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

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- 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.
This commit is contained in:
wwq
2026-04-24 17:02:03 +08:00
parent cedf47b3bc
commit cf8db47389
5 changed files with 158 additions and 40 deletions
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27
api/app/controllers/app_log_controller.py
View File

@@ -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)
detail.node_executions_map = node_executions_map
# 构建基础会话信息（不经过 ORM relationship）
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)

2
api/app/core/workflow/nodes/cycle_graph/iteration.py
View File

@@ -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")

2
api/app/core/workflow/nodes/cycle_graph/loop.py
View File

@@ -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")

2
api/app/schemas/app_log_schema.py
View File

@@ -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

165
api/app/services/app_log_service.py
View File

@@ -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
) -> Tuple[Conversation, dict[str, list[AppLogNodeExecution]]]:
workspace_id: uuid.UUID,
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）
Args:
app_id: 应用 ID
conversation_id: 会话 ID
workspace_id: 工作空间 ID
查询会话详情
Returns:
Tuple[Conversation, 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
Tuple[Conversation, list[AppLogMessage|Message], dict[str, list[AppLogNodeExecution]]]
"""
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）
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
# 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
)
if app_type == AppType.WORKFLOW:
messages, node_executions_map = self._get_workflow_messages_and_nodes(conversation_id)
else:
messages = self.message_repository.get_messages_by_conversation(
conversation_id=conversation_id
)
_, 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()
if best_dt is None or dt < best_dt:
best_dt = dt
delta = (msg.created_at - execution.started_at).total_seconds()
if best_dt is None or delta < best_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)