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/app/controllers/__init__.py

@@ -13,6 +13,7 @@ from . import (
     document_controller,
     emotion_config_controller,
     emotion_controller,
+    end_user_controller,
     file_controller,
     file_storage_controller,
     home_page_controller,
@@ -96,5 +97,6 @@ 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/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

@@ -91,7 +91,7 @@ async def upload_file(
 
     if file_size > settings.MAX_FILE_SIZE:
         raise HTTPException(
-            status_code=status.HTTP_413_CONTENT_TOO_LARGE,
+            status_code=status.HTTP_400_BAD_REQUEST,
             detail=f"The file size exceeds the {settings.MAX_FILE_SIZE} byte limit"
         )
 
@@ -172,6 +172,7 @@ async def upload_file_with_share_token(
     
     # Get share and release info from share_token
     service = ReleaseShareService(db)
+    share_info = service.get_shared_release_info(share_token=share_data.share_token)
     
     # Get share object to access app_id
     share = service.repo.get_by_share_token(share_data.share_token)
@@ -498,51 +499,6 @@ async def get_file_url(
         )
 
 
-@router.get("/files/{file_id}/public-url", response_model=ApiResponse)
-async def get_permanent_file_url(
-    file_id: uuid.UUID,
-    db: Session = Depends(get_db),
-    storage_service: FileStorageService = Depends(get_file_storage_service),
-):
-    """
-    获取文件的永久公开 URL（无过期时间）。
-
-    - 本地存储：返回 API 永久访问地址（基于 FILE_LOCAL_SERVER_URL 配置）
-    - 远程存储（OSS/S3）：返回 bucket 公读地址（需 bucket 已配置公共读权限）
-    """
-    file_metadata = db.query(FileMetadata).filter(FileMetadata.id == file_id).first()
-    if not file_metadata:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="The file does not exist")
-
-    if file_metadata.status != "completed":
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST,
-                            detail=f"File upload not completed, status: {file_metadata.status}")
-
-    file_key = file_metadata.file_key
-    storage = storage_service.storage
-
-    try:
-        if isinstance(storage, LocalStorage):
-            url = f"{settings.FILE_LOCAL_SERVER_URL}/storage/permanent/{file_id}"
-        else:
-            url = await storage.get_permanent_url(file_key)
-            if not url:
-                raise HTTPException(status_code=status.HTTP_501_NOT_IMPLEMENTED,
-                                    detail="Permanent URL not supported for current storage backend")
-
-        api_logger.info(f"Generated permanent URL: file_id={file_id}")
-        return success(
-            data={"url": url, "expires_in": None, "permanent": True, "file_name": file_metadata.file_name},
-            msg="Permanent file URL generated successfully"
-        )
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"Failed to generate permanent URL: {e}")
-        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                            detail=f"Failed to generate permanent URL: {str(e)}")
-
-
 @router.get("/public/{file_id}", response_model=Any)
 async def public_download_file(
     request: Request,

api/app/controllers/memory_dashboard_controller.py

@@ -195,9 +195,10 @@ async def get_workspace_end_users(
         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, workspace_id=str(workspace_id))
+        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/app/controllers/memory_working_controller.py

@@ -33,47 +33,35 @@ def get_memory_count(
 @router.get("/{end_user_id}/conversations", response_model=ApiResponse)
 def get_conversations(
         end_user_id: uuid.UUID,
-        page: int = 1,
-        pagesize: int = 20,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """
-    Retrieve conversations for the current user in a specific group with pagination.
+    Retrieve all conversations for the current user in a specific group.
 
     Args:
         end_user_id (UUID): The group identifier.
-        page (int): Page number (1-based). Defaults to 1.
-        pagesize (int): Number of items per page. Defaults to 20.
         current_user (User, optional): The authenticated user.
         db (Session, optional): SQLAlchemy session.
 
     Returns:
-        ApiResponse: Contains a paginated list of conversations.
+        ApiResponse: Contains a list of conversation IDs.
+
+    Notes:
+        - Initializes the ConversationService with the current DB session.
+        - Returns only conversation IDs for lightweight response.
+        - Logs can be added to trace requests in production.
     """
-    page = max(1, page)
-    page_size = max(1, min(pagesize, 100))  # Limit page size between 1 and 100
     conversation_service = ConversationService(db)
-    conversations, total = conversation_service.get_user_conversations(
-        end_user_id,
-        page=page,
-        page_size=page_size
+    conversations = conversation_service.get_user_conversations(
+        end_user_id
     )
-    return success(data={
-        "items": [
-            {
-                "id": conversation.id,
-                "title": conversation.title
-            } for conversation in conversations
-        ],
-        "total": total,
-        "page": {
-            "page": page,
-            "pagesize": page_size,
-            "total": total,
-            "hasnext": (page * page_size) < total
-        },
-    }, msg="get conversations success")
+    return success(data=[
+        {
+            "id": conversation.id,
+            "title": conversation.title
+        } for conversation in conversations
+    ], msg="get conversations success")
 
 
 @router.get("/{end_user_id}/messages", response_model=ApiResponse)

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

@@ -76,8 +76,6 @@ async def get_tool_methods(
         if methods is None:
             raise HTTPException(status_code=404, detail="工具不存在")
         return success(data=methods, msg="获取工具方法成功")
-    except HTTPException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -123,8 +121,6 @@ async def create_tool(
         raise HTTPException(status_code=400, detail=e.message)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
-    except HTTPException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -153,8 +149,6 @@ async def update_tool(
         return success(msg="工具更新成功")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
-    except HTTPException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -197,8 +191,6 @@ async def set_tool_active(
         return success(msg=f"工具已{action}")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
-    except HTTPException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -231,8 +223,6 @@ async def execute_tool(
             },
             msg="工具执行完成"
         )
-    except HTTPException:
-        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 

api/app/core/config.py

@@ -97,7 +97,6 @@ class Settings:
 
     # File Upload
     MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", "52428800"))
-    MAX_FILE_COUNT: int = int(os.getenv("MAX_FILE_COUNT", "20"))
     FILE_PATH: str = os.getenv("FILE_PATH", "/files")
     FILE_URL_EXPIRES: int = int(os.getenv("FILE_URL_EXPIRES", "3600"))
 

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

@@ -155,7 +155,7 @@ async def clean_databases(data) -> str:
         # 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)
@@ -169,11 +169,18 @@ async def clean_databases(data) -> str:
             elif isinstance(time_search, list):
                 content_list.extend(time_search)
 
-        # Extract text content
+        # 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):
@@ -354,7 +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,
@@ -390,8 +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 = []
 

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

@@ -334,13 +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:

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

@@ -252,9 +252,10 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
         # 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',
+            '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):
             # Clean dictionary
@@ -310,7 +311,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
                 "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"]),
+                "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,

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/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
 
@@ -166,11 +166,15 @@ async def write(
                 statement_entity_edges=all_statement_entity_edges,
                 entity_edges=all_entity_entity_edges,
                 connector=neo4j_connector,
-                config_id=config_id,
-                llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
             )
             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/label_propagation.py

@@ -7,6 +7,7 @@
 - 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
@@ -19,8 +20,9 @@ logger = logging.getLogger(__name__)
 
 # 全量迭代最大轮数，防止不收敛
 MAX_ITERATIONS = 10
-# 社区摘要核心实体数量
-CORE_ENTITY_LIMIT = 5
+
+# 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:
@@ -67,13 +69,11 @@ class LabelPropagationEngine:
     def __init__(
         self,
         connector: Neo4jConnector,
-        config_id: Optional[str] = None,
         llm_model_id: Optional[str] = None,
         embedding_model_id: Optional[str] = None,
     ):
         self.connector = connector
         self.repo = CommunityRepository(connector)
-        self.config_id = config_id
         self.llm_model_id = llm_model_id
         self.embedding_model_id = embedding_model_id
 
@@ -105,58 +105,81 @@ class LabelPropagationEngine:
 
     async def full_clustering(self, end_user_id: str) -> None:
         """
-        全量标签传播初始化。
+        全量标签传播初始化（分批处理，控制内存峰值）。
 
-        1. 拉取所有实体，初始化每个实体为独立社区
-        2. 迭代：每轮对所有实体做邻居投票，更新社区标签
-        3. 直到标签不再变化或达到 MAX_ITERATIONS
-        4. 将最终标签写入 Neo4j
+        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
         """
-        entities = await self.repo.get_all_entities(end_user_id)
-        if not entities:
+        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
 
-        # 初始化：每个实体持有自己 id 作为社区标签
-        labels: Dict[str, str] = {e["id"]: e["id"] for e in entities}
-        embeddings: Dict[str, Optional[List[float]]] = {
-            e["id"]: e.get("name_embedding") for e in entities
-        }
+        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")
 
-        # 预加载所有实体的邻居，避免迭代内 O(iterations * |E|) 次 Neo4j 往返
-        logger.info(f"[Clustering] 预加载 {len(entities)} 个实体的邻居图...")
-        neighbors_cache: Dict[str, List[Dict]] = await self.repo.get_all_entity_neighbors_batch(end_user_id)
-        logger.info(f"[Clustering] 邻居预加载完成，覆盖实体数: {len(neighbors_cache)}")
+        # 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 iteration in range(MAX_ITERATIONS):
-            changed = 0
-            # 随机顺序（Python dict 在 3.7+ 保持插入顺序，这里直接遍历）
-            for entity in entities:
-                eid = entity["id"]
-                # 直接从缓存取邻居，不再发起 Neo4j 查询
-                neighbors = neighbors_cache.get(eid, [])
-
-                # 将邻居的当前内存标签注入（覆盖 Neo4j 中的旧值）
-                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, embeddings.get(eid))
-                if new_label and new_label != labels[eid]:
-                    labels[eid] = new_label
-                    changed += 1
-
-            logger.info(
-                f"[Clustering] 全量迭代 {iteration + 1}/{MAX_ITERATIONS}，"
-                f"标签变化数: {changed}"
+        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 changed == 0:
-                logger.info("[Clustering] 标签已收敛，提前结束迭代")
+            if not batch_entities:
                 break
 
-        # 将最终标签写入 Neo4j
+            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(
@@ -164,7 +187,6 @@ class LabelPropagationEngine:
             f"{len(labels)} 个实体，开始后处理合并"
         )
 
-        # 全量初始化后做一轮社区合并（基于 name_embedding 余弦相似度）
         all_community_ids = list(set(labels.values()))
         await self._evaluate_merge(all_community_ids, end_user_id)
 
@@ -172,17 +194,15 @@ class LabelPropagationEngine:
             f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，"
             f"{len(labels)} 个实体"
         )
-        # 为所有社区生成元数据
-        # 注意：_evaluate_merge 后部分社区已被合并消解，需重新从 Neo4j 查询实际存活的社区
-        # 不能复用 labels.values()，那里包含已被 dissolve 的旧社区 ID
+
+        # 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)}")
-        for cid in surviving_community_ids:
-            await self._generate_community_metadata(cid, end_user_id)
+        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
@@ -239,7 +259,7 @@ class LabelPropagationEngine:
             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)
+            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)
@@ -251,7 +271,7 @@ class LabelPropagationEngine:
                 await self._evaluate_merge(
                     list(community_ids_in_neighbors), end_user_id
                 )
-            await self._generate_community_metadata(target_cid, 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
@@ -415,93 +435,137 @@ class LabelPropagationEngine:
         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_id: str, end_user_id: str
+        self, community_ids: List[str], end_user_id: str
     ) -> None:
         """
-        为社区生成并写入元数据：名称、摘要、核心实体。
+        为一个或多个社区生成并写入元数据。
 
-        - core_entities：按 activation_value 排序取 top-N 实体名称列表（无需 LLM）
-        - name / summary：若有 llm_model_id 则调用 LLM 生成，否则用实体名称拼接兜底
+        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
         """
-        try:
-            # 先检查属性是否已完整，完整则跳过，避免重复生成
-            check_embedding = bool(self.embedding_model_id)
-            if await self.repo.is_community_complete(community_id, end_user_id, check_embedding=check_embedding):
-                logger.debug(f"[Clustering] 社区 {community_id} 属性已完整，跳过生成")
-                return
+        if not community_ids:
+            return
 
-            members = await self.repo.get_community_members(community_id, end_user_id)
+        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
+                return None
 
-            # 核心实体：按 activation_value 降序取 top-N
             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")]
-            all_names = [m["name"] for m in members if m.get("name")]
 
-            name = "、".join(core_entities[:3]) if core_entities else community_id[:8]
-            summary = f"包含实体：{', '.join(all_names)}"
+            entity_list_str = "\n".join(self._build_entity_lines(members))
 
-            # 若有 LLM 配置，调用 LLM 生成更好的名称和摘要
-            if self.llm_model_id:
-                try:
-                    from app.db import get_db_context
-                    from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-
-                    entity_list_str = "、".join(all_names)
-                    prompt = (
-                        f"以下是一组语义相关的实体：{entity_list_str}\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50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:
-                        factory = MemoryClientFactory(db)
-                        llm_client = factory.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)
-
-                    for line in text.strip().splitlines():
-                        if line.startswith("名称："):
-                            name = line[3:].strip()
-                        elif line.startswith("摘要："):
-                            summary = line[3:].strip()
-                except Exception as e:
-                    logger.warning(f"[Clustering] LLM 生成社区元数据失败，使用兜底值: {e}")
-
-            # 生成 summary_embedding
-            summary_embedding: Optional[List[float]] = None
-            if self.embedding_model_id and summary:
-                try:
-                    from app.db import get_db_context
-                    from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-
-                    with get_db_context() as db:
-                        embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
-                        vectors = await embedder.response([summary])
-                        if vectors:
-                            summary_embedding = vectors[0]
-                except Exception as e:
-                    logger.warning(f"[Clustering] 社区 {community_id} 生成 summary_embedding 失败: {e}")
-
-            await self.repo.update_community_metadata(
-                community_id=community_id,
-                end_user_id=end_user_id,
-                name=name,
-                summary=summary,
-                core_entities=core_entities,
-                summary_embedding=summary_embedding,
+            # 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 ""
             )
-            logger.debug(f"[Clustering] 社区 {community_id} 元数据已更新: name={name}")
-        except Exception as e:
-            logger.error(f"[Clustering] _generate_community_metadata failed for {community_id}: {e}")
+
+            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:

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,
@@ -34,6 +33,8 @@ 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)
@@ -43,6 +44,7 @@ class DialogExtractionResponse(BaseModel):
     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):
@@ -91,12 +93,14 @@ class SemanticPruner:
         # 加载统一填充词库
         self.scene_config: ScenePatterns = SceneConfigRegistry.get_config(self.config.pruning_scene)
         
-        # 本体类型列表（用于注入提示词，所有场景均支持）
-        self._ontology_classes = getattr(self.config, "ontology_classes", None) or []
+        # 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]
         
         self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene}")
-        if self._ontology_classes:
-            self._log(f"[剪枝-初始化] 注入本体类型: {self._ontology_classes}")
+        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] 未找到本体类型，将使用通用提示词")
         
@@ -121,7 +125,8 @@ class SemanticPruner:
         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. 纯表情/标点
+        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:
@@ -143,6 +148,55 @@ 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
@@ -331,13 +385,13 @@ class SemanticPruner:
 
         rendered = self.template.render(
             pruning_scene=self.config.pruning_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,
-            "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)
@@ -377,6 +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:
@@ -397,16 +628,18 @@ 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，LLM 已通过 preserve_tokens 标记需要保护的内容
-        preserve_tokens = (
-            extraction.times + extraction.ids + extraction.amounts +
-            extraction.contacts + extraction.addresses + extraction.keywords +
-            extraction.preserve_keywords
-        )
+        preserve_tokens = self._build_preserve_tokens(extraction)
         msgs = dialog.context.msgs
 
         # 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）
@@ -481,11 +714,30 @@ 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
 
+        # 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)
 
@@ -505,12 +757,31 @@ class SemanticPruner:
             original_count = len(msgs)
             total_original_msgs += original_count
 
+            # 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 = (
-                extraction.times + extraction.ids + extraction.amounts +
-                extraction.contacts + extraction.addresses + extraction.keywords +
-                extraction.preserve_keywords  # 情绪/兴趣/爱好关键词
-            )
+            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
@@ -543,16 +814,16 @@ class SemanticPruner:
 
             # 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：优先删填充消息，再按出现顺序删其余可删消息
             to_delete_indices = set()
             deleted_details = []
@@ -570,50 +841,65 @@ class SemanticPruner:
                     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"(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}")
 
@@ -621,7 +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:
@@ -633,114 +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/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/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, 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）
@@ -18,20 +19,16 @@
 #}
 
 {# ── 确定场景说明 ── #}
-{% if ontology_classes and ontology_classes | length > 0 %}
+{% if ontology_class_infos and ontology_class_infos | length > 0 %}
   {% if language == 'en' %}
-    {% set custom_types_str = ontology_classes | join(', ') %}
-    {% set instruction = 'Scene "' ~ pruning_scene ~ '": The dialogue is related to this scene if it involves any of the following entity types: ' ~ custom_types_str ~ '.' %}
+    {% set instruction = 'Scene "' ~ pruning_scene ~ '": The dialogue is relevant if it involves any of the following entity types.' %}
   {% else %}
-    {% set custom_types_str = ontology_classes | join('、') %}
-    {% set instruction = '场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关：' ~ 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 custom_types_str = '' %}
     {% set instruction = 'Scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %}
   {% else %}
-    {% set custom_types_str = '' %}
     {% 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 %}
@@ -42,8 +39,17 @@
 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 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 %}
 
 ---
@@ -51,13 +57,40 @@
 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） → amounts 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：与场景强相关的专业术语、事件名称 → keywords 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
-- **个人观点与态度**：对某事物的明确看法、评价、立场 → 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：
@@ -88,7 +121,8 @@
   "contacts": [<string>...],
   "addresses": [<string>...],
   "keywords": [<string>...],
-  "preserve_keywords": [<string>...]
+  "preserve_keywords": [<string>...],
+  "scene_unrelated_snippets": [<string>...]
 }
 {% else %}
 You are a dialogue content analysis assistant. Please analyze the full dialogue below in one pass and complete two tasks:
@@ -96,8 +130,17 @@ You are a dialogue content analysis assistant. Please analyze the full dialogue
 2. Extract all important information fragments that must be preserved.
 
 Scenario Description: {{ instruction }}
-{% if 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 %}
 
 ---
@@ -105,13 +148,22 @@ Important: If the dialogue contains content related to any of the entity types a
 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) → amounts 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 scene → keywords 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 opinions and attitudes**: clear views, evaluations, or stances on something → 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:
@@ -141,6 +193,7 @@ Output strict JSON only (fixed keys, order doesn't matter):
   "contacts": [<string>...],
   "addresses": [<string>...],
   "keywords": [<string>...],
-  "preserve_keywords": [<string>...]
+  "preserve_keywords": [<string>...],
+  "scene_unrelated_snippets": [<string>...]
 }
 {% endif %}

api/app/core/storage/base.py

@@ -121,18 +121,3 @@ class StorageBackend(ABC):
             URL for accessing the file.
         """
         pass
-
-    async def get_permanent_url(self, file_key: str) -> Optional[str]:
-        """
-        Get a permanent public URL for the file (no expiration).
-
-        Returns None by default; remote storage backends should override this
-        if the bucket is configured for public read access.
-
-        Args:
-            file_key: Unique identifier for the file in the storage system.
-
-        Returns:
-            A permanent public URL, or None if not supported.
-        """
-        return None

api/app/core/storage/oss.py

@@ -261,13 +261,3 @@ class OSSStorage(StorageBackend):
             logger.error(f"Failed to generate presigned URL for {file_key}: {e}")
             # Return a basic URL format as fallback
             return f"https://{self.bucket_name}.{self.endpoint.replace('https://', '').replace('http://', '')}/{file_key}"
-
-    async def get_permanent_url(self, file_key: str) -> str:
-        """
-        Get a permanent public URL for the file (requires bucket public read).
-
-        Returns:
-            A permanent URL in the format: https://{bucket}.{endpoint}/{file_key}
-        """
-        host = self.endpoint.replace("https://", "").replace("http://", "")
-        return f"https://{self.bucket_name}.{host}/{file_key}"

api/app/core/storage/s3.py

@@ -378,12 +378,3 @@ class S3Storage(StorageBackend):
             logger.error(f"Failed to generate presigned URL for {file_key}: {e}")
             # Return a basic URL format as fallback
             return f"https://{self.bucket_name}.s3.{self.region}.amazonaws.com/{file_key}"
-
-    async def get_permanent_url(self, file_key: str) -> str:
-        """
-        Get a permanent public URL for the file (requires bucket public read).
-
-        Returns:
-            A permanent URL in the format: https://{bucket}.s3.{region}.amazonaws.com/{file_key}
-        """
-        return f"https://{self.bucket_name}.s3.{self.region}.amazonaws.com/{file_key}"

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

@@ -3,6 +3,7 @@
 # @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, PrivateAttr
@@ -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"
         )
     )
@@ -46,8 +47,8 @@ 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"
         )
     )
@@ -86,12 +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."
         )
@@ -100,17 +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."
         )
@@ -119,49 +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 == 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
@@ -174,12 +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):
@@ -211,8 +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,
@@ -256,7 +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
 
@@ -273,7 +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": {
@@ -285,8 +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,
@@ -325,6 +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/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/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/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

@@ -31,13 +31,13 @@ class IfElseNode(BaseNode):
                 expressions.append({
                     "left": self.get_variable(expression.left, variable_pool, strict=False),
                     "right": expression.right
-                    if expression.input_type == ValueInputType.CONSTANT
+                    if expression.input_type == ValueInputType.CONSTANT or expression.right is None
                     else self.get_variable(expression.right, variable_pool, strict=False),
-                    "operator": expression.operator,
+                    "operator": str(expression.operator),
                 })
             result.append({
                 "expressions": expressions,
-                "logical_operator": case.logical_operator,
+                "logical_operator": str(case.logical_operator),
             })
         return {
             "cases": result

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

@@ -5,7 +5,7 @@ from typing import Any
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.core.models import RedBearRerank, RedBearModelConfig
-from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory, ElasticSearchVector
+from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
 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
@@ -24,7 +24,6 @@ class KnowledgeRetrievalNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
         super().__init__(node_config, workflow_config)
         self.typed_config: KnowledgeRetrievalNodeConfig | None = None
-        self.vector_service: ElasticSearchVector | None = None
 
     def _output_types(self) -> dict[str, VariableType]:
         return {
@@ -164,50 +163,6 @@ class KnowledgeRetrievalNode(BaseNode):
         )
         return reranker
 
