Merge branch 'develop' into feature/ui_upgrade_zy

.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_400_BAD_REQUEST,
+            status_code=status.HTTP_413_CONTENT_TOO_LARGE,
             detail=f"The file size exceeds the {settings.MAX_FILE_SIZE} byte limit"
         )
 
@@ -172,7 +172,6 @@ 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)
@@ -499,6 +498,51 @@ 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

@@ -195,10 +195,9 @@ 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)
+        init_community_clustering_for_users.delay(end_user_ids=end_user_ids, workspace_id=str(workspace_id))
         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

@@ -33,35 +33,47 @@ 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 all conversations for the current user in a specific group.
+    Retrieve conversations for the current user in a specific group with pagination.
 
     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 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.
+        ApiResponse: Contains a paginated list of conversations.
     """
+    page = max(1, page)
+    page_size = max(1, min(pagesize, 100))  # Limit page size between 1 and 100
     conversation_service = ConversationService(db)
-    conversations = conversation_service.get_user_conversations(
-        end_user_id
+    conversations, total = conversation_service.get_user_conversations(
+        end_user_id,
+        page=page,
+        page_size=page_size
     )
-    return success(data=[
-        {
-            "id": conversation.id,
-            "title": conversation.title
-        } for conversation in conversations
-    ], msg="get conversations success")
+    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")
 
 
 @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

@@ -76,6 +76,8 @@ 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))
 
@@ -121,6 +123,8 @@ 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))
 
@@ -149,6 +153,8 @@ 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))
 
@@ -191,6 +197,8 @@ 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))
 
@@ -223,6 +231,8 @@ 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

@@ -97,6 +97,7 @@ 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/utils/write_tools.py

@@ -166,15 +166,12 @@ 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,
-                    config_id=config_id,
                     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,
                 )

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

@@ -69,15 +69,15 @@ class LabelPropagationEngine:
     def __init__(
         self,
         connector: Neo4jConnector,
-        config_id: Optional[str] = None,
         llm_model_id: Optional[str] = None,
         embedding_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
+        self.embedding_model_id = embedding_model_id
 
     # ──────────────────────────────────────────────────────────────────────────
     # 公开接口
@@ -439,15 +439,17 @@ class LabelPropagationEngine:
 
     @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。"""
+        """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 ""
-            lines.append(f"- {m_name}{aliases_str}{desc_str}")
+            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(
@@ -481,11 +483,24 @@ class LabelPropagationEngine:
             core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")]
 
             entity_list_str = "\n".join(self._build_entity_lines(members))
+
+            # 方案四：注入社区内实体间关系三元组
+            relationships = await self.repo.get_community_relationships(cid, end_user_id)
+            rel_lines = [
+                f"- {r['subject']} → {r['predicate']} → {r['object']}"
+                for r in relationships
+                if r.get("subject") and r.get("predicate") and r.get("object")
+            ]
+            rel_section = (
+                f"\n实体间关系：\n" + "\n".join(rel_lines)
+                if rel_lines else ""
+            )
+
             prompt = (
-                f"以下是一组语义相关的实体：\n{entity_list_str}\n\n"
+                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50个字）\n\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>"
             )

api/app/core/storage/base.py

@@ -121,3 +121,18 @@ 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

@@ -261,3 +261,13 @@ 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

@@ -378,3 +378,12 @@ 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
+from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory, ElasticSearchVector
 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
@@ -24,6 +24,7 @@ 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 {
@@ -163,6 +164,50 @@ 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.
@@ -191,56 +236,19 @@ 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:
-                vector_service.reranker = self.get_reranker_model()
-                final_rs = vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
+                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)
             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

@@ -506,10 +506,13 @@ 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
@@ -534,7 +537,7 @@ 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=translated_message)
+        content=fail(code=exc.status_code, msg=translated_message, error=exc.detail)
     )
 
 

api/app/repositories/conversation_repository.py

@@ -90,27 +90,27 @@ class ConversationRepository:
             self,
             user_id: uuid.UUID,
             workspace_id: uuid.UUID = None,
-            limit: int = 10,
-            is_activate: bool = True
-    ) -> list[Conversation]:
+            is_activate: bool = True,
+            page: int = 1,
+            page_size: int = 20
+    ) -> tuple[list[Conversation], int]:
         """
-        Retrieve recent conversations for a specific user.
+        Retrieve recent conversations for a specific user with pagination.
 
