")
@@ -195,42 +209,33 @@ class GraphBuilder:
             if not output:
                 continue
 
-            # Regex to split output into:
-            #    - variable placeholders: {{ ... }}
-            #    - normal literal text
-            #
-            # Example:
-            #   "Hello {{user.name}}!" ->
-            #   ["Hello ", "{{user.name}}", "!"]
-            pattern = r'\{\{.*?\}\}|[^{}]+'
-
-            # Strict variable format: {{ node_id.field_name }}
-            variable_pattern_string = r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+\s*\}\}'
-            variable_pattern = re.compile(variable_pattern_string)
-
             # Split output into ordered segments
-            output_template = list(re.findall(pattern, output))
+            output_template = list(_OUTPUT_PATTERN.findall(output))
 
             # Determine whether each segment is literal text
             #    True  -> literal (can be directly output)
             #    False -> variable placeholder (needs runtime value)
             output_flag = [
-                not bool(variable_pattern.match(item))
+                not bool(_VARIABLE_PATTERN.match(item))
                 for item in output_template
             ]
 
             # Stream mode: output activation depends on upstream branch nodes
             if self.stream:
                 # Find upstream branch nodes that can control this End node
-                has_branch, control_nodes = self._find_upstream_branch_node(end_node_id)
-
+                upstream_control_nodes, upstream_output_nodes = self._find_upstream_activation_dep(end_node_id)
+                activate = not bool(upstream_control_nodes) and not bool(upstream_output_nodes)
                 # Build StreamOutputConfig for this End node
                 self.end_node_map[end_node_id] = StreamOutputConfig(
+                    id=end_node_id,
                     # If there is no upstream branch, output is active immediately
-                    activate=not has_branch,
+                    activate=activate,
 
                     # Branch nodes that control activation of this End node
-                    control_nodes=self._merge_control_nodes(control_nodes),
+                    control_nodes=self._merge_control_nodes(upstream_control_nodes),
+                    upstream_output_nodes=list(upstream_output_nodes),
+                    control_resolved=not bool(upstream_control_nodes),
+                    output_resolved=not bool(upstream_output_nodes),
 
                     # Convert output segments into OutputContent objects
                     outputs=list(
@@ -249,14 +254,16 @@ class GraphBuilder:
                     cursor=0
                 )
                 logger.info(f"[Stream Analysis] end_id: {end_node_id}, "
-                            f"activate: {not has_branch}, "
-                            f"control_nodes: {control_nodes},"
+                            f"activate: {activate}, "
+                            f"control_nodes: {upstream_control_nodes},"
+                            f"ref_outputs: {upstream_output_nodes},"
                             f"output: {output_template},"
                             f"output_activate: {output_flag}")
 
             # Non-stream mode: all outputs are activated by default
             else:
                 self.end_node_map[end_node_id] = StreamOutputConfig(
+                    id=end_node_id,
                     activate=True,
                     control_nodes={},
                     outputs=list(
@@ -269,7 +276,10 @@ class GraphBuilder:
                             for output_string, activate in zip(output_template, output_flag)
                         ]
                     ),
-                    cursor=0
+                    cursor=0,
+                    upstream_output_nodes=[],
+                    control_resolved=True,
+                    output_resolved=True,
                 )
 
     def add_nodes(self):
@@ -304,8 +314,6 @@ class GraphBuilder:
             # Record start and end node IDs
             if node_type in [NodeType.START, NodeType.CYCLE_START]:
                 self.start_node_id = node_id
-            elif node_type == NodeType.END:
-                self.end_node_ids.append(node_id)
 
             # Create node instance (start and end nodes are also created)
             # NOTE:Loop node creation automatically removes the nodes and edges of the subgraph from the current graph
@@ -448,7 +456,7 @@ class GraphBuilder:
                     branch_activate = []
                     new_state = state.copy()
                     new_state["activate"] = dict(state.get("activate", {}))  # deep copy of activate
-                    node_output = variable_pool.get_node_output(src, defalut=dict(), strict=False)
+                    node_output = variable_pool.get_node_output(src, default=dict(), strict=False)
                     for label, branch in unique_branch.items():
                         if node_output and evaluate_condition(
                                 branch["condition"],
@@ -494,9 +502,11 @@ class GraphBuilder:
                 logger.debug(f"Added waiting edge: {sources} -> {target}")
 
         # Connect End nodes to the global END node
-        for end_node_id in self.end_node_ids:
-            self.graph.add_edge(end_node_id, END)
-            logger.debug(f"Added edge: {end_node_id} -> END")
+        for end_node in self.end_nodes:
+            end_node_id = end_node.get("id")
+            if end_node_id:
+                self.graph.add_edge(end_node_id, END)
+                logger.debug(f"Added edge: {end_node_id} -> END")
         return
 
     def build(self) -> CompiledStateGraph:

api/app/core/workflow/engine/result_builder.py

@@ -12,6 +12,7 @@ class WorkflowResultBuilder:
             variable_pool: VariablePool,
             elapsed_time: float,
             final_output: str,
+            success: bool
     ):
         """Construct the final standardized output of the workflow execution.
 
@@ -29,6 +30,7 @@ class WorkflowResultBuilder:
             elapsed_time (float): Total execution time in seconds.
             final_output (Any): The aggregated or final output content of the workflow
                 (e.g., combined messages from all End nodes).
+            success (bool): Whether the execution was successful.
 
         Returns:
             dict: A dictionary containing the final workflow execution result with keys:
@@ -49,7 +51,7 @@ class WorkflowResultBuilder:
         conversation_id = variable_pool.get_value("sys.conversation_id")
 
         return {
-            "status": "completed",
+            "status": "completed" if success else "failed",
             "output": final_output,
             "variables": {
                 "conv": variable_pool.get_all_conversation_vars(),

api/app/core/workflow/engine/stream_output_coordinator.py

@@ -3,9 +3,10 @@
 # @Email: 1533512157@qq.com
 # @Time : 2026/2/9 15:11
 import re
+from queue import Queue
 from typing import AsyncGenerator
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, PrivateAttr
 
 from app.core.logging_config import get_logger
 from app.core.workflow.engine.variable_pool import VariablePool
@@ -37,8 +38,8 @@ class OutputContent(BaseModel):
     activate: bool = Field(
         ...,
         description=(
-            "Whether this output segment is currently active.\n"
-            "- True: allowed to be emitted/output\n"
+            "Whether this output segment is currently active."
+            "- True: allowed to be emitted/output"
             "- False: blocked until activated by branch control"
         )
     )
@@ -46,16 +47,17 @@ class OutputContent(BaseModel):
     is_variable: bool = Field(
         ...,
         description=(
-            "Whether this segment represents a variable placeholder.\n"
-            "True  -> variable (e.g. {{ node.field }})\n"
+            "Whether this segment represents a variable placeholder."
+            "True  -> variable (e.g. {{ node.field }})"
             "False -> literal text"
         )
     )
 
-    _SCOPE: str | None = None
+    _SCOPE: str | None = PrivateAttr(default=None)
 
-    def get_scope(self) -> str:
-        self._SCOPE = SCOPE_PATTERN.findall(self.literal)[0]
+    def get_scope(self) -> str | None:
+        matches = SCOPE_PATTERN.findall(self.literal)
+        self._SCOPE = matches[0] if matches else None
         return self._SCOPE
 
     def depends_on_scope(self, scope: str) -> bool:
@@ -68,6 +70,8 @@ class OutputContent(BaseModel):
         Returns:
             bool: True if this segment references the given scope.
         """
+        if not self.is_variable:
+            return False
         if self._SCOPE:
             return self._SCOPE == scope
         return self.get_scope() == scope
@@ -83,12 +87,16 @@ class StreamOutputConfig(BaseModel):
     - which upstream branch/control nodes gate the activation
     - how each parsed output segment is streamed and activated
     """
+    id: str = Field(
+        ...,
+        description="ID of the End node this configuration belongs to."
+    )
 
     activate: bool = Field(
         ...,
         description=(
-            "Global activation flag for the End node output.\n"
-            "When False, output segments should not be emitted even if available.\n"
+            "Global activation flag for the End node output."
+            "When False, output segments should not be emitted even if available."
             "This flag typically becomes True once required control branch conditions "
             "are satisfied."
         )
@@ -97,17 +105,46 @@ class StreamOutputConfig(BaseModel):
     control_nodes: dict[str, list[str]] = Field(
         ...,
         description=(
-            "Control branch conditions for this End node output.\n"
-            "Mapping of `branch_node_id -> expected_branch_label`.\n"
+            "Control branch conditions for this End node output."
+            "Mapping of `branch_node_id -> expected_branch_label`."
             "The End node output becomes globally active when a controlling branch node "
             "reports a matching completion status."
         )
     )
 