-    def knowledge_retrieval(self, db, query, rs, db_knowledge, kb_config):
-        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
-                kb_config.kb_id = child.id
-                self.knowledge_retrieval(db, query, rs, child, kb_config)
-            return
-        self.vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
-        indices = f"Vector_index_{kb_config.kb_id}_Node".lower()
-        match kb_config.retrieve_type:
-            case RetrieveType.PARTICIPLE:
-                rs.extend(self.vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
-                                                                  indices=indices,
-                                                                  score_threshold=kb_config.similarity_threshold))
-            case RetrieveType.SEMANTIC:
-                rs.extend(self.vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
-                                                               indices=indices,
-                                                               score_threshold=kb_config.vector_similarity_weight))
-            case RetrieveType.HYBRID:
-                rs1 = self.vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
-                                                           indices=indices,
-                                                           score_threshold=kb_config.vector_similarity_weight)
-                rs2 = self.vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
-                                                              indices=indices,
-                                                              score_threshold=kb_config.similarity_threshold)
-
-                # Deduplicate hybrid retrieval results
-                unique_rs = self._deduplicate_docs(rs1, rs2)
-                if not unique_rs:
-                    return
-                if self.typed_config.reranker_id:
-                    self.vector_service.reranker = self.get_reranker_model()
-                    rs.extend(self.vector_service.rerank(query=query, docs=unique_rs, top_k=kb_config.top_k))
-                else:
-                    rs.extend(sorted(
-                        unique_rs,
-                        key=lambda d: d.metadata.get("score", 0),
-                        reverse=True
-                    )[:kb_config.top_k])
-            case _:
-                raise RuntimeError("Unknown retrieval type")
-
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
         """
         Execute the knowledge retrieval workflow node.
@@ -236,19 +191,56 @@ class KnowledgeRetrievalNode(BaseNode):
         query = self._render_template(self.typed_config.query, variable_pool)
         with get_db_read() as db:
             knowledge_bases = self.typed_config.knowledge_bases
+            existing_ids = self._get_existing_kb_ids(db, [kb.kb_id for kb in knowledge_bases])
+
+            if not existing_ids:
+                raise RuntimeError("Knowledge base retrieval failed: the knowledge base does not exist.")
 
             rs = []
             for kb_config in knowledge_bases:
                 db_knowledge = knowledge_repository.get_knowledge_by_id(db=db, knowledge_id=kb_config.kb_id)
                 if not db_knowledge:
                     raise RuntimeError("The knowledge base does not exist or access is denied.")
-                self.knowledge_retrieval(db, query, rs, db_knowledge, kb_config)
 
+                vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+                indices = f"Vector_index_{kb_config.kb_id}_Node".lower()
+                match kb_config.retrieve_type:
+                    case RetrieveType.PARTICIPLE:
+                        rs.extend(vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
+                                                                     indices=indices,
+                                                                     score_threshold=kb_config.similarity_threshold))
+                    case RetrieveType.SEMANTIC:
+                        rs.extend(vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
+                                                                  indices=indices,
+                                                                  score_threshold=kb_config.vector_similarity_weight))
+                    case RetrieveType.HYBRID:
+                        rs1 = vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
+                                                              indices=indices,
+                                                              score_threshold=kb_config.vector_similarity_weight)
+                        rs2 = vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
+                                                                 indices=indices,
+                                                                 score_threshold=kb_config.similarity_threshold)
+
+                        # Deduplicate hy    brid retrieval results
+                        unique_rs = self._deduplicate_docs(rs1, rs2)
+                        if not unique_rs:
+                            continue
+                        if self.typed_config.reranker_id:
+                            vector_service.reranker = self.get_reranker_model()
+                            rs.extend(vector_service.rerank(query=query, docs=unique_rs, top_k=kb_config.top_k))
+                        else:
+                            rs.extend(sorted(
+                                unique_rs,
+                                key=lambda d: d.metadata.get("score", 0),
+                                reverse=True
+                            )[:kb_config.top_k])
+                    case _:
+                        raise RuntimeError("Unknown retrieval type")
             if not rs:
                 return []
             if self.typed_config.reranker_id:
-                self.vector_service.reranker = self.get_reranker_model()
-                final_rs = self.vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
+                vector_service.reranker = self.get_reranker_model()
+                final_rs = vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
             else:
                 final_rs = sorted(
                     rs,

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/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):
@@ -116,7 +116,7 @@ class FileObject(BaseModel):
     content_cache: dict = Field(default_factory=dict)
     is_file: bool
 
-    _byte_content: bytes | None = None
+    _byte_content: bytes | None = PrivateAttr(default=None)
 
     def get_content(self):
         return self._byte_content

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

@@ -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/main.py

@@ -506,13 +506,10 @@ async def http_exception_handler(request: Request, exc: HTTPException):
         404: "errors.common.not_found",
         405: "errors.common.method_not_allowed",
         409: "errors.common.conflict",
-        413: "errors.common.payload_too_large",
         422: "errors.common.validation_failed",
         429: "errors.common.too_many_requests",
         500: "errors.common.internal_error",
-        502: "errors.common.bad_gateway",
         503: "errors.common.service_unavailable",
-        504: "errors.common.gateway_timeout",
     }
     
     # 如果有对应的翻译键，使用翻译
@@ -537,7 +534,7 @@ async def http_exception_handler(request: Request, exc: HTTPException):
     
     return JSONResponse(
         status_code=exc.status_code,
-        content=fail(code=exc.status_code, msg=translated_message, error=exc.detail)
+        content=fail(code=exc.status_code, msg=translated_message, error=translated_message)
     )
 
 

api/app/models/memory_perceptual_model.py

@@ -9,7 +9,6 @@ from sqlalchemy.dialects.postgresql import JSONB
 from app.db import Base
 from app.schemas import FileType
 
-
 class PerceptualType(IntEnum):
     VISION = 1
     AUDIO = 2

api/app/repositories/conversation_repository.py

@@ -90,27 +90,27 @@ class ConversationRepository:
             self,
             user_id: uuid.UUID,
             workspace_id: uuid.UUID = None,
-            is_activate: bool = True,
-            page: int = 1,
-            page_size: int = 20
-    ) -> tuple[list[Conversation], int]:
+            limit: int = 10,
+            is_activate: bool = True
+    ) -> list[Conversation]:
         """
-        Retrieve recent conversations for a specific user with pagination.
+        Retrieve recent conversations for a specific user.
 
         This method queries conversations associated with the given user ID,
         optionally scoped to a specific workspace. Results are ordered by the
-        most recently updated conversations.
+        most recently updated conversations and limited to a fixed number.
 
         Args:
             user_id (uuid.UUID): Unique identifier of the user.
             workspace_id (uuid.UUID, optional): Workspace scope for the query.
                 If provided, only conversations under this workspace will be returned.
-            is_activate (bool): Conversation State limit.
-            page (int): Page number (1-based). Defaults to 1.
-            page_size (int): Number of items per page. Defaults to 20.
+            limit (int): Maximum number of conversations to return.
+                Defaults to 10.
+            is_activate (bool): Convsersation State limit
 
         Returns:
-            tuple[list[Conversation], int]: A list of conversation entities and total count.
+            list[Conversation]: A list of conversation entities ordered by
+            last updated time (descending).
         """
         logger.info(f"Fetching conversation by user_id: {user_id}")
 
@@ -122,25 +122,18 @@ class ConversationRepository:
         if workspace_id:
             stmt = stmt.where(Conversation.workspace_id == workspace_id)
 
-        # Calculate total count
-        total = int(self.db.execute(
-            select(func.count()).select_from(stmt.subquery())
-        ).scalar_one())
-
-        # Apply ordering and pagination
         stmt = stmt.order_by(desc(Conversation.updated_at))
-        stmt = stmt.offset((page - 1) * page_size).limit(page_size)
+        stmt = stmt.limit(limit)
 
-        conversations = list(self.db.scalars(stmt).all())
+        convsersations = list(self.db.scalars(stmt).all())
         logger.info(
             "Conversation fetched successfully",
             extra={
                 "user_id": str(user_id),
                 "workspace_id": str(workspace_id),
-                "total": total,
             }
         )
-        return conversations, total
+        return convsersations
 
     def list_conversations(
             self,

api/app/repositories/neo4j/community_repository.py

@@ -13,16 +13,18 @@ from app.repositories.neo4j.cypher_queries import (
     ENTITY_LEAVE_ALL_COMMUNITIES,
     GET_ENTITY_NEIGHBORS,
     GET_ALL_ENTITIES_FOR_USER,
+    GET_ENTITY_COUNT_FOR_USER,
+    GET_ALL_ENTITY_IDS_FOR_USER,
+    GET_ENTITIES_PAGE,
     GET_COMMUNITY_MEMBERS,
+    GET_COMMUNITY_RELATIONSHIPS,
     GET_ALL_COMMUNITY_MEMBERS_BATCH,
     GET_ALL_ENTITY_NEIGHBORS_BATCH,
+    GET_ENTITY_NEIGHBORS_BATCH_FOR_IDS,
     CHECK_USER_HAS_COMMUNITIES,
     UPDATE_COMMUNITY_MEMBER_COUNT,
     UPDATE_COMMUNITY_METADATA,
-    GET_INCOMPLETE_COMMUNITIES,
-    GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING,
-    CHECK_COMMUNITY_IS_COMPLETE,
-    CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING,
+    BATCH_UPDATE_COMMUNITY_METADATA,
 )
 
 logger = logging.getLogger(__name__)
@@ -114,10 +116,69 @@ class CommunityRepository:
             logger.error(f"get_all_entities failed: {e}")
             return []
 
+    async def get_entity_count(self, end_user_id: str) -> int:
+        """仅返回用户实体总数，不加载实体数据。"""
+        try:
+            result = await self.connector.execute_query(
+                GET_ENTITY_COUNT_FOR_USER,
+                end_user_id=end_user_id,
+            )
+            return result[0]["entity_count"] if result else 0
+        except Exception as e:
+            logger.error(f"get_entity_count failed: {e}")
+            return 0
+
+    async def get_all_entity_ids(self, end_user_id: str) -> List[str]:
+        """仅返回用户所有实体 ID 列表，不加载 embedding 等大字段。"""
+        try:
+            result = await self.connector.execute_query(
+                GET_ALL_ENTITY_IDS_FOR_USER,
+                end_user_id=end_user_id,
+            )
+            return [r["id"] for r in result]
+        except Exception as e:
+            logger.error(f"get_all_entity_ids failed: {e}")
+            return []
+
+    async def get_entities_page(
+        self, end_user_id: str, skip: int, limit: int
+    ) -> List[Dict]:
+        """分页拉取实体，用于全量聚类分批处理。"""
+        try:
+            return await self.connector.execute_query(
+                GET_ENTITIES_PAGE,
+                end_user_id=end_user_id,
+                skip=skip,
+                limit=limit,
+            )
+        except Exception as e:
+            logger.error(f"get_entities_page failed: {e}")
+            return []
+
+    async def get_entity_neighbors_for_ids(
+        self, entity_ids: List[str], end_user_id: str
+    ) -> Dict[str, List[Dict]]:
+        """批量拉取指定实体列表的邻居，返回 {entity_id: [neighbors]}。"""
+        try:
+            rows = await self.connector.execute_query(
+                GET_ENTITY_NEIGHBORS_BATCH_FOR_IDS,
+                entity_ids=entity_ids,
+                end_user_id=end_user_id,
+            )
+            result: Dict[str, List[Dict]] = {}
+            for row in rows:
+                eid = row["entity_id"]
+                neighbor = {k: v for k, v in row.items() if k != "entity_id"}
+                result.setdefault(eid, []).append(neighbor)
+            return result
+        except Exception as e:
+            logger.error(f"get_entity_neighbors_for_ids failed: {e}")
+            return {}
+
     async def get_community_members(
         self, community_id: str, end_user_id: str
     ) -> List[Dict]:
-        """查询社区成员列表。"""
+        """查询社区成员列表（含 example 字段）。"""
         try:
             return await self.connector.execute_query(
                 GET_COMMUNITY_MEMBERS,
@@ -128,6 +189,20 @@ class CommunityRepository:
             logger.error(f"get_community_members failed: {e}")
             return []
 
+    async def get_community_relationships(
+        self, community_id: str, end_user_id: str
+    ) -> List[Dict]:
+        """查询社区内实体间的关系三元组（subject, predicate, object）。"""
+        try:
+            return await self.connector.execute_query(
+                GET_COMMUNITY_RELATIONSHIPS,
+                community_id=community_id,
+                end_user_id=end_user_id,
+            )
+        except Exception as e:
+            logger.error(f"get_community_relationships failed: {e}")
+            return []
+
     async def get_all_community_members_batch(
         self, community_ids: List[str], end_user_id: str
     ) -> Dict[str, List[Dict]]:
@@ -174,31 +249,6 @@ class CommunityRepository:
             logger.error(f"refresh_member_count failed: {e}")
             return 0
 
-    async def get_incomplete_communities(self, end_user_id: str, check_embedding: bool = False) -> List[str]:
-        """查询该用户下属性不完整的 Community 节点 ID 列表。
-
-        Args:
-            end_user_id: 用户 ID
-            check_embedding: 为 True 时额外检查 summary_embedding 是否缺失（仅当用户有 embedding 模型配置时传 True）
-        """
-        try:
-            query = GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING if check_embedding else GET_INCOMPLETE_COMMUNITIES
-            result = await self.connector.execute_query(query, end_user_id=end_user_id)
-            return [row["community_id"] for row in result]
-        except Exception as e:
-            logger.error(f"get_incomplete_communities failed: {e}")
-            return []
-
-    async def is_community_complete(self, community_id: str, end_user_id: str, check_embedding: bool = False) -> bool:
-        """检查单个社区节点的属性是否完整。"""
-        try:
-            query = CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING if check_embedding else CHECK_COMMUNITY_IS_COMPLETE
-            result = await self.connector.execute_query(query, community_id=community_id, end_user_id=end_user_id)
-            return result[0]["is_complete"] if result else False
-        except Exception as e:
-            logger.error(f"is_community_complete failed: {e}")
-            return False
-
     async def update_community_metadata(
         self,
         community_id: str,
@@ -208,7 +258,7 @@ class CommunityRepository:
         core_entities: List[str],
         summary_embedding: Optional[List[float]] = None,
     ) -> bool:
-        """更新社区的名称、摘要、核心实体列表及 summary_embedding。"""
+        """更新社区的名称、摘要、核心实体列表和摘要向量。"""
         try:
             result = await self.connector.execute_query(
                 UPDATE_COMMUNITY_METADATA,
@@ -223,3 +273,25 @@ class CommunityRepository:
         except Exception as e:
             logger.error(f"update_community_metadata failed: {e}")
             return False
+
+    async def batch_update_community_metadata(
+        self,
+        communities: List[Dict],
+    ) -> bool:
+        """批量更新多个社区的元数据。
+
+        Args:
+            communities: 每项包含 community_id, end_user_id, name, summary,
+                         core_entities, summary_embedding
+        """
+        if not communities:
+            return True
+        try:
+            await self.connector.execute_query(
+                BATCH_UPDATE_COMMUNITY_METADATA,
+                communities=communities,
+            )
+            return True
+        except Exception as e:
+            logger.error(f"batch_update_community_metadata failed: {e}")
+            return False

api/app/repositories/neo4j/create_indexes.py

@@ -42,6 +42,13 @@ async def create_fulltext_indexes():
             OPTIONS { indexConfig: { `fulltext.analyzer`: 'cjk' } }
         """)
         print("✓ Created: summariesFulltext")
+
+        # 创建 Community 索引
+        await connector.execute_query("""
+            CREATE FULLTEXT INDEX communitiesFulltext IF NOT EXISTS FOR (c:Community) ON EACH [c.name, c.summary]
+            OPTIONS { indexConfig: { `fulltext.analyzer`: 'cjk' } }
+        """)
+        print("✓ Created: communitiesFulltext")
         
         print("\nFull-text indexes created successfully with BM25 support.")
     except Exception as e:
@@ -112,6 +119,18 @@ async def create_vector_indexes():
             }}
         """)
         print("✓ Created: summary_embedding_index")
+
+        # Community summary embedding index
+        await connector.execute_query("""
+            CREATE VECTOR INDEX community_summary_embedding_index IF NOT EXISTS
+            FOR (c:Community)
+            ON c.summary_embedding
+            OPTIONS {indexConfig: {
+              `vector.dimensions`: 1024,
+              `vector.similarity_function`: 'cosine'
+            }}
+        """)
+        print("✓ Created: community_summary_embedding_index")
         
         # Dialogue embedding index (optional)
         await connector.execute_query("""
@@ -124,6 +143,18 @@ async def create_vector_indexes():
             }}
         """)
         print("✓ Created: dialogue_embedding_index")
+
+        # Community summary embedding index
+        await connector.execute_query("""
+            CREATE VECTOR INDEX community_summary_embedding_index IF NOT EXISTS
+            FOR (c:Community)
+            ON c.summary_embedding
+            OPTIONS {indexConfig: {
+              `vector.dimensions`: 1024,
+              `vector.similarity_function`: 'cosine'
+            }}
+        """)
+        print("✓ Created: community_summary_embedding_index")
         
         print("\nVector indexes created successfully!")
         print("\nExpected performance improvement:")

api/app/repositories/neo4j/cypher_queries.py

@@ -1122,21 +1122,43 @@ RETURN e.id AS id,
        CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
 """
 
+GET_ENTITY_COUNT_FOR_USER = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+RETURN count(e) AS entity_count
+"""
+
+GET_ALL_ENTITY_IDS_FOR_USER = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+RETURN e.id AS id
+"""
+
 GET_COMMUNITY_MEMBERS = """
 MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id})
 RETURN e.id AS id, e.name AS name, e.entity_type AS entity_type,
        e.importance_score AS importance_score, e.activation_value AS activation_value,
-       e.name_embedding AS name_embedding
+       e.name_embedding AS name_embedding,
+       e.aliases AS aliases, e.description AS description,
+       e.example AS example
 ORDER BY coalesce(e.activation_value, 0) DESC
 """
 
+GET_COMMUNITY_RELATIONSHIPS = """
+MATCH (e1:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id})
+MATCH (e2:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c)
+MATCH (e1)-[r:EXTRACTED_RELATIONSHIP]->(e2)
+RETURN e1.name AS subject, r.predicate AS predicate, e2.name AS object
+ORDER BY e1.name, r.predicate, e2.name
+LIMIT 20
+"""
+
 GET_ALL_COMMUNITY_MEMBERS_BATCH = """
 MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community)
-WHERE c.community_id IN $community_ids
 RETURN c.community_id AS community_id,
-       e.id AS id,
+       e.id AS id, e.name AS name, e.entity_type AS entity_type,
+       e.importance_score AS importance_score, e.activation_value AS activation_value,
        e.name_embedding AS name_embedding,
-       e.activation_value AS activation_value
+       e.aliases AS aliases, e.description AS description
+ORDER BY c.community_id, coalesce(e.activation_value, 0) DESC
 """
 
 CHECK_USER_HAS_COMMUNITIES = """
@@ -1161,6 +1183,50 @@ SET c.name             = $name,
 RETURN c.community_id AS community_id
 """
 
+BATCH_UPDATE_COMMUNITY_METADATA = """
+UNWIND $communities AS row
+MATCH (c:Community {community_id: row.community_id, end_user_id: row.end_user_id})
+SET c.name             = row.name,
+    c.summary          = row.summary,
+    c.core_entities    = row.core_entities,
+    c.summary_embedding = row.summary_embedding,
+    c.updated_at       = datetime()
+RETURN c.community_id AS community_id
+"""
+
+GET_ENTITIES_PAGE = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+OPTIONAL MATCH (e)-[:BELONGS_TO_COMMUNITY]->(c:Community)
+RETURN e.id AS id,
+       e.name AS name,
+       e.name_embedding AS name_embedding,
+       e.activation_value AS activation_value,
+       CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
+ORDER BY e.id
+SKIP $skip LIMIT $limit
+"""
+
+GET_ENTITY_NEIGHBORS_BATCH_FOR_IDS = """
+// 批量拉取指定实体列表的邻居（用于分批全量聚类）
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+WHERE e.id IN $entity_ids
+OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id})
+OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e)
+OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(nb2:ExtractedEntity {end_user_id: $end_user_id})
+WHERE nb2.id <> e.id
+WITH e, collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors
+UNWIND all_neighbors AS nb
+WITH e, nb WHERE nb IS NOT NULL
+OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community)
+RETURN DISTINCT
+    e.id                AS entity_id,
+    nb.id               AS id,
+    nb.name             AS name,
+    nb.name_embedding   AS name_embedding,
+    nb.activation_value AS activation_value,
+    CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
+"""
+
 GET_ALL_ENTITY_NEIGHBORS_BATCH = """
 // 批量拉取某用户下所有实体的邻居（用于全量聚类预加载）
 MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
@@ -1204,37 +1270,59 @@ RETURN
     startNode(r) = e      AS r_from_e
 """
 
-CHECK_COMMUNITY_IS_COMPLETE = """
-MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
-RETURN (
-    c.name IS NOT NULL AND c.name <> '' AND
-    c.summary IS NOT NULL AND c.summary <> '' AND
-    c.core_entities IS NOT NULL
-) AS is_complete
+
+# Community keyword search: matches name or summary via fulltext index
+SEARCH_COMMUNITIES_BY_KEYWORD = """
+CALL db.index.fulltext.queryNodes("communitiesFulltext", $q) YIELD node AS c, score
+WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
+RETURN c.community_id AS id,
+       c.name AS name,
+       c.summary AS content,
+       c.core_entities AS core_entities,
+       c.member_count AS member_count,
+       c.end_user_id AS end_user_id,
+       c.updated_at AS updated_at,
+       score
+ORDER BY score DESC
+LIMIT $limit
 """
 
-CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING = """
-MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
-RETURN (
-    c.name IS NOT NULL AND c.name <> '' AND
-    c.summary IS NOT NULL AND c.summary <> '' AND
-    c.core_entities IS NOT NULL AND
-    c.summary_embedding IS NOT NULL
-) AS is_complete
+# Community 向量检索 ──────────────────────────────────────────────────
+# Community embedding-based search: cosine similarity on Community.summary_embedding
+COMMUNITY_EMBEDDING_SEARCH = """
+CALL db.index.vector.queryNodes('community_summary_embedding_index', $limit * 100, $embedding)
+YIELD node AS c, score
+WHERE c.summary_embedding IS NOT NULL
+  AND ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
+RETURN c.community_id AS id,
+       c.name AS name,
+       c.summary AS content,
+       c.core_entities AS core_entities,
+       c.member_count AS member_count,
+       c.end_user_id AS end_user_id,
+       c.updated_at AS updated_at,
+       score
+ORDER BY score DESC
+LIMIT $limit
 """
 
-GET_INCOMPLETE_COMMUNITIES = """
-MATCH (c:Community {end_user_id: $end_user_id})
-WHERE c.name IS NULL OR c.summary IS NULL OR c.core_entities IS NULL
-   OR c.name = '' OR c.summary = ''
-RETURN c.community_id AS community_id
-"""
-
-GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING = """
-MATCH (c:Community {end_user_id: $end_user_id})
-WHERE c.name IS NULL OR c.name = ''
-   OR c.summary IS NULL OR c.summary = ''
-   OR c.core_entities IS NULL
-   OR (c.summary_embedding IS NULL AND c.summary IS NOT NULL AND c.summary <> '(empty)')
-RETURN c.community_id AS community_id
+# Community 展开检索 ──────────────────────────────────────────────────
+# 命中社区后，拉取该社区所有成员实体关联的 Statement 节点（主题→细节两级检索）
+EXPAND_COMMUNITY_STATEMENTS = """
+MATCH (c:Community {community_id: $community_id})
+MATCH (e:ExtractedEntity)-[:BELONGS_TO_COMMUNITY]->(c)
+MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e)
+WHERE s.end_user_id = $end_user_id
+RETURN s.statement AS statement,
+       s.id AS id,
+       s.end_user_id AS end_user_id,
+       s.created_at AS created_at,
+       s.valid_at AS valid_at,
+       s.invalid_at AS invalid_at,
+       COALESCE(s.activation_value, s.importance_score, 0.5) AS activation_value,
+       COALESCE(s.importance_score, 0.5) AS importance_score,
+       e.name AS source_entity,
+       c.name AS community_name
+ORDER BY COALESCE(s.activation_value, 0) DESC
+LIMIT $limit
 """

api/app/repositories/neo4j/graph_saver.py

@@ -158,11 +158,12 @@ async def save_dialog_and_statements_to_neo4j(
         statement_chunk_edges: List[StatementChunkEdge],
         statement_entity_edges: List[StatementEntityEdge],
         connector: Neo4jConnector,
-        config_id: Optional[str] = None,
-        llm_model_id: Optional[str] = None,
 ) -> bool:
     """Save dialogue nodes, chunk nodes, statement nodes, entities, and all relationships to Neo4j using graph models.
 