         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 and limited to a fixed number.
+        most recently updated conversations.
 
         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.
-            limit (int): Maximum number of conversations to return.
-                Defaults to 10.
-            is_activate (bool): Convsersation State limit
+            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.
 
         Returns:
-            list[Conversation]: A list of conversation entities ordered by
-            last updated time (descending).
+            tuple[list[Conversation], int]: A list of conversation entities and total count.
         """
         logger.info(f"Fetching conversation by user_id: {user_id}")
 
@@ -122,18 +122,25 @@ class ConversationRepository:
         if workspace_id:
             stmt = stmt.where(Conversation.workspace_id == workspace_id)
 
-        stmt = stmt.order_by(desc(Conversation.updated_at))
-        stmt = stmt.limit(limit)
+        # Calculate total count
+        total = int(self.db.execute(
+            select(func.count()).select_from(stmt.subquery())
+        ).scalar_one())
 
-        convsersations = list(self.db.scalars(stmt).all())
+        # Apply ordering and pagination
+        stmt = stmt.order_by(desc(Conversation.updated_at))
+        stmt = stmt.offset((page - 1) * page_size).limit(page_size)
+
+        conversations = 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 convsersations
+        return conversations, total
 
     def list_conversations(
             self,

api/app/repositories/neo4j/community_repository.py

@@ -17,12 +17,17 @@ from app.repositories.neo4j.cypher_queries import (
     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,
 )
 
@@ -177,7 +182,7 @@ class CommunityRepository:
     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,
@@ -188,6 +193,20 @@ 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]]:
@@ -234,6 +253,31 @@ 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,
@@ -243,7 +287,7 @@ class CommunityRepository:
         core_entities: List[str],
         summary_embedding: Optional[List[float]] = None,
     ) -> bool:
-        """更新社区的名称、摘要、核心实体列表和摘要向量。"""
+        """更新社区的名称、摘要、核心实体列表及 summary_embedding。"""
         try:
             result = await self.connector.execute_query(
                 UPDATE_COMMUNITY_METADATA,

api/app/repositories/neo4j/cypher_queries.py

@@ -1137,10 +1137,20 @@ MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(
 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.aliases AS aliases, e.description AS description
+       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)
 RETURN c.community_id AS community_id,
@@ -1316,3 +1326,38 @@ RETURN s.statement AS statement,
 ORDER BY COALESCE(s.activation_value, 0) DESC
 LIMIT $limit
 """
+
+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
+"""
+
+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
+"""
+
+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
+"""

api/app/repositories/neo4j/graph_saver.py

@@ -1,4 +1,5 @@
 import asyncio
+import os
 from typing import List, Optional
 
 # 使用新的仓储层
@@ -304,7 +305,6 @@ 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:
@@ -325,13 +325,12 @@ 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, embedding_model_id=embedding_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:
@@ -343,7 +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, embedding_model_id=embedding_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/workflow_repository.py

@@ -43,6 +43,7 @@ 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
@@ -53,6 +54,7 @@ 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:
@@ -82,6 +84,7 @@ 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

@@ -149,18 +149,26 @@ 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=100)
+    document_max_size_mb: int = Field(default=50)
     document_allowed_extensions: List[str] = Field(
-        default=["pdf", "docx", "xlsx", "txt", "csv", "json", "md"]
+        default=["pdf", "docx", "doc", "xlsx", "xls", "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=500)
+    video_max_size_mb: int = Field(default=50)
     video_allowed_extensions: List[str] = Field(
-        default=["mp4", "mov"]
+        default=["mp4"]
     )
     # 最大文件数量
-    max_file_count: int = Field(default=5, ge=1, le=20)
+    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
 
 
 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/services/app_chat_service.py