+    upstream_output_nodes: list[str] = Field(
+        ...,
+        description=(
+            "Upstream output node dependencies (data flow)."
+            "Represents END/output nodes that this output depends on."
+            "These nodes provide data sources required before this output can be activated "
+            "or streamed."
+            "Used to ensure correct ordering and dependency resolution in streaming mode."
+        )
+    )
+
+    control_resolved: bool = Field(
+        ...,
+        description=(
+            "Whether all upstream branch control dependencies have been satisfied."
+            "True if no upstream branch nodes exist or the required branch "
+            "conditions have been met."
+        )
+    )
+
+    output_resolved: bool = Field(
+        ...,
+        description=(
+            "Whether all upstream output node dependencies have been completed."
+            "True if no upstream output nodes exist or all upstream output "
+            "nodes have finished their output."
+        )
+    )
+
     outputs: list[OutputContent] = Field(
         ...,
         description=(
-            "Ordered list of output segments parsed from the output template.\n"
+            "Ordered list of output segments parsed from the output template."
             "Each segment represents either a literal text block or a variable placeholder "
             "that may be activated independently."
         )
@@ -116,49 +153,97 @@ class StreamOutputConfig(BaseModel):
     cursor: int = Field(
         ...,
         description=(
-            "Streaming cursor index.\n"
-            "Indicates the next output segment index to be emitted.\n"
+            "Streaming cursor index."
+            "Indicates the next output segment index to be emitted."
             "Segments with index < cursor are considered already streamed."
         )
     )
 
+    force: bool = Field(
+        default=False,
+        description=(
+            "Force flag for output emission."
+            "When True, all output segments are emitted regardless of activation state."
+            "Triggered when this output node has finished execution."
+        )
+    )
+
     def update_activate(self, scope: str, status=None):
         """
-        Update streaming activation state based on an upstream node or special variable.
+        Update streaming activation state based on upstream events.
 
         Args:
             scope (str):
                 Identifier of the completed upstream entity.
                 - If a control branch node, it should match a key in `control_nodes`.
-                - If a variable placeholder (e.g., "sys.xxx"), it may appear in output segments.
+                - If an upstream output node, it should match an entry in `upstream_output_nodes`.
+                - If a variable placeholder (e.g., "sys.xxx" or "node_id.field"),
+                  it may appear in output segments.
+
             status (optional):
                 Completion status of the control branch node.
                 Required when `scope` refers to a control node.
 
         Behavior:
-        1. Control branch nodes:
-           - If `scope` matches a key in `control_nodes` and `status` matches the expected
-             branch label, the End node output becomes globally active (`activate = True`).
+        1. Force activation:
+           - If `self.force` is True, the method returns immediately.
+           - If `scope == self.id`, the node marks itself as completed:
+               - `activate = True`
+               - `force = True`
+             This is typically used for final flushing when the node finishes execution.
 
-        2. Variable output segments:
-           - For each segment that is a variable (`is_variable=True`):
-               - If the segment literal references `scope`, mark the segment as active.
-               - This applies both to regular node variables (e.g., "node_id.field")
-                 and special system variables (e.g., "sys.xxx").
+        2. Control dependency resolution:
+           - If `scope` matches a key in `control_nodes`:
+               - `status` must be provided.
+               - If `status` matches expected branch labels, mark control as resolved
+                 (`control_resolved = True`).
+
+        3. Upstream output dependency resolution:
+           - If `scope` is in `upstream_output_nodes`,
+             mark data dependency as resolved (`output_resolved = True`).
+
+        4. Global activation condition:
+           - The node becomes active when BOTH conditions are satisfied:
+               - control_resolved == True
+               - output_resolved == True
+           - Once activated, `activate` remains True.
+
+        5. Variable segment activation:
+           - For each output segment that is a variable (`is_variable=True`):
+               - If the segment depends on the given `scope`,
+                 mark the segment as active.
+           - This applies to both node variables (e.g., "node_id.field")
+             and system variables (e.g., "sys.xxx").
 
         Notes:
-        - This method does not emit output or advance the streaming cursor.
-        - It only updates activation flags based on upstream events or special variables.
+        - This method does NOT emit output or advance the streaming cursor.
+        - It only updates activation and dependency resolution states.
+        - Activation is driven by both control flow (branch nodes) and
+          data flow (upstream output nodes).
         """
+        if self.force:
+            return
 
-        # Case 1: resolve control branch dependency
-        if scope in self.control_nodes.keys():
+        if scope == self.id:
+            self.activate = True
+            self.force = True
+            return
+
+        # resolve control branch dependency
+        if scope in self.control_nodes:
             if status is None:
                 raise RuntimeError("[Stream Output] Control node activation status not provided")
             if status in self.control_nodes[scope]:
-                self.activate = True
+                self.control_resolved = True
 
-        # Case 2: activate variable segments related to this node
+        if scope in self.upstream_output_nodes:
+            self.upstream_output_nodes.remove(scope)
+        if not self.upstream_output_nodes:
+            self.output_resolved = True
+
+        self.activate = self.activate or (self.control_resolved and self.output_resolved)
+
+        # activate variable segments related to this node
         for i in range(len(self.outputs)):
             if (
                     self.outputs[i].is_variable
@@ -171,12 +256,17 @@ class StreamOutputCoordinator:
     def __init__(self):
         self.end_outputs: dict[str, StreamOutputConfig] = {}
         self.activate_end: str | None = None
+        self.output_queue: Queue = Queue()
+        self.processed_outputs = []
 
     def initialize_end_outputs(
             self,
             end_node_map: dict[str, StreamOutputConfig]
     ):
         self.end_outputs = end_node_map
+        self.processed_outputs = []
+        self.activate_end = None
+        self.output_queue = Queue()
 
     @property
     def current_activate_end_info(self):
@@ -208,8 +298,11 @@ class StreamOutputCoordinator:
         """
         for node in self.end_outputs.keys():
             self.end_outputs[node].update_activate(scope, status)
-            if self.end_outputs[node].activate and self.activate_end is None:
-                self.activate_end = node
+            if self.end_outputs[node].activate and node not in self.processed_outputs:
+                self.output_queue.put(node)
+                self.processed_outputs.append(node)
+        if self.activate_end is None and not self.output_queue.empty():
+            self.activate_end = self.output_queue.get_nowait()
 
     async def emit_activate_chunk(
             self,
@@ -253,7 +346,7 @@ class StreamOutputCoordinator:
             final_chunk = ''
             current_segment = end_info.outputs[end_info.cursor]
 
-            if not current_segment.activate and not force:
+            if not current_segment.activate and not force and not end_info.force:
                 # Stop processing until this segment becomes active
                 break
 
@@ -270,7 +363,7 @@ class StreamOutputCoordinator:
                     logger.warning(f"[STREAM] Failed to evaluate segment: {current_segment.literal}, error: {e}")
 
             if final_chunk:
-                logger.info(f"[STREAM] StreamOutput Node:{self.activate_end}, chunk:{final_chunk}")
+                logger.info(f"[STREAM] StreamOutput Node:{self.activate_end}, chunk_length:{len(final_chunk)}")
                 yield {
                     "event": "message",
                     "data": {
@@ -282,8 +375,7 @@ class StreamOutputCoordinator:
             end_info.cursor += 1
 
         if end_info.cursor >= len(end_info.outputs):
-            self.end_outputs.pop(self.activate_end)
-            self.activate_end = None
+            self.pop_current_activate_end()
 
     async def flush_remaining_chunk(
             self,
@@ -322,6 +414,8 @@ class StreamOutputCoordinator:
                 async for msg_event in self.emit_activate_chunk(variable_pool, force=True):
                     yield msg_event
 
+                if not self.output_queue.empty():
+                    self.activate_end = self.output_queue.get_nowait()
                 # Move to next active End node if current one is done
                 if not self.activate_end and self.end_outputs:
                     self.activate_end = list(self.end_outputs.keys())[0]

api/app/core/workflow/engine/variable_pool.py

@@ -351,12 +351,12 @@ class VariablePool:
             }
         return runtime_vars
 
-    def get_node_output(self, node_id: str, defalut: Any = None, strict: bool = True) -> dict[str, Any] | None:
+    def get_node_output(self, node_id: str, default: Any = None, strict: bool = True) -> dict[str, Any] | None:
         """获取指定节点的输出（运行时变量）
         
         Args:
             node_id: 节点 ID
-            defalut: 默认值
+            default: 默认值
             strict: 是否严格模式
         
