Merge pull request #992 from wanxunyang/develop-wxy

fix(workflow): rectify error handling and bolster execution logging

api/app/controllers/app_log_controller.py

@@ -9,7 +9,7 @@ from app.core.logging_config import get_business_logger
 from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user, cur_workspace_access_guard
-from app.schemas.app_log_schema import AppLogConversation, AppLogConversationDetail
+from app.schemas.app_log_schema import AppLogConversation, AppLogConversationDetail, AppLogMessage
 from app.schemas.response_schema import PageData, PageMeta
 from app.services.app_service import AppService
 from app.services.app_log_service import AppLogService
@@ -78,17 +78,32 @@ def get_app_log_detail(
 
     # 验证应用访问权限
     app_service = AppService(db)
-    app_service.get_app(app_id, workspace_id)
+    app = app_service.get_app(app_id, workspace_id)
 
     # 使用 Service 层查询
     log_service = AppLogService(db)
-    conversation, node_executions_map = log_service.get_conversation_detail(
+    conversation, messages, node_executions_map = log_service.get_conversation_detail(
         app_id=app_id,
         conversation_id=conversation_id,
-        workspace_id=workspace_id
+        workspace_id=workspace_id,
+        app_type=app.type
     )
 
-    detail = AppLogConversationDetail.model_validate(conversation)
+    # 构建基础会话信息（不经过 ORM relationship）
-    detail.node_executions_map = node_executions_map
+    base = AppLogConversation.model_validate(conversation)
+
+    # 单独处理 messages，避免触发 SQLAlchemy relationship 校验
+    if messages and isinstance(messages[0], AppLogMessage):
+        # 工作流：已经是 AppLogMessage 实例
+        msg_list = messages
+    else:
+        # Agent：ORM Message 对象逐个转换
+        msg_list = [AppLogMessage.model_validate(m) for m in messages]
+
+    detail = AppLogConversationDetail(
+        **base.model_dump(),
+        messages=msg_list,
+        node_executions_map=node_executions_map,
+    )
 
     return success(data=detail)

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

@@ -73,6 +73,7 @@ class CustomTool(BaseTool):
         # 添加通用参数（基于第一个操作的参数）
         if self._parsed_operations:
             first_operation = next(iter(self._parsed_operations.values()))
+            # path/query 参数
             for param_name, param_info in first_operation.get("parameters", {}).items():
                 params.append(ToolParameter(
                     name=param_name,
@@ -85,6 +86,23 @@ class CustomTool(BaseTool):
                     maximum=param_info.get("maximum"),
                     pattern=param_info.get("pattern")
                 ))
+            # requestBody 参数 — 将 body 字段平铺为独立参数暴露给模型
+            request_body = first_operation.get("request_body")
+            if request_body:
+                body_schema = request_body.get("properties", {})
+                required_fields = request_body.get("required", [])
+                for prop_name, prop_schema in body_schema.items():
+                    params.append(ToolParameter(
+                        name=prop_name,
+                        type=self._convert_openapi_type(prop_schema.get("type", "string")),
+                        description=prop_schema.get("description", ""),
+                        required=prop_name in required_fields,
+                        default=prop_schema.get("default"),
+                        enum=prop_schema.get("enum"),
+                        minimum=prop_schema.get("minimum"),
+                        maximum=prop_schema.get("maximum"),
+                        pattern=prop_schema.get("pattern")
+                    ))
         
         return params
     

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

@@ -180,6 +180,9 @@ class IterationRuntime:
                                 "cycle_id": self.node_id,
                                 "cycle_idx": idx,
                                 "node_id": node_name,
+                                "node_type": node_type,
+                                "node_name": node_cfg.get("data", {}).get("label") if node_cfg else node_name,
+                                "status": result.get("node_outputs", {}).get(node_name, {}).get("status", "completed"),
                                 "input": result.get("node_outputs", {}).get(node_name, {}).get("input")
                                 if not cycle_variable else cycle_variable,
                                 "output": result.get("node_outputs", {}).get(node_name, {}).get("output")