@@ -118,28 +118,54 @@ 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 = [
-            {"role": msg.role, "content": msg.content}
-            for msg in messages
-        ]
+        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
+            })
 
         # 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")
@@ -313,31 +339,54 @@ 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 = []
-            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
-                ]
+            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
+                })
 
             # 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")
@@ -347,8 +396,14 @@ 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_obj,
+                features_config, api_key_config,
                 text_queue=text_queue,
                 tenant_id=tenant_id, workspace_id=workspace_id
             )

api/app/services/app_dsl_service.py

@@ -16,6 +16,7 @@ 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
@@ -84,7 +85,9 @@ 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["tools"])
+                enriched["tools"] = self._enrich_tools(cfg.get("tools"))
+            if "skills" in cfg:
+                enriched["skills"] = self._enrich_skills(cfg.get("skills"))
             return enriched
         if app_type == AppType.MULTI_AGENT:
             enriched = {**cfg}
@@ -108,6 +111,7 @@ 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 {}
@@ -123,7 +127,8 @@ 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": config.skills if config else {},
+                "skills": self._enrich_skills(config.skills) if config else {},
+                "features": config.features if config else {}
             } if config else {}
             dsl = {**meta, "app": app_meta, "agent_config": config_data}
 
@@ -185,6 +190,22 @@ 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
@@ -249,7 +270,8 @@ 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=cfg.get("skills", {}),
+                skills=self._resolve_skills(cfg.get("skills", {}), tenant_id, warnings),
+                features=cfg.get("features", {}),
                 is_active=True,
                 created_at=now,
                 updated_at=now,
@@ -290,6 +312,7 @@ 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,
             )
@@ -444,6 +467,46 @@ 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

@@ -833,8 +833,6 @@ 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:
@@ -849,28 +847,29 @@ class AppService:
 
                 if source_config:
                     if is_cross_workspace:
-                        # 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
+                        # 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
                         )
-                        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
+                        _, available_kb_ids = self._preload_cross_workspace_resources(
+                            target_tenant_id, target_workspace_id, set(), kb_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 = self._clean_tools(
-                            source_config.tools, 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_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(),
@@ -879,9 +878,11 @@ 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=copy.deepcopy(source_config.memory) if source_config.memory else None,
+                        memory=new_memory,
                         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,
@@ -894,28 +895,14 @@ 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=new_nodes,
+                        nodes=copy.deepcopy(source_config.nodes) if source_config.nodes else [],
                         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,
@@ -929,24 +916,15 @@ class AppService:
                 ).first()
 
                 if source_config:
-                    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
-
+                    # 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
                     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=new_model_config_id,
-                        model_parameters=source_config.model_parameters,
+                        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,
                         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,
@@ -1037,8 +1015,7 @@ class AppService:
     @staticmethod
     def _collect_resource_ids_from_config(
             model_config_id: Optional[uuid.UUID],
-            knowledge_retrieval: Optional[dict],
-            tools: Optional[list]
+            knowledge_retrieval: Optional[dict]
     ) -> tuple:
         """Extract all model config IDs and knowledge base IDs from an app config."""
         model_ids: set = set()
@@ -1048,62 +1025,12 @@ class AppService:
             model_ids.add(model_config_id)
 
         if knowledge_retrieval and isinstance(knowledge_retrieval, dict):
-            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))
+            if "knowledge_bases" in knowledge_retrieval:
+                for kid in knowledge_retrieval.get("knowledge_bases", []):
+                    kb_ids.add(str(kid.get("kb_id")))
 
         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:
@@ -1124,95 +1051,53 @@ class AppService:
 
         cleaned = copy.deepcopy(knowledge_retrieval)
 
-        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_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 "knowledge_id" in cleaned:
-            cleaned["knowledge_id"] = self._is_kb_available(
-                cleaned.get("knowledge_id"), available_kb_ids
-            )
-
         return cleaned
 
-    def _clean_tools(
+    def _clean_memory_cross_workspace(
             self,
-            tools: Optional[list],
-            available_kb_ids: set
-    ) -> list:
-        """Clean tools config, keeping built-in tools and tools with available KBs."""
-        if not tools:
-            return []
+            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
 