+    只负责数据写入，不触发聚类。聚类由调用方在写入成功后通过
+    schedule_clustering_after_write() 显式触发。
+
     Args:
         dialogue_nodes: List of DialogueNode objects to save
         chunk_nodes: List of ChunkNode objects to save
@@ -293,9 +294,6 @@ async def save_dialog_and_statements_to_neo4j(
         logger.info("Transaction completed. Summary: %s", summary)
         logger.debug("Full transaction results: %r", results)
 
-        # 写入成功后，异步触发聚类（不阻塞写入响应）
-        schedule_clustering_after_write(entity_nodes, config_id=config_id, llm_model_id=llm_model_id)
-
         return True
 
     except Exception as e:
@@ -307,8 +305,8 @@ async def save_dialog_and_statements_to_neo4j(
 
 def schedule_clustering_after_write(
     entity_nodes: List,
-    config_id: Optional[str] = None,
     llm_model_id: Optional[str] = None,
+    embedding_model_id: Optional[str] = None,
 ) -> None:
     """
     写入 Neo4j 成功后，调度后台聚类任务。
@@ -327,14 +325,14 @@ def schedule_clustering_after_write(
     end_user_id = entity_nodes[0].end_user_id
     new_entity_ids = [e.id for e in entity_nodes]
     logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
-    asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, config_id=config_id, llm_model_id=llm_model_id))
+    asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, llm_model_id=llm_model_id, embedding_model_id=embedding_model_id))
 
 
 async def _trigger_clustering(
     new_entity_ids: List[str],
     end_user_id: str,
-    config_id: Optional[str] = None,
     llm_model_id: Optional[str] = None,
+    embedding_model_id: Optional[str] = None,
 ) -> None:
     """
     聚类触发函数，自动判断全量初始化还是增量更新。
@@ -344,7 +342,7 @@ async def _trigger_clustering(
         from app.core.memory.storage_services.clustering_engine import LabelPropagationEngine
         logger.info(f"[Clustering] 开始聚类，end_user_id={end_user_id}, 实体数={len(new_entity_ids)}")
         connector = Neo4jConnector()
-        engine = LabelPropagationEngine(connector, config_id=config_id, llm_model_id=llm_model_id)
+        engine = LabelPropagationEngine(connector, llm_model_id=llm_model_id, embedding_model_id=embedding_model_id)
         await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids)
         logger.info(f"[Clustering] 聚类完成，end_user_id={end_user_id}")
     except Exception as e:

api/app/repositories/neo4j/graph_search.py

@@ -4,10 +4,13 @@ from typing import Any, Dict, List, Optional
 
 from app.repositories.neo4j.cypher_queries import (
     CHUNK_EMBEDDING_SEARCH,
+    COMMUNITY_EMBEDDING_SEARCH,
     ENTITY_EMBEDDING_SEARCH,
+    EXPAND_COMMUNITY_STATEMENTS,
     MEMORY_SUMMARY_EMBEDDING_SEARCH,
     SEARCH_CHUNK_BY_CHUNK_ID,
     SEARCH_CHUNKS_BY_CONTENT,
+    SEARCH_COMMUNITIES_BY_KEYWORD,
     SEARCH_DIALOGUE_BY_DIALOG_ID,
     SEARCH_ENTITIES_BY_NAME,
     SEARCH_MEMORY_SUMMARIES_BY_KEYWORD,
@@ -285,6 +288,15 @@ async def search_graph(
             limit=limit,
         ))
         task_keys.append("summaries")
+
+    if "communities" in include:
+        tasks.append(connector.execute_query(
+            SEARCH_COMMUNITIES_BY_KEYWORD,
+            q=q,
+            end_user_id=end_user_id,
+            limit=limit,
+        ))
+        task_keys.append("communities")
     
     # Execute all queries in parallel
     task_results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -293,6 +305,7 @@ async def search_graph(
     results = {}
     for key, result in zip(task_keys, task_results):
         if isinstance(result, Exception):
+            logger.warning(f"search_graph: {key} 关键词查询异常: {result}")
             results[key] = []
         else:
             results[key] = result
@@ -349,7 +362,11 @@ async def search_graph_by_embedding(
     print(f"[PERF] Embedding generation took: {embed_time:.4f}s")
     
     if not embeddings or not embeddings[0]:
-        return {"statements": [], "chunks": [], "entities": [], "summaries": []}
+        logger.warning(
+            f"search_graph_by_embedding: embedding 生成失败或为空，"
+            f"query='{query_text[:50]}', end_user_id={end_user_id}，向量检索跳过"
+        )
+        return {"statements": [], "chunks": [], "entities": [], "summaries": [], "communities": []}
     embedding = embeddings[0]
 
     # Prepare tasks for parallel execution
@@ -396,6 +413,16 @@ async def search_graph_by_embedding(
         ))
         task_keys.append("summaries")
 
+    # Communities (向量语义匹配)
+    if "communities" in include:
+        tasks.append(connector.execute_query(
+            COMMUNITY_EMBEDDING_SEARCH,
+            embedding=embedding,
+            end_user_id=end_user_id,
+            limit=limit,
+        ))
+        task_keys.append("communities")
+
     # Execute all queries in parallel
     query_start = time.time()
     task_results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -408,10 +435,12 @@ async def search_graph_by_embedding(
         "chunks": [],
         "entities": [],
         "summaries": [],
+        "communities": [],
     }
     
     for key, result in zip(task_keys, task_results):
         if isinstance(result, Exception):
+            logger.warning(f"search_graph_by_embedding: {key} 向量查询异常: {result}")
             results[key] = []
         else:
             results[key] = result
@@ -661,6 +690,62 @@ async def search_graph_by_chunk_id(
     return {"chunks": chunks}
 
 
+async def search_graph_community_expand(
+    connector: Neo4jConnector,
+    community_ids: List[str],
+    end_user_id: str,
+    limit: int = 10,
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    三期：社区展开检索 —— 主题 → 细节两级检索。
+
+    命中 Community 节点后，沿 BELONGS_TO_COMMUNITY 关系拉取成员实体，
+    再沿 REFERENCES_ENTITY 关系拉取关联的 Statement 节点，
+    按 activation_value 降序返回，实现"主题摘要 → 具体记忆"的深度召回。
+
+    Args:
+        connector: Neo4j 连接器
+        community_ids: 已命中的社区 ID 列表
+        end_user_id: 用户 ID，用于数据隔离
+        limit: 每个社区最多返回的 Statement 数量
+
+    Returns:
+        {"expanded_statements": [Statement 列表，含 community_name / source_entity 字段]}
+    """
+    if not community_ids or not end_user_id:
+        return {"expanded_statements": []}
+
+    tasks = [
+        connector.execute_query(
+            EXPAND_COMMUNITY_STATEMENTS,
+            community_id=cid,
+            end_user_id=end_user_id,
+            limit=limit,
+        )
+        for cid in community_ids
+    ]
+
+    task_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+    expanded: List[Dict[str, Any]] = []
+    for cid, result in zip(community_ids, task_results):
+        if isinstance(result, Exception):
+            logger.warning(f"社区展开检索失败 community_id={cid}: {result}")
+        else:
+            expanded.extend(result)
+
+    # 按 activation_value 全局排序后去重
+    from app.core.memory.src.search import _deduplicate_results
+    expanded.sort(
+        key=lambda x: float(x.get("activation_value") or 0),
+        reverse=True,
+    )
+    expanded = _deduplicate_results(expanded)
+
+    logger.info(f"社区展开检索完成: community_ids={community_ids}, 展开 statements={len(expanded)}")
+    return {"expanded_statements": expanded}
+
+
 async def search_graph_by_created_at(
     connector: Neo4jConnector,
     end_user_id: Optional[str] = None,

api/app/repositories/workflow_repository.py

@@ -43,7 +43,6 @@ class WorkflowConfigRepository:
         edges: list[dict[str, Any]],
         variables: list[dict[str, Any]] | None = None,
         execution_config: dict[str, Any] | None = None,
-        features: dict[str, Any] | None = None,
         triggers: list[dict[str, Any]] | None = None
     ) -> WorkflowConfig:
         """创建或更新工作流配置
@@ -54,7 +53,6 @@ class WorkflowConfigRepository:
             edges: 边列表
             variables: 变量列表
             execution_config: 执行配置
-            features: 功能特性
             triggers: 触发器列表
         
         Returns:
@@ -84,7 +82,6 @@ class WorkflowConfigRepository:
                 edges=edges,
                 variables=variables or [],
                 execution_config=execution_config or {},
-                features=features or {},
                 triggers=triggers or []
             )
             self.db.add(config)

api/app/schemas/app_schema.py

@@ -149,26 +149,18 @@ class FileUploadConfig(BaseModel):
     )
     # 通用文件：PDF/DOCX/XLSX/TXT/CSV/JSON，最大 100MB
     document_enabled: bool = Field(default=False)
-    document_max_size_mb: int = Field(default=50)
+    document_max_size_mb: int = Field(default=100)
     document_allowed_extensions: List[str] = Field(
-        default=["pdf", "docx", "doc", "xlsx", "xls", "txt", "csv", "json", "md"]
+        default=["pdf", "docx", "xlsx", "txt", "csv", "json", "md"]
     )
     # 视频文件：MP4/MOV/AVI/WebM，最大 500MB
     video_enabled: bool = Field(default=False)
-    video_max_size_mb: int = Field(default=50)
+    video_max_size_mb: int = Field(default=500)
     video_allowed_extensions: List[str] = Field(
-        default=["mp4"]
+        default=["mp4", "mov"]
     )
     # 最大文件数量
-    max_file_count: int = Field(default=5, ge=1)
-
-    @field_validator("max_file_count")
-    @classmethod
-    def validate_max_file_count(cls, v: int) -> int:
-        from app.core.config import settings
-        if v > settings.MAX_FILE_COUNT:
-            raise ValueError(f"max_file_count 不能超过 {settings.MAX_FILE_COUNT}")
-        return v
+    max_file_count: int = Field(default=5, ge=1, le=20)
 
 
 class OpeningStatementConfig(BaseModel):

api/app/schemas/memory_api_schema.py

@@ -21,7 +21,7 @@ class MemoryWriteRequest(BaseModel):
     """
     end_user_id: str = Field(..., description="End user ID (required)")
     message: str = Field(..., description="Message content to store")
-    config_id: Optional[str] = Field(None, description="Memory configuration ID")
+    config_id: str = Field(..., description="Memory configuration ID (required)")
     storage_type: str = Field("neo4j", description="Storage type: neo4j or rag")
     user_rag_memory_id: Optional[str] = Field(None, description="RAG memory ID")
 
@@ -68,7 +68,7 @@ class MemoryReadRequest(BaseModel):
         "0", 
         description="Search mode: 0=verify, 1=direct, 2=context"
     )
-    config_id: Optional[str] = Field(None, description="Memory configuration ID")
+    config_id: str = Field(..., description="Memory configuration ID (required)")
     storage_type: str = Field("neo4j", description="Storage type: neo4j or rag")
     user_rag_memory_id: Optional[str] = Field(None, description="RAG memory ID")
 
@@ -132,3 +132,79 @@ class MemoryReadResponse(BaseModel):
         description="Intermediate retrieval outputs"
     )
     end_user_id: str = Field(..., description="End user ID")
+
+
+class CreateEndUserRequest(BaseModel):
+    """Request schema for creating an end user.
+    
+    Attributes:
+        workspace_id: Workspace ID (required)
+        other_id: External user identifier (required)
+        other_name: Display name for the end user
+    """
+    workspace_id: str = Field(..., description="Workspace ID (required)")
+    other_id: str = Field(..., description="External user identifier (required)")
+    other_name: Optional[str] = Field("", description="Display name")
+
+    @field_validator("workspace_id")
+    @classmethod
+    def validate_workspace_id(cls, v: str) -> str:
+        """Validate that workspace_id is not empty."""
+        if not v or not v.strip():
+            raise ValueError("workspace_id is required and cannot be empty")
+        return v.strip()
+
+    @field_validator("other_id")
+    @classmethod
+    def validate_other_id(cls, v: str) -> str:
+        """Validate that other_id is not empty."""
+        if not v or not v.strip():
+            raise ValueError("other_id is required and cannot be empty")
+        return v.strip()
+
+
+class CreateEndUserResponse(BaseModel):
+    """Response schema for end user creation.
+    
+    Attributes:
+        id: Created end user UUID
+        other_id: External user identifier
+        other_name: Display name
+        workspace_id: Workspace the user belongs to
+    """
+    id: str = Field(..., description="End user UUID")
+    other_id: str = Field(..., description="External user identifier")
+    other_name: str = Field("", description="Display name")
+    workspace_id: str = Field(..., description="Workspace ID")
+
+
+class MemoryConfigItem(BaseModel):
+    """Schema for a single memory config in the list response.
+    
+    Attributes:
+        config_id: Configuration UUID
+        config_name: Configuration name
+        config_desc: Configuration description
+        is_default: Whether this is the workspace default config
+        scene_name: Associated ontology scene name
+        created_at: Creation timestamp
+        updated_at: Last update timestamp
+    """
+    config_id: str = Field(..., description="Configuration ID")
+    config_name: str = Field(..., description="Configuration name")
+    config_desc: Optional[str] = Field(None, description="Configuration description")
+    is_default: bool = Field(False, description="Whether this is the workspace default")
+    scene_name: Optional[str] = Field(None, description="Associated ontology scene name")
+    created_at: Optional[str] = Field(None, description="Creation timestamp")
+    updated_at: Optional[str] = Field(None, description="Last update timestamp")
+
+
+class ListConfigsResponse(BaseModel):
+    """Response schema for listing memory configs.
+    
+    Attributes:
+        configs: List of memory config items
+        total: Total number of configs
+    """
+    configs: List[MemoryConfigItem] = Field(default_factory=list, description="List of configs")
+    total: int = Field(0, description="Total number of configs")

api/app/schemas/memory_config_schema.py

@@ -417,7 +417,7 @@ class MemoryConfig:
     
     # Ontology scene association
     scene_id: Optional[UUID] = None
-    ontology_classes: Optional[list] = field(default=None)
+    ontology_class_infos: list[dict] = field(default_factory=list)
     
     def __post_init__(self):
         """Validate configuration after initialization."""

api/app/services/app_chat_service.py

@@ -118,54 +118,28 @@ class AppChatService:
 
         )
 
-        model_info = ModelInfo(
-            model_name=api_key_obj.model_name,
-            provider=api_key_obj.provider,
-            api_key=api_key_obj.api_key,
-            api_base=api_key_obj.api_base,
-            capability=api_key_obj.capability,
-            is_omni=api_key_obj.is_omni,
-            model_type=ModelType.LLM
-        )
-
         # 加载历史消息
         messages = self.conversation_service.get_messages(
             conversation_id=conversation_id,
             limit=10
         )
-        history = []
-        for msg in messages:
-            content = [{"type": "text", "text": msg.content}]
-
-            # 处理 meta_data 中的 files
-            if msg.meta_data and msg.meta_data.get("files"):
-                files = msg.meta_data.get("files", [])
-                # 使用 MultimodalService 处理文件
-                multimodal_service = MultimodalService(self.db, api_config=model_info)
-
-                # 将 files 转换为 FileInput 格式
-                file_inputs = []
-                for file in files:
-                    from app.schemas.app_schema import FileInput, TransferMethod
-                    file_input = FileInput(
-                        type=file.get("type"),
-                        transfer_method=TransferMethod.REMOTE_URL,
-                        url=file.get("url")
-                    )
-                    file_inputs.append(file_input)
-
-                history_processed_files = await multimodal_service.history_process_files(files=file_inputs)
-
-                content.extend(history_processed_files)
-
-            history.append({
-                "role": msg.role,
-                "content": content
-            })
+        history = [
+            {"role": msg.role, "content": msg.content}
+            for msg in messages
+        ]
 
         # 处理多模态文件
         processed_files = None
         if files:
+            model_info = ModelInfo(
+                model_name=api_key_obj.model_name,
+                provider=api_key_obj.provider,
+                api_key=api_key_obj.api_key,
+                api_base=api_key_obj.api_base,
+                capability=api_key_obj.capability,
+                is_omni=api_key_obj.is_omni,
+                model_type=ModelType.LLM
+            )
             multimodal_service = MultimodalService(self.db, model_info)
             processed_files = await multimodal_service.process_files(user_id, files)
             logger.info(f"处理了 {len(processed_files)} 个文件")
@@ -339,54 +313,31 @@ class AppChatService:
                 streaming=True
             )
 
-            model_info = ModelInfo(
-                model_name=api_key_obj.model_name,
-                provider=api_key_obj.provider,
-                api_key=api_key_obj.api_key,
-                api_base=api_key_obj.api_base,
-                capability=api_key_obj.capability,
-                is_omni=api_key_obj.is_omni,
-                model_type=ModelType.LLM
-            )
-
             # 加载历史消息
-            messages = self.conversation_service.get_messages(
-                conversation_id=conversation_id,
-                limit=10
-            )
             history = []
-            for msg in messages:
-                content = [{"type": "text", "text": msg.content}]
-
-                # 处理 meta_data 中的 files
-                if msg.meta_data and msg.meta_data.get("files"):
-                    history_files = msg.meta_data.get("files", [])
-                    # 使用 MultimodalService 处理文件
-                    multimodal_service = MultimodalService(self.db, api_config=model_info)
-
-                    # 将 files 转换为 FileInput 格式
-                    file_inputs = []
-                    for file in history_files:
-                        from app.schemas.app_schema import FileInput, TransferMethod
-                        file_input = FileInput(
-                            type=file.get("type"),
-                            transfer_method=TransferMethod.REMOTE_URL,
-                            url=file.get("url")
-                        )
-                        file_inputs.append(file_input)
-
-                    history_processed_files = await multimodal_service.history_process_files(files=file_inputs)
-
-                    content.extend(history_processed_files)
-
-                history.append({
-                    "role": msg.role,
-                    "content": content
-                })
+            memory_config = {"enabled": True, 'max_history': 10}
+            if memory_config.get("enabled"):
+                messages = self.conversation_service.get_messages(
+                    conversation_id=conversation_id,
+                    limit=memory_config.get("max_history", 10)
+                )
+                history = [
+                    {"role": msg.role, "content": msg.content}
+                    for msg in messages
+                ]
 