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

@@ -210,6 +210,9 @@ class LoopRuntime:
                                 "cycle_id": self.node_id,
                                 "cycle_idx": idx,
                                 "node_id": node_name,
+                                "node_type": node_type,
+                                "node_name": node_name,
+                                "status": result.get("node_outputs", {}).get(node_name, {}).get("status", "completed"),
                                 "input": result.get("node_outputs", {}).get(node_name, {}).get("input")
                                 if not cycle_variable else cycle_variable,
                                 "output": result.get("node_outputs", {}).get(node_name, {}).get("output")

api/app/schemas/app_log_schema.py

@@ -14,6 +14,7 @@ class AppLogMessage(BaseModel):
     conversation_id: uuid.UUID
     role: str = Field(description="角色: user / assistant / system")
     content: str
+    status: Optional[str] = Field(default=None, description="执行状态（工作流专用）: completed / failed")
     meta_data: Optional[Dict[str, Any]] = None
     created_at: datetime.datetime
 
@@ -58,6 +59,7 @@ class AppLogNodeExecution(BaseModel):
     input: Optional[Any] = None
     process: Optional[Any] = None
     output: Optional[Any] = None
+    cycle_items: Optional[List[Any]] = None
     elapsed_time: Optional[float] = None
     token_usage: Optional[Dict[str, Any]] = None
 

api/app/services/api_key_service.py

@@ -65,6 +65,11 @@ class ApiKeyService:
                         BizCode.BAD_REQUEST
                     )
 
+            if data.resource_id:
+                app = db.get(App, data.resource_id)
+                if not app or not app.current_release_id:
+                    raise BusinessException("该应用未发布", BizCode.APP_NOT_PUBLISHED)
+
             # 生成 API Key
             api_key = generate_api_key(data.type)
 

api/app/services/app_log_service.py

@@ -1,16 +1,17 @@
 """应用日志服务层"""
 import uuid
+import datetime as dt
 from typing import Optional, Tuple
-from datetime import datetime
 
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_business_logger
+from app.models.app_model import AppType
 from app.models.conversation_model import Conversation, Message
 from app.models.workflow_model import WorkflowExecution
 from app.repositories.conversation_repository import ConversationRepository, MessageRepository
-from app.schemas.app_log_schema import AppLogNodeExecution
+from app.schemas.app_log_schema import AppLogMessage, AppLogNodeExecution
 
 logger = get_business_logger()
 
@@ -83,51 +84,40 @@ class AppLogService:
         self,
         app_id: uuid.UUID,
         conversation_id: uuid.UUID,
-        workspace_id: uuid.UUID
+        workspace_id: uuid.UUID,
-    ) -> Tuple[Conversation, dict[str, list[AppLogNodeExecution]]]:
+        app_type: str = AppType.AGENT
+    ) -> Tuple[Conversation, list, dict[str, list[AppLogNodeExecution]]]:
         """
-        查询会话详情（包含消息和工作流节点执行记录）
+        查询会话详情
-
-        Args:
-            app_id: 应用 ID
-            conversation_id: 会话 ID
-            workspace_id: 工作空间 ID
 
         Returns:
-            Tuple[Conversation, dict[str, list[AppLogNodeExecution]]]:
+            Tuple[Conversation, list[AppLogMessage|Message], dict[str, list[AppLogNodeExecution]]]
-                (包含消息的会话对象, 按消息ID分组的节点执行记录)
-
-        Raises:
-            ResourceNotFoundException: 当会话不存在时
         """
         logger.info(
             "查询应用日志会话详情",
             extra={
                 "app_id": str(app_id),
                 "conversation_id": str(conversation_id),
-                "workspace_id": str(workspace_id)
+                "workspace_id": str(workspace_id),
+                "app_type": app_type
             }
         )
 
-        # 查询会话
         conversation = self.conversation_repository.get_conversation_for_app_log(
             conversation_id=conversation_id,
             app_id=app_id,
             workspace_id=workspace_id
         )
 
-        # 查询消息（按时间正序）
+        if app_type == AppType.WORKFLOW:
-        messages = self.message_repository.get_messages_by_conversation(
+            messages, node_executions_map = self._get_workflow_messages_and_nodes(conversation_id)
-            conversation_id=conversation_id
+        else:
-        )
+            messages = self.message_repository.get_messages_by_conversation(
-
+                conversation_id=conversation_id
-        # 将消息附加到会话对象
+            )
-        conversation.messages = messages
+            node_executions_map = self._get_workflow_node_executions_with_map(
-
+                conversation_id, messages
-        # 查询工作流节点执行记录（按消息分组）
+            )
-        _, node_executions_map = self._get_workflow_node_executions_with_map(
-            conversation_id, messages
-        )
 
         logger.info(
             "查询应用日志会话详情成功",
@@ -139,13 +129,129 @@ class AppLogService:
             }
         )
 