-        cleaned = []
-        for tool in tools:
-            if not isinstance(tool, dict):
-                cleaned.append(tool)
-                continue
+        from app.models.memory_config_model import MemoryConfig
 
-            tool_type = tool.get("type", "")
-            if tool_type in ("builtin", "built_in", "system"):
-                cleaned.append(copy.deepcopy(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
 
-            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
+            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
 
-            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

@@ -21,6 +21,7 @@ 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
 
@@ -119,25 +120,27 @@ class ConversationService:
 
     def get_user_conversations(
             self,
-            user_id: uuid.UUID
-    ) -> list[Conversation]:
+            user_id: uuid.UUID,
+            page: int = 1,
+            page_size: int = 20
+    ) -> tuple[list[Conversation], int]:
         """
-        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).
+        Retrieve recent conversations for a specific user with pagination.
 
         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:
-            list[Conversation]: A list of recent conversation entities.
+            tuple[list[Conversation], int]: A list of recent conversation entities and total count.
         """
-        conversations = self.conversation_repo.get_conversation_by_user_id(
+        conversations, total = self.conversation_repo.get_conversation_by_user_id(
             user_id,
-            limit=10
+            page=page,
+            page_size=page_size
         )
-        return conversations
+        return conversations, total
 
     def list_conversations(
             self,
@@ -267,10 +270,11 @@ class ConversationService:
 
         return messages
 
-    def get_conversation_history(
+    async def get_conversation_history(
             self,
             conversation_id: uuid.UUID,
-            max_history: Optional[int] = None
+            max_history: Optional[int] = None,
+            api_config: Optional[ModelInfo] = None
     ) -> List[dict]:
         """
         Retrieve historical conversation messages formatted as dictionaries.
@@ -278,6 +282,7 @@ 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'.
@@ -288,13 +293,37 @@ class ConversationService:
         )
 
         # 转换为字典格式
-        history = [
-            {
+        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({
                 "role": msg.role,
-                "content": msg.content
-            }
-            for msg in messages
-        ]
+                "content": content
+            })
 
         return history
 
@@ -522,9 +551,18 @@ class ConversationService:
             type=ModelType(model_type)
         )
 
-        conversation_messages = self.get_conversation_history(
+        conversation_messages = await self.get_conversation_history(
             conversation_id=conversation_id,
-            max_history=20
+            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
+            )
         )
         if len(conversation_messages) == 0:
             return ConversationOut(

api/app/services/draft_run_service.py

@@ -579,9 +579,20 @@ 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
             )
 
@@ -589,15 +600,6 @@ 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)
@@ -815,9 +817,20 @@ 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)
             )
 
@@ -825,15 +838,6 @@ 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)
@@ -1115,6 +1119,7 @@ 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
@@ -1129,9 +1134,11 @@ class AgentRunService:
         try:
 
             conversation_service = ConversationService(self.db)
-            history = conversation_service.get_conversation_history(
+            # 获取 API 配置用于多模态处理
+            history = await conversation_service.get_conversation_history(
                 conversation_id=uuid.UUID(conversation_id),
-                max_history=max_history
+                max_history=max_history,
+                api_config=api_config
             )
 
             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_forget_service.py

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

api/app/services/multimodal_service.py

@@ -11,6 +11,8 @@
 import base64
 import io
 import uuid
+import zipfile
+import chardet
 from abc import ABC, abstractmethod
 from typing import List, Dict, Any, Optional
 
@@ -42,12 +44,10 @@ 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']
@@ -418,6 +418,71 @@ 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,
@@ -588,12 +653,12 @@ 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 file_content.decode("utf-8")
+                return self._decode_text_safe(file_content)
             elif file_mime_type in PDF_MIME:
                 return await self._extract_pdf_text(file_content)
-            elif file_mime_type in DOC_MIME and file.file_type.endswith(('docx', 'doc')):
+            elif self._is_word_file(file_content, file_mime_type):
                 return await self._extract_word_text(file_content)
-            elif file_mime_type in XLSX_MIME and file.file_type.endswith(("xlsx", "xls")):
+            elif self._is_excel_file(file_content, file_mime_type):
                 return await self._extract_xlsx_text(file_content)
             elif file_mime_type in CSV_MIME:
                 return await self._extract_csv_text(file_content)
@@ -622,52 +687,156 @@ class MultimodalService:
 