         Returns:
@@ -368,7 +368,7 @@ class VariablePool:
         if strict:
             raise KeyError(f"node {node_id} output not exist")
         else:
-            return defalut
+            return default
 
     def copy(self, pool: 'VariablePool'):
         self.variables = deepcopy(pool.variables)

api/app/core/workflow/executor.py

@@ -128,89 +128,100 @@ class WorkflowExecutor:
                   - token_usage: aggregated token usage if available
                   - error: error message if any
         """
-        logger.info(f"Starting workflow execution: execution_id={self.execution_context.execution_id}")
-
-        start_time = datetime.datetime.now()
-
-        # Execute the workflow
-        try:
-            # Build the workflow graph
-            graph = self.build_graph()
-
-            # Initialize the variable pool with input data
-            await self.variable_initializer.initialize(
-                variable_pool=self.variable_pool,
-                input_data=input_data,
-                execution_context=self.execution_context
-            )
-            initial_state = self.state_manager.create_initial_state(
-                workflow_config=self.workflow_config,
-                input_data=input_data,
-                execution_context=self.execution_context,
-                start_node_id=self.start_node_id
-            )
-
-            result = await graph.ainvoke(initial_state, config=self.execution_context.checkpoint_config)
-
-            # Aggregate output from all End nodes
-            full_content = ''
-            for end_id in self.stream_coordinator.end_outputs.keys():
-                full_content += self.variable_pool.get_value(f"{end_id}.output", default="", strict=False)
-
-            # Append messages for user and assistant
-            if input_data.get("files"):
-                result["messages"].extend(
-                    [
-                        {
-                            "role": "user",
-                            "content": input_data.get("message", '')
-                        },
-                        {
-                            "role": "user",
-                            "content": input_data.get("files")
-                        },
-                        {
-                            "role": "assistant",
-                            "content": full_content
-                        }
-                    ]
-                )
-            else:
-                result["messages"].extend(
-                    [
-                        {
-                            "role": "user",
-                            "content": input_data.get("message", '')
-                        },
-                        {
-                            "role": "assistant",
-                            "content": full_content
-                        }
-                    ]
-                )
-            # Calculate elapsed time
-            end_time = datetime.datetime.now()
-            elapsed_time = (end_time - start_time).total_seconds()
-
-            logger.info(
-                f"Workflow execution completed: execution_id={self.execution_context.execution_id}, elapsed_time={elapsed_time:.2f}ms")
-
-            return self.result_builder.build_final_output(result, self.variable_pool, elapsed_time, full_content)
-
-        except Exception as e:
-            end_time = datetime.datetime.now()
-            elapsed_time = (end_time - start_time).total_seconds()
-
-            logger.error(f"Workflow execution failed: execution_id={self.execution_context.execution_id}, error={e}",
-                         exc_info=True)
-            return {
-                "status": "failed",
-                "error": str(e),
-                "output": None,
-                "node_outputs": {},
-                "elapsed_time": elapsed_time,
-                "token_usage": None
-            }
+        start = datetime.datetime.now()
+        async for event in self.execute_stream(input_data):
+            if event.get("event") == "workflow_end":
+                return event.get("data")
+        return self.result_builder.build_final_output(
+            {"error": "Workflow execution did not end as expected"},
+            self.variable_pool,
+            (datetime.datetime.now() - start).total_seconds(),
+            "",
+            success=False
+        )
+        # logger.info(f"Starting workflow execution: execution_id={self.execution_context.execution_id}")
+        #
+        # start_time = datetime.datetime.now()
+        #
+        # # Execute the workflow
+        # try:
+        #     # Build the workflow graph
+        #     graph = self.build_graph()
+        #
+        #     # Initialize the variable pool with input data
+        #     await self.variable_initializer.initialize(
+        #         variable_pool=self.variable_pool,
+        #         input_data=input_data,
+        #         execution_context=self.execution_context
+        #     )
+        #     initial_state = self.state_manager.create_initial_state(
+        #         workflow_config=self.workflow_config,
+        #         input_data=input_data,
+        #         execution_context=self.execution_context,
+        #         start_node_id=self.start_node_id
+        #     )
+        #
+        #     result = await graph.ainvoke(initial_state, config=self.execution_context.checkpoint_config)
+        #
+        #     # Aggregate output from all End nodes
+        #     full_content = ''
+        #     for end_id in self.stream_coordinator.end_outputs.keys():
+        #         full_content += self.variable_pool.get_value(f"{end_id}.output", default="", strict=False)
+        #
+        #     # Append messages for user and assistant
+        #     if input_data.get("files"):
+        #         result["messages"].extend(
+        #             [
+        #                 {
+        #                     "role": "user",
+        #                     "content": input_data.get("message", '')
+        #                 },
+        #                 {
+        #                     "role": "user",
+        #                     "content": input_data.get("files")
+        #                 },
+        #                 {
+        #                     "role": "assistant",
+        #                     "content": full_content
+        #                 }
+        #             ]
+        #         )
+        #     else:
+        #         result["messages"].extend(
+        #             [
+        #                 {
+        #                     "role": "user",
+        #                     "content": input_data.get("message", '')
+        #                 },
+        #                 {
+        #                     "role": "assistant",
+        #                     "content": full_content
+        #                 }
+        #             ]
+        #         )
+        #     # Calculate elapsed time
+        #     end_time = datetime.datetime.now()
+        #     elapsed_time = (end_time - start_time).total_seconds()
+        #
+        #     logger.info(
+        #         f"Workflow execution completed: execution_id={self.execution_context.execution_id}, elapsed_time={elapsed_time:.2f}ms")
+        #
+        #     return self.result_builder.build_final_output(result, self.variable_pool, elapsed_time, full_content)
+        #
+        # except Exception as e:
+        #     end_time = datetime.datetime.now()
+        #     elapsed_time = (end_time - start_time).total_seconds()
+        #
+        #     logger.error(f"Workflow execution failed: execution_id={self.execution_context.execution_id}, error={e}",
+        #                  exc_info=True)
+        #     return {
+        #         "status": "failed",
+        #         "error": str(e),
+        #         "output": None,
+        #         "node_outputs": {},
+        #         "elapsed_time": elapsed_time,
+        #         "token_usage": None
+        #     }
 
     async def execute_stream(
             self,
@@ -248,7 +259,8 @@ class WorkflowExecutor:
                 "timestamp": int(start_time.timestamp() * 1000)
             }
         }
-
+        result = None
+        full_content = ''
         try:
             # Build the workflow graph in streaming mode
             graph = self.build_graph(stream=True)
@@ -266,7 +278,6 @@ class WorkflowExecutor:
                 start_node_id=self.start_node_id
             )
 
-            full_content = ''
             self.stream_coordinator.update_scope_activation("sys")
 
             # Execute the workflow with streaming
@@ -363,7 +374,12 @@ class WorkflowExecutor:
 
             yield {
                 "event": "workflow_end",
-                "data": self.result_builder.build_final_output(result, self.variable_pool, elapsed_time, full_content)
+                "data": self.result_builder.build_final_output(
+                    result,
+                    self.variable_pool,
+                    elapsed_time,
+                    full_content,
+                    success=True)
             }
 
         except Exception as e:
@@ -372,16 +388,19 @@ class WorkflowExecutor:
 
             logger.error(f"Workflow execution failed: execution_id={self.execution_context.execution_id}, error={e}",
                          exc_info=True)
-
+            if result is None:
+                result = {"error": str(e)}
+            else:
+                result["error"] = str(e)
             yield {
                 "event": "workflow_end",
-                "data": {
-                    "execution_id": self.execution_context.execution_id,
-                    "status": "failed",
-                    "error": str(e),
-                    "elapsed_time": elapsed_time,
-                    "timestamp": end_time.isoformat()
-                }
+                "data": self.result_builder.build_final_output(
+                    result,
+                    self.variable_pool,
+                    elapsed_time,
+                    full_content,
+                    success=False
+                )
             }
 
 

api/app/core/workflow/nodes/base_node.py

@@ -1,5 +1,6 @@
 import asyncio
 import logging
+import uuid
 from abc import ABC, abstractmethod
 from datetime import datetime
 from functools import cached_property
@@ -15,6 +16,7 @@ from app.core.workflow.variable.base_variable import VariableType, FileObject
 from app.db import get_db_read
 from app.models import ModelConfig, ModelApiKey, LoadBalanceStrategy
 from app.schemas import FileInput
+from app.schemas.model_schema import ModelInfo
 from app.services.multimodal_service import MultimodalService
 
 logger = logging.getLogger(__name__)
@@ -619,11 +621,12 @@ class BaseNode(ABC):
 