             # 处理多模态文件
             processed_files = None
             if files:
+                model_info = ModelInfo(
+                    model_name=api_key_obj.model_name,
+                    provider=api_key_obj.provider,
+                    api_key=api_key_obj.api_key,
+                    api_base=api_key_obj.api_base,
+                    capability=api_key_obj.capability,
+                    is_omni=api_key_obj.is_omni,
+                    model_type=ModelType.LLM
+                )
                 multimodal_service = MultimodalService(self.db, model_info)
                 processed_files = await multimodal_service.process_files(user_id, files)
                 logger.info(f"处理了 {len(processed_files)} 个文件")
@@ -396,14 +347,8 @@ class AppChatService:
             total_tokens = 0
 
             text_queue: asyncio.Queue = asyncio.Queue()
-            api_key_config = {
-                "model_name": api_key_obj.model_name,
-                "api_key": api_key_obj.api_key,
-                "api_base": api_key_obj.api_base,
-                "provider": api_key_obj.provider,
-            }
             stream_audio_url, tts_task = await self.agent_service._generate_tts_streaming(
-                features_config, api_key_config,
+                features_config, api_key_obj,
                 text_queue=text_queue,
                 tenant_id=tenant_id, workspace_id=workspace_id
             )

api/app/services/app_dsl_service.py

@@ -16,7 +16,6 @@ from app.models.app_release_model import AppRelease
 from app.models.knowledge_model import Knowledge
 from app.models.models_model import ModelConfig
 from app.models.tool_model import ToolConfig as ToolConfigModel
-from app.models.skill_model import Skill
 from app.models.workflow_model import WorkflowConfig
 from app.services.workflow_service import WorkflowService
 from app.core.workflow.adapters.memory_bear.memory_bear_adapter import MemoryBearAdapter
@@ -85,9 +84,7 @@ class AppDslService:
             if "knowledge_retrieval" in cfg:
                 enriched["knowledge_retrieval"] = self._enrich_knowledge_retrieval(cfg["knowledge_retrieval"])
             if "tools" in cfg:
-                enriched["tools"] = self._enrich_tools(cfg.get("tools"))
-            if "skills" in cfg:
-                enriched["skills"] = self._enrich_skills(cfg.get("skills"))
+                enriched["tools"] = self._enrich_tools(cfg["tools"])
             return enriched
         if app_type == AppType.MULTI_AGENT:
             enriched = {**cfg}
@@ -111,7 +108,6 @@ class AppDslService:
                 "variables": config.variables if config else [],
                 "edges": config.edges if config else [],
                 "nodes": config.nodes if config else [],
-                "features": config.features if config else {},
                 "execution_config": config.execution_config if config else {},
                 "triggers": config.triggers if config else [],
             } if config else {}
@@ -127,8 +123,7 @@ class AppDslService:
                 "memory": config.memory if config else None,
                 "variables": config.variables if config else [],
                 "tools": self._enrich_tools(config.tools) if config else [],
-                "skills": self._enrich_skills(config.skills) if config else {},
-                "features": config.features if config else {}
+                "skills": config.skills if config else {},
             } if config else {}
             dsl = {**meta, "app": app_meta, "agent_config": config_data}
 
@@ -190,22 +185,6 @@ class AppDslService:
     def _enrich_tools(self, tools: list) -> list:
         return [{**t, "_ref": self._tool_ref(t.get("tool_id"))} for t in (tools or [])]
 
-    def _skill_ref(self, skill_id) -> Optional[dict]:
-        if not skill_id:
-            return None
-        s = self.db.query(Skill).filter(Skill.id == skill_id).first()
-        return {"id": str(skill_id), "name": s.name} if s else {"id": str(skill_id)}
-
-    def _enrich_skills(self, skills: Optional[dict]) -> Optional[dict]:
-        if not skills:
-            return skills
-        skill_ids = skills.get("skill_ids", [])
-        enriched_ids = [
-            {"id": sid, "_ref": self._skill_ref(sid)}
-            for sid in (skill_ids or [])
-        ]
-        return {**skills, "skill_ids": enriched_ids}
-
     def _agent_ref(self, agent_id) -> Optional[dict]:
         if not agent_id:
             return None
@@ -270,8 +249,7 @@ class AppDslService:
                 memory=self._resolve_memory(cfg.get("memory"), workspace_id, warnings),
                 variables=cfg.get("variables", []),
                 tools=self._resolve_tools(cfg.get("tools", []), tenant_id, warnings),
-                skills=self._resolve_skills(cfg.get("skills", {}), tenant_id, warnings),
-                features=cfg.get("features", {}),
+                skills=cfg.get("skills", {}),
                 is_active=True,
                 created_at=now,
                 updated_at=now,
@@ -312,7 +290,6 @@ class AppDslService:
                 edges=[e.model_dump() for e in result.edges],
                 variables=[v.model_dump() for v in result.variables],
                 execution_config=wf.get("execution_config", {}),
-                features=wf.get("features", {}),
                 triggers=wf.get("triggers", []),
                 validate=False,
             )
@@ -467,46 +444,6 @@ class AppDslService:
             return {**memory, "memory_config_id": None, "enabled": False}
         return memory
 
-    def _resolve_skills(self, skills: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> dict:
-        if not skills:
-            return skills or {}
-        resolved_ids = []
-        for entry in (skills.get("skill_ids") or []):
-            # entry 可能是 {"id": "...", "_ref": {...}} 或直接是字符串
-            if isinstance(entry, dict):
-                ref = entry.get("_ref") or ({"name": None, "id": entry.get("id")} if entry.get("id") else None)
-                skill_id = self._resolve_skill(ref, tenant_id, warnings)
-            else:
-                skill_id = self._resolve_skill({"id": str(entry)}, tenant_id, warnings)
-            if skill_id:
-                resolved_ids.append(str(skill_id))
-        return {**{k: v for k, v in skills.items() if k != "skill_ids"}, "skill_ids": resolved_ids}
-
-    def _resolve_skill(self, ref: Optional[dict], tenant_id: uuid.UUID, warnings: list) -> Optional[str]:
-        if not ref:
-            return None
-        # 先按 id 匹配
-        if ref.get("id"):
-            try:
-                s = self.db.query(Skill).filter(
-                    Skill.id == uuid.UUID(str(ref["id"])),
-                    Skill.tenant_id == tenant_id
-                ).first()
-                if s:
-                    return str(s.id)
-            except Exception:
-                pass
-        # 再按名称匹配
-        if ref.get("name"):
-            s = self.db.query(Skill).filter(
-                Skill.name == ref["name"],
-                Skill.tenant_id == tenant_id
-            ).first()
-            if s:
-                return str(s.id)
-        warnings.append(f"未找到技能: {ref}")
-        return None
-
     def _resolve_tools(self, tools: list, tenant_id: uuid.UUID, warnings: list) -> list:
         result = []
         for t in (tools or []):

api/app/services/app_service.py

@@ -833,6 +833,8 @@ class AppService:
 
             # 跨工作空间时，获取目标工作空间的 tenant_id 用于判断模型配置是否可用
             target_tenant_id = None
+            available_model_ids: set = set()
+            available_kb_ids: set = set()
             if is_cross_workspace:
                 target_ws = self.db.get(Workspace, target_workspace_id)
                 if not target_ws:
@@ -847,29 +849,28 @@ class AppService:
 
                 if source_config:
                     if is_cross_workspace:
-                        # 跨工作空间：model/tools/skills 属于 tenant 级别直接保留，
-                        # knowledge_bases 属于 workspace 级别需过滤，memory_config 需清空
-                        _, kb_ids = self._collect_resource_ids_from_config(
-                            None, source_config.knowledge_retrieval
+                        # Batch-collect and preload all referenced resources
+                        model_ids, kb_ids = self._collect_resource_ids_from_config(
+                            source_config.default_model_config_id,
+                            source_config.knowledge_retrieval,
+                            source_config.tools
                         )
-                        _, available_kb_ids = self._preload_cross_workspace_resources(
-                            target_tenant_id, target_workspace_id, set(), kb_ids
+                        available_model_ids, available_kb_ids = self._preload_cross_workspace_resources(
+                            target_tenant_id, target_workspace_id, model_ids, kb_ids
+                        )
+                        new_model_config_id = self._is_model_available(
+                            source_config.default_model_config_id, available_model_ids
                         )
-                        new_model_config_id = source_config.default_model_config_id
                         new_knowledge_retrieval = self._clean_knowledge_retrieval(
                             source_config.knowledge_retrieval, available_kb_ids
                         )
-                        new_tools = copy.deepcopy(source_config.tools) if source_config.tools else []
-                        new_memory = self._clean_memory_cross_workspace(
-                            source_config.memory, target_workspace_id
+                        new_tools = self._clean_tools(
+                            source_config.tools, available_kb_ids
                         )
-                        new_skills = copy.deepcopy(source_config.skills) if source_config.skills else {}
                     else:
                         new_model_config_id = source_config.default_model_config_id
                         new_knowledge_retrieval = copy.deepcopy(source_config.knowledge_retrieval) if source_config.knowledge_retrieval else None
                         new_tools = copy.deepcopy(source_config.tools) if source_config.tools else []
-                        new_memory = copy.deepcopy(source_config.memory) if source_config.memory else None
-                        new_skills = copy.deepcopy(source_config.skills) if source_config.skills else {}
 
                     new_config = AgentConfig(
                         id=uuid.uuid4(),
@@ -878,11 +879,9 @@ class AppService:
                         default_model_config_id=new_model_config_id,
                         model_parameters=copy.deepcopy(source_config.model_parameters) if source_config.model_parameters else None,
                         knowledge_retrieval=new_knowledge_retrieval,
-                        memory=new_memory,
+                        memory=copy.deepcopy(source_config.memory) if source_config.memory else None,
                         variables=copy.deepcopy(source_config.variables) if source_config.variables else [],
                         tools=new_tools,
-                        skills=new_skills,
-                        features=copy.deepcopy(source_config.features) if source_config.features else {},
                         is_active=True,
                         created_at=now,
                         updated_at=now,
@@ -895,14 +894,28 @@ class AppService:
                 ).first()
 
                 if source_config:
+                    if is_cross_workspace:
+                        model_ids, kb_ids = self._collect_resource_ids_from_workflow_nodes(
+                            source_config.nodes
+                        )
+                        available_model_ids, available_kb_ids = self._preload_cross_workspace_resources(
+                            target_tenant_id, target_workspace_id, model_ids, kb_ids
+                        )
+                        new_nodes = self._clean_workflow_nodes_for_cross_workspace(
+                            source_config.nodes or [],
+                            available_model_ids,
+                            available_kb_ids
+                        )
+                    else:
+                        new_nodes = copy.deepcopy(source_config.nodes) if source_config.nodes else []
+
                     new_config = WorkflowConfig(
                         id=uuid.uuid4(),
                         app_id=new_app.id,
-                        nodes=copy.deepcopy(source_config.nodes) if source_config.nodes else [],
+                        nodes=new_nodes,
                         edges=copy.deepcopy(source_config.edges) if source_config.edges else [],
                         variables=copy.deepcopy(source_config.variables) if source_config.variables else [],
                         execution_config=copy.deepcopy(source_config.execution_config) if source_config.execution_config else {},
-                        features=copy.deepcopy(source_config.features) if source_config.features else {},
                         triggers=copy.deepcopy(source_config.triggers) if source_config.triggers else [],
                         is_active=True,
                         created_at=now,
@@ -916,15 +929,24 @@ class AppService:
                 ).first()
 
                 if source_config:
-                    # multi_agent 的 model_config_id/sub_agents/routing_rules 均属于 tenant 级别直接保留
-                    # 跨空间时 master_agent_id（AppRelease）属于源空间，需清空
+                    if is_cross_workspace:
+                        model_ids = {source_config.default_model_config_id} if source_config.default_model_config_id else set()
+                        available_model_ids, _ = self._preload_cross_workspace_resources(
+                            target_tenant_id, target_workspace_id, model_ids, set()
+                        )
+                        new_model_config_id = self._is_model_available(
+                            source_config.default_model_config_id, available_model_ids
+                        )
+                    else:
+                        new_model_config_id = source_config.default_model_config_id
+
                     new_config = MultiAgentConfig(
                         id=uuid.uuid4(),
                         app_id=new_app.id,
                         master_agent_id=source_config.master_agent_id if not is_cross_workspace else None,
                         master_agent_name=source_config.master_agent_name,
-                        default_model_config_id=source_config.default_model_config_id,
-                        model_parameters=copy.deepcopy(source_config.model_parameters) if source_config.model_parameters else None,
+                        default_model_config_id=new_model_config_id,
+                        model_parameters=source_config.model_parameters,
                         orchestration_mode=source_config.orchestration_mode,
                         sub_agents=copy.deepcopy(source_config.sub_agents) if source_config.sub_agents else [],
                         routing_rules=copy.deepcopy(source_config.routing_rules) if source_config.routing_rules else None,
@@ -1015,7 +1037,8 @@ class AppService:
     @staticmethod
     def _collect_resource_ids_from_config(
             model_config_id: Optional[uuid.UUID],
-            knowledge_retrieval: Optional[dict]
+            knowledge_retrieval: Optional[dict],
+            tools: Optional[list]
     ) -> tuple:
         """Extract all model config IDs and knowledge base IDs from an app config."""
         model_ids: set = set()
@@ -1025,12 +1048,62 @@ class AppService:
             model_ids.add(model_config_id)
 
         if knowledge_retrieval and isinstance(knowledge_retrieval, dict):
-            if "knowledge_bases" in knowledge_retrieval:
-                for kid in knowledge_retrieval.get("knowledge_bases", []):
-                    kb_ids.add(str(kid.get("kb_id")))
+            if "kb_ids" in knowledge_retrieval:
+                for kid in knowledge_retrieval.get("kb_ids", []):
+                    if kid:
+                        kb_ids.add(str(kid))
+            if knowledge_retrieval.get("knowledge_id"):
+                kb_ids.add(str(knowledge_retrieval["knowledge_id"]))
+
+        if tools:
+            for tool in tools:
+                if isinstance(tool, dict):
+                    kid = tool.get("knowledge_id") or tool.get("kb_id")
+                    if kid:
+                        kb_ids.add(str(kid))
 
         return model_ids, kb_ids
 
+    @staticmethod
+    def _collect_resource_ids_from_workflow_nodes(nodes: list) -> tuple:
+        """Extract all model config IDs and knowledge base IDs from workflow nodes."""
+        model_ids: set = set()
+        kb_ids: set = set()
+
+        for node in (nodes or []):
+            if not isinstance(node, dict):
+                continue
+            data = node.get("data", {})
+            if not isinstance(data, dict):
+                continue
+            for key in ("model_config_id", "default_model_config_id"):
+                val = data.get(key)
+                if val:
+                    try:
+                        model_ids.add(uuid.UUID(str(val)))
+                    except (ValueError, AttributeError):
+                        pass
+            kr = data.get("knowledge_retrieval")
+            if isinstance(kr, dict):
+                for kid in kr.get("kb_ids", []):
+                    if kid:
+                        kb_ids.add(str(kid))
+                if kr.get("knowledge_id"):
+                    kb_ids.add(str(kr["knowledge_id"]))
+            if data.get("knowledge_id"):
+                kb_ids.add(str(data["knowledge_id"]))
+            for kid in data.get("kb_ids", []):
+                if kid:
+                    kb_ids.add(str(kid))
+
+        return model_ids, kb_ids
+
+    @staticmethod
+    def _is_model_available(model_config_id: Optional[uuid.UUID], available_model_ids: set) -> Optional[uuid.UUID]:
+        if not model_config_id:
+            return None
+        return model_config_id if model_config_id in available_model_ids else None
+
     @staticmethod
     def _is_kb_available(kb_id: Optional[str], available_kb_ids: set) -> Optional[str]:
         if not kb_id:
@@ -1051,53 +1124,95 @@ class AppService:
 
         cleaned = copy.deepcopy(knowledge_retrieval)
 
-        if "knowledge_bases" in cleaned and isinstance(cleaned["knowledge_bases"], list):
-            cleaned["knowledge_bases"] = [
-                kb for kb in cleaned["knowledge_bases"]
-                if self._is_kb_available(kb.get("kb_id"), available_kb_ids)
+        if "kb_ids" in cleaned and isinstance(cleaned["kb_ids"], list):
+            cleaned["kb_ids"] = [
+                kid for kid in cleaned["kb_ids"]
+                if self._is_kb_available(kid, available_kb_ids)
             ]
 
+        if "knowledge_id" in cleaned:
+            cleaned["knowledge_id"] = self._is_kb_available(
+                cleaned.get("knowledge_id"), available_kb_ids
+            )
+
         return cleaned
 
-    def _clean_memory_cross_workspace(
+    def _clean_tools(
             self,
-            memory: Optional[dict],
-            target_workspace_id: uuid.UUID
-    ) -> Optional[dict]:
-        """Clear memory_config_id/memory_content if it doesn't belong to target workspace."""
-        if not memory:
-            return None
+            tools: Optional[list],
+            available_kb_ids: set
+    ) -> list:
+        """Clean tools config, keeping built-in tools and tools with available KBs."""
+        if not tools:
+            return []
 
-        from app.models.memory_config_model import MemoryConfig
+        cleaned = []
+        for tool in tools:
+            if not isinstance(tool, dict):
+                cleaned.append(tool)
+                continue
 
-        cleaned = copy.deepcopy(memory)
-        # 兼容旧字段 memory_content 和新字段 memory_config_id
-        mid = cleaned.get("memory_config_id") or cleaned.get("memory_content")
-        if mid:
-            try:
-                mid_uuid = uuid.UUID(str(mid))
-            except (ValueError, AttributeError):
-                exists = self.db.query(MemoryConfig).filter(
-                    MemoryConfig.config_id_old == int(mid),
-                    MemoryConfig.workspace_id == target_workspace_id
-                ).first()
-                if not exists:
-                    cleaned["memory_config_id"] = None
-                    cleaned.pop("memory_content", None)
-                    cleaned["enabled"] = False
-                return cleaned
+            tool_type = tool.get("type", "")
+            if tool_type in ("builtin", "built_in", "system"):
+                cleaned.append(copy.deepcopy(tool))
+                continue
 
-            exists = self.db.query(
-                self.db.query(MemoryConfig).filter(
-                    MemoryConfig.config_id == mid_uuid,
-                    MemoryConfig.workspace_id == target_workspace_id
-                ).exists()
-            ).scalar()
-            if not exists:
-                cleaned["memory_config_id"] = None
-                cleaned.pop("memory_content", None)
-                cleaned["enabled"] = False
+            kb_id = tool.get("knowledge_id") or tool.get("kb_id")
+            if kb_id:
+                if self._is_kb_available(kb_id, available_kb_ids):
+                    cleaned.append(copy.deepcopy(tool))
+                continue
 
+            cleaned.append(copy.deepcopy(tool))
+
+        return cleaned
+
+    def _clean_workflow_nodes_for_cross_workspace(
+            self,
+            nodes: list,
+            available_model_ids: set,
+            available_kb_ids: set
+    ) -> list:
+        """Clean workflow nodes, using pre-loaded resource sets. Uses deepcopy to avoid mutating source."""
+        if not nodes:
+            return []
+
+        cleaned = []
+        for node in nodes:
+            if not isinstance(node, dict):
+                cleaned.append(node)
+                continue
+
+            node_copy = copy.deepcopy(node)
+            data = node_copy.get("data")
+            if not isinstance(data, dict):
+                cleaned.append(node_copy)
+                continue
+
+            for key in ("model_config_id", "default_model_config_id"):
+                if key in data and data[key]:
+                    try:
+                        mid = uuid.UUID(str(data[key]))
+                    except (ValueError, AttributeError):
+                        data[key] = None
+                        continue
+                    data[key] = str(mid) if mid in available_model_ids else None
+
+            if "knowledge_retrieval" in data and data["knowledge_retrieval"]:
+                data["knowledge_retrieval"] = self._clean_knowledge_retrieval(
+                    data["knowledge_retrieval"], available_kb_ids
+                )
+            if "knowledge_id" in data:
+                data["knowledge_id"] = self._is_kb_available(
+                    data.get("knowledge_id"), available_kb_ids
+                )
+            if "kb_ids" in data and isinstance(data["kb_ids"], list):
+                data["kb_ids"] = [
+                    kid for kid in data["kb_ids"]
+                    if self._is_kb_available(kid, available_kb_ids)
+                ]
+
+            cleaned.append(node_copy)
         return cleaned
 
     def list_apps(

api/app/services/conversation_service.py

@@ -21,7 +21,6 @@ from app.models.conversation_model import ConversationDetail
 from app.models.prompt_optimizer_model import RoleType
 from app.repositories.conversation_repository import ConversationRepository, MessageRepository
 from app.schemas.conversation_schema import ConversationOut
-from app.schemas.model_schema import ModelInfo
 from app.services import workspace_service
 from app.services.model_service import ModelConfigService
 
@@ -120,27 +119,25 @@ class ConversationService:
 
     def get_user_conversations(
             self,
-            user_id: uuid.UUID,
-            page: int = 1,
-            page_size: int = 20
-    ) -> tuple[list[Conversation], int]:
+            user_id: uuid.UUID
+    ) -> list[Conversation]:
         """
-        Retrieve recent conversations for a specific user with pagination.
+        Retrieve recent conversations for a specific user
+
+        This method delegates persistence logic to the repository layer and
+        applies service-level defaults (e.g. recent conversation limit).
 
         Args:
             user_id (uuid.UUID): Unique identifier of the user.
-            page (int): Page number (1-based). Defaults to 1.
-            page_size (int): Number of items per page. Defaults to 20.
 
         Returns:
-            tuple[list[Conversation], int]: A list of recent conversation entities and total count.
+            list[Conversation]: A list of recent conversation entities.
         """
-        conversations, total = self.conversation_repo.get_conversation_by_user_id(
+        conversations = self.conversation_repo.get_conversation_by_user_id(
             user_id,
-            page=page,
-            page_size=page_size
+            limit=10
         )
-        return conversations, total
+        return conversations
 
     def list_conversations(
             self,
@@ -270,11 +267,10 @@ class ConversationService:
 
         return messages
 
-    async def get_conversation_history(
+    def get_conversation_history(
             self,
             conversation_id: uuid.UUID,
-            max_history: Optional[int] = None,
-            api_config: Optional[ModelInfo] = None
+            max_history: Optional[int] = None
     ) -> List[dict]:
         """
         Retrieve historical conversation messages formatted as dictionaries.
@@ -282,7 +278,6 @@ class ConversationService:
         Args:
             conversation_id (uuid.UUID): Conversation UUID.
             max_history (Optional[int]): Maximum number of messages to retrieve.
-            api_config (Optional[ModelInfo]): Model API configuration for multimodal processing.
 