     @staticmethod
     async def _extract_word_text(file_content: bytes) -> str:
-        """提取 Word 文档文本"""
+        """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）
         try:
-            word_file = io.BytesIO(file_content)
-            doc = Document(word_file)
-            text_parts = [paragraph.text for paragraph in doc.paragraphs]
-            return '\n'.join(text_parts)
+            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
         except Exception as e:
-            logger.error(f"提取 Word 文本失败: {e}")
-            return f"[Word 提取失败: {str(e)}]"
+            logger.error(f"提取 doc 文本失败: {e}")
+            return f"[doc 提取失败: {str(e)}]"
 
     @staticmethod
     async def _extract_xlsx_text(file_content: bytes) -> str:
-        """提取 Excel 文本"""
+        """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）
         try:
-            wb = openpyxl.load_workbook(io.BytesIO(file_content), read_only=True, data_only=True)
+            import xlrd
+            wb = xlrd.open_workbook(file_contents=file_content)
             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))
+            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)))
             return '\n'.join(parts)
         except Exception as e:
-            logger.error(f"提取 Excel 文本失败: {e}")
-            return f"[Excel 提取失败: {str(e)}]"
+            logger.error(f"提取 xls 文本失败: {e}")
+            return f"[xls 提取失败: {str(e)}]"
 
-    @staticmethod
-    async def _extract_csv_text(file_content: bytes) -> str:
+    async def _extract_csv_text(self, file_content: bytes) -> str:
         """提取 CSV 文本"""
         try:
-            text = file_content.decode('utf-8-sig')
+            text = self._decode_text_safe(file_content)
             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)}]"
 
-    @staticmethod
-    async def _extract_json_text(file_content: bytes) -> str:
+    async def _extract_json_text(self, file_content: bytes) -> str:
         """提取 JSON 文本"""
         try:
-            data = json.loads(file_content.decode('utf-8'))
+            text = self._decode_text_safe(file_content)
+            data = json.loads(text)
             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/user_memory_service.py

@@ -1408,12 +1408,11 @@ async def analytics_memory_types(
     if end_user_id:
         try:
             conversation_repo = ConversationRepository(db)
-            conversations = conversation_repo.get_conversation_by_user_id(
+            conversations, total = 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 = len(conversations)
+            work_count = total
             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
@@ -55,6 +55,7 @@ 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:
@@ -66,6 +67,7 @@ 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
 
@@ -81,6 +83,7 @@ class WorkflowService:
             "edges": edges,
             "variables": variables or [],
             "execution_config": execution_config or {},
+            "features": features or {},
             "triggers": triggers or []
         }
 
@@ -101,6 +104,7 @@ class WorkflowService:
             edges=edges,
             variables=variables,
             execution_config=execution_config,
+            features=features,
             triggers=triggers
         )
 

api/app/tasks.py

@@ -2675,13 +2675,15 @@ 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]) -> Dict[str, Any]:
+def init_community_clustering_for_users(self, end_user_ids: List[str], workspace_id: Optional[str] = None) -> 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
@@ -2707,6 +2709,7 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
 
             # 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
@@ -2718,21 +2721,54 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
                             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 LLM 配置失败，将使用 None: {e}")
+                                logger.warning(f"[CommunityCluster] 用户 {uid} 加载配置失败，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 LLM 配置失败，所有用户将使用 None: {e}")
+                logger.warning(f"[CommunityCluster] 批量获取配置失败，所有用户将使用 None: {e}")
 
             for end_user_id in end_user_ids:
                 try:
-                    # 已有社区节点则跳过
+                    # 已有社区节点时，检查是否存在属性不完整的节点
                     has_communities = await repo.has_communities(end_user_id)
                     if has_communities:
-                        skipped += 1
-                        logger.debug(f"[CommunityCluster] 用户 {end_user_id} 已有社区节点，跳过")
+                        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
                         continue
 
