""" 基于LangGraph的工作流管理器 负责任务的创建、编排和执行，使用LangGraph进行状态管理 新增功能： - 任务终止管理 - 终止信号设置和检测 """ import asyncio import time from typing import Dict, Optional, Any from datetime import datetime from langgraph.graph import StateGraph, END from langchain_core.messages import BaseMessage, HumanMessage, AIMessage from foundation.observability.logger.loggering import server_logger as logger from foundation.observability.monitoring.time_statistics import track_execution_time from foundation.infrastructure.cache.redis_connection import RedisConnectionFactory from .progress_manager import ProgressManager from .redis_duplicate_checker import RedisDuplicateChecker from .task_models import TaskFileInfo, TaskChain from ..construction_review.workflows import DocumentWorkflow, AIReviewWorkflow, ReportWorkflow from ..construction_review.workflows.types import TaskChainState class ProgressManagerRegistry: """ProgressManager注册表 - 为每个任务管理独立的ProgressManager实例""" _registry = {} # {callback_task_id: ProgressManager} @classmethod def register_progress_manager(cls, callback_task_id: str, progress_manager: ProgressManager): """注册ProgressManager实例""" cls._registry[callback_task_id] = progress_manager logger.info(f"注册ProgressManager实例: {callback_task_id}, ID: {id(progress_manager)}") @classmethod def get_progress_manager(cls, callback_task_id: str) -> ProgressManager: """获取ProgressManager实例""" return cls._registry.get(callback_task_id) @classmethod def unregister_progress_manager(cls, callback_task_id: str): """注销ProgressManager实例""" if callback_task_id in cls._registry: del cls._registry[callback_task_id] logger.info(f"注销ProgressManager实例: {callback_task_id}") class WorkflowManager: """工作流管理器""" def __init__(self, max_concurrent_docs: int = 5, max_concurrent_reviews: int = 10): self.max_concurrent_docs = max_concurrent_docs self.max_concurrent_reviews = max_concurrent_reviews # 并发控制 self.doc_semaphore = asyncio.Semaphore(max_concurrent_docs) self.review_semaphore = asyncio.Semaphore(max_concurrent_reviews) # 服务组件 self.progress_manager = ProgressManager() self.redis_duplicate_checker = RedisDuplicateChecker() # 活跃任务跟踪 self.active_chains: Dict[str, TaskChain] = {} self._cleanup_task_started = False # 任务终止管理 self._terminate_signal_prefix = "ai_review:terminate_signal:" self._task_expire_time = 7200 # 2小时 # LangGraph 任务链工作流（方案D） self.task_chain_graph = None # 延迟初始化，避免循环导入 async def submit_task_processing(self, file_info: dict) -> str: """异步提交任务处理（用于file_upload层）""" from foundation.infrastructure.messaging.tasks import submit_task_processing_task from foundation.infrastructure.tracing.celery_trace import CeleryTraceManager try: logger.info(f"提交文档处理任务到Celery: {file_info['file_id']}") # 使用CeleryTraceManager提交任务，自动传递trace_id task = CeleryTraceManager.submit_celery_task( submit_task_processing_task, file_info ) logger.info(f"Celery任务已提交，Task ID: {task.id}") return task.id except Exception as e: logger.error(f"提交Celery任务失败: {str(e)}") raise @track_execution_time def submit_task_processing_sync(self, file_info: dict) -> dict: """ 同步提交任务处理（用于Celery worker） Note: 已切换到 LangGraph 任务链工作流（方案D） 使用统一的状态管理和嵌套子图架构 """ try: logger.info(f"提交文档处理任务（LangGraph方案D）: {file_info['file_id']}") # 1. 创建TaskFileInfo对象（封装任务文件信息） task_file_info = TaskFileInfo(file_info) logger.info(f"创建任务文件信息: {task_file_info}") # 2. 生成任务链ID callback_task_id = task_file_info.callback_task_id # 3. 