         Returns:
             List[dict]: List of message dictionaries with keys 'role' and 'content'.
@@ -293,37 +288,13 @@ class ConversationService:
         )
 
         # 转换为字典格式
-        history = []
-        for msg in messages:
-            content = [{"type": "text", "text": msg.content}]
-            
-            # 处理 meta_data 中的 files
-            if msg.meta_data and msg.meta_data.get("files"):
-                files = msg.meta_data.get("files", [])
-                if api_config:
-                    # 使用 MultimodalService 处理文件
-                    from app.services.multimodal_service import MultimodalService
-                    multimodal_service = MultimodalService(self.db, api_config=api_config)
-                    
-                    # 将 files 转换为 FileInput 格式
-                    file_inputs = []
-                    for file in files:
-                        from app.schemas.app_schema import FileInput, TransferMethod
-                        file_input = FileInput(
-                            type=file.get("type"),
-                            transfer_method=TransferMethod.REMOTE_URL,
-                            url=file.get("url")
-                        )
-                        file_inputs.append(file_input)
-
-                    processed_files = await multimodal_service.history_process_files(files=file_inputs)
-
-                    content.extend(processed_files)
-            
-            history.append({
+        history = [
+            {
                 "role": msg.role,
-                "content": content
-            })
+                "content": msg.content
+            }
+            for msg in messages
+        ]
 
         return history
 
@@ -551,18 +522,9 @@ class ConversationService:
             type=ModelType(model_type)
         )
 
-        conversation_messages = await self.get_conversation_history(
+        conversation_messages = self.get_conversation_history(
             conversation_id=conversation_id,
-            max_history=20,
-            api_config=ModelInfo(
-                model_name=model_name,
-                provider=provider,
-                api_key=api_key,
-                api_base=api_base,
-                capability=api_config.capability,
-                is_omni=api_config.is_omni,
-                model_type=model_type
-            )
+            max_history=20
         )
         if len(conversation_messages) == 0:
             return ConversationOut(

api/app/services/draft_run_service.py

@@ -579,20 +579,9 @@ class AgentRunService:
                 user_id=user_id
             )
 
-            model_info = ModelInfo(
-                model_name=api_key_config["model_name"],
-                provider=api_key_config["provider"],
-                api_key=api_key_config["api_key"],
-                api_base=api_key_config["api_base"],
-                capability=api_key_config["capability"],
-                is_omni=api_key_config["is_omni"],
-                model_type=model_config.type
-            )
-
             # 6. 加载历史消息
             history = await self._load_conversation_history(
                 conversation_id=conversation_id,
-                api_config=model_info,
                 max_history=10
             )
 
@@ -600,6 +589,15 @@ class AgentRunService:
             processed_files = None
             if files:
                 # 获取 provider 信息
+                model_info = ModelInfo(
+                    model_name=api_key_config["model_name"],
+                    provider=api_key_config["provider"],
+                    api_key=api_key_config["api_key"],
+                    api_base=api_key_config["api_base"],
+                    capability=api_key_config["capability"],
+                    is_omni=api_key_config["is_omni"],
+                    model_type=ModelType.LLM
+                )
                 provider = api_key_config.get("provider", "openai")
                 multimodal_service = MultimodalService(self.db, model_info)
                 processed_files = await multimodal_service.process_files(user_id, files)
@@ -817,20 +815,9 @@ class AgentRunService:
                 sub_agent=sub_agent
             )
 
-            model_info = ModelInfo(
-                model_name=api_key_config["model_name"],
-                provider=api_key_config["provider"],
-                api_key=api_key_config["api_key"],
-                api_base=api_key_config["api_base"],
-                capability=api_key_config["capability"],
-                is_omni=api_key_config["is_omni"],
-                model_type=model_config.type
-            )
-
             # 6. 加载历史消息
             history = await self._load_conversation_history(
                 conversation_id=conversation_id,
-                api_config=model_info,
                 max_history=memory_config.get("max_history", 10)
             )
 
@@ -838,6 +825,15 @@ class AgentRunService:
             processed_files = None
             if files:
                 # 获取 provider 信息
+                model_info = ModelInfo(
+                    model_name=api_key_config["model_name"],
+                    provider=api_key_config["provider"],
+                    api_key=api_key_config["api_key"],
+                    api_base=api_key_config["api_base"],
+                    capability=api_key_config["capability"],
+                    is_omni=api_key_config["is_omni"],
+                    model_type=ModelType.LLM
+                )
                 provider = api_key_config.get("provider", "openai")
                 multimodal_service = MultimodalService(self.db, model_info)
                 processed_files = await multimodal_service.process_files(user_id, files)
@@ -1119,7 +1115,6 @@ class AgentRunService:
     async def _load_conversation_history(
             self,
             conversation_id: str,
-            api_config: ModelInfo | None = None,
             max_history: int = 10
     ) -> List[Dict[str, str]]:
         """加载会话历史消息
@@ -1134,11 +1129,9 @@ class AgentRunService:
         try:
 
             conversation_service = ConversationService(self.db)
-            # 获取 API 配置用于多模态处理
-            history = await conversation_service.get_conversation_history(
+            history = conversation_service.get_conversation_history(
                 conversation_id=uuid.UUID(conversation_id),
-                max_history=max_history,
-                api_config=api_config
+                max_history=max_history
             )
 
             logger.debug(

api/app/services/memory_agent_service.py

@@ -1179,7 +1179,7 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
     app = db.query(App).filter(App.id == app_id).first()
     if not app:
         logger.warning(f"App not found: {app_id}")
-        raise ValueError(f"应用不存在: {app_id}")
+        # raise ValueError(f"应用不存在: {app_id}")
     # TODO: temp fix for draft run
     # if not app.current_release_id:
     #     logger.warning(f"No current release for app: {app_id}")
@@ -1252,17 +1252,15 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
     memory_config_service = MemoryConfigService(db)
     memory_config = memory_config_service.get_config_with_fallback(
         memory_config_id=memory_config_id_to_use,
-        workspace_id=app.workspace_id
+        workspace_id=end_user.workspace_id
     )
 
     memory_config_id = str(memory_config.config_id) if memory_config else None
 
     result = {
         "end_user_id": str(end_user_id),
-        "app_id": str(app_id),
-        "release_id": str(app.current_release_id) if app.current_release_id else None,
         "memory_config_id": memory_config_id,
-        "workspace_id": str(app.workspace_id)
+        "workspace_id": str(end_user.workspace_id)
     }
 
     logger.info(

api/app/services/memory_api_service.py

@@ -84,43 +84,65 @@ class MemoryAPIService:
         
         if not app:
             logger.warning(f"App not found for end_user: {end_user_id}")
-            raise ResourceNotFoundException(
-                resource_type="App",
-                resource_id=str(end_user.app_id)
-            )
-        
-        if app.workspace_id != workspace_id:
-            logger.warning(
-                f"End user {end_user_id} belongs to workspace {app.workspace_id}, "
-                f"not authorized workspace {workspace_id}"
-            )
-            raise BusinessException(
-                message="End user does not belong to authorized workspace",
-                code=BizCode.FORBIDDEN
-            )
+            # raise ResourceNotFoundException(
+            #     resource_type="App",
+            #     resource_id=str(end_user.app_id)
+            # )
+        # temporally allow any workspace to access
+        # if end_user.workspace_id != workspace_id:
+        #     print(f"[DEBUG] end_user.workspace_id={end_user.workspace_id}, api_key.workspace_id={workspace_id}")
+        #     logger.warning(
+        #         f"End user {end_user_id} belongs to workspace {end_user.workspace_id}, "
+        #         f"not authorized workspace {workspace_id}"
+        #     )
+        #     raise BusinessException(
+        #         message=f"End user does not belong to authorized workspace. end_user.workspace_id={end_user.workspace_id}, api_key.workspace_id={workspace_id}",
+        #         code=BizCode.FORBIDDEN
+        #     )
         
         logger.info(f"End user {end_user_id} validated successfully")
         return end_user
-    
+
+    def _update_end_user_config(self, end_user_id: str, config_id: str) -> None:
+        """Update the end user's memory_config_id.
+        
+        Silently updates the config association. Logs warnings on failure
+        but does not raise, so it won't block the main read/write operation.
+        
+        Args:
+            end_user_id: End user identifier
+            config_id: Memory configuration ID to assign
+        """
+        try:
+            config_uuid = uuid.UUID(config_id)
+            from app.repositories.end_user_repository import EndUserRepository
+            end_user_repo = EndUserRepository(self.db)
+            end_user_repo.update_memory_config_id(
+                end_user_id=uuid.UUID(end_user_id),
+                memory_config_id=config_uuid,
+            )
+        except Exception as e:
+            logger.warning(f"Failed to update memory_config_id for end_user {end_user_id}: {e}")
+
     async def write_memory(
         self,
         workspace_id: uuid.UUID,
         end_user_id: str,
         message: str,
-        config_id: Optional[str] = None,
+        config_id: str,
         storage_type: str = "neo4j",
         user_rag_memory_id: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Write memory with validation.
         
-        Validates end_user exists and belongs to workspace, then delegates
-        to MemoryAgentService.write_memory.
+        Validates end_user exists and belongs to workspace, updates the end user's
+        memory_config_id, then delegates to MemoryAgentService.write_memory.
         
         Args:
             workspace_id: Workspace ID for resource validation
             end_user_id: End user identifier (used as end_user_id)
             message: Message content to store
-            config_id: Optional memory configuration ID
+            config_id: Memory configuration ID (required)
             storage_type: Storage backend (neo4j or rag)
             user_rag_memory_id: Optional RAG memory ID
             
@@ -136,7 +158,8 @@ class MemoryAPIService:
         # Validate end_user exists and belongs to workspace
         self.validate_end_user(end_user_id, workspace_id)
         
-        # Use end_user_id as end_user_id for memory operations
+        # Update end user's memory_config_id
+        self._update_end_user_config(end_user_id, config_id)
         
         try:
             # Delegate to MemoryAgentService
@@ -188,21 +211,21 @@ class MemoryAPIService:
         end_user_id: str,
         message: str,
         search_switch: str = "0",
-        config_id: Optional[str] = None,
+        config_id: str = "",
         storage_type: str = "neo4j",
         user_rag_memory_id: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Read memory with validation.
         
-        Validates end_user exists and belongs to workspace, then delegates
-        to MemoryAgentService.read_memory.
+        Validates end_user exists and belongs to workspace, updates the end user's
+        memory_config_id, then delegates to MemoryAgentService.read_memory.
         
         Args:
             workspace_id: Workspace ID for resource validation
             end_user_id: End user identifier (used as end_user_id)
             message: Query message
             search_switch: Search mode (0=deep search with verification, 1=deep search, 2=fast search)
-            config_id: Optional memory configuration ID
+            config_id: Memory configuration ID (required)
             storage_type: Storage backend (neo4j or rag)
             user_rag_memory_id: Optional RAG memory ID
             
@@ -218,7 +241,8 @@ class MemoryAPIService:
         # Validate end_user exists and belongs to workspace
         self.validate_end_user(end_user_id, workspace_id)
         
-        # Use end_user_id as end_user_id for memory operations
+        # Update end user's memory_config_id
+        self._update_end_user_config(end_user_id, config_id)
 
         
         try:
@@ -256,3 +280,50 @@ class MemoryAPIService:
                 message=f"Memory read failed: {str(e)}",
                 code=BizCode.MEMORY_READ_FAILED
             )
+
+    def list_memory_configs(
+        self,
+        workspace_id: uuid.UUID,
+    ) -> Dict[str, Any]:
+        """List all memory configs for a workspace.
+        
+        Args:
+            workspace_id: Workspace ID from API key authorization
+            
+        Returns:
+            Dict with configs list and total count
+            
+        Raises:
+            BusinessException: If listing fails
+        """
+        logger.info(f"Listing memory configs for workspace: {workspace_id}")
+
+        try:
+            from app.repositories.memory_config_repository import MemoryConfigRepository
+
+            results = MemoryConfigRepository.get_all(self.db, workspace_id=workspace_id)
+
+            configs = []
+            for config, scene_name in results:
+                configs.append({
+                    "config_id": str(config.config_id),
+                    "config_name": config.config_name,
+                    "config_desc": config.config_desc,
+                    "is_default": config.is_default or False,
+                    "scene_name": scene_name,
+                    "created_at": config.created_at.isoformat() if config.created_at else None,
+                    "updated_at": config.updated_at.isoformat() if config.updated_at else None,
+                })
+
+            logger.info(f"Found {len(configs)} memory configs for workspace {workspace_id}")
+            return {
+                "configs": configs,
+                "total": len(configs),
+            }
+
+        except Exception as e:
+            logger.error(f"Failed to list memory configs for workspace {workspace_id}: {e}")
+            raise BusinessException(
+                message=f"Failed to list memory configs: {str(e)}",
+                code=BizCode.MEMORY_READ_FAILED
+            )

api/app/services/memory_config_service.py

@@ -107,28 +107,29 @@ def _validate_config_id(config_id, db: Session = None):
     )
 
 
-def _load_ontology_classes(db: Session, scene_id, pruning_scene: Optional[str]) -> Optional[list]:
-    """从 ontology_class 表加载场景类型名称列表，用于注入提示词。
+def _load_ontology_class_infos(db: Session, scene_id) -> list:
+    """从 ontology_class 表加载完整本体类型信息（name + description），用于注入剪枝提示词。
 
     Args:
         db: 数据库会话
         scene_id: 本体场景 UUID
-        pruning_scene: 语义剪枝场景名称（保留参数，暂未使用）
 
     Returns:
-        class_name 字符串列表，或 None（无数据时）
+        [{"class_name": ..., "class_description": ...}, ...] 或空列表
     """
     if not scene_id:
-        return None
+        return []
     try:
         from app.repositories.ontology_class_repository import OntologyClassRepository
         repo = OntologyClassRepository(db)
         classes = repo.get_classes_by_scene(scene_id)
-        names = [c.class_name for c in classes if c.class_name]
-        return names if names else None
+        return [
+            {"class_name": c.class_name, "class_description": c.class_description or ""}
+            for c in classes if c.class_name
+        ]
     except Exception as e:
-        logger.warning(f"Failed to load ontology classes for scene_id={scene_id}: {e}")
-        return None
+        logger.warning(f"Failed to load ontology class infos for scene_id={scene_id}: {e}")
+        return []
 
 
 class MemoryConfigService:
@@ -383,7 +384,7 @@ class MemoryConfigService:
                 pruning_threshold=float(memory_config.pruning_threshold) if memory_config.pruning_threshold is not None else 0.5,
                 # Ontology scene association
                 scene_id=memory_config.scene_id,
-                ontology_classes=_load_ontology_classes(self.db, memory_config.scene_id, memory_config.pruning_scene),
+                ontology_class_infos=_load_ontology_class_infos(self.db, memory_config.scene_id),
             )
 
             elapsed_ms = (time.time() - start_time) * 1000
@@ -550,11 +551,13 @@ class MemoryConfigService:
             - pruning_switch: bool
             - pruning_scene: str
             - pruning_threshold: float
+            - ontology_class_infos: list of {class_name, class_description} dicts
         """
         return {
             "pruning_switch": memory_config.pruning_enabled,
             "pruning_scene": memory_config.pruning_scene,
             "pruning_threshold": memory_config.pruning_threshold,
+            "ontology_class_infos": memory_config.ontology_class_infos or [],
         }
 
     def get_ontology_types(self, memory_config: MemoryConfig):

api/app/services/memory_forget_service.py

@@ -619,7 +619,7 @@ class MemoryForgetService:
                     recent_trends.append({
                         'date': date_str,
                         'merged_count': record.merged_count,
-                        'average_activation': round(record.average_activation_value, 2) if record.average_activation_value is not None else None,
+                        'average_activation': record.average_activation_value,
                         'total_nodes': record.total_nodes,
                         'execution_time': int(record.execution_time.timestamp() * 1000)
                     })

api/app/services/multimodal_service.py

@@ -11,8 +11,6 @@
 import base64
 import io
 import uuid
-import zipfile
-import chardet
 from abc import ABC, abstractmethod
 from typing import List, Dict, Any, Optional
 
@@ -44,10 +42,12 @@ PDF_MIME = ['application/pdf']
 DOC_MIME = [
     'application/msword',
     'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+    'application/zip'
 ]
 XLSX_MIME = [
     'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
     'application/vnd.ms-excel',
+    'application/zip'
 ]
 CSV_MIME = ['text/csv', 'application/csv']
 JSON_MIME = ['application/json']
@@ -418,71 +418,6 @@ class MultimodalService:
         logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}")
         return result
 
-    async def history_process_files(
-            self,
-            files: Optional[List[FileInput]],
-    ) -> List[Dict[str, Any]]:
-        """
-        处理文件列表，返回 LLM 可用的格式
-
-        Args:
-            files: 文件输入列表
-
-        Returns:
-            List[Dict]: LLM 可用的内容格式列表（根据 provider 返回不同格式）
-        """
-        if not files:
-            return []
-
-        # 获取对应的策略
-        # dashscope 的 omni 模型使用 OpenAI 兼容格式
-        if self.provider == "dashscope" and self.is_omni:
-            strategy_class = OpenAIFormatStrategy
-        else:
-            strategy_class = PROVIDER_STRATEGIES.get(self.provider)
-            if not strategy_class:
-                logger.warning(f"未找到 provider '{self.provider}' 的策略，使用默认策略")
-                strategy_class = DashScopeFormatStrategy
-
-        result = []
-        for idx, file in enumerate(files):
-            strategy = strategy_class(file)
-            if not file.url:
-                file.url = await self.get_file_url(file)
-            try:
-                if file.type == FileType.IMAGE and "vision" in self.capability:
-                    is_support, content = await self._process_image(file, strategy)
-                    result.append(content)
-                elif file.type == FileType.DOCUMENT:
-                    is_support, content = await self._process_document(file, strategy)
-                    result.append(content)
-                elif file.type == FileType.AUDIO and "audio" in self.capability:
-                    is_support, content = await self._process_audio(file, strategy)
-                    result.append(content)
-                elif file.type == FileType.VIDEO and "video" in self.capability:
-                    is_support, content = await self._process_video(file, strategy)
-                    result.append(content)
-                else:
-                    logger.warning(f"不支持的文件类型: {file.type}")
-            except Exception as e:
-                logger.error(
-                    f"处理文件失败",
-                    extra={
-                        "file_index": idx,
-                        "file_type": file.type,
-                        "error": str(e)
-                    },
-                    exc_info=True
-                )
-                # 继续处理其他文件，不中断整个流程
-                result.append({
-                    "type": "text",
-                    "text": f"[文件处理失败: {str(e)}]"
-                })
-
-        logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}")
-        return result
-
     def write_perceptual_memory(
             self,
             end_user_id: str,
@@ -653,12 +588,12 @@ class MultimodalService:
                     file.set_content(file_content)
             file_mime_type = magic.from_buffer(file_content, mime=True)
             if file_mime_type in TEXT_MIME:
-                return self._decode_text_safe(file_content)
+                return file_content.decode("utf-8")
             elif file_mime_type in PDF_MIME:
                 return await self._extract_pdf_text(file_content)
-            elif self._is_word_file(file_content, file_mime_type):
+            elif file_mime_type in DOC_MIME and file.file_type.endswith(('docx', 'doc')):
                 return await self._extract_word_text(file_content)
-            elif self._is_excel_file(file_content, file_mime_type):
+            elif file_mime_type in XLSX_MIME and file.file_type.endswith(("xlsx", "xls")):
                 return await self._extract_xlsx_text(file_content)
             elif file_mime_type in CSV_MIME:
                 return await self._extract_csv_text(file_content)
@@ -687,156 +622,52 @@ class MultimodalService:
 
     @staticmethod
     async def _extract_word_text(file_content: bytes) -> str:
-        """提取 Word 文档文本（支持 .docx 和旧版 .doc）"""
-        # 先尝试 docx（ZIP 格式）
-        if file_content[:2] == b'PK':
-            try:
-                word_file = io.BytesIO(file_content)
-                doc = Document(word_file)
-                return '\n'.join(p.text for p in doc.paragraphs)
-            except Exception as e:
-                logger.error(f"提取 docx 文本失败: {e}")
-                return f"[docx 提取失败: {str(e)}]"
-
-        # 旧版 .doc（OLE2 格式）
+        """提取 Word 文档文本"""
         try:
-            import olefile
-            ole = olefile.OleFileIO(io.BytesIO(file_content))
-            if not ole.exists('WordDocument'):
-                return "[doc 提取失败: 未找到 WordDocument 流]"
-            # 读取 WordDocument 流，提取可见 ASCII/Unicode 文本
-            stream = ole.openstream('WordDocument').read()
-            # Word Binary Format: 文本在流中以 UTF-16-LE 编码存储
-            # 简单提取：过滤出可打印字符段
-            try:
-                text = stream.decode('utf-16-le', errors='ignore')
-            except Exception:
-                text = stream.decode('latin-1', errors='ignore')
-            # 过滤控制字符，保留可打印内容
-            import re
-            text = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]', '', text)
-            text = re.sub(r' +', ' ', text).strip()
-            ole.close()
-            return text
+            word_file = io.BytesIO(file_content)
+            doc = Document(word_file)
+            text_parts = [paragraph.text for paragraph in doc.paragraphs]
+            return '\n'.join(text_parts)
         except Exception as e:
-            logger.error(f"提取 doc 文本失败: {e}")
-            return f"[doc 提取失败: {str(e)}]"
+            logger.error(f"提取 Word 文本失败: {e}")
+            return f"[Word 提取失败: {str(e)}]"
 
     @staticmethod
     async def _extract_xlsx_text(file_content: bytes) -> str:
-        """提取 Excel 文本（支持 .xlsx 和旧版 .xls）"""
-        # xlsx（ZIP 格式）
-        if file_content[:2] == b'PK':
-            try:
-                wb = openpyxl.load_workbook(io.BytesIO(file_content), read_only=True, data_only=True)
-                parts = []
-                for sheet in wb.worksheets:
-                    parts.append(f"[Sheet: {sheet.title}]")
-                    for row in sheet.iter_rows(values_only=True):
-                        parts.append('\t'.join('' if v is None else str(v) for v in row))
-                return '\n'.join(parts)
-            except Exception as e:
-                logger.error(f"提取 xlsx 文本失败: {e}")
-                return f"[xlsx 提取失败: {str(e)}]"
-
-        # xls（OLE2/BIFF 格式）
+        """提取 Excel 文本"""
         try:
-            import xlrd
-            wb = xlrd.open_workbook(file_contents=file_content)
+            wb = openpyxl.load_workbook(io.BytesIO(file_content), read_only=True, data_only=True)
             parts = []
-            for sheet in wb.sheets():
-                parts.append(f"[Sheet: {sheet.name}]")
-                for row_idx in range(sheet.nrows):
-                    parts.append('\t'.join(str(sheet.cell_value(row_idx, col)) for col in range(sheet.ncols)))
+            for sheet in wb.worksheets:
+                parts.append(f"[Sheet: {sheet.title}]")
+                for row in sheet.iter_rows(values_only=True):
+                    parts.append('\t'.join('' if v is None else str(v) for v in row))
             return '\n'.join(parts)
         except Exception as e:
-            logger.error(f"提取 xls 文本失败: {e}")
-            return f"[xls 提取失败: {str(e)}]"
+            logger.error(f"提取 Excel 文本失败: {e}")
+            return f"[Excel 提取失败: {str(e)}]"
 
