cf8db47389
- 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.
336 lines
12 KiB
Python
336 lines
12 KiB
Python
"""应用日志服务层"""
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import uuid
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import datetime as dt
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from typing import Optional, Tuple
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.core.logging_config import get_business_logger
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from app.models.app_model import AppType
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from app.models.conversation_model import Conversation, Message
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from app.models.workflow_model import WorkflowExecution
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from app.repositories.conversation_repository import ConversationRepository, MessageRepository
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from app.schemas.app_log_schema import AppLogMessage, AppLogNodeExecution
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logger = get_business_logger()
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class AppLogService:
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"""应用日志服务"""
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def __init__(self, db: Session):
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self.db = db
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self.conversation_repository = ConversationRepository(db)
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self.message_repository = MessageRepository(db)
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def list_conversations(
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self,
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app_id: uuid.UUID,
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workspace_id: uuid.UUID,
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page: int = 1,
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pagesize: int = 20,
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is_draft: Optional[bool] = None,
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keyword: Optional[str] = None,
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) -> Tuple[list[Conversation], int]:
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"""
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查询应用日志会话列表
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Args:
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app_id: 应用 ID
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workspace_id: 工作空间 ID
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page: 页码（从 1 开始）
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pagesize: 每页数量
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is_draft: 是否草稿会话（None表示返回全部）
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keyword: 搜索关键词（匹配消息内容）
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Returns:
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Tuple[list[Conversation], int]: (会话列表，总数)
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"""
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logger.info(
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"查询应用日志会话列表",
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extra={
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"app_id": str(app_id),
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"workspace_id": str(workspace_id),
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"page": page,
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"pagesize": pagesize,
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"is_draft": is_draft,
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"keyword": keyword
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}
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)
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# 使用 Repository 查询
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conversations, total = self.conversation_repository.list_app_conversations(
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app_id=app_id,
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workspace_id=workspace_id,
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is_draft=is_draft,
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keyword=keyword,
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page=page,
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pagesize=pagesize
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)
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logger.info(
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"查询应用日志会话列表成功",
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extra={
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"app_id": str(app_id),
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"total": total,
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"returned": len(conversations)
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}
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)
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return conversations, total
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def get_conversation_detail(
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self,
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app_id: uuid.UUID,
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conversation_id: uuid.UUID,
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workspace_id: uuid.UUID,
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app_type: str = AppType.AGENT
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) -> Tuple[Conversation, list, dict[str, list[AppLogNodeExecution]]]:
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"""
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查询会话详情
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Returns:
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Tuple[Conversation, list[AppLogMessage|Message], dict[str, list[AppLogNodeExecution]]]
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"""
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logger.info(
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"查询应用日志会话详情",
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extra={
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"app_id": str(app_id),
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"conversation_id": str(conversation_id),
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"workspace_id": str(workspace_id),
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"app_type": app_type
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}
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)
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conversation = self.conversation_repository.get_conversation_for_app_log(
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conversation_id=conversation_id,
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app_id=app_id,
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workspace_id=workspace_id
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)
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if app_type == AppType.WORKFLOW:
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messages, node_executions_map = self._get_workflow_messages_and_nodes(conversation_id)
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else:
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messages = self.message_repository.get_messages_by_conversation(
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conversation_id=conversation_id
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)
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_, node_executions_map = self._get_workflow_node_executions_with_map(
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conversation_id, messages
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)
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logger.info(
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"查询应用日志会话详情成功",
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extra={
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"app_id": str(app_id),
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"conversation_id": str(conversation_id),
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"message_count": len(messages),
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"message_with_nodes_count": len(node_executions_map)
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}
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)
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return conversation, messages, node_executions_map
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def _get_workflow_messages_and_nodes(
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self,
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conversation_id: uuid.UUID,
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) -> Tuple[list[AppLogMessage], dict[str, list[AppLogNodeExecution]]]:
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"""
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工作流应用专用：从 workflow_executions 构建 messages 和节点日志。
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每条 WorkflowExecution 对应一轮对话：
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- user message：来自 execution.input_data
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- assistant message：来自 execution.output_data（失败时内容为错误信息）
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节点日志以 execution id 为 key 分组。
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Returns:
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(messages 列表, node_executions_map)
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"""
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stmt = (
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select(WorkflowExecution)
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.where(
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WorkflowExecution.conversation_id == conversation_id,
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WorkflowExecution.status.in_(["completed", "failed"])
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)
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.order_by(WorkflowExecution.started_at.asc())
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)
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executions = list(self.db.scalars(stmt).all())
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messages: list[AppLogMessage] = []
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node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
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for execution in executions:
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started_at = execution.started_at or dt.datetime.now()
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completed_at = execution.completed_at or started_at
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# assistant message 的 id，同时作为 node_executions_map 的 key
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assistant_msg_id = uuid.uuid5(execution.id, "assistant")
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# --- user message（输入）---
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input_content = _extract_text(execution.input_data)
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user_msg = AppLogMessage(
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id=uuid.uuid5(execution.id, "user"),
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conversation_id=conversation_id,
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role="user",