-        return conversation, node_executions_map
+        return conversation, messages, node_executions_map
+
+    def _get_workflow_messages_and_nodes(
+        self,
+        conversation_id: uuid.UUID,
+    ) -> Tuple[list[AppLogMessage], dict[str, list[AppLogNodeExecution]]]:
+        """
+        工作流应用专用：从 workflow_executions 构建 messages 和节点日志。
+
+        每条 WorkflowExecution 对应一轮对话：
+          - user message：来自 execution.input_data（content 取 message 字段，files 放 meta_data）
+          - assistant message：来自 execution.output_data（失败时内容为错误信息）
+        开场白的 suggested_questions 合并到第一条 assistant message 的 meta_data 里。
+
+        Returns:
+            (messages 列表, node_executions_map)
+        """
+        stmt = (
+            select(WorkflowExecution)
+            .where(
+                WorkflowExecution.conversation_id == conversation_id,
+                WorkflowExecution.status.in_(["completed", "failed"])
+            )
+            .order_by(WorkflowExecution.started_at.asc())
+        )
+        executions = list(self.db.scalars(stmt).all())
+
+        # 查开场白：Message 表里 meta_data 含 suggested_questions 的第一条 assistant 消息
+        opening_stmt = (
+            select(Message)
+            .where(
+                Message.conversation_id == conversation_id,
+                Message.role == "assistant",
+            )
+            .order_by(Message.created_at.asc())
+            .limit(10)
+        )
+        early_messages = list(self.db.scalars(opening_stmt).all())
+        suggested_questions: list = []
+        for m in early_messages:
+            if isinstance(m.meta_data, dict) and "suggested_questions" in m.meta_data:
+                suggested_questions = m.meta_data.get("suggested_questions") or []
+                break
+
+        messages: list[AppLogMessage] = []
+        node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
+
+        # 如果有开场白，作为第一条 assistant 消息插入
+        if suggested_questions or early_messages:
+            opening_msg = next(
+                (m for m in early_messages
+                 if isinstance(m.meta_data, dict) and "suggested_questions" in m.meta_data),
+                None
+            )
+            if opening_msg:
+                messages.append(AppLogMessage(
+                    id=opening_msg.id,
+                    conversation_id=conversation_id,
+                    role="assistant",
+                    content=opening_msg.content,
+                    status=None,
+                    meta_data={"suggested_questions": suggested_questions},
+                    created_at=opening_msg.created_at,
+                ))
+
+        for execution in executions:
+            started_at = execution.started_at or dt.datetime.now()
+            completed_at = execution.completed_at or started_at
+
+            # assistant message 的 id，同时作为 node_executions_map 的 key
+            assistant_msg_id = uuid.uuid5(execution.id, "assistant")
+
+            # --- user message（输入）---
+            input_data = execution.input_data or {}
+            input_content = input_data.get("message") or _extract_text(input_data)
+
+            # 跳过没有用户输入的 execution（如开场白触发的记录）
+            if not input_content or not input_content.strip():
+                continue
+
+            files = input_data.get("files") or []
+            user_msg = AppLogMessage(
+                id=uuid.uuid5(execution.id, "user"),
+                conversation_id=conversation_id,
+                role="user",
+                content=input_content,
+                meta_data={"files": files} if files else None,
+                created_at=started_at,
+            )
+            messages.append(user_msg)
+
+            # --- assistant message（输出）---
+            if execution.status == "completed":
+                output_content = _extract_text(execution.output_data)
+                meta = {"usage": execution.token_usage or {}, "elapsed_time": execution.elapsed_time}
+            else:
+                output_content = _extract_text(execution.output_data) or ""
+                meta = {"error": execution.error_message, "error_node_id": execution.error_node_id}
+
+            assistant_msg = AppLogMessage(
+                id=assistant_msg_id,
+                conversation_id=conversation_id,
+                role="assistant",
+                content=output_content,
+                status=execution.status,
+                meta_data=meta,
+                created_at=completed_at,
+            )
+            messages.append(assistant_msg)
+
+            # --- 节点执行记录，从 workflow_executions.output_data["node_outputs"] 读取 ---
+            execution_nodes = _build_nodes_from_output_data(execution.output_data)
+
+            if execution_nodes:
+                node_executions_map[str(assistant_msg_id)] = execution_nodes
+
+        return messages, node_executions_map
 
     def _get_workflow_node_executions_with_map(
         self,
         conversation_id: uuid.UUID,
         messages: list[Message]
-    ) -> Tuple[list[AppLogNodeExecution], dict[str, list[AppLogNodeExecution]]]:
+    ) -> dict[str, list[AppLogNodeExecution]]:
         """
         从 workflow_executions 表中提取节点执行记录，并按 assistant message 分组
 