                     # 检查是否有 ExtractedEntity 节点
@@ -2742,11 +2778,13 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
                         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
+                    # 每个用户使用自己的 llm_model_id / embedding_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

@@ -1,4 +1,38 @@
 {
+  "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/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

@@ -52,6 +52,10 @@ 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: 2026-03-13 10:48:41
+ * @Last Modified time: 2026-03-19 18:35:10
  */
 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) => {
-  return request.get(`/memory/work/${end_user_id}/conversations`)
+export const getConversations = (end_user_id: string, page = 1, pagesize = 20) => {
+  return request.get(`/memory/work/${end_user_id}/conversations`, { page, pagesize })
 }
 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

@@ -143,15 +143,20 @@ 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('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>}
+                        {(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
+                        }
                       </div>
                     )
                   })}

web/src/components/Chat/ChatToolbar.tsx

@@ -49,6 +49,7 @@ interface FormValues {
   memory?: boolean;
 }
 
+const max_file_count = 1;
 const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
   features,
   leftExtra,
@@ -86,10 +87,16 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
 
   // Append newly uploaded file to the file list when upload is complete
   const fileChange = (file?: any) => {
-    if (file?.status !== 'done') return
-    const files = [...(queryValues?.files || []), file]
-    form.setFieldValue('files', files)
-    onFilesChange?.(files)
+    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])
   }
 
   // Append recorded audio file to the file list and notify parent
@@ -129,8 +136,8 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
       key: 'url',
       label: t('memoryConversation.addRemoteFile'),
       onClick: () => {
-        if ((queryValues?.files?.length || 0) >= file_upload.max_file_count) {
-          messageApi.warning(t('common.fileNumTip', { num: file_upload.max_file_count }))
+        if ((queryValues?.files?.length || 0) >= max_file_count) {
+          messageApi.warning(t('common.fileNumTip', { num: max_file_count }))
           return
         }
         uploadFileListModalRef.current?.handleOpen()
@@ -146,7 +153,7 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
           onChange={fileChange}
           requestConfig={uploadRequestConfig}
           featureConfig={file_upload}
-          disabled={(queryValues?.files?.length || 0) >= file_upload.max_file_count}
+          disabled={(queryValues?.files?.length || 0) >= max_file_count}
         />
       )
     })
@@ -184,7 +191,7 @@ const ChatToolbar = forwardRef<ChatToolbarRef, ChatToolbarProps>(({
           {rightExtra}
           {file_upload?.audio_enabled && file_upload?.allowed_transfer_methods?.includes('local_file')  &&
             <AudioRecorder
-              disabled={(queryValues?.files?.length || 0) >= file_upload.max_file_count}
+              disabled={(queryValues?.files?.length || 0) >= 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: 2026-03-18 18:35:53
+ * @LastEditTime: 2026-03-20 12:12:20
  */
 import { useState, useEffect, useRef, useCallback, type FC } from 'react';
 import { Spin, Alert, Button, Table, InputNumber, Image } from 'antd';
@@ -309,23 +309,64 @@ 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 {
-      const arrayBuffer = await fetchFileBuffer(fileUrl);
-
-      // CSV 文件需要处理编码问题（可能是 GBK/GB2312）
+      // 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;
+          }
+        }
+
         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 {
@@ -334,19 +375,35 @@ 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];
-          const data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
+          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;
+          }
           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 => {
+      const sheets = workbook.SheetNames.map((sheetName: string) => {
         const worksheet = workbook.Sheets[sheetName];
         const data = XLSX.utils.sheet_to_json(worksheet, { header: 1 }) as any[][];
         return { sheetName, data };
@@ -522,9 +579,14 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
         )
       )}
 