     @staticmethod
     async def process_message(
-            provider: str,
-            is_omni: bool,
+            api_config: ModelInfo,
             content: str | dict | FileObject,
+            end_user_id: str,
             enable_file=False
     ) -> list | str | None:
+        provider = api_config.provider
         if isinstance(content, dict):
             content = FileObject(
                 type=content.get("type"),
@@ -642,16 +645,20 @@ class BaseNode(ABC):
             if content.content_cache.get(provider):
                 return content.content_cache[provider]
             with get_db_read() as db:
-                multimodel_service = MultimodalService(db, provider, is_omni=is_omni)
-                message = await multimodel_service.process_files(
-                    [FileInput.model_construct(
-                        type=content.type,
-                        url=content.url,
-                        transfer_method=content.transfer_method,
-                        file_type=content.origin_file_type,
-                        upload_file_id=content.file_id
-                    )]
+                multimodel_service = MultimodalService(db, api_config=api_config)
+                file_obj = FileInput(
+                    type=content.type,
+                    url=content.url,
+                    transfer_method=content.transfer_method,
+                    origin_file_type=content.origin_file_type,
+                    upload_file_id=uuid.UUID(content.file_id) if content.file_id else None,
                 )
+                file_obj.set_content(content.get_content())
+                message = await multimodel_service.process_files(
+                    end_user_id,
+                    [file_obj],
+                )
+                content.set_content(file_obj.get_content())
                 if message:
                     content.content_cache[provider] = message
                     return message

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

@@ -128,7 +128,7 @@ class CodeNode(BaseNode):
         else:
             raise ValueError(f"Unsupported language: {self.typed_config.language}")
 
-        async with httpx.AsyncClient() as client:
+        async with httpx.AsyncClient(timeout=60) as client:
             response = await client.post(
                 "http://sandbox:8194/v1/sandbox/run",
                 headers={

api/app/core/workflow/nodes/configs.py

@@ -23,6 +23,7 @@ from app.core.workflow.nodes.question_classifier.config import QuestionClassifie
 from app.core.workflow.nodes.start.config import StartNodeConfig
 from app.core.workflow.nodes.tool.config import ToolNodeConfig
 from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig
+from app.core.workflow.nodes.notes.config import NoteNodeConfig
 
 __all__ = [
     # 基础类
@@ -47,5 +48,6 @@ __all__ = [
     "ToolNodeConfig",
     "MemoryReadNodeConfig",
     "MemoryWriteNodeConfig",
-    "CodeNodeConfig"
+    "CodeNodeConfig",
+    "NoteNodeConfig"
 ]

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

@@ -51,7 +51,7 @@ class ConditionDetail(BaseModel):
     )
 
     right: Any = Field(
-        ...,
+        default=None,
         description="Right-hand operand of the comparison expression"
     )
 

api/app/core/workflow/nodes/cycle_graph/loop.py

@@ -158,7 +158,7 @@ class LoopRuntime:
         self.variable_pool.variables["conv"].update(
             self.child_variable_pool.variables["conv"]
         )
-        loop_vars = self.child_variable_pool.get_node_output(self.node_id, defalut={}, strict=False)
+        loop_vars = self.child_variable_pool.get_node_output(self.node_id, default={}, strict=False)
         loopstate["node_outputs"][self.node_id] = loop_vars
 
     def evaluate_conditional(self) -> bool:
@@ -261,4 +261,4 @@ class LoopRuntime:
             idx += 1
 
         logger.info(f"loop node {self.node_id}: execution completed")
-        return self.child_variable_pool.get_node_output(self.node_id) | {"__child_state": child_state}
+        return self.child_variable_pool.get_node_output(self.node_id, default={}, strict=False) | {"__child_state": child_state}

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

@@ -4,6 +4,7 @@ from pydantic import Field, BaseModel, field_validator
 
 from app.core.workflow.nodes.base_config import BaseNodeConfig
 from app.core.workflow.nodes.enums import HttpRequestMethod, HttpAuthType, HttpContentType, HttpErrorHandle
+from app.core.workflow.variable.base_variable import FileObject
 
 
 class HttpAuthConfig(BaseModel):
@@ -260,6 +261,11 @@ class HttpRequestNodeOutput(BaseModel):
         description="Http response headers"
     )
 
+    files: list[FileObject] = Field(
+        default_factory=list,
+        description="List of files",
+    )
+
     output: str = Field(
         default="SUCCESS",
         description="HTTP response body",

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

@@ -1,24 +1,146 @@
 import asyncio
 import json
 import logging
+import mimetypes
 import uuid
+import imghdr
+from email.message import Message
 from typing import Any, Callable, Coroutine
 
 import httpx
-# import filetypes # TODO: File support (Feature)
 from httpx import AsyncClient, Response, Timeout
+import magic
 
 from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.enums import HttpRequestMethod, HttpErrorHandle, HttpAuthType, HttpContentType
 from app.core.workflow.nodes.http_request.config import HttpRequestNodeConfig, HttpRequestNodeOutput
-from app.core.workflow.variable.base_variable import VariableType
+from app.core.workflow.utils.file_processer import mime_to_file_type
+from app.core.workflow.variable.base_variable import VariableType, FileObject
 from app.core.workflow.variable.variable_objects import FileVariable, ArrayVariable
+from app.schemas import FileType, TransferMethod
 
 logger = logging.getLogger(__file__)
 
 
+class HttpResponse:
+    def __init__(self, response: httpx.Response):
+        self.response = response
+        self.headers = dict(response.headers)
+
+        self._is_file: bool | None = None
+
+    @property
+    def content_type(self) -> str:
+        return self.headers.get("content-type", "")
+
+    @property
+    def content_disposition(self) -> Message | None:
+        content_disposition = self.headers.get("content-disposition", "")
+        if content_disposition:
+            msg = Message()
+            msg["content-disposition"] = content_disposition
+            return msg
+        return None
+
+    @property
+    def is_file(self) -> bool:
+        if self._is_file is not None:
+            return self._is_file
+        content_type = self.content_type.split(";")[0].strip().lower()
+
+        parsed_content_disposition = self.content_disposition
+        if parsed_content_disposition:
+            disp_type = parsed_content_disposition.get_content_disposition()
+            filename = parsed_content_disposition.get_filename()
+            if disp_type == "attachment" or filename:
+                self._is_file = True
+                return True
+
+        if content_type.startswith("text/") and "csv" not in content_type:
+            return False
+
+        if content_type.startswith("application/"):
+            if any(
+                    text_type in content_type
+                    for text_type in {"json", "xml", "javascript", "x-www-form-urlencoded", "yaml", "graphql"}
+            ):
+                self._is_file = False
+                return False
+            try:
+                content_sample = self.response.content[:1024]
+                content_sample.decode("utf-8")
+                text_markers = (b"{", b"[", b"<", b"function", b"var ", b"const ", b"let ")
+                if any(marker in content_sample for marker in text_markers):
+                    return False
+            except UnicodeDecodeError:
+                self._is_file = True
+                return True
+
+        main_type, _ = mimetypes.guess_type("dummy" + (mimetypes.guess_extension(content_type) or ""))
+        if main_type:
+            self._is_file = main_type.split("/")[0] in ("application", "image", "audio", "video")
+            return self._is_file
+        self._is_file = any(media_type in content_type for media_type in ("image/", "audio/", "video/"))
+        return self._is_file
+
+    @property
+    def is_image(self):
+        if self.is_file:
+            kind = imghdr.what(None, h=self.response.content)
+            return kind is not None
+        return False
+
+    @property
+    def url(self) -> str:
+        return str(self.response.url)
+
+    @property
+    def body(self) -> str:
+        if self.is_file:
+            return f"{'!' if self.is_image else ''}[file]({self.url})"
+        return self.response.text
+
+    @staticmethod
+    def get_file_type(file_bytes) -> tuple[FileType | None, str | None]:
+        mime = magic.from_buffer(file_bytes, mime=True)
+
+        if mime.startswith("image"):
+            return FileType.IMAGE, mime
+        elif mime.startswith("video"):
+            return FileType.VIDEO, mime
+        elif mime.startswith("audio"):
+            return FileType.AUDIO, mime
+        elif mime in ["application/pdf",
+                      "application/msword",
+                      "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+                      "application/vnd.ms-excel",
+                      "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+                      "text/plain"]:
+            return FileType.DOCUMENT, mime
+        return None, None
+
+    @property
+    def files(self) -> list[FileObject]:
+        file_type, mime_type = self.get_file_type(self.response.content)
+        origin_file_type = mime_to_file_type(mime_type)
+        if self.is_file and file_type and origin_file_type:
+            file_obj = FileObject(
+                type=file_type,
+                url=self.url,
+                transfer_method=TransferMethod.REMOTE_URL.value,
+                origin_file_type=origin_file_type,
+                file_id=None,
+                is_file=True
+            )
+            file_obj.set_content(self.response.content)
+            return [
+                file_obj
+            ]
+        return []
+
+
 class HttpRequestNode(BaseNode):
     """
     HTTP Request Workflow Node.
@@ -44,6 +166,7 @@ class HttpRequestNode(BaseNode):
             "body": VariableType.STRING,
             "status_code": VariableType.NUMBER,
             "headers": VariableType.OBJECT,
+            "files": VariableType.ARRAY_FILE,
             "output": VariableType.STRING
         }
 