@@ -157,13 +263,12 @@ class AppLogService:
             Tuple[list[AppLogNodeExecution], dict[str, list[AppLogNodeExecution]]]:
                 (所有节点执行记录列表, 按 message_id 分组的节点执行记录字典)
         """
-        node_executions = []
         node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
 
         # 查询该会话关联的所有工作流执行记录（按时间正序）
         stmt = select(WorkflowExecution).where(
             WorkflowExecution.conversation_id == conversation_id,
-            WorkflowExecution.status == "completed"
+            WorkflowExecution.status.in_(["completed", "failed"])
         ).order_by(WorkflowExecution.started_at.asc())
 
         executions = self.db.scalars(stmt).all()
@@ -188,10 +293,18 @@ class AppLogService:
         used_message_ids: set[str] = set()
 
         for execution in executions:
-            if not execution.output_data:
+            # 构建节点执行记录列表，从 workflow_executions.output_data["node_outputs"] 读取
+            execution_nodes = _build_nodes_from_output_data(execution.output_data)
+
+            if not execution_nodes:
                 continue
 
-            # 找到该 execution 对应的 assistant message
+            # 失败的执行没有 assistant message，直接用 execution id 作为 key
+            if execution.status == "failed":
+                node_executions_map[f"execution_{str(execution.id)}"] = execution_nodes
+                continue
+
+            # completed：通过时序匹配关联到对应的 assistant message
             # 逻辑：找 execution.started_at 之后最近的、未使用的 assistant message
             best_msg = None
             best_dt = None
@@ -200,9 +313,9 @@ class AppLogService:
                 if msg_id_str in used_message_ids:
                     continue
                 if msg.created_at and msg.created_at >= execution.started_at:
-                    dt = (msg.created_at - execution.started_at).total_seconds()
+                    delta = (msg.created_at - execution.started_at).total_seconds()
-                    if best_dt is None or dt < best_dt:
+                    if best_dt is None or delta < best_dt:
-                        best_dt = dt
+                        best_dt = delta
                         best_msg = msg
 
             if not best_msg:
@@ -210,31 +323,76 @@ class AppLogService:
 
             msg_id_str = str(best_msg.id)
             used_message_ids.add(msg_id_str)
+            node_executions_map[msg_id_str] = execution_nodes
 
-            # 提取节点输出
+        return node_executions_map
-            output_data = execution.output_data
-            if isinstance(output_data, dict):
-                node_outputs = output_data.get("node_outputs", {})
-                execution_nodes = []
-                for node_id, node_data in node_outputs.items():
-                    if not isinstance(node_data, dict):
-                        continue
-                    node_execution = AppLogNodeExecution(
-                        node_id=node_data.get("node_id", node_id),
-                        node_type=node_data.get("node_type", "unknown"),
-                        node_name=node_data.get("node_name"),
-                        status=node_data.get("status", "unknown"),
-                        error=node_data.get("error"),
-                        input=node_data.get("input"),
-                        process=node_data.get("process"),
-                        output=node_data.get("output"),
-                        elapsed_time=node_data.get("elapsed_time"),
-                        token_usage=node_data.get("token_usage"),
-                    )
-                    node_executions.append(node_execution)
-                    execution_nodes.append(node_execution)
 
-                # 将节点记录关联到 message_id
-                node_executions_map[msg_id_str] = execution_nodes
 