-      {/* Excel 预览 */}
+      {/* Excel/CSV 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>
@@ -541,6 +603,7 @@ const DocumentPreview: FC<DocumentPreviewProps> = ({
                   scroll={{ x: 'max-content' }}
                   size="small"
                   bordered
+                  virtual
                 />
               )}
             </div>

web/src/i18n/en.ts

@@ -469,6 +469,7 @@ export const en = {
       download: 'Download',
       view: 'View',
       updated_at: 'Updated At',
+      callbackUrlInvalid: 'Please enter a valid URL',
     },
     model: {
       searchPlaceholder: 'search model…',

web/src/i18n/zh.ts

@@ -1106,6 +1106,7 @@ export const zh = {
       download: '下载',
       view: '查看',
       updated_at: '更新时间',
+      callbackUrlInvalid: '请输入有效的 URL',
     },
     model: {
       searchPlaceholder: '搜索模型…',

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() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     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() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     const variables = toolbarRef.current?.getVariables() || []
     const { isCanSend, params } = buildVariableParams(variables)
     if (!isCanSend) return

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

@@ -189,7 +189,7 @@ const Chat: FC<ChatProps> = ({
       .then(() => {
         const message = msg
         if (!message?.trim()) return
-        const files = toolbarRef.current?.getFiles() || []
+        const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
         // Validate required variables before sending
         let isCanSend = true
         const params: Record<string, any> = {}
@@ -350,7 +350,7 @@ const Chat: FC<ChatProps> = ({
       .then(() => {
         const message = msg
         if (!message || message.trim() === '') return
-        const files = toolbarRef.current?.getFiles() || []
+        const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
         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>
-                      {fu.max_file_count} {t('application.unix')}
+                      {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 15:18:20
+ * @Last Modified time: 2026-03-19 20:19:14
  */
 import { forwardRef, useImperativeHandle, useState } from 'react';
 import { Form, InputNumber, Flex, Switch, Row, Col, Radio } from 'antd';
@@ -82,28 +82,27 @@ 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"
+    "json",
+    "md",
   ],
   video_enabled: false,
   video_max_size_mb: 100,
   video_allowed_extensions: [
     "mp4",
     "mov",
-    "avi",
-    "webm"
   ],
-  max_file_count: 5,
+  max_file_count: 1,
   allowed_transfer_methods: 'both'
 }
 
@@ -168,8 +167,8 @@ 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">
+        {/* <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>
           <InputNumber min={1} max={20} precision={0} className="rb:w-full!" placeholder={t('common.pleaseEnter')} />
         </Form.Item>
 

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

@@ -23,7 +23,7 @@
 import { useState, useEffect, forwardRef, useImperativeHandle, useMemo } from 'react';
 import { Upload, Progress, App, Flex } from 'antd';
 import type { UploadProps, UploadFile } from 'antd';
-import type { UploadProps as RcUploadProps } from 'antd/es/upload/interface';
+import type { UploadProps as RcUploadProps, RcFile, UploadFileStatus } from 'antd/es/upload/interface';
 import { useTranslation } from 'react-i18next';
 
 import { request } from '@/utils/request'
@@ -221,17 +221,29 @@ const UploadFiles = forwardRef<UploadFilesRef, UploadFilesProps>(({
    */
   const handleCustomRequest: RcUploadProps['customRequest'] = async (options) => {
     const { file, onSuccess, onError } = options;
-    
-    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);
+    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)
     }
+    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: 2026-03-18 21:10:01
+ * @Last Modified time: 2026-03-19 20:32:32
  */
 /**
  * Upload File List Modal Component
@@ -19,7 +19,10 @@
  * @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'
@@ -105,9 +108,11 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
       onOk={handleSave}
       confirmLoading={loading}
     >
-      <Form form={form} layout="vertical">
+      <Form form={form} layout="vertical" initialValues={{ files: [{ type: undefined, url: undefined }] }}>
         <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 }) => (
@@ -116,6 +121,9 @@ 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')}
@@ -126,22 +134,25 @@ const UploadFileListModal = forwardRef<UploadFileListModalRef, UploadFileListMod
                   <FormItem
                     {...restField}
                     name={[name, 'url']}
-                    rules={[{ required: true, message: t('common.pleaseEnter') }]}
+                    rules={[
+                      { required: true, message: t('common.pleaseEnter') },
+                      { type: 'url', message: t('common.callbackUrlInvalid') },
+                    ]}
                     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