@@ -232,10 +355,12 @@ class HttpRequestNode(BaseNode):
                     )
                     resp.raise_for_status()
                     logger.info(f"Node {self.node_id}: HTTP request succeeded")
+                    response = HttpResponse(resp)
                     return HttpRequestNodeOutput(
-                        body=resp.text,
+                        body=response.body,
                         status_code=resp.status_code,
                         headers=resp.headers,
+                        files=response.files
                     ).model_dump()
                 except (httpx.HTTPStatusError, httpx.RequestError) as e:
                     logger.error(f"HTTP request node exception: {e}")

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

@@ -18,7 +18,7 @@ class ConditionDetail(BaseModel):
     )
 
     right: Any = Field(
-        ...,
+        default=None,
         description="Value to compare with"
     )
 

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

@@ -5,7 +5,7 @@ from typing import Any
 from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
-from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator
+from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator, ValueInputType
 from app.core.workflow.nodes.if_else import IfElseNodeConfig
 from app.core.workflow.nodes.operators import ConditionExpressionResolver, CompareOperatorInstance
 from app.core.workflow.variable.base_variable import VariableType
@@ -23,6 +23,26 @@ class IfElseNode(BaseNode):
             "output": VariableType.STRING
         }
 
+    def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
+        result = []
+        for case in self.typed_config.cases:
+            expressions = []
+            for expression in case.expressions:
+                expressions.append({
+                    "left": self.get_variable(expression.left, variable_pool, strict=False),
+                    "right": expression.right
+                    if expression.input_type == ValueInputType.CONSTANT or expression.right is None
+                    else self.get_variable(expression.right, variable_pool, strict=False),
+                    "operator": str(expression.operator),
+                })
+            result.append({
+                "expressions": expressions,
+                "logical_operator": str(case.logical_operator),
+            })
+        return {
+            "cases": result
+        }
+
     @staticmethod
     def _evaluate(operator, instance: CompareOperatorInstance) -> Any:
         match operator:

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

@@ -30,6 +30,12 @@ class KnowledgeRetrievalNode(BaseNode):
             "output": VariableType.ARRAY_STRING
         }
 
+    def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
+        return {
+            "query": self._render_template(self.typed_config.query, variable_pool),
+            "knowledge_bases": [kb_config.model_dump(mode="json") for kb_config in self.typed_config.knowledge_bases],
+        }
+
     @staticmethod
     def _build_kb_filter(kb_ids: list[uuid.UUID], permission: knowledge_model.PermissionType):
         """

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

@@ -20,6 +20,7 @@ from app.core.workflow.nodes.llm.config import LLMNodeConfig
 from app.core.workflow.variable.base_variable import VariableType
 from app.db import get_db_context
 from app.models import ModelType
+from app.schemas.model_schema import ModelInfo
 from app.services.model_service import ModelConfigService
 
 logger = logging.getLogger(__name__)
@@ -113,12 +114,15 @@ class LLMNode(BaseNode):
 
             # 在 Session 关闭前提取所有需要的数据
             api_config = self.model_balance(config)
-            model_name = api_config.model_name
-            provider = api_config.provider
-            api_key = api_config.api_key
-            api_base = api_config.api_base
-            is_omni = api_config.is_omni
-            model_type = config.type
+            model_info = ModelInfo(
+                model_name=api_config.model_name,
+                model_type=ModelType(config.type),
+                api_key=api_config.api_key,
+                api_base=api_config.api_base,
+                provider=api_config.provider,
+                is_omni=api_config.is_omni,
+                capability=api_config.capability
+            )
 
         # 4. 创建 LLM 实例（使用已提取的数据）
         # 注意：对于流式输出，需要在模型初始化时设置 streaming=True
@@ -126,17 +130,18 @@ class LLMNode(BaseNode):
 
         llm = RedBearLLM(
             RedBearModelConfig(
-                model_name=model_name,
-                provider=provider,
-                api_key=api_key,
-                base_url=api_base,
+                model_name=model_info.model_name,
+                provider=model_info.provider,
+                api_key=model_info.api_key,
+                base_url=model_info.api_base,
                 extra_params=extra_params,
-                is_omni=is_omni
+                is_omni=model_info.is_omni
             ),
-            type=ModelType(model_type)
+            type=model_info.model_type
         )
 
-        logger.debug(f"创建 LLM 实例: provider={provider}, model={model_name}, streaming={stream}")
+        logger.debug(
+            f"创建 LLM 实例: provider={model_info.provider}, model={model_info.model_name}, streaming={stream}")
 
         messages_config = self.typed_config.messages
 
@@ -148,35 +153,40 @@ class LLMNode(BaseNode):
                 content_template = msg_config.content
                 content_template = self._render_context(content_template, variable_pool)
                 content = self._render_template(content_template, variable_pool)
-
+                user_id = self.get_variable("sys.user_id", variable_pool)
                 # 根据角色创建对应的消息对象
                 if role == "system":
                     messages.append({
                         "role": "system",
-                        "content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
+                        "content": await self.process_message(
+                            model_info,
+                            content,
+                            user_id,
+                            self.typed_config.vision,
+                        )
                     })
                 elif role in ["user", "human"]:
                     messages.append({
                         "role": "user",
-                        "content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
+                        "content": await self.process_message(model_info, content, user_id, self.typed_config.vision)
                     })
                 elif role in ["ai", "assistant"]:
                     messages.append({
                         "role": "assistant",
-                        "content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
+                        "content": await self.process_message(model_info, content, user_id, self.typed_config.vision)
                     })
                 else:
                     logger.warning(f"未知的消息角色: {role}，默认使用 user")
                     messages.append({
                         "role": "user",
-                        "content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
+                        "content": await self.process_message(model_info, content, user_id, self.typed_config.vision)
                     })
 
             if self.typed_config.vision_input and self.typed_config.vision:
                 file_content = []
                 files = variable_pool.get_instance(self.typed_config.vision_input)
                 for file in files.value:
-                    content = await self.process_message(provider, is_omni, file.value, self.typed_config.vision)
+                    content = await self.process_message(model_info, file.value, user_id, self.typed_config.vision)
                     if content:
                         file_content.extend(content)
                 if messages and messages[-1]["role"] == 'user':
@@ -190,14 +200,19 @@ class LLMNode(BaseNode):
                     if isinstance(message["content"], list):
                         file_content = []
                         for file in message["content"]:
-                            content = await self.process_message(provider, is_omni, file, self.typed_config.vision)
+                            content = await self.process_message(model_info, file, user_id, self.typed_config.vision)
                             if content:
                                 file_content.extend(content)
                         history_message.append(
                             {"role": message["role"], "content": file_content}
                         )
                     else:
-                        message["content"] = await self.process_message(provider, is_omni, message["content"], self.typed_config.vision)
+                        message["content"] = await self.process_message(
+                            model_info,
+                            message["content"],
+                            user_id,
+                            self.typed_config.vision
+                        )
                         history_message.append(message)
                 messages = messages[:-1] + history_message + messages[-1:]
             self.messages = messages
@@ -293,7 +308,7 @@ class LLMNode(BaseNode):
 
         # 调用 LLM（流式，支持字符串或消息列表）
         last_meta_data = {}
-        async for chunk in llm.astream(self.messages, stream_usage=True):
+        async for chunk in llm.astream(self.messages):
             # 提取内容
             if hasattr(chunk, 'content'):
                 content = self.process_model_output(chunk.content)

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

@@ -0,0 +1,12 @@
+from pydantic import Field
+
+from app.core.workflow.nodes.base_config import BaseNodeConfig
+
+
+class NoteNodeConfig(BaseNodeConfig):
+    author: str = Field(default="", description="author")
+    text: str = Field(default="", description="note content")
+    width: int = Field(default=80)
+    height: int = Field(default=80)
+    theme: str = Field(default="blue")
+    show_author: bool = Field(default=True)

api/app/core/workflow/nodes/operators.py

@@ -250,6 +250,8 @@ class ConditionBase(ABC):
         self.type_limit = getattr(self, "type_limit", None)
 
     def resolve_right_literal_value(self):
+        if self.right_selector is None:
+            return None
         if self.input_type == ValueInputType.VARIABLE:
             pattern = r"\{\{\s*(.*?)\s*\}\}"
             right_expression = re.sub(pattern, r"\1", self.right_selector).strip()