创建任务链（引用 TaskFileInfo，避免数据重复） task_chain = TaskChain(task_file_info) # 4. 标记任务开始 task_chain.start_processing() # 5. 添加到活跃任务跟踪 self.active_chains[callback_task_id] = task_chain # 6. 初始化进度管理 asyncio.run(self.progress_manager.initialize_progress( callback_task_id=callback_task_id, user_id=task_file_info.user_id, stages=[] )) # 7. 构建 LangGraph 任务链工作流（延迟初始化） if self.task_chain_graph is None: self.task_chain_graph = self._build_task_chain_workflow() # 8. 构建初始状态 initial_state = TaskChainState( file_id=task_file_info.file_id, callback_task_id=callback_task_id, user_id=task_file_info.user_id, file_name=task_file_info.file_name, file_type=task_file_info.file_type, file_content=task_file_info.file_content, current_stage="start", overall_task_status="processing", stage_status={ "document": "pending", "ai_review": "pending", "report": "pending" }, document_result=None, ai_review_result=None, report_result=None, error_message=None, progress_manager=self.progress_manager, task_file_info=task_file_info, messages=[HumanMessage(content=f"开始任务链: {task_file_info.file_id}")] ) # 9. 执行 LangGraph 任务链工作流 loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) result = loop.run_until_complete(self.task_chain_graph.ainvoke(initial_state)) loop.close() # 10. 清理任务注册 asyncio.run(self.redis_duplicate_checker.unregister_task(task_chain.file_id)) logger.info(f"施工方案审查任务已完成（LangGraph方案D）！") logger.info(f"文件ID: {task_file_info.file_id}") logger.info(f"文件名: {task_file_info.file_name}") logger.info(f"整体状态: {result.get('overall_task_status', 'unknown')}") # 构建可序列化的返回结果（移除不可序列化的对象） serializable_result = { "file_id": result.get("file_id"), "callback_task_id": result.get("callback_task_id"), "user_id": result.get("user_id"), "file_name": result.get("file_name"), "current_stage": result.get("current_stage"), "overall_task_status": result.get("overall_task_status"), "stage_status": result.get("stage_status"), "error_message": result.get("error_message"), # 注意：不包含 progress_manager, task_file_info, messages 等不可序列化对象 } return serializable_result except Exception as e: logger.error(f"提交文档处理任务失败: {str(e)}", exc_info=True) # 标记任务失败 if callback_task_id in self.active_chains: self.active_chains[callback_task_id].fail_processing(str(e)) # 清理任务注册 asyncio.run(self.redis_duplicate_checker.unregister_task(task_file_info.file_id)) # 通知SSE连接任务失败 error_data = { "error": str(e), "status": "failed", "overall_task_status": "failed", "timestamp": datetime.now().isoformat() } asyncio.run(self.progress_manager.complete_task(callback_task_id, task_file_info.user_id, error_data)) raise finally: # 清理活跃任务 if callback_task_id in self.active_chains: del self.active_chains[callback_task_id] async def set_terminate_signal(self, callback_task_id: str, operator: str = "unknown") -> Dict[str, any]: """ 设置任务终止信号 Args: callback_task_id: 任务回调ID operator: 操作人（用户ID或系统标识） Returns: Dict: 操作结果 {"success": bool, "message": str, "task_info": dict} Note: 将终止信号写入 Redis，支持跨进程检测 AI审查节点在执行前会检查此信号 """ try: # 检查任务是否在活跃列表中 if callback_task_id not in self.active_chains: return { "success": False, "message": f"任务不存在或已完成: {callback_task_id}", "task_info": None } task_chain = self.active_chains[callback_task_id] # 检查任务状态 if task_chain.status != "processing": return { "success": False, "message": f"任务状态不是 processing，无需终止: {callback_task_id} (当前状态: {task_chain.status})", "task_info": { "callback_task_id": callback_task_id, "status": task_chain.status, "file_name": task_chain.file_name } } # 设置 Redis 终止信号 redis_client = await RedisConnectionFactory.get_connection() terminate_key = f"{self._terminate_signal_prefix}{callback_task_id}" # 存储终止信号和操作人、时间 terminate_data = { "operator": operator, "terminate_time": str(time.time()), "task_id": callback_task_id } # 使用 hash 存储更多信息 await redis_client.hset(terminate_key, mapping=terminate_data) # 设置过期时间（2小时） await redis_client.expire(terminate_key, self._task_expire_time) logger.info(f"已设置终止信号: {callback_task_id} (操作人: {operator}, 文件: {task_chain.file_name})") return { "success": True, "message": f"终止信号已设置，任务将在当前节点完成后终止", "task_info": { "callback_task_id": callback_task_id, "file_id": task_chain.file_id, "file_name": task_chain.file_name, "user_id": task_chain.user_id, "status": task_chain.status, "current_stage": task_chain.current_stage } } except Exception as e: logger.error(f"设置终止信号失败: {str(e)}", exc_info=True) return { "success": False, "message": f"设置终止信号失败: {str(e)}", "task_info": None } async def check_terminate_signal(self, callback_task_id: str) -> bool: """ 检查是否有终止信号 Args: callback_task_id: 任务回调ID Returns: bool: 有终止信号返回 True Note: 从 Redis 读取终止信号 工作流节点在执行前调用此方法检查是否需要终止 """ try: redis_client = await RedisConnectionFactory.get_connection() terminate_key = f"{self._terminate_signal_prefix}{callback_task_id}" # 检查键是否存在 exists = await redis_client.exists(terminate_key) if exists: # 读取终止信息 terminate_info = await redis_client.hgetall(terminate_key) logger.warning(f"检测到终止信号: {callback_task_id}, 操作人: {terminate_info.get(b'operator', b'unknown').decode()}") return True return False except RuntimeError as e: # 事件循环相关的错误处理 error_msg = str(e) if "Event loop is closed" in error_msg: # 事件循环关闭是正常情况（任务结束），不记录错误 logger.debug(f"检查终止信号时事件循环已关闭: {callback_task_id}") return False elif "bound to a different event loop" in error_msg: # 事件循环不匹配，记录警告但不中断流程 logger.warning(f"检查终止信号时检测到事件循环不匹配: {callback_task_id}，将忽略本次检查") return False else: # 其他 RuntimeError 记录错误 logger.error(f"检查终止信号失败(RuntimeError): {error_msg}", exc_info=True) return False except Exception as e: # 其他异常仍然记录错误 logger.error(f"检查终止信号失败: {str(e)}", exc_info=True) return False async def clear_terminate_signal(self, callback_task_id: str): """ 清理 Redis 中的终止信号 Args: callback_task_id: 任务回调ID """ try: redis_client = await RedisConnectionFactory.get_connection() terminate_key = f"{self._terminate_signal_prefix}{callback_task_id}" await redis_client.delete(terminate_key) logger.debug(f"清理终止信号: {callback_task_id}") except Exception as e: logger.warning(f"清理终止信号失败: {str(e)}") async def get_active_tasks(self) -> list: """ 获取活跃任务列表 Returns: list: 活跃任务信息列表 """ try: active_tasks = [] current_time = time.time() for task_id, task_chain in self.active_chains.items(): if task_chain.status == "processing": task_info = { "callback_task_id": task_id, "file_id": task_chain.file_id, "file_name": task_chain.file_name, "user_id": task_chain.user_id, "status": task_chain.status, "current_stage": task_chain.current_stage, "start_time": task_chain.start_time, "running_duration": int(current_time - task_chain.start_time) if task_chain.start_time else 0 } active_tasks.append(task_info) return active_tasks except Exception as e: logger.error(f"获取活跃任务列表失败: {str(e)}", exc_info=True) return [] async def get_task_info(self, callback_task_id: str) -> Optional[Dict]: """ 获取任务信息 Args: callback_task_id: 任务回调ID Returns: Optional[Dict]: 任务信息字典，不存在返回 None """ try: task_chain = self.active_chains.get(callback_task_id) if task_chain: current_time = time.time() return { "callback_task_id": callback_task_id, "file_id": task_chain.file_id, "file_name": task_chain.file_name, "user_id": task_chain.user_id, "status": task_chain.status, "current_stage": task_chain.current_stage, "start_time": task_chain.start_time, "running_duration": int(current_time - task_chain.start_time) if task_chain.start_time else 0, "results": task_chain.results } return None except Exception as e: logger.error(f"获取任务信息失败: {str(e)}", exc_info=True) return None def _build_task_chain_workflow(self) -> StateGraph: """ 构建 LangGraph 任务链工作流图（方案D） Returns: StateGraph: 配置完成的 LangGraph 任务链图实例 Note: 创建包含文档处理、AI审查（嵌套子图）、报告生成的完整任务链 设置节点间的转换关系和条件边，支持终止检查和错误处理 工作流路径: start → document_processing → ai_review_subgraph → report_generation → complete → END """ logger.info("开始构建 LangGraph 任务链工作流图") workflow = StateGraph(TaskChainState) # 添加节点 workflow.add_node("start", self._start_chain_node) workflow.add_node("document_processing", self._document_processing_node) workflow.add_node("ai_review_subgraph", self._ai_review_subgraph_node) workflow.add_node("report_generation", self._report_generation_node) workflow.add_node("complete", self._complete_chain_node) workflow.add_node("error_handler", self._error_handler_chain_node) workflow.add_node("terminate", self._terminate_chain_node) # 设置入口点 workflow.set_entry_point("start") # 添加边和条件边 workflow.add_edge("start", "document_processing") # 文档处理后检查终止信号 workflow.add_conditional_edges( "document_processing", self._should_terminate_or_error_chain, { "terminate": "terminate", "error": "error_handler", "continue": "ai_review_subgraph" } ) # AI审查后检查终止信号 workflow.add_conditional_edges( "ai_review_subgraph", self._should_terminate_or_error_chain, { "terminate": "terminate", "error": "error_handler", "continue": "report_generation" } ) # 报告生成后检查终止信号 workflow.add_conditional_edges( "report_generation", self._should_terminate_or_error_chain, { "terminate": "terminate", "error": "error_handler", "continue": "complete" } ) # 完成节点直接结束 workflow.add_edge("complete", END) workflow.add_edge("error_handler", END) workflow.add_edge("terminate", END) # 编译工作流图 compiled_graph = workflow.compile() logger.info("LangGraph 任务链工作流图构建完成") return compiled_graph async def _start_chain_node(self, state: TaskChainState) -> TaskChainState: """ 任务链开始节点 Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态 """ logger.info(f"任务链工作流启动: {state['callback_task_id']}") return { "current_stage": "start", "overall_task_status": "processing", "stage_status": { "document": "pending", "ai_review": "pending", "report": "pending" }, "messages": [AIMessage(content="任务链工作流启动")] } async def _document_processing_node(self, state: TaskChainState) -> TaskChainState: """ 文档处理节点 Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态，包含文档处理结果 """ try: logger.info(f"开始文档处理阶段: {state['callback_task_id']}") # 检查终止信号 if await self.check_terminate_signal(state["callback_task_id"]): logger.warning(f"文档处理阶段检测到终止信号: {state['callback_task_id']}") return { "current_stage": "document_processing", "overall_task_status": "terminated", "stage_status": {**state["stage_status"], "document": "terminated"}, "messages": [AIMessage(content="文档处理阶段检测到终止信号")] } # 