feat(workflow): augment logging capabilities with execution status and loop support

- Augment workflow logs with execution status fields and loop node information.
- Refactor log service to handle distinct processing logic for workflows and agents.
- Construct message and node logs derived from workflow_executions data.

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/workflow/nodes/cycle_graph/iteration.py

@@ -180,6 +180,8 @@ 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,
                                 "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 +210,8 @@ class LoopRuntime:
                                 "cycle_id": self.node_id,
                                 "cycle_idx": idx,
                                 "node_id": node_name,
+                                "node_type": node_type,
+                                "node_name": node_name,
                                 "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/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",
@@ -139,7 +129,97 @@ 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
+          - assistant message：来自 execution.output_data（失败时内容为错误信息）
+        节点日志以 execution id 为 key 分组。
+
+        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())
+
+        messages: list[AppLogMessage] = []
+        node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
+
+        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_content = _extract_text(execution.input_data)
+            user_msg = AppLogMessage(
+                id=uuid.uuid5(execution.id, "user"),
+                conversation_id=conversation_id,
+                role="user",
+                content=input_content,
+                meta_data=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，key 与 assistant message id 一致 ---
+            execution_nodes = []
+            for node_exec in execution.node_executions:
+                output_data = dict(node_exec.output_data or {})
+                cycle_items = output_data.pop("cycle_items", None)
+                execution_nodes.append(AppLogNodeExecution(
+                    node_id=node_exec.node_id,
+                    node_type=node_exec.node_type,
+                    node_name=node_exec.node_name,
+                    status=node_exec.status,
+                    error=node_exec.error_message,
+                    input=node_exec.input_data,
+                    process=None,
+                    output=output_data,
+                    cycle_items=cycle_items,
+                    elapsed_time=node_exec.elapsed_time,
+                    token_usage=node_exec.token_usage,
+                ))
+
+            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,
@@ -191,6 +271,8 @@ class AppLogService:
             # 构建节点执行记录列表
             execution_nodes = []
             for node_exec in execution.node_executions:
+                output_data = dict(node_exec.output_data or {})
+                cycle_items = output_data.pop("cycle_items", None)
                 node_execution = AppLogNodeExecution(
                     node_id=node_exec.node_id,
                     node_type=node_exec.node_type,
@@ -199,7 +281,8 @@ class AppLogService:
                     error=node_exec.error_message,
                     input=node_exec.input_data,
                     process=None,
-                    output=node_exec.output_data,
+                    output=output_data,
+                    cycle_items=cycle_items,
                     elapsed_time=node_exec.elapsed_time,
                     token_usage=node_exec.token_usage,
                 )
@@ -223,9 +306,9 @@ 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:
@@ -236,3 +319,17 @@ class AppLogService:
             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 ("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)