-    async def _extract_csv_text(self, file_content: bytes) -> str:
+    @staticmethod
+    async def _extract_csv_text(file_content: bytes) -> str:
         """提取 CSV 文本"""
         try:
-            text = self._decode_text_safe(file_content)
+            text = file_content.decode('utf-8-sig')
             reader = csv.reader(io.StringIO(text))
             return '\n'.join('\t'.join(row) for row in reader)
         except Exception as e:
             logger.error(f"提取 CSV 文本失败: {e}")
             return f"[CSV 提取失败: {str(e)}]"
 
-    async def _extract_json_text(self, file_content: bytes) -> str:
+    @staticmethod
+    async def _extract_json_text(file_content: bytes) -> str:
         """提取 JSON 文本"""
         try:
-            text = self._decode_text_safe(file_content)
-            data = json.loads(text)
+            data = json.loads(file_content.decode('utf-8'))
             return json.dumps(data, ensure_ascii=False, indent=2)
         except Exception as e:
             logger.error(f"提取 JSON 文本失败: {e}")
             return f"[JSON 提取失败: {str(e)}]"
 
-    def _is_word_file(self, file_content: bytes, mime_type: str) -> bool:
-        """判断是不是 Word 文件（doc / docx），不依赖后缀"""
-        # 旧版 .doc
-        if mime_type == 'application/msword':
-            return True
-
-        # 新版 .docx（ZIP 内部包含 word/document.xml）
-        header = file_content[:4]
-        if header == b'PK\x03\x04':
-            try:
-                with zipfile.ZipFile(io.BytesIO(file_content)) as zf:
-                    return "word/document.xml" in zf.namelist()
-            except:
-                pass
-
-        return False
-
-    def _is_excel_file(self, file_content: bytes, mime_type: str) -> bool:
-        """判断是不是 Excel 文件（xls / xlsx），不依赖后缀"""
-        # 旧版 .xls
-        if mime_type == 'application/vnd.ms-excel':
-            return True
-
-        # 新版 .xlsx（ZIP 内部包含 xl/workbook.xml）
-        header = file_content[:4]
-        if header == b'PK\x03\x04':
-            try:
-                with zipfile.ZipFile(io.BytesIO(file_content)) as zf:
-                    return "xl/workbook.xml" in zf.namelist()
-            except:
-                pass
-
-        return False
-
-    @staticmethod
-    def _decode_text_safe(file_content: bytes) -> str:
-        """
-        【万能文本解码】
-        自动检测编码，支持 utf-8 / gbk / gb2312 / utf-8-sig / ascii 等
-        永远不报错，永远不乱码
-        """
-        if not file_content:
-            return ""
-
-        # 1. 自动检测文件编码
-        detect = chardet.detect(file_content)
-        encoding = detect.get("encoding") or "utf-8"
-        encoding = encoding.lower()
-
-        # 2. 兼容常见中文编码
-        compatible_encodings = ["utf-8", "gbk", "gb18030", "gb2312", "ascii", "latin-1"]
-
-        # 3. 按优先级尝试解码
-        for enc in [encoding] + compatible_encodings:
-            if not enc:
-                continue
-            try:
-                return file_content.decode(enc.strip())
-            except (UnicodeDecodeError, LookupError):
-                continue
-
-        # 终极兜底
-        return file_content.decode("utf-8", errors="replace")
-
 
 def get_multimodal_service(db: Session) -> MultimodalService:
     """获取多模态服务实例（依赖注入）"""

api/app/services/pilot_run_service.py

@@ -121,7 +121,7 @@ async def run_pilot_extraction(
                     "pruning_scene": memory_config.pruning_scene,
                     "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,
                 }
                 config = PruningConfig(**pruning_config_dict)
                 
@@ -232,9 +232,11 @@ async def run_pilot_extraction(
                 "chunker_strategy": memory_config.chunker_strategy,
             }
             
-            # 添加剪枝统计信息
-            if pruning_stats:
-                preprocessing_summary["pruning"] = pruning_stats
+            # 添加剪枝统计信息（始终包含 pruning 字段，确保前端不会因字段缺失报错）
+            preprocessing_summary["pruning"] = pruning_stats if pruning_stats else {
+                "enabled": memory_config.pruning_enabled,
+                "deleted_count": 0,
+            }
             
             await progress_callback("text_preprocessing_complete", "预处理文本完成（剪枝 + 分块）", preprocessing_summary)
 

api/app/services/user_memory_service.py

@@ -1408,11 +1408,12 @@ async def analytics_memory_types(
     if end_user_id:
         try:
             conversation_repo = ConversationRepository(db)
-            conversations, total = conversation_repo.get_conversation_by_user_id(
+            conversations = conversation_repo.get_conversation_by_user_id(
                 user_id=uuid.UUID(end_user_id),
+                limit=100,  # 获取更多会话以准确统计
                 is_activate=True
             )
-            work_count = total
+            work_count = len(conversations)
             logger.debug(f"工作记忆数量（会话数）: {work_count} (end_user_id={end_user_id})")
         except Exception as e:
             logger.warning(f"获取会话数量失败，工作记忆数量设为0: {str(e)}")

api/app/services/workflow_service.py

@@ -25,7 +25,7 @@ from app.repositories.workflow_repository import (
     WorkflowExecutionRepository,
     WorkflowNodeExecutionRepository
 )
-from app.schemas import DraftRunRequest, FileInput, FileType
+from app.schemas import DraftRunRequest, FileInput
 from app.services.conversation_service import ConversationService
 from app.services.multi_agent_service import convert_uuids_to_str
 from app.services.multimodal_service import MultimodalService
@@ -55,7 +55,6 @@ class WorkflowService:
             edges: list[dict[str, Any]],
             variables: list[dict[str, Any]] | None = None,
             execution_config: dict[str, Any] | None = None,
-            features: dict[str, Any] | None = None,
             triggers: list[dict[str, Any]] | None = None,
             validate: bool = True
     ) -> WorkflowConfig:
@@ -67,7 +66,6 @@ class WorkflowService:
             edges: 边列表
             variables: 变量列表
             execution_config: 执行配置
-            features: 功能特性
             triggers: 触发器列表
             validate: 是否验证配置
 
@@ -83,7 +81,6 @@ class WorkflowService:
             "edges": edges,
             "variables": variables or [],
             "execution_config": execution_config or {},
-            "features": features or {},
             "triggers": triggers or []
         }
 
@@ -104,7 +101,6 @@ class WorkflowService:
             edges=edges,
             variables=variables,
             execution_config=execution_config,
-            features=features,
             triggers=triggers
         )
 

api/app/tasks.py

@@ -2675,15 +2675,13 @@ def write_perceptual_memory(
     time_limit=7200,   # 2小时硬超时
     soft_time_limit=6900,
 )
-def init_community_clustering_for_users(self, end_user_ids: List[str], workspace_id: Optional[str] = None) -> Dict[str, Any]:
+def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]:
     """触发型任务：检查指定用户列表，对有 ExtractedEntity 但无 Community 节点的用户执行全量聚类。
 
     由 /dashboard/end_users 接口触发，已有社区节点的用户直接跳过。
-    任务完成且所有用户数据均完整时，写入 Redis 标记，避免下次重复投递。
 
     Args:
         end_user_ids: 需要检查的用户 ID 列表
-        workspace_id: 工作空间 ID，用于完成标记
 
     Returns:
         包含任务执行结果的字典
@@ -2709,7 +2707,6 @@ def init_community_clustering_for_users(self, end_user_ids: List[str], workspace
 
             # 批量预取所有用户的配置（内置兜底：用户配置不可用时自动回退到工作空间默认配置）
             user_llm_map: Dict[str, Optional[str]] = {}
-            user_embedding_map: Dict[str, Optional[str]] = {}
             try:
                 with get_db_context() as db:
                     from app.services.memory_agent_service import get_end_users_connected_configs_batch
@@ -2721,54 +2718,21 @@ def init_community_clustering_for_users(self, end_user_ids: List[str], workspace
                             try:
                                 cfg = MemoryConfigService(db).load_memory_config(config_id=config_id)
                                 user_llm_map[uid] = str(cfg.llm_model_id) if cfg.llm_model_id else None
-                                user_embedding_map[uid] = str(cfg.embedding_model_id) if cfg.embedding_model_id else None
                             except Exception as e:
-                                logger.warning(f"[CommunityCluster] 用户 {uid} 加载配置失败，将使用 None: {e}")
+                                logger.warning(f"[CommunityCluster] 用户 {uid} 加载 LLM 配置失败，将使用 None: {e}")
                                 user_llm_map[uid] = None
-                                user_embedding_map[uid] = None
                         else:
                             user_llm_map[uid] = None
-                            user_embedding_map[uid] = None
             except Exception as e:
-                logger.warning(f"[CommunityCluster] 批量获取配置失败，所有用户将使用 None: {e}")
+                logger.warning(f"[CommunityCluster] 批量获取 LLM 配置失败，所有用户将使用 None: {e}")
 
             for end_user_id in end_user_ids:
                 try:
-                    # 已有社区节点时，检查是否存在属性不完整的节点
+                    # 已有社区节点则跳过
                     has_communities = await repo.has_communities(end_user_id)
                     if has_communities:
-                        llm_model_id = user_llm_map.get(end_user_id)
-                        embedding_model_id = user_embedding_map.get(end_user_id)
-                        incomplete_ids = await repo.get_incomplete_communities(
-                            end_user_id, check_embedding=bool(embedding_model_id)
-                        )
-                        if not incomplete_ids:
-                            skipped += 1
-                            logger.debug(f"[CommunityCluster] 用户 {end_user_id} 社区节点均完整，跳过")
-                            continue
-
-                        # 对不完整的社区节点逐一补全元数据
-                        engine = LabelPropagationEngine(
-                            connector=connector,
-                            llm_model_id=llm_model_id,
-                            embedding_model_id=embedding_model_id,
-                        )
-                        logger.info(
-                            f"[CommunityCluster] 用户 {end_user_id} 发现 {len(incomplete_ids)} 个属性不完整的社区，开始补全"
-                        )
-                        patch_ok = 0
-                        patch_fail = 0
-                        for cid in incomplete_ids:
-                            try:
-                                await engine._generate_community_metadata(cid, end_user_id)
-                                patch_ok += 1
-                            except Exception as patch_err:
-                                patch_fail += 1
-                                logger.error(f"[CommunityCluster] 社区 {cid} 元数据补全失败: {patch_err}")
-                        logger.info(
-                            f"[CommunityCluster] 用户 {end_user_id} 社区补全完成: 成功={patch_ok}, 失败={patch_fail}"
-                        )
-                        initialized += 1
+                        skipped += 1
+                        logger.debug(f"[CommunityCluster] 用户 {end_user_id} 已有社区节点，跳过")
                         continue
 
                     # 检查是否有 ExtractedEntity 节点
@@ -2778,13 +2742,11 @@ def init_community_clustering_for_users(self, end_user_ids: List[str], workspace
                         logger.debug(f"[CommunityCluster] 用户 {end_user_id} 无实体节点，跳过")
                         continue
 
-                    # 每个用户使用自己的 llm_model_id / embedding_model_id
+                    # 每个用户使用自己的 llm_model_id
                     llm_model_id = user_llm_map.get(end_user_id)
-                    embedding_model_id = user_embedding_map.get(end_user_id)
                     engine = LabelPropagationEngine(
                         connector=connector,
                         llm_model_id=llm_model_id,
-                        embedding_model_id=embedding_model_id,
                     )
 
                     logger.info(f"[CommunityCluster] 用户 {end_user_id} 有 {len(entities)} 个实体，开始全量聚类，llm_model_id={llm_model_id}")

api/app/version_info.json

@@ -1,38 +1,4 @@
 {
-  "v0.2.8": {
-    "introduction": {
-      "codeName": "景玉",
-      "releaseDate": "2026-3-20",
-      "upgradePosition": "🐻 MemoryBear v0.2.8 社区版全面升级应用共享、多模态交互与平台基础设施，引入语音交互、感知记忆和云端存储，打造更强大的开放 AI 记忆平台",
-      "coreUpgrades": [
-        "1. 应用共享与发布<br>* 应用共享（Agent、工作流、Agent 集群）：全类型应用共享至其他空间<br>* 分享应用默认开启记忆功能：发布分享后记忆默认开启，关闭时提醒<br>* 工作流记忆分享规则：按记忆配置自动控制分享页记忆开关<br>* 分享会话联网搜索修复：恢复分享应用的联网搜索能力",
-        "2. 多模态与交互 💬<br>* 语音输入：模型接口和应用支持语音输入<br>* 语音回复：应用支持语音回复模态<br>* 多模态感知记忆：记忆系统支持视觉、音频、图片和文件的感知记忆<br>* 对话框文件展示：试运行和体验分享中正确展示上传文件",
-        "3. 平台与基础设施 ⚙️<br>* i18n 国际化：全面多语言多地区支持<br>* 云端文件存储（OSS + S3）：支持阿里云 OSS 和 S3 云端上传<br>* Flower 容器监控：Celery 异步任务监控与管理",
-        "4. EndUser 身份迁移 🔐<br>* EndUser 从 app_id 迁移至 workspace_id：身份从应用级迁移至工作空间级",
-        "5. 情景记忆 🧠<br>* 情景记忆聚类算法：基于社区图谱的聚类算法，支持老用户图谱生成",
-        "6. 稳健性与缺陷修复 🔧<br>* MCP 服务删除后工具 404：修复删除 MCP 服务后接口报错<br>* 应用导出配置不一致：导出已保存配置而非画布状态<br>* 工作流节点 ID 重复：修复复制节点后 ID 冲突<br>* 条件分支连线错误：修复保存刷新后连线错乱<br>* 回复节点内容丢失：修复点击画布后内容消失<br>* 连接桩规则优化：禁止非法连接方向<br>* 知识库状态列宽度：锁定或自适应宽度<br>* 等待中文档预览：支持未完成解析文档预览<br>* 知识库关联修复：统一修复关联问题<br>* 多模态对话连续性：修复多模态内容后无法继续对话<br>* 时区统一：环境变量统一控制存储和任务时区<br>* 遗忘强度精度：修复小数显示过长",
-        "<br>",
-        "v0.2.8 社区版在应用共享和多模态交互方面实现重大升级，感知记忆扩展了平台的认知维度。后续将深化多智能体协作、情景记忆聚类，并持续优化平台稳定性与开放生态。",
-        "MemoryBear —— 让 AI 拥有记忆 🐻✨"
-      ]
-    },
-    "introduction_en": {
-      "codeName": "JingYu",
-      "releaseDate": "2026-3-20",
-      "upgradePosition": "🐻 MemoryBear v0.2.8 Community delivers multimodal interaction, perceptual memory, cloud storage, and workspace-level identity for a more capable open AI memory platform",
-      "coreUpgrades": [
-        "1. Application Sharing & Publishing<br>* Application Sharing (Agent, Workflow, Agent Cluster): Full sharing across all app types<br>* Memory Enabled by Default: Memory auto-enabled on shared apps with disable reminder<br>* Workflow Memory Sharing Rules: Auto-controlled based on memory configuration<br>* Shared Session Web Search Fix: Restored web search for shared apps",
-        "2. Multimodal & Interaction 💬<br>* Voice Input: Model interfaces and apps support voice input<br>* Voice Reply: Apps support voice reply modality<br>* Multimodal Perceptual Memory: Memory system supports visual, audio, image, and file perception<br>* File Display in Chat: Uploaded files display correctly in dry-run and sharing",
-        "3. Platform & Infrastructure ⚙️<br>* i18n Internationalization: Full multi-language multi-region support<br>* Cloud File Storage (OSS + S3): Alibaba Cloud OSS and S3 cloud uploads<br>* Flower Container Monitoring: Celery async task monitoring and management",
-        "4. EndUser Identity Migration 🔐<br>* EndUser Migration from app_id to workspace_id: Identity migrated to workspace level",
-        "5. Episodic Memory 🧠<br>* Episodic Memory Clustering: Community-graph-based clustering with legacy user support",
-        "6. Robustness & Bug Fixes 🔧<br>* MCP Service Deletion 404: Fixed tool endpoint error after MCP removal<br>* App Export Config Mismatch: Exports saved config instead of canvas state<br>* Workflow Duplicate Node ID: Fixed ID conflict on node duplication<br>* Conditional Branch Wiring: Fixed wiring reset after save/refresh<br>* Reply Node Content Loss: Fixed content disappearing on canvas click<br>* Port Connection Rules: Prohibited invalid connection directions<br>* Knowledge Base Status Width: Locked or adaptive column width<br>* Pending Document Preview: Preview support for unparsed documents<br>* Knowledge Base Association Fixes: Consolidated association fixes<br>* Multimodal Conversation Continuity: Fixed single-round limit after multimodal input<br>* Timezone Unification: Env-var controlled unified timezone<br>* Forgetting Strength Precision: Fixed excessive decimal display",
-        "<br>",
-        "v0.2.8 Community delivers major upgrades in application sharing and multimodal interaction, with perceptual memory expanding the platform's cognitive dimensions. Multi-agent collaboration, episodic clustering, and continued platform stability improvements are ahead.",
-        "MemoryBear — Give AI Memory 🐻✨"
-      ]
-    }
-  },
   "v0.2.7": {
     "introduction": {
       "codeName": "武陵",

api/migrations/versions/74b51dfece29_20260311000.py

@@ -0,0 +1,156 @@
+"""20260311000
+
+Revision ID: 74b51dfece29
+Revises: f017efe4831c
+Create Date: 2026-03-19 10:15:42.488027
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '74b51dfece29'
+down_revision: Union[str, None] = 'f017efe4831c'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # 先删除旧的触发器（如果存在）
+    op.execute("DROP TRIGGER IF EXISTS tr_documents_update_stats ON documents;")
+
+    # 创建或更新 knowledges 统计信息的函数
+    op.execute("""
+CREATE OR REPLACE FUNCTION update_knowledge_stats()
+RETURNS TRIGGER AS $$
+DECLARE
+    -- 声明变量用于存储当前处理的知识库ID
+    current_kb_id UUID;
+    -- 声明变量用于存储文件夹知识库ID（如果存在）
+    folder_kb_id UUID;
+    -- 声明变量用于存储递归查询结果
+    folder_ids UUID[];
+BEGIN
+    -- 处理 documents 表的插入、更新或删除
+    IF TG_TABLE_NAME = 'documents' THEN
+        -- 1. 更新 knowledges 表的 doc_num
+        UPDATE knowledges SET doc_num = (
+            SELECT COUNT(*) FROM documents
+            WHERE kb_id = knowledges.id AND status = 1
+        )
+        WHERE id = NEW.kb_id OR id = OLD.kb_id;
+
+        -- 2. 更新 knowledges 表的 chunk_num
+        UPDATE knowledges SET chunk_num = (
+            SELECT COALESCE(SUM(chunk_num), 0) FROM documents
+            WHERE kb_id = knowledges.id AND status = 1
+        )
+        WHERE id = NEW.kb_id OR id = OLD.kb_id;
+
+        -- 通过 knowledge_shares 表同步统计信息
+        -- 1. 使用 source_kb_id 的 doc_num 更新 target_kb_id 的 doc_num
+        UPDATE knowledges AS target
+        SET doc_num = source.doc_num
+        FROM knowledge_shares ks
+        JOIN knowledges AS source ON source.id = ks.source_kb_id
+        WHERE ks.target_kb_id = target.id
+          AND (source.id = NEW.kb_id OR source.id = OLD.kb_id);
+
+        -- 2. 使用 source_kb_id 的 chunk_num 更新 target_kb_id 的 chunk_num
+        UPDATE knowledges AS target
+        SET chunk_num = source.chunk_num
+        FROM knowledge_shares ks
+        JOIN knowledges AS source ON source.id = ks.source_kb_id
+        WHERE ks.target_kb_id = target.id
+          AND (source.id = NEW.kb_id OR source.id = OLD.kb_id);
+
+        -- 处理文件夹知识库的统计更新
+        -- 获取当前处理的知识库ID（可能是NEW或OLD中的kb_id）
+        IF NEW.kb_id IS NOT NULL THEN
+            current_kb_id := NEW.kb_id;
+        ELSIF OLD.kb_id IS NOT NULL THEN
+            current_kb_id := OLD.kb_id;
+        ELSE
+            RETURN NULL;
+        END IF;
+
+        -- 查找当前知识库的父文件夹（如果有）
+        SELECT id INTO folder_kb_id FROM knowledges
+        WHERE id IN (
+            SELECT parent_id FROM knowledges WHERE id = current_kb_id
+        ) AND type = 'Folder';
+
+        -- 如果存在父文件夹，递归处理所有父文件夹
+        IF folder_kb_id IS NOT NULL THEN
+            -- 使用递归CTE获取所有父文件夹ID（包括多级嵌套）
+            WITH RECURSIVE folder_hierarchy AS (
+                -- 基础查询：获取直接父文件夹
+                SELECT id FROM knowledges
+                WHERE id = folder_kb_id AND type = 'Folder'
+                UNION ALL
+                -- 递归查询：获取父文件夹的父文件夹
+                SELECT k.id FROM knowledges k
+                JOIN folder_hierarchy fh ON k.id = k.parent_id
+                WHERE k.type = 'Folder'
+            )
+            -- 将结果存入数组以便处理
+            SELECT array_agg(id) INTO folder_ids FROM folder_hierarchy;
+
+            -- 遍历所有父文件夹并更新统计信息
+            FOR i IN 1..array_length(folder_ids, 1) LOOP
+                -- 更新文件夹的doc_num（汇总所有子知识库的doc_num）
+                UPDATE knowledges SET doc_num = (
+                    -- 汇总直接子知识库的doc_num
+                    SELECT COALESCE(SUM(child.doc_num), 0)
+                    FROM knowledges child
+                    WHERE child.parent_id = folder_ids[i] AND child.status = 1
+                    -- 加上直接属于该文件夹的文档数（如果有）
+                    UNION ALL
+                    SELECT COALESCE(COUNT(*), 0)
+                    FROM documents
+                    WHERE kb_id = folder_ids[i] AND status = 1
+                    LIMIT 1
+                )
+                WHERE id = folder_ids[i];
+
+                -- 更新文件夹的chunk_num（汇总所有子知识库的chunk_num）
+                UPDATE knowledges SET chunk_num = (
+                    -- 汇总直接子知识库的chunk_num
+                    SELECT COALESCE(SUM(child.chunk_num), 0)
+                    FROM knowledges child
+                    WHERE child.parent_id = folder_ids[i] AND child.status = 1
+                    -- 加上直接属于该文件夹的文档的chunk_num（如果有）
+                    UNION ALL
+                    SELECT COALESCE(SUM(d.chunk_num), 0)
+                    FROM documents d
+                    WHERE d.kb_id = folder_ids[i] AND d.status = 1
+                    LIMIT 1
+                )
+                WHERE id = folder_ids[i];
+            END LOOP;
+        END IF;
+    END IF;
+
+    RETURN NULL;
+END;
+$$ LANGUAGE plpgsql;
+    """)
+
+    # documents 表上的触发器（插入、更新、删除后）
+    op.execute("""
+CREATE TRIGGER tr_documents_update_stats
+    AFTER INSERT OR UPDATE OR DELETE ON documents
+    FOR EACH ROW
+    EXECUTE FUNCTION update_knowledge_stats();
+    """)
+
+
+def downgrade() -> None:
+    # 删除触发器
+    op.execute("DROP TRIGGER IF EXISTS tr_documents_update_stats ON documents;")
+    # 删除函数
+    op.execute("DROP FUNCTION IF EXISTS update_knowledge_stats();")
+

api/tests/workflow/executor/test_vairable_pool.py

@@ -303,7 +303,7 @@ async def test_get_node_output_not_exist_with_default():
     """测试获取不存在的节点输出（使用默认值）"""
     pool = VariablePool()
     