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

@@ -37,6 +37,14 @@ class ParameterExtractorNode(BaseNode):
                 }
         return None
 
+    def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
+        return {
+            "text": self._render_template(self.typed_config.text, variable_pool),
+            "prompt": self._render_template(self.typed_config.prompt, variable_pool),
+            "params": [param.model_dump(mode="json") for param in self.typed_config.params],
+            "model_id": str(self.typed_config.model_id),
+        }
+
     def _output_types(self) -> dict[str, VariableType]:
         outputs = {}
         for param in self.typed_config.params:

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

@@ -27,7 +27,6 @@ class ToolNode(BaseNode):
     def _output_types(self) -> dict[str, VariableType]:
         return {
             "data": VariableType.STRING,
-            "error_code": VariableType.STRING,
             "execution_time": VariableType.NUMBER
         }
 
@@ -48,10 +47,7 @@ class ToolNode(BaseNode):
 
         if not tenant_id:
             logger.error(f"节点 {self.node_id} 缺少租户ID")
-            return {
-                "success": False,
-                "data": "缺少租户ID"
-            }
+            raise ValueError("缺少租户ID")
 
         # 渲染工具参数
         rendered_parameters = {}
@@ -83,13 +79,8 @@ class ToolNode(BaseNode):
             logger.info(f"节点 {self.node_id} 工具执行成功")
             return {
                 "data": result.data if isinstance(result.data, str) else json.dumps(result.data, ensure_ascii=False),
-                "error_code": "",
                 "execution_time": result.execution_time
             }
         else:
             logger.error(f"节点 {self.node_id} 工具执行失败: {result.error}")
-            return {
-                "data": result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False),
-                "error_code": result.error_code,
-                "execution_time": result.execution_time
-            }
+            raise ValueError(f"工具执行失败: {result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False)}")

api/app/core/workflow/utils/file_processer.py

@@ -0,0 +1,56 @@
+# -*- coding: UTF-8 -*-
+# Author: Eternity
+# @Email: 1533512157@qq.com
+# @Time : 2026/3/10 13:36
+TRANSFORM_FILE_TYPE = {
+    'text/plain': 'document/text',
+    'text/markdown': 'document/markdown',
+    'text/x-markdown': 'document/x-markdown',
+
+    'application/pdf': 'document/pdf',
+
+    'application/msword': 'document/doc',
+    'application/vnd.openxmlformats-officedocument.wordprocessingml.document': 'document/docx',
+
+    'application/vnd.ms-powerpoint': 'document/ppt',
+    'application/vnd.openxmlformats-officedocument.presentationml.presentation': 'document/pptx',
+}
+ALLOWED_FILE_TYPES = [
+    'text/plain',
+    'text/markdown',
+    'text/x-markdown',
+    'application/pdf',
+    'application/msword',
+    'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+    'application/vnd.ms-powerpoint',
+    'application/vnd.openxmlformats-officedocument.presentationml.presentation',
+    'image/jpg',
+    'image/jpeg',
+    'image/png',
+    'image/gif',
+    'image/bmp',
+    'image/webp',
+    'image/svg+xml',
+    'video/mp4',
+    'video/quicktime',
+    'video/x-msvideo',
+    'video/x-matroska',
+    'video/webm',
+    'video/x-flv',
+    'video/x-ms-wmv',
+    'audio/mpeg',
+    'audio/wav',
+    'audio/ogg',
+    'audio/aac',
+    'audio/flac',
+    'audio/mp4',
+    'audio/x-ms-wma',
+    'audio/x-m4a',
+]
+
+
+def mime_to_file_type(mime_type):
+    if mime_type not in ALLOWED_FILE_TYPES:
+        return None
+
+    return TRANSFORM_FILE_TYPE.get(mime_type, mime_type)

api/app/core/workflow/validator.py

@@ -170,7 +170,7 @@ class WorkflowValidator:
                 # 仅在发布时验证所有节点可达
                 # 6. 验证所有节点可达（从 start 节点出发）
                 if start_nodes and not errors:  # 只有在前面验证通过时才检查可达性
-                    reachable = WorkflowValidator._get_reachable_nodes(
+                    reachable = WorkflowValidator.get_reachable_nodes(
                         start_nodes[0]["id"],
                         edges
                     )
@@ -194,7 +194,7 @@ class WorkflowValidator:
         return len(errors) == 0, errors
 
     @staticmethod
-    def _get_reachable_nodes(start_id: str, edges: list[dict]) -> set[str]:
+    def get_reachable_nodes(start_id: str, edges: list[dict]) -> set[str]:
         """获取从 start 节点可达的所有节点
         
         Args:

api/app/core/workflow/variable/base_variable.py

@@ -2,7 +2,7 @@ from enum import StrEnum
 from abc import abstractmethod, ABC
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, PrivateAttr
 
 from app.schemas import FileType
 
@@ -41,10 +41,10 @@ class VariableType(StrEnum):
         """
         if isinstance(var, str):
             return cls.STRING
-        elif isinstance(var, (int, float)):
-            return cls.NUMBER
         elif isinstance(var, bool):
             return cls.BOOLEAN
+        elif isinstance(var, (int, float)):
+            return cls.NUMBER
         elif isinstance(var, FileObject) or (isinstance(var, dict) and var.get('is_file')):
             return cls.FILE
         elif isinstance(var, dict):
@@ -114,9 +114,16 @@ class FileObject(BaseModel):
     file_id: str | None
 
     content_cache: dict = Field(default_factory=dict)
-
     is_file: bool
 
+    _byte_content: bytes | None = PrivateAttr(default=None)
+
+    def get_content(self):
+        return self._byte_content
+
+    def set_content(self, byte_content):
+        self._byte_content = byte_content
+
 
 class BaseVariable(ABC):
     """Abstract base class for all workflow variables.

api/app/core/workflow/variable/variable_objects.py

@@ -10,6 +10,7 @@ T = TypeVar("T", bound=BaseVariable)
 
 
 class StringVariable(BaseVariable):
+    value: str
     type = 'str'
 
     def valid_value(self, value) -> str:
@@ -22,6 +23,7 @@ class StringVariable(BaseVariable):
 
 
 class NumberVariable(BaseVariable):
+    value: int | float
     type = 'number'
 
     def valid_value(self, value) -> int | float:
@@ -34,6 +36,7 @@ class NumberVariable(BaseVariable):
 
 
 class BooleanVariable(BaseVariable):
+    value: bool
     type = 'boolean'
 
     def valid_value(self, value) -> bool:
@@ -46,6 +49,7 @@ class BooleanVariable(BaseVariable):
 
 
 class DictVariable(BaseVariable):
+    value: dict
     type = 'object'
 
     def valid_value(self, value) -> dict:
@@ -58,6 +62,7 @@ class DictVariable(BaseVariable):
 
 
 class FileVariable(BaseVariable):
+    value: FileObject
     type = 'file'
 
     def valid_value(self, value) -> FileObject:
@@ -102,6 +107,7 @@ class FileVariable(BaseVariable):
 
 
 class ArrayVariable(BaseVariable, Generic[T]):
+    value: list[T]
     type = 'array'
 
     def __init__(self, child_type: Type[T], value: list[Any]):
@@ -129,6 +135,7 @@ class ArrayVariable(BaseVariable, Generic[T]):
 
 
 class NestedArrayVariable(BaseVariable):
+    value: list[ArrayVariable]
     type = 'array_nest'
 
     def valid_value(self, value: list[T]) -> list[T]:
@@ -153,6 +160,7 @@ class NestedArrayVariable(BaseVariable):
     category=RuntimeWarning
 )
 class AnyVariable(BaseVariable):
+    value: Any
     type = 'any'
 
     def valid_value(self, value: Any) -> Any:

api/app/db.py

@@ -16,7 +16,7 @@ engine = create_engine(
     pool_recycle=settings.DB_POOL_RECYCLE,
     pool_timeout=settings.DB_POOL_TIMEOUT,
     connect_args={
-        "options": "-c timezone=Asia/Shanghai -c statement_timeout=60000"
+        "options": "-c timezone=UTC -c statement_timeout=60000"
     },
 )
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
@@ -65,6 +65,7 @@ def get_db_read() -> Generator[Session, None, None]:
             yield db
         finally:
             db.rollback()  # 只读任务无需 commit
+            db.close()
 
 
 def get_pool_status():