-        return node_executions, node_executions_map
+def _extract_text(data: Optional[dict]) -> str:
+    """从 workflow execution 的 input_data / output_data 中提取可读文本。
+
+    优先取 'text'、'content'、'output' 字段；若都没有则 JSON 序列化整个 dict。
+    """
+    if not data:
+        return ""
+    for key in ("message", "text", "content", "output", "result", "answer"):
+        if key in data and isinstance(data[key], str):
+            return data[key]
+    import json
+    return json.dumps(data, ensure_ascii=False)
+
+
+def _build_nodes_from_output_data(output_data: Optional[dict]) -> list[AppLogNodeExecution]:
+    """从 workflow_executions.output_data["node_outputs"] 构建节点执行记录列表。
+
+    output_data 结构：
+    {
+        "node_outputs": {
+            "<node_id>": {
+                "node_type": ...,
+                "node_name": ...,
+                "status": ...,
+                "input": ...,
+                "output": ...,
+                "elapsed_time": ...,
+                "token_usage": ...,
+                "error": ...,
+                "cycle_items": [...],
+                ...
+            }
+        },
+        "error": ...,
+        ...
+    }
+    """
+    if not output_data:
+        return []
+    node_outputs: dict = output_data.get("node_outputs") or {}
+    result = []
+    for node_id, node_data in node_outputs.items():
+        if not isinstance(node_data, dict):
+            continue
+        output = dict(node_data)
+        cycle_items = output.pop("cycle_items", None)
+        # 把已知的顶层字段剥离，剩余的作为 output
+        node_type = output.pop("node_type", "unknown")
+        node_name = output.pop("node_name", None)
+        status = output.pop("status", "completed")
+        error = output.pop("error", None)
+        inp = output.pop("input", None)
+        elapsed_time = output.pop("elapsed_time", None)
+        token_usage = output.pop("token_usage", None)
+        result.append(AppLogNodeExecution(
+            node_id=node_id,
+            node_type=node_type,
+            node_name=node_name,
+            status=status,
+            error=error,
+            input=inp,
+            process=None,
+            output=output if output else None,
+            cycle_items=cycle_items,
+            elapsed_time=elapsed_time,
+            token_usage=token_usage,
+        ))
+    return result

api/app/services/tool_service.py

@@ -815,11 +815,12 @@ class ToolService:
                         "default": param_info.get("default")
                     })
                 
-                # 请求体参数
+                # 请求体参数 — _extract_request_body 返回 {"schema": {...}, "required": bool, ...}
                 request_body = operation.get("request_body")
                 if request_body:
-                    schema_props = request_body.get("schema", {}).get("properties", {})
+                    body_schema = request_body.get("schema", {})
-                    required_props = request_body.get("schema", {}).get("required", [])
+                    schema_props = body_schema.get("properties", {})
+                    required_props = body_schema.get("required", [])
                     
                     for prop_name, prop_schema in schema_props.items():
                         parameters.append({

api/app/services/workflow_service.py

@@ -17,8 +17,9 @@ from app.core.workflow.executor import execute_workflow, execute_workflow_stream
 from app.core.workflow.nodes.enums import NodeType
 from app.core.workflow.validator import validate_workflow_config
 from app.db import get_db
+from sqlalchemy import select
 from app.models import App
-from app.models.workflow_model import WorkflowConfig, WorkflowExecution
+from app.models.workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution
 from app.repositories import knowledge_repository
 from app.repositories.workflow_repository import (
     WorkflowConfigRepository,
@@ -918,6 +919,7 @@ class WorkflowService:
                 input_data["conv_messages"] = conv_messages
             init_message_length = len(input_data.get("conv_messages", []))
             message_id = uuid.uuid4()
+            _cycle_items: dict[str, list] = {}
 
             # 新会话时写入开场白
             is_new_conversation = init_message_length == 0
@@ -948,6 +950,15 @@ class WorkflowService:
                     memory_storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id
             ):
+                event_type = event.get("event")
+                event_data = event.get("data", {})
+
+                if event_type == "cycle_item":
+                    cycle_id = event_data.get("cycle_id")
+                    if cycle_id not in _cycle_items:
+                        _cycle_items[cycle_id] = []
+                    _cycle_items[cycle_id].append(event_data)
+
                 if event.get("event") == "workflow_end":
                     status = event.get("data", {}).get("status")
                     token_usage = event.get("data", {}).get("token_usage", {}) or {}
@@ -1019,6 +1030,18 @@ class WorkflowService:
                         )
                     else:
                         logger.error(f"unexpect workflow run status, status: {status}")
+                    # 把积累的 cycle_item 写入 workflow_executions.output_data["node_outputs"]
+                    if _cycle_items and execution.output_data:
+                        import copy
+                        new_output_data = copy.deepcopy(execution.output_data)
+                        node_outputs = new_output_data.setdefault("node_outputs", {})
+                        for cycle_node_id, items in _cycle_items.items():
+                            if cycle_node_id in node_outputs:
+                                node_outputs[cycle_node_id]["cycle_items"] = items
+                            else:
+                                node_outputs[cycle_node_id] = {"cycle_items": items}
+                        execution.output_data = new_output_data
+                        self.db.commit()
                 elif event.get("event") == "workflow_start":
                     event["data"]["message_id"] = str(message_id)
                 event = self._emit(public, event)