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content=input_content,
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meta_data=None,
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created_at=started_at,
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)
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messages.append(user_msg)
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# --- assistant message（输出）---
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if execution.status == "completed":
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output_content = _extract_text(execution.output_data)
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meta = {"usage": execution.token_usage or {}, "elapsed_time": execution.elapsed_time}
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else:
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output_content = _extract_text(execution.output_data) or ""
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meta = {"error": execution.error_message, "error_node_id": execution.error_node_id}
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assistant_msg = AppLogMessage(
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id=assistant_msg_id,
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conversation_id=conversation_id,
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role="assistant",
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content=output_content,
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status=execution.status,
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meta_data=meta,
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created_at=completed_at,
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)
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messages.append(assistant_msg)
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# --- 节点执行记录，key 与 assistant message id 一致 ---
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execution_nodes = []
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for node_exec in execution.node_executions:
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output_data = dict(node_exec.output_data or {})
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cycle_items = output_data.pop("cycle_items", None)
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execution_nodes.append(AppLogNodeExecution(
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node_id=node_exec.node_id,
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node_type=node_exec.node_type,
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node_name=node_exec.node_name,
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status=node_exec.status,
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error=node_exec.error_message,
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input=node_exec.input_data,
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process=None,
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output=output_data,
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cycle_items=cycle_items,
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elapsed_time=node_exec.elapsed_time,
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token_usage=node_exec.token_usage,
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))
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if execution_nodes:
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node_executions_map[str(assistant_msg_id)] = execution_nodes
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return messages, node_executions_map
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def _get_workflow_node_executions_with_map(
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self,
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conversation_id: uuid.UUID,
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messages: list[Message]
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) -> Tuple[list[AppLogNodeExecution], dict[str, list[AppLogNodeExecution]]]:
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"""
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从 workflow_executions 表中提取节点执行记录，并按 assistant message 分组
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Args:
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conversation_id: 会话 ID
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messages: 消息列表
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Returns:
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Tuple[list[AppLogNodeExecution], dict[str, list[AppLogNodeExecution]]]:
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(所有节点执行记录列表, 按 message_id 分组的节点执行记录字典)
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"""
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node_executions = []
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node_executions_map: dict[str, list[AppLogNodeExecution]] = {}
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# 查询该会话关联的所有工作流执行记录（按时间正序）
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stmt = select(WorkflowExecution).where(
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WorkflowExecution.conversation_id == conversation_id,
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WorkflowExecution.status.in_(["completed", "failed"])
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).order_by(WorkflowExecution.started_at.asc())
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executions = self.db.scalars(stmt).all()
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logger.info(
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f"查询到 {len(executions)} 条工作流执行记录",
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extra={
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"conversation_id": str(conversation_id),
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"execution_count": len(executions),
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"execution_ids": [str(e.id) for e in executions]
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}
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)
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# 筛选出 workflow 执行产生的 assistant 消息（排除开场白）
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# workflow 结果的 meta_data 包含 usage，而开场白包含 suggested_questions
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assistant_messages = [
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m for m in messages
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if m.role == "assistant" and m.meta_data and "usage" in m.meta_data
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]
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# 通过时序匹配，将 execution 和 assistant message 关联
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used_message_ids: set[str] = set()
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for execution in executions:
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# 构建节点执行记录列表
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execution_nodes = []
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for node_exec in execution.node_executions:
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output_data = dict(node_exec.output_data or {})
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cycle_items = output_data.pop("cycle_items", None)
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node_execution = AppLogNodeExecution(
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node_id=node_exec.node_id,
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node_type=node_exec.node_type,
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node_name=node_exec.node_name,
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status=node_exec.status,
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error=node_exec.error_message,
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input=node_exec.input_data,
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process=None,
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output=output_data,
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cycle_items=cycle_items,
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elapsed_time=node_exec.elapsed_time,
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token_usage=node_exec.token_usage,
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)
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node_executions.append(node_execution)
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execution_nodes.append(node_execution)
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if not execution_nodes:
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continue
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# 失败的执行没有 assistant message，直接用 execution id 作为 key
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if execution.status == "failed":
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node_executions_map[f"execution_{str(execution.id)}"] = execution_nodes
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continue
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# completed：通过时序匹配关联到对应的 assistant message
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# 逻辑：找 execution.started_at 之后最近的、未使用的 assistant message
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best_msg = None
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best_dt = None
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for msg in assistant_messages:
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msg_id_str = str(msg.id)
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if msg_id_str in used_message_ids:
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continue
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if msg.created_at and msg.created_at >= execution.started_at:
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delta = (msg.created_at - execution.started_at).total_seconds()
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if best_dt is None or delta < best_dt:
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best_dt = delta
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best_msg = msg
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if not best_msg:
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continue
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msg_id_str = str(best_msg.id)
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used_message_ids.add(msg_id_str)
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node_executions_map[msg_id_str] = execution_nodes
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return node_executions, node_executions_map
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def _extract_text(data: Optional[dict]) -> str:
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"""从 workflow execution 的 input_data / output_data 中提取可读文本。
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优先取 'text'、'content'、'output' 字段；若都没有则 JSON 序列化整个 dict。
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"""
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if not data:
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return ""
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for key in ("text", "content", "output", "result", "answer"):
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if key in data and isinstance(data[key], str):
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return data[key]
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import json
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return json.dumps(data, ensure_ascii=False)
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