api/app/i18n/README.md

@@ -0,0 +1,61 @@
+# Internationalization (i18n) Module
+
+This module provides internationalization support for the MemoryBear API.
+
+## Components
+
+- `service.py` - Translation service and core translation logic
+- `middleware.py` - Language detection middleware
+- `dependencies.py` - FastAPI dependency injection functions
+- `exceptions.py` - Internationalized exception classes
+
+## Usage
+
+### Basic Translation
+
+```python
+from app.i18n import t
+
+# Simple translation
+message = t("common.success.created")
+
+# Parameterized translation
+message = t("common.validation.required", field="Name")
+```
+
+### Enum Translation
+
+```python
+from app.i18n import t_enum
+
+# Translate enum value
+role_display = t_enum("workspace_role", "manager")
+```
+
+### In FastAPI Endpoints
+
+```python
+from fastapi import Depends
+from app.i18n.dependencies import get_translator
+
+@router.post("/workspaces")
+async def create_workspace(
+    data: WorkspaceCreate,
+    t: Callable = Depends(get_translator)
+):
+    workspace = await workspace_service.create(data)
+    return {
+        "success": True,
+        "message": t("workspace.created_successfully"),
+        "data": workspace
+    }
+```
+
+## Configuration
+
+See `app/core/config.py` for i18n configuration options:
+
+- `I18N_DEFAULT_LANGUAGE` - Default language (default: "zh")
+- `I18N_SUPPORTED_LANGUAGES` - Supported languages (default: "zh,en")
+- `I18N_ENABLE_TRANSLATION_CACHE` - Enable caching (default: true)
+- `I18N_LOG_MISSING_TRANSLATIONS` - Log missing translations (default: true)

api/app/i18n/__init__.py

@@ -0,0 +1,124 @@
+"""
+Internationalization (i18n) module for MemoryBear Enterprise.
+
+This module provides complete i18n support for the backend API including:
+- Translation loading from multiple directories (community + enterprise)
+- Translation service with caching and fallback
+- Language detection middleware
+- Dependency injection for FastAPI
+- Convenience functions for easy usage
+
+Usage:
+    from app.i18n import t, t_enum
+
+    # Simple translation
+    message = t("common.success.created")
+
+    # Parameterized translation
+    error = t("common.validation.required", field="名称")
+
+    # Enum translation
+    role_display = t_enum("workspace_role", "manager")
+"""
+
+from app.i18n.dependencies import (
+    get_current_language,
+    get_enum_translator,
+    get_translator,
+)
+from app.i18n.exceptions import (
+    BadRequestError,
+    ConflictError,
+    FileNotFoundError,
+    FileTooLargeError,
+    ForbiddenError,
+    I18nException,
+    InternalServerError,
+    InvalidCredentialsError,
+    InvalidFileTypeError,
+    NotFoundError,
+    QuotaExceededError,
+    RateLimitExceededError,
+    ServiceUnavailableError,
+    TenantNotFoundError,
+    TenantSuspendedError,
+    TokenExpiredError,
+    TokenInvalidError,
+    UnauthorizedError,
+    UserAlreadyExistsError,
+    UserNotFoundError,
+    ValidationError,
+    WorkspaceNotFoundError,
+    WorkspacePermissionDeniedError,
+    get_current_locale,
+    set_current_locale,
+)
+from app.i18n.loader import TranslationLoader
+from app.i18n.logger import (
+    TranslationLogger,
+    get_translation_logger,
+    log_missing_translation,
+    log_translation_error,
+)
+from app.i18n.middleware import LanguageMiddleware
+from app.i18n.serializers import (
+    I18nResponseMixin,
+    WorkspaceSerializer,
+    WorkspaceMemberSerializer,
+    WorkspaceInviteSerializer,
+)
+from app.i18n.service import (
+    TranslationService,
+    get_translation_service,
+    t,
+    t_enum,
+)
+
+__all__ = [
+    "TranslationLoader",
+    "LanguageMiddleware",
+    "TranslationService",
+    "get_translation_service",
+    "t",
+    "t_enum",
+    "get_current_language",
+    "get_translator",
+    "get_enum_translator",
+    # Context management
+    "get_current_locale",
+    "set_current_locale",
+    # Logging
+    "TranslationLogger",
+    "get_translation_logger",
+    "log_missing_translation",
+    "log_translation_error",
+    # Serializers
+    "I18nResponseMixin",
+    "WorkspaceSerializer",
+    "WorkspaceMemberSerializer",
+    "WorkspaceInviteSerializer",
+    # Exception classes
+    "I18nException",
+    "BadRequestError",
+    "UnauthorizedError",
+    "ForbiddenError",
+    "NotFoundError",
+    "ConflictError",
+    "ValidationError",
+    "InternalServerError",
+    "ServiceUnavailableError",
+    "WorkspaceNotFoundError",
+    "WorkspacePermissionDeniedError",
+    "UserNotFoundError",
+    "UserAlreadyExistsError",
+    "TenantNotFoundError",
+    "TenantSuspendedError",
+    "InvalidCredentialsError",
+    "TokenExpiredError",
+    "TokenInvalidError",
+    "FileNotFoundError",
+    "FileTooLargeError",
+    "InvalidFileTypeError",
+    "RateLimitExceededError",
+    "QuotaExceededError",
+]