-    result = pool.get_node_output("nonexistent_node", defalut=None, strict=False)
+    result = pool.get_node_output("nonexistent_node", default=None, strict=False)
     
     assert result is None
 

web/src/api/knowledgeBase.ts

@@ -52,10 +52,6 @@ export const getKnowledgeBaseTypeList = async (): Promise<string[]> => {
   // 如果不是数组，返回空数组
   return [];
 };
-// 获取文件地址
-export const getFileUrl = (fileId: string) => {
-  return `${apiPrefix}/files/${fileId}`;
-};
 // 知识库文档解析类型
 export const getKnowledgeBaseDocumentParseTypeList = async () => {
   const response = await request.get(`${apiPrefix}/knowledges/parsertype`);

web/src/api/memory.ts

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-03 14:00:06 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 18:35:10
+ * @Last Modified time: 2026-03-13 10:48:41
  */
 import { request } from '@/utils/request'
 import type { AxiosRequestConfig } from 'axios'
@@ -218,8 +218,8 @@ export const getExplicitMemory = (end_user_id: string) => {
 export const getExplicitMemoryDetails = (data: { end_user_id: string, memory_id: string; }) => {
   return request.post(`/memory/explicit-memory/details`, data)
 }
-export const getConversations = (end_user_id: string, page = 1, pagesize = 20) => {
-  return request.get(`/memory/work/${end_user_id}/conversations`, { page, pagesize })
+export const getConversations = (end_user_id: string) => {
+  return request.get(`/memory/work/${end_user_id}/conversations`)
 }
 export const getConversationMessages = (end_user_id: string, conversation_id: string) => {
   return request.get(`/memory/work/${end_user_id}/messages`, { conversation_id })

web/src/components/Chat/ChatContent.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2025-12-10 16:46:17 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 19:45:40
+ * @Last Modified time: 2026-03-19 13:38:20
  */
 import { type FC, useRef, useEffect, useState } from 'react'
 import clsx from 'clsx'
@@ -143,20 +143,15 @@ const ChatContent: FC<ChatContentProps> = ({
                     }
                     return (
                       <div key={file.url || file.uid} className="rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:p-1! rb:cursor-pointer" onClick={() => handleDownload(file)}>
-                        {(file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv'))
-                          ? <div
-                            className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel.svg')]"
-                          ></div>
-                          :(file.type.includes('pdf'))
-                          ? <div
-                            className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf.svg')]"
-                          ></div>
-                          : (file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document'))
-                          ? <div
-                            className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word.svg')]"
-                          ></div>
-                          : null
-                        }
+                        {(file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document')) && <div
+                          className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word.svg')]"
+                        ></div>}
+                        {(file.type.includes('pdf')) && <div
+                          className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf.svg')]"
+                        ></div>}
+                        {(file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv')) && <div
+                          className="rb:size-10 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel.svg')]"
+                        ></div>}
                       </div>
                     )
                   })}

web/src/components/Chat/ChatInput.tsx

@@ -2,11 +2,10 @@
  * @Author: ZhaoYing 
  * @Date: 2025-12-10 16:46:14 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 18:44:51
+ * @Last Modified time: 2026-03-19 16:05:56
  */
 import { type FC, useEffect, useMemo } from 'react'
-import { Flex, Input, Form, Spin } from 'antd'
-import clsx from 'clsx'
+import { Flex, Input, Form } from 'antd'
 
 import SendIcon from '@/assets/images/conversation/send.svg'
 import SendDisabledIcon from '@/assets/images/conversation/sendDisabled.svg'
@@ -70,8 +69,6 @@ const ChatInput: FC<ChatInputProps> = ({
     onSend(values.message)
   }
 
-  console.log('previewFileList', previewFileList)
-
   return (
     <div className={`rb:absolute rb:bottom-3 rb:left-0 rb:right-0 rb:w-full ${className}`}>
       <Flex vertical justify="space-between" className="rb:border rb:border-[#DFE4ED] rb:rounded-xl rb:min-h-30">
@@ -79,78 +76,62 @@ const ChatInput: FC<ChatInputProps> = ({
           {previewFileList.map((file) => {
             if (file.type.includes('image')) {
               return (
-                <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
-                  <div key={file.url || file.uid} className={clsx("rb:inline-block rb:group rb:relative rb:rounded-lg", {
-                    'rb:border rb:border-[#FF5D34]': file.status === 'error'
-                  })}>
-                    <img src={file.url} alt={file.name} className="rb:size-12! rb:rounded-lg rb:object-cover rb:cursor-pointer" />
-                    <div
-                      className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
-                      onClick={() => handleDelete(file)}
-                    ></div>
-                  </div>
-                </Spin>
-              )
-            }
-            if (file.type.includes('video')) {
-              return (
-                <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
-                  <div key={file.url || file.uid} className={clsx("rb:w-45 rb:h-16 rb:inline-block rb:group rb:relative rb:rounded-lg", {
-                    'rb:border rb:border-[#FF5D34]': file.status === 'error'
-                  })}>
-                    <video src={file.url} controls className="rb:w-45 rb:h-15.5 rb:rounded-lg rb:object-cover rb:cursor-pointer" />
-                    <div
-                      className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
-                      onClick={() => handleDelete(file)}
-                    ></div>
-                  </div>
-                </Spin>
-              )
-            }
-            if (file.type.includes('audio')) {
-              return (
-                <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
-                  <div key={file.url || file.uid} className={clsx("rb:w-45 rb:h-16 rb:inline-flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5 rb:gap-2", {
-                    'rb:border rb:border-[#FF5D34]': file.status === 'error'
-                  })}>
-                    <audio src={file.url} controls className="rb:w-45 rb:h-15.5" />
-                    <div
-                      className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
-                      onClick={() => handleDelete(file)}
-                    ></div>
-                  </div>
-                </Spin>
-              )
-            }
-            return (
-              <Spin key={`${file.url || file.uid}_${file.status}`} spinning={file.status === 'uploading'}>
-                <div key={file.url || file.uid} className={clsx("rb:w-45 rb:text-[12px] rb:gap-2.5 rb:flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5", {
-                  'rb:border rb:border-[#FF5D34]': file.status === 'error'
-                })}>
-                  {file.type.includes('pdf')
-                    ? <div
-                      className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/pdf.svg')]"
-                    ></div>
-                    : (file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv'))
-                    ? <div
-                      className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/excel.svg')]"
-                    ></div>
-                    : (file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document'))
-                    ? <div
-                      className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/word.svg')]"
-                    ></div>
-                    : null
-                  }
-                  <div className="rb:flex-1 rb:w-32.5">
-                    <div className="rb:leading-4 rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.name}</div>
-                    <div className="rb:leading-3.5 rb:mt-0.5 rb:text-[#5B6167] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.type} · {file.size}</div>
-                  </div>
+                <div key={file.url || file.uid} className="rb:inline-block rb:group rb:relative rb:rounded-lg">
+                  <img src={file.url} alt={file.name} className="rb:size-12! rb:rounded-lg rb:object-cover rb:cursor-pointer" />
                   <div
                     className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
                     onClick={() => handleDelete(file)}
                   ></div>
-                  </div>
-              </Spin>
+                </div>
+              )
+            }
+            if (file.type.includes('video')) {
+              return (
+                <div key={file.url || file.uid} className="rb:w-45 rb:h-16 rb:inline-block rb:group rb:relative rb:rounded-lg">
+                  <video src={file.url} controls className="rb:w-45 rb:h-16 rb:rounded-lg rb:object-cover rb:cursor-pointer" />
+                  <div
+                    className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
+                    onClick={() => handleDelete(file)}
+                  ></div>
+                </div>
+              )
+            }
+            if (file.type.includes('audio')) {
+              return (
+                <div key={file.url || file.uid} className="rb:w-45 rb:h-16 rb:inline-flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5 rb:gap-2">
+                  <audio src={file.url} controls className="rb:w-45 rb:h-16" />
+                  <div
+                    className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
+                    onClick={() => handleDelete(file)}
+                  ></div>
+                </div>
+              )
+            }
+            return (
+              <div key={file.url || file.uid} className="rb:w-45 rb:text-[12px] rb:gap-2.5 rb:flex rb:items-center rb:group rb:relative rb:rounded-lg rb:bg-[#F0F3F8] rb:py-2 rb:px-2.5">
+                {file.type.includes('pdf')
+                  ? <div
+                    className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/pdf_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/pdf.svg')]"
+                  ></div>
+                  : (file.type.includes('excel') || file.type.includes('spreadsheetml.sheet') || file.type.includes('csv'))
+                  ? <div
+                    className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/excel_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/excel.svg')]"
+                  ></div>
+                  : (file.type.includes('doc') || file.type.includes('docx') || file.type.includes('word') || file.type.includes('wordprocessingml.document'))
+                  ? <div
+                    className="rb:size-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/word_disabled.svg')] rb:hover:bg-[url('@/assets/images/conversation/word.svg')]"
+                  ></div>
+                  : null
+                }
+                <div className="rb:flex-1 rb:w-32.5">
+                  <div className="rb:leading-4 rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.name}</div>
+                  <div className="rb:leading-3.5 rb:mt-0.5 rb:text-[#5B6167] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{file.type} · {file.size}</div>
+                </div>
+                <div
+                  className="rb:hidden rb:group-hover:block rb:absolute rb:-right-1 rb:-top-1 rb:size-3.5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/conversation/delete.svg')] rb:hover:bg-[url('@/assets/images/conversation/delete_hover.svg')]"
+                  onClick={() => handleDelete(file)}
+                ></div>
+              </div>
             )
           })}
         </Flex></div>}

web/src/components/Chat/ChatToolbar.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-03-17 14:22:25 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 18:59:37
+ * @Last Modified time: 2026-03-18 15:55:13
  */
 // Toolbar component for chat input area, supporting file upload, audio recording, and variable configuration
 import { useRef, forwardRef, useImperativeHandle, type ReactNode, useEffect } from 'react'
@@ -49,7 +49,6 @@ interface FormValues {
   memory?: boolean;
 }
 
-const max_file_count = 1;
 const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
   features,
   extra,
@@ -86,18 +85,10 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
 
   // Append newly uploaded file to the file list when upload is complete
   const fileChange = (file?: any) => {
-    console.log('file', file)
-    const lastFiles = form.getFieldValue('files') || [];
-    const index = lastFiles.findIndex((item: any) => item.uid === file.uid)
-    if (index > -1) {
-      lastFiles[index] = file
-    } else {
-      lastFiles.push(file)
-    }
-    form.setFieldValue('files', [...lastFiles])
-    onFilesChange?.([...lastFiles])
-
-    console.log('lastFiles', lastFiles)
+    if (file?.status !== 'done') return
+    const files = [...(queryValues?.files || []), file]
+    form.setFieldValue('files', files)
+    onFilesChange?.(files)
   }
 
   // Append recorded audio file to the file list and notify parent
@@ -137,8 +128,8 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
       key: 'url',
       label: t('memoryConversation.addRemoteFile'),
       onClick: () => {
-        if ((queryValues?.files?.length || 0) >= max_file_count) {
-          messageApi.warning(t('common.fileNumTip', { num: max_file_count }))
+        if ((queryValues?.files?.length || 0) >= file_upload.max_file_count) {
+          messageApi.warning(t('common.fileNumTip', { num: file_upload.max_file_count }))
           return
         }
         uploadFileListModalRef.current?.handleOpen()
@@ -154,7 +145,7 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
           onChange={fileChange}
           requestConfig={uploadRequestConfig}
           featureConfig={file_upload}
-          disabled={(queryValues?.files?.length || 0) >= max_file_count}
+          disabled={(queryValues?.files?.length || 0) >= file_upload.max_file_count}
         />
       )
     })
@@ -186,7 +177,7 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
         {file_upload?.audio_enabled && file_upload?.allowed_transfer_methods?.includes('local_file') && (
           <Flex align="center">
             <AudioRecorder
-              disabled={(queryValues?.files?.length || 0) >= max_file_count}
+              disabled={(queryValues?.files?.length || 0) >= file_upload.max_file_count}
               action={uploadAction}
               requestConfig={uploadRequestConfig}
               onRecordingComplete={handleRecordingComplete}

web/src/components/DocumentPreview/index.tsx

@@ -4,7 +4,7 @@
  * @Author: yujiangping
  * @Date: 2026-03-16 19:01:12
  * @LastEditors: yujiangping
- * @LastEditTime: 2026-03-20 12:12:20
+ * @LastEditTime: 2026-03-18 18:35:53
  */
 import { useState, useEffect, useRef, useCallback, type FC } from 'react';
 import { Spin, Alert, Button, Table, InputNumber, Image } from 'antd';
@@ -309,64 +309,23 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
     }
   };
 
-  const [csvTruncated, setCsvTruncated] = useState(false);
-
   const isCsvFile = () => getFileExtension() === '.csv';
 
-  // CSV 预览大小限制：1MB
-  const CSV_PREVIEW_SIZE = 1 * 1024 * 1024;
-  // 最大预览行数
-  const MAX_PREVIEW_ROWS = 500;
-
-  const fetchFileBufferWithLimit = async (url: string, maxBytes?: number): Promise<ArrayBuffer> => {
-    const requestUrl = getRequestUrl(url);
-    const headers: Record<string, string> = {
-      'Authorization': `Bearer ${cookieUtils.get('authToken') || ''}`,
-    };
-    if (maxBytes) {
-      headers['Range'] = `bytes=0-${maxBytes - 1}`;
-    }
-    const response = await fetch(requestUrl, {
-      credentials: 'include',
-      headers,
-    });
-    if (!response.ok && response.status !== 206) {
-      throw new Error(`HTTP ${response.status}: ${response.statusText}`);
-    }
-    return response.arrayBuffer();
-  };
-
   const loadExcelFile = async () => {
     setLoading(true);
     setError(false);
     setErrorMessage('');
-    setCsvTruncated(false);
     try {
-      // CSV 文件需要处理编码问题（可能是 GBK/GB2312），且大文件只取前 1MB
-      if (isCsvFile()) {
-        let arrayBuffer: ArrayBuffer;
-        let truncated = false;
-        try {
-          // 先尝试 Range 请求只取前 1MB
-          arrayBuffer = await fetchFileBufferWithLimit(fileUrl, CSV_PREVIEW_SIZE);
-          // 如果返回的数据刚好等于限制大小，说明可能被截断了
-          if (arrayBuffer.byteLength >= CSV_PREVIEW_SIZE) {
-            truncated = true;
-          }
-        } catch {
-          // Range 请求不支持时，全量获取后截断
-          const fullBuffer = await fetchFileBuffer(fileUrl);
-          if (fullBuffer.byteLength > CSV_PREVIEW_SIZE) {
-            arrayBuffer = fullBuffer.slice(0, CSV_PREVIEW_SIZE);
-            truncated = true;
-          } else {
-            arrayBuffer = fullBuffer;
-          }
-        }
+      const arrayBuffer = await fetchFileBuffer(fileUrl);
 
+      // CSV 文件需要处理编码问题（可能是 GBK/GB2312）
+      if (isCsvFile()) {
         let csvText: string;
+        // 先尝试 UTF-8 解码
         const utf8Text = new TextDecoder('utf-8').decode(arrayBuffer);
+        // 检测是否有乱码特征（常见的 GBK 被错误解析为 UTF-8 的替换字符）
         if (utf8Text.includes('\uFFFD') || /[\x80-\xff]/.test(utf8Text.slice(0, 200))) {
+          // 尝试 GBK 解码
           try {
             csvText = new TextDecoder('gbk').decode(arrayBuffer);
           } catch {
@@ -375,35 +334,19 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
         } else {
           csvText = utf8Text;
         }
-
-        // 如果被截断，去掉最后一行不完整的数据
-        if (truncated) {
-          const lastNewline = csvText.lastIndexOf('\n');
-          if (lastNewline > 0) {
-            csvText = csvText.substring(0, lastNewline);
-          }
-        }
-
         const workbook = XLSX.read(csvText, { type: 'string' });
         const sheets = workbook.SheetNames.map(sheetName => {
           const worksheet = workbook.Sheets[sheetName];
-          let data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
-          // 限制最大行数
-          if (data.length > MAX_PREVIEW_ROWS + 1) {
-            data = data.slice(0, MAX_PREVIEW_ROWS + 1); // +1 保留表头
-            truncated = true;
-          }
+          const data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
           return { sheetName, data };
         });
-        setCsvTruncated(truncated);
         setExcelData(sheets);
         setLoading(false);
         return;
       }
 
-      const arrayBuffer = await fetchFileBuffer(fileUrl);
       const workbook = XLSX.read(arrayBuffer, { type: 'array' });
-      const sheets = workbook.SheetNames.map((sheetName: string) => {
+      const sheets = workbook.SheetNames.map(sheetName => {
         const worksheet = workbook.Sheets[sheetName];
         const data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
         return { sheetName, data };
@@ -579,14 +522,9 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
         )
       )}
 
-      {/* Excel/CSV 预览 */}
+      {/* Excel 预览 */}
       {isExcelFile() && !error && !loading && (
         <div className="rb:w-full rb:flex-1 rb:overflow-auto rb:bg-white rb:p-4 rb:rounded rb:border rb:border-gray-200">
-          {csvTruncated && (
-            <div className="rb:mb-3 rb:px-3 rb:py-2 rb:bg-yellow-50 rb:border rb:border-yellow-200 rb:rounded rb:text-sm rb:text-yellow-700">
-              文件较大，仅预览前 {MAX_PREVIEW_ROWS} 行数据
-            </div>
-          )}
           {excelData.map((sheet, index) => (
             <div key={index} className="rb:mb-6">
               <h3 className="rb:text-lg rb:font-semibold rb:mb-3">{sheet.sheetName}</h3>
@@ -603,7 +541,6 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
                   scroll={{ x: 'max-content' }}
                   size="small"
                   bordered
-                  virtual
                 />
               )}
             </div>

web/src/i18n/en.ts

@@ -460,7 +460,6 @@ export const en = {
       nameInvalid: 'Name cannot start or end with a space',
       notAllSpaces: 'Cannot be all spaces',
       view: 'View',
-      callbackUrlInvalid: 'Please enter a valid URL',
     },
     model: {
       searchPlaceholder: 'search model…',
@@ -1774,6 +1773,8 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re
       memoryConversationAnalysisEmpty: 'No conversation analysis available.',
       memoryConversationAnalysisEmptySubTitle: 'Conversation analysis will appear here.',
 
+      communities: 'Cluster Communities',
+      summaries: 'Memory Summaries',
       uploadFile: 'Upload File',
       fileType: 'File Type',
       image: 'Image',

web/src/i18n/zh.ts

@@ -1093,7 +1093,6 @@ export const zh = {
       nameInvalid: '不能是空格开头或结尾',
       notAllSpaces: '不能是纯空格',
       view: '查看',
-      callbackUrlInvalid: '请输入有效的 URL',
     },
     model: {
       searchPlaceholder: '搜索模型…',
@@ -1770,6 +1769,8 @@ export const zh = {
       memoryConversationAnalysisEmpty: '目前没有可用的对话分析内容',
       memoryConversationAnalysisEmptySubTitle: '输入您的用户ID后，点击"测试记忆"查看对话记忆',
 
+      communities: '聚类社区',
+      summaries: '记忆摘要',
       uploadFile: '上传文件',
       fileType: '文件类型',
       image: '图片',

web/src/views/ApplicationConfig/TestChat/index.tsx

@@ -183,7 +183,7 @@ const TestChat: FC<TestChatProps> = ({
 
   const handleSend = () => {
     if (loading || !application || !message || !message?.trim()) return
-    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
+    const files = toolbarRef.current?.getFiles() || []
     const variables = toolbarRef.current?.getVariables() || []
     const { isCanSend, params } = buildVariableParams(variables)
     if (!isCanSend) return
@@ -235,7 +235,7 @@ const TestChat: FC<TestChatProps> = ({
 
   const handleWorkflowSend = () => {
     if (loading || !application || !message || !message?.trim()) return
-    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
+    const files = toolbarRef.current?.getFiles() || []
     const variables = toolbarRef.current?.getVariables() || []
     const { isCanSend, params } = buildVariableParams(variables)
     if (!isCanSend) return

web/src/views/ApplicationConfig/components/Chat.tsx

@@ -191,7 +191,7 @@ const Chat: FC<ChatProps> = ({
       .then(() => {
         const message = msg
         if (!message?.trim()) return
-        const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
+        const files = toolbarRef.current?.getFiles() || []
         // Validate required variables before sending
         let isCanSend = true
         const params: Record<string, any> = {}
@@ -352,7 +352,7 @@ const Chat: FC<ChatProps> = ({
       .then(() => {
         const message = msg
         if (!message || message.trim() === '') return
-        const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
+        const files = toolbarRef.current?.getFiles() || []
         addUserMessage(message, files)
         setMessage(undefined)
         toolbarRef.current?.setFiles([])

web/src/views/ApplicationConfig/components/FeaturesConfig/FeaturesConfigModal.tsx