@@ -194,7 +194,7 @@ const Conversation: FC = () => {
   /** Send message and handle streaming response */
   const handleSend = () => {
     if (!token || !shareToken) return
-    const files = toolbarRef.current?.getFiles() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     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 } from '@/api/knowledgeBase';
+import { getDocumentDetail, getDocumentChunkList, downloadFile, updateDocument, updateDocumentChunk, createDocumentChunk, getFileUrl } 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 = `${imagePath}/api/files/${response.file_id}`
+      const url = `${window.location.origin}/api/files/${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/UserMemoryDetail/components/RelationshipNetwork.tsx

@@ -191,24 +191,28 @@ const RelationshipNetwork: FC = () => {
           })}>
             {(selectedNode as RawCommunityNode).properties.community_id
               ? <div>
-                <div className="rb:font-medium rb:text-[#212332] rb:text-[16px] rb:leading-5.5 rb:pl-1">
-                  {(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>
-                <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>
+                  <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}
+                  </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>
 
-                <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 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>
+                  </>}
+                </div>
               : <>
                 {(selectedNode as Node).name &&
                   <div className="rb:font-medium rb:text-[16px] rb:text-[#212332] rb:leading-5.5 rb:mb-3">

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

@@ -4,12 +4,14 @@
  * @Last Modified by:   ZhaoYing 
  * @Last Modified time: 2026-03-16 15:10:17 
  */
-import { type FC, useEffect, useState, useMemo } from 'react'
+import { type FC, useEffect, useState, useMemo, useRef } from 'react'
 import clsx from 'clsx'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Row, Col, Skeleton, Button, Divider, Tooltip, Flex } from 'antd'
 
+
+import InfiniteScroll from 'react-infinite-scroll-component'
 import RbCard from '@/components/RbCard/Card'
 import {
   getConversations,
@@ -61,6 +63,8 @@ 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)
@@ -80,17 +84,30 @@ const WorkingDetail: FC = () => {
     setSelected(null)
     setDetail(null)
     setData([])
-    getConversations(id).then((res) => {
-      const response = res as Conversation[] 
-      setData(response)
-      setSelected(response[0] || null)
+    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)
     })
     .finally(() => {
       setLoading(false)
     })
   }
 
-  /* Load messages and AI insight whenever the selected conversation changes. */
+  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)
@@ -138,16 +155,16 @@ const WorkingDetail: FC = () => {
         : data.length === 0
         ? <Empty />
         :(
-          <Row gutter={16} className="rb:h-full">
-            <Col flex='360px' className="rb:h-full">
-              <RbCard
-                title={t('workingDetail.conversation')}
-                headerType="borderless"
-                headerClassName="rb:min-h-[54px]! rb:font-[MiSans-Bold] rb:font-bold"
-                bodyClassName='rb:p-3! rb:pt-0! rb:h-[calc(100%-54px)]'
-                className="rb:h-full!"
-              >
-                <Flex gap={8} vertical>
+          <Row gutter={16}>
+            <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 => (
                     <Flex
                       key={item.id}
@@ -166,8 +183,8 @@ const WorkingDetail: FC = () => {
                       </Tooltip>
                     </Flex>
                   ))}
-                </Flex>
-              </RbCard>
+                </InfiniteScroll>
+              </div>
             </Col>
             {selected && <>
               <Col flex="auto" className="rb:h-full">

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

@@ -151,7 +151,7 @@ const Chat = forwardRef<ChatRef, { appId: string; graphRef: GraphRef; data: Work
 
     setLoading(true)
     const message = msg
-    const files = toolbarRef.current?.getFiles() || []
+    const files = (toolbarRef.current?.getFiles() || []).filter(item => !['uploading', 'error'].includes(item.status))
     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(() => {
-    // 监听编辑器变化，标记是否为用户输入
-    const removeListener = editor.registerUpdateListener(({ editorState }) => {
+    const removeListener = editor.registerUpdateListener(({ editorState, tags }) => {
+      if (tags.has('programmatic')) return;
       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 });
+        }, { discrete: true, tag: 'programmatic' });
       });
     }