api/app/i18n/cache.py

@@ -0,0 +1,291 @@
+"""
+Advanced caching system for i18n translations.
+
+This module provides:
+- LRU cache for hot translations
+- Lazy loading mechanism
+- Memory optimization
+- Cache statistics
+"""
+
+import logging
+from functools import lru_cache
+from typing import Any, Dict, Optional
+from collections import OrderedDict
+import time
+
+logger = logging.getLogger(__name__)
+
+
+class TranslationCache:
+    """
+    Advanced translation cache with LRU eviction and lazy loading.
+    
+    Features:
+    - LRU cache for frequently accessed translations
+    - Lazy loading to reduce startup time
+    - Memory-efficient storage
+    - Cache hit/miss statistics
+    """
+    
+    def __init__(self, max_lru_size: int = 1000, enable_lazy_load: bool = True):
+        """
+        Initialize the translation cache.
+        
+        Args:
+            max_lru_size: Maximum size of LRU cache for hot translations
+            enable_lazy_load: Enable lazy loading of locales
+        """
+        self.max_lru_size = max_lru_size
+        self.enable_lazy_load = enable_lazy_load
+        
+        # Main cache: {locale: {namespace: {key: value}}}
+        self._main_cache: Dict[str, Dict[str, Any]] = {}
+        
+        # LRU cache for hot translations
+        self._lru_cache: OrderedDict = OrderedDict()
+        
+        # Loaded locales tracker
+        self._loaded_locales: set = set()
+        
+        # Statistics
+        self._stats = {
+            "hits": 0,
+            "misses": 0,
+            "lru_hits": 0,
+            "lru_misses": 0,
+            "lazy_loads": 0
+        }
+        
+        logger.info(
+            f"TranslationCache initialized with LRU size: {max_lru_size}, "
+            f"lazy loading: {enable_lazy_load}"
+        )
+    
+    def set_locale_data(self, locale: str, data: Dict[str, Any]):
+        """
+        Set translation data for a locale.
+        
+        Args:
+            locale: Locale code
+            data: Translation data dictionary
+        """
+        self._main_cache[locale] = data
+        self._loaded_locales.add(locale)
+        logger.debug(f"Loaded locale '{locale}' into cache")
+    
+    def get_translation(
+        self,
+        locale: str,
+        namespace: str,
+        key_path: list
+    ) -> Optional[str]:
+        """
+        Get translation from cache with LRU optimization.
+        
+        Args:
+            locale: Locale code
+            namespace: Translation namespace
+            key_path: List of nested keys
+            
+        Returns:
+            Translation string or None if not found
+        """
+        # Build cache key for LRU
+        cache_key = f"{locale}:{namespace}:{'.'.join(key_path)}"
+        
+        # Check LRU cache first (hot translations)
+        if cache_key in self._lru_cache:
+            self._stats["lru_hits"] += 1
+            self._stats["hits"] += 1
+            # Move to end (most recently used)
+            self._lru_cache.move_to_end(cache_key)
+            return self._lru_cache[cache_key]
+        
+        self._stats["lru_misses"] += 1
+        
+        # Check main cache
+        if locale not in self._main_cache:
+            self._stats["misses"] += 1
+            return None
+        
+        if namespace not in self._main_cache[locale]:
+            self._stats["misses"] += 1
+            return None
+        
+        # Navigate through nested keys
+        current = self._main_cache[locale][namespace]
+        for key in key_path:
+            if isinstance(current, dict) and key in current:
+                current = current[key]
+            else:
+                self._stats["misses"] += 1
+                return None
+        
+        # Return only if it's a string value
+        if not isinstance(current, str):
+            self._stats["misses"] += 1
+            return None
+        
+        self._stats["hits"] += 1
+        
+        # Add to LRU cache
+        self._add_to_lru(cache_key, current)
+        
+        return current
+    
+    def _add_to_lru(self, key: str, value: str):
+        """
+        Add translation to LRU cache.
+        
+        Args:
+            key: Cache key
+            value: Translation value
+        """
+        # Remove oldest if cache is full
+        if len(self._lru_cache) >= self.max_lru_size:
+            self._lru_cache.popitem(last=False)
+        
+        self._lru_cache[key] = value
+    
+    def is_locale_loaded(self, locale: str) -> bool:
+        """
+        Check if a locale is loaded.
+        
+        Args:
+            locale: Locale code
+            
+        Returns:
+            True if locale is loaded
+        """
+        return locale in self._loaded_locales
+    
+    def get_loaded_locales(self) -> list:
+        """
+        Get list of loaded locales.
+        
+        Returns:
+            List of locale codes
+        """
+        return list(self._loaded_locales)
+    
+    def clear_lru(self):
+        """Clear the LRU cache."""
+        self._lru_cache.clear()
+        logger.info("LRU cache cleared")
+    
+    def clear_locale(self, locale: str):
+        """
+        Clear cache for a specific locale.
+        
+        Args:
+            locale: Locale code
+        """
+        if locale in self._main_cache:
+            del self._main_cache[locale]
+            self._loaded_locales.discard(locale)
+        
+        # Clear related LRU entries
+        keys_to_remove = [k for k in self._lru_cache if k.startswith(f"{locale}:")]
+        for key in keys_to_remove:
+            del self._lru_cache[key]
+        
+        logger.info(f"Cleared cache for locale '{locale}'")
+    
+    def clear_all(self):
+        """Clear all caches."""
+        self._main_cache.clear()
+        self._lru_cache.clear()
+        self._loaded_locales.clear()
+        logger.info("All caches cleared")
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """
+        Get cache statistics.
+        
+        Returns:
+            Dictionary with cache statistics
+        """
+        total_requests = self._stats["hits"] + self._stats["misses"]
+        hit_rate = (
+            self._stats["hits"] / total_requests * 100
+            if total_requests > 0
+            else 0
+        )
+        
+        lru_total = self._stats["lru_hits"] + self._stats["lru_misses"]
+        lru_hit_rate = (
+            self._stats["lru_hits"] / lru_total * 100
+            if lru_total > 0
+            else 0
+        )
+        
+        return {
+            "total_requests": total_requests,
+            "hits": self._stats["hits"],
+            "misses": self._stats["misses"],
+            "hit_rate": round(hit_rate, 2),
+            "lru_hits": self._stats["lru_hits"],
+            "lru_misses": self._stats["lru_misses"],
+            "lru_hit_rate": round(lru_hit_rate, 2),
+            "lru_size": len(self._lru_cache),
+            "lru_max_size": self.max_lru_size,
+            "loaded_locales": len(self._loaded_locales),
+            "lazy_loads": self._stats["lazy_loads"]
+        }
+    
+    def reset_stats(self):
+        """Reset cache statistics."""
+        self._stats = {
+            "hits": 0,
+            "misses": 0,
+            "lru_hits": 0,
+            "lru_misses": 0,
+            "lazy_loads": 0
+        }
+        logger.info("Cache statistics reset")
+    
+    def get_memory_usage(self) -> Dict[str, Any]:
+        """
+        Estimate memory usage of the cache.
+        
+        Returns:
+            Dictionary with memory usage information
+        """
+        import sys
+        
+        main_cache_size = sys.getsizeof(self._main_cache)
+        lru_cache_size = sys.getsizeof(self._lru_cache)
+        
+        # Rough estimate of nested data
+        for locale_data in self._main_cache.values():
+            main_cache_size += sys.getsizeof(locale_data)
+            for namespace_data in locale_data.values():
+                main_cache_size += sys.getsizeof(namespace_data)
+        
+        return {
+            "main_cache_bytes": main_cache_size,
+            "lru_cache_bytes": lru_cache_size,
+            "total_bytes": main_cache_size + lru_cache_size,
+            "main_cache_mb": round(main_cache_size / 1024 / 1024, 2),
+            "lru_cache_mb": round(lru_cache_size / 1024 / 1024, 2),
+            "total_mb": round((main_cache_size + lru_cache_size) / 1024 / 1024, 2)
+        }
+
+
+@lru_cache(maxsize=128)
+def get_cached_translation_key(locale: str, namespace: str, key: str) -> str:
+    """
+    LRU cached function for building translation cache keys.
+    
+    This reduces string concatenation overhead for frequently accessed keys.
+    
+    Args:
+        locale: Locale code
+        namespace: Translation namespace
+        key: Translation key
+        
+    Returns:
+        Cache key string
+    """
+    return f"{locale}:{namespace}:{key}"

api/app/i18n/dependencies.py

@@ -0,0 +1,158 @@
+"""
+FastAPI dependency injection functions for i18n.
+
+This module provides dependency injection functions that can be used
+in FastAPI route handlers to access the current language and translator.
+"""
+
+import logging
+from typing import Callable
+
+from fastapi import Request
+
+from app.i18n.service import get_translation_service
+
+logger = logging.getLogger(__name__)
+
+
+async def get_current_language(request: Request) -> str:
+    """
+    Get the current language from the request context.
+
+    This dependency extracts the language that was determined by the
+    LanguageMiddleware and stored in request.state.
+
+    Args:
+        request: FastAPI request object
+
+    Returns:
+        Language code (e.g., "zh", "en")
+
+    Usage:
+        @router.get("/example")
+        async def example(language: str = Depends(get_current_language)):
+            return {"language": language}
+    """
+    # Get language from request state (set by LanguageMiddleware)
+    language = getattr(request.state, "language", None)
+
+    if language is None:
+        # Fallback to default language if not set
+        from app.core.config import settings
+        language = settings.I18N_DEFAULT_LANGUAGE
+        logger.warning(
+            "Language not found in request.state, using default: "
+            f"{language}"
+        )
+
+    return language
+
+
+async def get_translator(request: Request) -> Callable:
+    """
+    Get a translator function bound to the current request's language.
+
+    This dependency returns a translation function that automatically
+    uses the current request's language, making it easy to translate
+    strings in route handlers.
+
+    Args:
+        request: FastAPI request object
+
+    Returns:
+        Translation function with signature: t(key: str, **params) -> str
+
+    Usage:
+        @router.post("/workspaces")
+        async def create_workspace(
+            data: WorkspaceCreate,
+            t: Callable = Depends(get_translator)
+        ):
+            workspace = await workspace_service.create(data)
+            return {
+                "success": True,
+                "message": t("workspace.created_successfully"),
+                "data": workspace
+            }
+
+        # With parameters
+        @router.get("/items")
+        async def get_items(t: Callable = Depends(get_translator)):
+            count = 5
+            return {
+                "message": t("items.found", count=count)
+            }
+    """
+    # Get current language
+    language = await get_current_language(request)
+
+    # Get translation service
+    service = get_translation_service()
+
+    # Return a bound translation function
+    def translate(key: str, **params) -> str:
+        """
+        Translate a key using the current request's language.
+
+        Args:
+            key: Translation key (e.g., "common.success.created")
+            **params: Parameters for parameterized messages
+
+        Returns:
+            Translated string
+        """
+        return service.translate(key, language, **params)
+
+    return translate
+
+
+async def get_enum_translator(request: Request) -> Callable:
+    """
+    Get an enum translator function bound to the current request's language.
+
+    This dependency returns a function for translating enum values
+    that automatically uses the current request's language.
+
+    Args:
+        request: FastAPI request object
+
+    Returns:
+        Enum translation function with signature:
+        t_enum(enum_type: str, value: str) -> str
+
+    Usage:
+        @router.get("/workspace/{id}")
+        async def get_workspace(
+            id: str,
+            t_enum: Callable = Depends(get_enum_translator)
+        ):
+            workspace = await workspace_service.get(id)
+            return {
+                "id": workspace.id,
+                "role": workspace.role,
+                "role_display": t_enum("workspace_role", workspace.role),
+                "status": workspace.status,
+                "status_display": t_enum("workspace_status", workspace.status)
+            }
+    """
+    # Get current language
+    language = await get_current_language(request)
+
+    # Get translation service
+    service = get_translation_service()
+
+    # Return a bound enum translation function
+    def translate_enum(enum_type: str, value: str) -> str:
+        """
+        Translate an enum value using the current request's language.
+
+        Args:
+            enum_type: Enum type name (e.g., "workspace_role")
+            value: Enum value (e.g., "manager")
+
+        Returns:
+            Translated enum display name
+        """
+        return service.translate_enum(enum_type, value, language)
+
+    return translate_enum