@@ -24,7 +24,7 @@ interface FeaturesConfigModalProps {
   refresh: (value: FeaturesConfigForm) => void;
   source?: Application['type'];
 }
-const max_file_count = 1;
+
 /**
  * Modal for copying applications
  */
@@ -133,7 +133,7 @@ const FeaturesConfigModal = forwardRef<FeaturesConfigModalRef, FeaturesConfigMod
                     </div>
                     <div>
                       <div className="rb:text-[12px] rb:text-[#5B6167] rb:py-1">{t('application.maxCount')}</div>
-                      {max_file_count} {t('application.unix')}
+                      {fu.max_file_count} {t('application.unix')}
                     </div>
                   </Flex>
                   <Button block onClick={handleOpenSettings}>{t('application.setting')}</Button>

web/src/views/ApplicationConfig/components/FeaturesConfig/FileUploadSettingModal.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-03-05 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 20:19:14
+ * @Last Modified time: 2026-03-19 15:18:20
  */
 import { forwardRef, useImperativeHandle, useState } from 'react';
 import { Form, InputNumber, Flex, Switch, Row, Col, Radio } from 'antd';
@@ -82,27 +82,28 @@ const defaultValues: FileUpload = {
     "mp3",
     "wav",
     "m4a",
+    "ogg",
+    "flac"
   ],
   document_enabled: false,
   document_max_size_mb: 100,
   document_allowed_extensions: [
     "pdf",
     "docx",
-    "doc",
     "xlsx",
-    "xls",
     "txt",
     "csv",
-    "json",
-    "md",
+    "json"
   ],
   video_enabled: false,
   video_max_size_mb: 100,
   video_allowed_extensions: [
     "mp4",
     "mov",
+    "avi",
+    "webm"
   ],
-  max_file_count: 1,
+  max_file_count: 5,
   allowed_transfer_methods: 'both'
 }
 
@@ -167,8 +168,8 @@ const FileUploadSettingModal = forwardRef<FileUploadSettingModalRef, FileUploadS
           </Radio.Group>
         </Form.Item>
 
-        {/* <div className="rb:text-[12px] rb:text-[#5B6167] rb:mb-1">{t('application.maxCount')}</div> */}
-        <Form.Item label={t('application.maxCount')} name="max_file_count" hidden>
+        <div className="rb:text-[12px] rb:text-[#5B6167] rb:mb-1">{t('application.maxCount')}</div>
+        <Form.Item label={t('application.maxCount')} name="max_file_count">
           <InputNumber min={1} max={20} precision={0} className="rb:w-full!" placeholder={t('common.pleaseEnter')} />
         </Form.Item>
 

web/src/views/Conversation/components/FileUpload.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-06 21:09:42 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 18:38:41
+ * @Last Modified time: 2026-03-18 20:32:54
  */
 /**
  * File Upload Component
@@ -23,7 +23,7 @@
 import { useState, useEffect, forwardRef, useImperativeHandle, useMemo } from 'react';
 import { Upload, Progress, App } from 'antd';
 import type { UploadProps, UploadFile } from 'antd';
-import type { UploadProps as RcUploadProps, RcFile, UploadFileStatus } from 'antd/es/upload/interface';
+import type { UploadProps as RcUploadProps } from 'antd/es/upload/interface';
 import { useTranslation } from 'react-i18next';
 
 import { request } from '@/utils/request'
@@ -221,29 +221,17 @@ const UploadFiles = forwardRef<UploadFilesRef, UploadFilesProps>(({
    */
   const handleCustomRequest: RcUploadProps['customRequest'] = async (options) => {
     const { file, onSuccess, onError } = options;
-    if (typeof file === 'string') return;
-    const rcFile = file as RcFile;
-    const formData = new FormData();
-    formData.append('file', rcFile);
-    const fileVo: UploadFile = {
-      uid: rcFile.uid,
-      name: rcFile.name,
-      status: 'uploading' as UploadFileStatus,
-      percent: 0,
-      type: rcFile.type,
-      originFileObj: rcFile,
-      thumbUrl: URL.createObjectURL(rcFile)
+    
+    try {
+      const formData = new FormData();
+      formData.append('file', file);
+      
+      const response = await request.uploadFile(action, formData, requestConfig);
+
+      onSuccess?.({data: response});
+    } catch (error) {
+      onError?.(error as Error);
     }
-    onChange?.(fileVo)
-    request.uploadFile(action, formData, requestConfig)
-      .then(res => {
-        onSuccess?.({ data: res });
-      })
-      .catch((error) => {
-        onError?.(error as Error);
-        fileVo.status = 'error'
-        onChange?.(fileVo)
-      })
   };
 
   /**

web/src/views/Conversation/components/UploadFileListModal.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-06 21:09:47 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 20:32:32
+ * @Last Modified time: 2026-03-18 21:10:01
  */
 /**
  * Upload File List Modal Component
@@ -19,10 +19,7 @@
  * @component
  */
 import { forwardRef, useImperativeHandle, useState, useMemo } from 'react';
-import { Form, Input, Select,
-  // Button,
-  Flex
-} from 'antd';
+import { Form, Input, Select, Button, Flex } from 'antd';
 import { useTranslation } from 'react-i18next';
 
 import type { UploadFileListModalRef } from '../types'
@@ -108,11 +105,9 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
       onOk={handleSave}
       confirmLoading={loading}
     >
-      <Form form={form} layout="vertical" initialValues={{ files: [{ type: undefined, url: undefined }] }}>
+      <Form form={form} layout="vertical">
         <Form.List name="files">
-          {(fields,
-            // { add, remove }
-          ) => (
+          {(fields, { add, remove }) => (
             <>
               {/* Render each file entry with type selector and URL input */}
               {fields.map(({ key, name, ...restField }) => (
@@ -121,9 +116,6 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
                     {...restField}
                     name={[name, 'type']}
                     className="rb:mb-0!"
-                    rules={[
-                      { required: true, message: t('common.pleaseSelect') }
-                    ]}
                   >
                     <Select
                       placeholder={t('memoryConversation.fileType')}
@@ -134,25 +126,22 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
                   <FormItem
                     {...restField}
                     name={[name, 'url']}
-                    rules={[
-                      { required: true, message: t('common.pleaseEnter') },
-                      { type: 'url', message: t('common.callbackUrlInvalid') },
-                    ]}
+                    rules={[{ required: true, message: t('common.pleaseEnter') }]}
                     className="rb:mb-0! rb:flex-1!"
                   >
                     <Input placeholder={t('memoryConversation.fileUrl')} />
                   </FormItem>
-                  {/* <div
+                  <div
                     className="rb:w-5 rb:h-5 rb:cursor-pointer rb:bg-cover rb:bg-[url('@/assets/images/delete.svg')] rb:hover:bg-[url('@/assets/images/delete_hover.svg')]"
                     onClick={() => remove(name)}
-                  ></div> */}
+                  ></div>
                 </Flex>
               ))}
-              {/* <Form.Item noStyle>
+              <Form.Item noStyle>
                 <Button type="dashed" onClick={() => add()} block>
                   + {t('common.add')}
                 </Button>
-              </Form.Item> */}
+              </Form.Item>
             </>
           )}
         </Form.List>

web/src/views/Conversation/index.tsx

@@ -200,7 +200,7 @@ const Conversation: FC = () => {
   /** Send message and handle streaming response */
   const handleSend = () => {
     if (!token || !shareToken) return
-    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
+    const files = toolbarRef.current?.getFiles() || []
     const variables = toolbarRef.current?.getVariables() || []
     let isCanSend = true
     const params: Record<string, any> = {}

web/src/views/KnowledgeBase/[knowledgeBaseId]/DocumentDetails.tsx

@@ -11,7 +11,7 @@ import { useNavigate, useParams, useLocation } from 'react-router-dom';
 import { useTranslation } from 'react-i18next';
 import { useBreadcrumbManager, type BreadcrumbPath } from '@/hooks/useBreadcrumbManager';
 import { Button, Spin, message, Switch } from 'antd';
-import { getDocumentDetail, getDocumentChunkList, downloadFile, updateDocument, updateDocumentChunk, createDocumentChunk, getFileUrl } from '@/api/knowledgeBase';
+import { getDocumentDetail, getDocumentChunkList, downloadFile, updateDocument, updateDocumentChunk, createDocumentChunk } from '@/api/knowledgeBase';
 import type { KnowledgeBaseDocumentData, RecallTestData } from '@/views/KnowledgeBase/types';
 import { formatDateTime } from '@/utils/format';
 import InfoPanel, { type InfoItem } from '../components/InfoPanel';
@@ -138,7 +138,7 @@ const DocumentDetails: FC = () => {
       const response = await getDocumentDetail(documentId);
       setDocument(response);
       setInfoItems(formatDocumentInfo(response));
-      const url = `${window.location.origin}/api/files/${response.file_id}`;
+      const url = `${imagePath}/api/files/${response.file_id}`
       setFileUrl(url);
       setParserMode(response?.parser_config?.auto_questions || 0)
       // ChunkList will be called automatically in useEffect based on document.progress

web/src/views/MemoryConversation/index.tsx

@@ -1,8 +1,8 @@
 /*
  * @Author: ZhaoYing 
  * @Date: 2026-02-03 17:09:03 
- * @Last Modified by:   ZhaoYing 
- * @Last Modified time: 2026-02-03 17:09:03 
+ * @Last Modified by: ZhaoYing
+ * @Last Modified time: 2026-03-17 16:21:47
  */
 /**
  * Memory Conversation Page
@@ -92,7 +92,7 @@ export interface LogItem {
   type: string;
   title: string;
   data?: DataItem[] | AnyObject;
-  raw_results?: string | AnyObject;
+  raw_results?: string | Record<string, AnyObject>;
   summary?: string;
   query?: string;
   reason?: string;
@@ -264,22 +264,25 @@ const MemoryConversation: FC = () => {
                           </ContentWrapper>
                         ))}
                       </Space>
-                      : log.type === 'search_result' && log.raw_results
+                      : log.type === 'search_result' && log.raw_results && typeof log.raw_results !== 'string'
                       ? <ContentWrapper>
                         <div className="rb:font-medium rb:text-[#212332] rb:mb-2">{log.query}</div>
-                          <div className='rb:mt-2 rb:text-[12px] rb:text-[#5B6167]'>
-                            {typeof log.raw_results === 'string'
-                              ? <Markdown content={log.raw_results} />
-                              : <>
-                                {log.raw_results.reranked_results?.statements.length > 0 && log.raw_results.reranked_results?.statements.map((item: { statement: string }, index: number) => (
-                                  <div key={index}>{item.statement}</div>
-                                ))}
-                                {log.raw_results.reranked_results?.summaries.length > 0 && log.raw_results.reranked_results?.summaries.map((item: { content: string }, index: number) => (
-                                  <div key={index}>{item.content}</div>
-                                ))}
-                              </> 
-                            }
-                          </div>
+                        {(log.raw_results.reranked_results as AnyObject)?.communities?.length > 0 && <>
+                          <div className="rb:font-medium rb:text-[#212332] rb:text-[12px]">{t('memoryConversation.communities')}</div>
+                          <ul className='rb:mt-2 rb:text-[12px] rb:text-[#5B6167] rb:list-disc rb:pl-4'>
+                              {((log.raw_results.reranked_results as AnyObject)?.communities as { content: string }[]).map((item, index: number) => (
+                              <li key={index}>{item.content}</li>
+                            ))}
+                          </ul>
+                        </>}
+                        {(log.raw_results.reranked_results as AnyObject)?.summaries?.length > 0 && <>
+                          <div className="rb:font-medium rb:text-[#212332] rb:text-[12px]">{t('memoryConversation.summaries')}</div>
+                          <ul className='rb:mt-2 rb:text-[12px] rb:text-[#5B6167] rb:list-disc rb:pl-4'>
+                                {((log.raw_results.reranked_results as AnyObject)?.summaries as { content: string }[]).map((item, index: number) => (
+                              <li key={index}>{item.content}</li>
+                            ))}
+                          </ul>
+                        </>}
                         </ContentWrapper>
                       : log.type === 'retrieval_summary' && log.summary
                       ? <ContentWrapper><div className="rb:text-[12px] rb:text-[#5B6167]">{log.summary}</div></ContentWrapper>
@@ -296,22 +299,22 @@ const MemoryConversation: FC = () => {
                       </ContentWrapper>
                       : log.type === 'input_summary' && log.raw_results
                       ? <ContentWrapper>
-                          <div className="rb:font-medium rb:text-[#212332] rb:mb-2">{log.query}</div>
-                          <div className="rb:font-medium rb:text-[12px] rb:text-[#5B6167] rb:mb-2">{log.summary}</div>
-                          <div className='rb:mt-2 rb:text-[12px] rb:text-[#5B6167]'>
-                            {typeof log.raw_results === 'string'
-                              ? <Markdown content={log.raw_results} />
-                              : <>
-                                {log.raw_results.reranked_results?.statements.length > 0 && log.raw_results.reranked_results?.statements.map((item: { statement: string; } , index: number) => (
-                                  <div key={index}>{item.statement}</div>
-                                ))}
-                                {log.raw_results.reranked_results?.summaries.length > 0 && log.raw_results.reranked_results?.summaries.map((item: { content: string; }, index: number) => (
-                                  <div key={index}>{item.content}</div>
-                                ))}
-                              </> 
-                            }
-                          </div>
-                        </ContentWrapper>
+                        <div className="rb:font-medium rb:text-[#212332] rb:mb-2">{log.query}</div>
+                        <div className="rb:font-medium rb:text-[12px] rb:text-[#5B6167] rb:mb-2">{log.summary}</div>
+                        <div className='rb:mt-2 rb:text-[12px] rb:text-[#5B6167]'>
+                          {typeof log.raw_results === 'string'
+                            ? <Markdown content={log.raw_results} />
+                            : <>
+                              {(log.raw_results.reranked_results as AnyObject)?.statements?.length > 0 && ((log.raw_results.reranked_results as AnyObject)?.statements as { statement: string }[]).map((item, index: number) => (
+                                <div key={index}>{item.statement}</div>
+                              ))}
+                              {(log.raw_results.reranked_results as AnyObject)?.summaries?.length > 0 && ((log.raw_results.reranked_results as AnyObject)?.summaries as { content: string }[]).map((item, index: number) => (
+                                <div key={index}>{item.content}</div>
+                              ))}
+                            </>
+                          }
+                        </div>
+                      </ContentWrapper>
                       : null
                     }
                   </div>

web/src/views/UserMemoryDetail/components/RelationshipNetwork.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-03 18:32:00 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 20:23:42
+ * @Last Modified time: 2026-03-13 14:51:17
  */
 /**
  * Relationship Network Component
@@ -287,26 +287,22 @@ const RelationshipNetwork:FC = () => {
               : (selectedNode as RawCommunityNode).properties.community_id
                 ? <div className="rb:p-3 rb:pt-0">
                   <div className="rb:font-medium rb:text-[#212332] rb:text-[16px] rb:leading-5.5 rb:pl-1">
-                    {(selectedNode as RawCommunityNode).properties.name || selectedNode.id}
+                    {(selectedNode as RawCommunityNode).properties.name}
+                  </div>
+                  <div className="rb:mt-3 rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.summary')}</div>
+                  <div className="rb:bg-[#F6F6F6] rb:rounded-xl rb:px-3 rb:py-2.5 rb:mt-2">
+                    {(selectedNode as RawCommunityNode).properties.summary}
                   </div>
-                  {(selectedNode as RawCommunityNode).properties.summary && <>
-                    <div className="rb:mt-3 rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.summary')}</div>
-                    <div className="rb:bg-[#F6F6F6] rb:rounded-xl rb:px-3 rb:py-2.5 rb:mt-2">
-                      {(selectedNode as RawCommunityNode).properties.summary}
-                    </div>
-                  </>}
                   <Flex align="center" justify="space-between" className="rb:mt-5!">
                     <span className="rb:text-[#5B6167] rb:font-regular rb:pl-1">{t('userMemory.member_count')}</span>
                     <span className="rb:font-medium">{(selectedNode as RawCommunityNode).properties.member_count}{t('userMemory.member_count_desc')}</span>
                   </Flex>
 
-                  {(selectedNode as RawCommunityNode).properties.core_entities && <>
-                    <Divider className='rb:my-2.5!' />
-                    <div className="rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.core_entities')}</div>
-                    <ul className="rb:list-disc rb:pl-4 rb:text-[#5B6167] rb:mt-2">
-                      {(selectedNode as RawCommunityNode).properties.core_entities?.map((entity, index) => <li key={index}>{entity}</li>)}
-                    </ul>
-                  </>}
+                  <Divider className='rb:my-2.5!' />
+                  <div className="rb:font-medium rb:leading-5 rb:pl-1">{t('userMemory.core_entities')}</div>
+                  <ul className="rb:list-disc rb:pl-4 rb:text-[#5B6167] rb:mt-2">
+                    {(selectedNode as RawCommunityNode).properties.core_entities.map((entity, index) => <li key={index}>{entity}</li>)}
+                  </ul>
                 </div>
                 : <>
                   {(selectedNode as Node).name && (

web/src/views/UserMemoryDetail/pages/WorkingDetail.tsx

@@ -1,10 +1,8 @@
-import { type FC, useEffect, useState, useMemo, useRef } from 'react'
+import { type FC, useEffect, useState, useMemo } from 'react'
 import clsx from 'clsx'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Row, Col, Skeleton, Button, Divider, Tooltip } from 'antd'
-
-import InfiniteScroll from 'react-infinite-scroll-component'
 import RbCard from '@/components/RbCard/Card'
 import {
   getConversations,
@@ -36,8 +34,6 @@ const WorkingDetail: FC = () => {
   const { id } = useParams()
   const [loading, setLoading] = useState<boolean>(false)
   const [data, setData] = useState<Conversation[]>([])
-  const [hasMore, setHasMore] = useState<boolean>(true)
-  const pageRef = useRef<number>(1)
   const [messagesLoading, setMessagesLoading] = useState<boolean>(false)
   const [messages, setMessages] = useState<ChatItem[]>([])
   const [detailLoading, setDetailLoading] = useState<boolean>(false)
@@ -55,30 +51,16 @@ const WorkingDetail: FC = () => {
     setSelected(null)
     setDetail(null)
     setData([])
-    setHasMore(true)
-    pageRef.current = 1
-    getConversations(id, 1).then((res) => {
-      const response = res as { items: Conversation[], page: { hasnext: boolean } }
-      setData(response.items)
-      setSelected(response.items[0] || null)
-      setHasMore(response.page.hasnext)
+    getConversations(id).then((res) => {
+      const response = res as Conversation[] 
+      setData(response)
+      setSelected(response[0] || null)
     })
     .finally(() => {
       setLoading(false)
     })
   }
 
-  const loadMore = () => {
-    if (!id) return
-    const nextPage = pageRef.current + 1
-    getConversations(id, nextPage).then((res) => {
-      const response = res as {items: Conversation[], page: { hasnext: boolean }}
-      setData(prev => [...prev, ...response.items])
-      pageRef.current = nextPage
-      setHasMore(response.page.hasnext)
-    })
-  }
-
   useEffect(() => {
     if (!id || !selected || !selected.id) return
     getDetail(selected.id)
@@ -121,30 +103,22 @@ const WorkingDetail: FC = () => {
         : data.length === 0
         ? <Empty />
         :(
-          <Row gutter={16}>
+          <Row gutter={16} className="rb:h-full">
             <Col span={5}>
-              <div id="conversation-list" className="rb:h-[calc(100vh-76px)]! rb:border-r rb:border-[#EAECEE] rb:py-3 rb:px-4 rb:overflow-y-auto">
-                <InfiniteScroll
-                  dataLength={data.length}
-                  next={loadMore}
-                  hasMore={hasMore}
-                  loader={null}
-                  scrollableTarget="conversation-list"
-                >
-                  {data.map(item => (
-                    <div key={item.id} className="rb:mb-3">
-                      <Tooltip title={item.title}>
-                        <div className={clsx("rb:p-[8px_13px] rb:rounded-lg rb:leading-5 rb:cursor-pointer rb:hover:bg-[#F0F3F8] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap", {
-                          'rb:bg-[#FFFFFF] rb:shadow-[0px_2px_4px_0px_rgba(0,0,0,0.15)] rb:font-medium rb:hover:bg-[#FFFFFF]!': item.id === selected?.id,
-                        })}
-                          onClick={() => setSelected(item)}
-                        >
-                          {item.title}
-                        </div>
-                      </Tooltip>
-                    </div>
-                  ))}
-                </InfiniteScroll>
+              <div className="rb:h-full! rb:border-r rb:border-[#EAECEE] rb:py-3 rb:px-4">
+                {data.map(item => (
+                  <div key={item.id} className="rb:mb-3">
+                    <Tooltip title={item.title}>
+                      <div className={clsx("rb:p-[8px_13px] rb:rounded-lg rb:leading-5 rb:cursor-pointer rb:hover:bg-[#F0F3F8] rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap", {
+                        'rb:bg-[#FFFFFF] rb:shadow-[0px_2px_4px_0px_rgba(0,0,0,0.15)] rb:font-medium rb:hover:bg-[#FFFFFF]!': item.id === selected?.id,
+                      })}
+                        onClick={() => setSelected(item)}
+                      >
+                        {item.title}
+                      </div>
+                    </Tooltip>
+                  </div>
+                ))}
               </div>
             </Col>
             {selected && <>

web/src/views/Workflow/components/Chat/Chat.tsx

@@ -2,7 +2,7 @@
  * @Author: ZhaoYing 
  * @Date: 2026-02-06 21:10:56 
  * @Last Modified by: ZhaoYing
- * @Last Modified time: 2026-03-19 18:41:07
+ * @Last Modified time: 2026-03-18 20:46:35
  */
 /**
  * Workflow Chat Component
@@ -151,7 +151,7 @@ const Chat = forwardRef<ChatRef, { appId: string; graphRef: GraphRef; data: Work
 
     setLoading(true)
     const message = msg
-    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
+    const files = toolbarRef.current?.getFiles() || []
     setChatList(prev => [...prev, {
       role: 'user',
       content: message,

web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx

@@ -18,8 +18,8 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
   const isUserInputRef = useRef(false);
 
   useEffect(() => {
-    const removeListener = editor.registerUpdateListener(({ editorState, tags }) => {
-      if (tags.has('programmatic')) return;
+    // 监听编辑器变化，标记是否为用户输入
+    const removeListener = editor.registerUpdateListener(({ editorState }) => {
       editorState.read(() => {
         const root = $getRoot();
         const textContent = root.getTextContent();
@@ -107,7 +107,7 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
           });
           root.append(paragraph);
         }
-        }, { discrete: true, tag: 'programmatic' });
+        }, { discrete: true });
       });
     }