获取 TaskFileInfo 实例 task_file_info = state["task_file_info"] # 创建文档工作流实例 document_workflow = DocumentWorkflow( task_file_info=task_file_info, progress_manager=state["progress_manager"], redis_duplicate_checker=self.redis_duplicate_checker ) # 执行文档处理 doc_result = await document_workflow.execute( state["file_content"], state["file_type"] ) logger.info(f"文档处理完成: {state['callback_task_id']}") return { "current_stage": "document_processing", "overall_task_status": "processing", "stage_status": {**state["stage_status"], "document": "completed"}, "document_result": doc_result, "messages": [AIMessage(content="文档处理完成")] } except Exception as e: logger.error(f"文档处理失败: {str(e)}", exc_info=True) return { "current_stage": "document_processing", "overall_task_status": "failed", "stage_status": {**state["stage_status"], "document": "failed"}, "error_message": f"文档处理失败: {str(e)}", "messages": [AIMessage(content=f"文档处理失败: {str(e)}")] } async def _ai_review_subgraph_node(self, state: TaskChainState) -> TaskChainState: """ AI审查子图节点（嵌套现有的 AIReviewWorkflow） Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态，包含AI审查结果 Note: 这是方案D的核心实现：将现有的 AIReviewWorkflow 作为子图嵌套 无需修改 AIReviewWorkflow 的代码，保持其独立性 """ try: logger.info(f"开始AI审查阶段: {state['callback_task_id']}") # 检查终止信号 if await self.check_terminate_signal(state["callback_task_id"]): logger.warning(f"AI审查阶段检测到终止信号: {state['callback_task_id']}") return { "current_stage": "ai_review", "overall_task_status": "terminated", "stage_status": {**state["stage_status"], "ai_review": "terminated"}, "messages": [AIMessage(content="AI审查阶段检测到终止信号")] } # 获取文档处理结果中的结构化内容 structured_content = state["document_result"].get("structured_content") if not structured_content: raise ValueError("文档处理结果中缺少结构化内容") # 获取 TaskFileInfo 实例 task_file_info = state["task_file_info"] # 读取AI审查配置 import configparser config = configparser.ConfigParser() config.read('config/config.ini', encoding='utf-8') max_review_units = config.getint('ai_review', 'MAX_REVIEW_UNITS', fallback=None) if max_review_units == 0: max_review_units = None review_mode = config.get('ai_review', 'REVIEW_MODE', fallback='all') logger.info(f"AI审查配置: 最大审查数量={max_review_units}, 审查模式={review_mode}") # 创建AI审查工作流实例（作为嵌套子图） ai_workflow = AIReviewWorkflow( task_file_info=task_file_info, structured_content=structured_content, progress_manager=state["progress_manager"], max_review_units=max_review_units, review_mode=review_mode ) # 执行AI审查（内部使用 LangGraph） ai_result = await ai_workflow.execute() logger.info(f"AI审查完成: {state['callback_task_id']}") return { "current_stage": "ai_review", "overall_task_status": "processing", "stage_status": {**state["stage_status"], "ai_review": "completed"}, "ai_review_result": ai_result, "messages": [AIMessage(content="AI审查完成")] } except Exception as e: logger.error(f"AI审查失败: {str(e)}", exc_info=True) return { "current_stage": "ai_review", "overall_task_status": "failed", "stage_status": {**state["stage_status"], "ai_review": "failed"}, "error_message": f"AI审查失败: {str(e)}", "messages": [AIMessage(content=f"AI审查失败: {str(e)}")] } async def _report_generation_node(self, state: TaskChainState) -> TaskChainState: """ 报告生成节点 Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态，包含报告生成结果 Note: 调用ReportWorkflow生成审查报告摘要（基于高中风险问题，使用LLM） 根据决策2（方案A-方式1），在此阶段生成完整报告后一次性保存 """ try: logger.info(f"开始报告生成阶段: {state['callback_task_id']}") # 检查终止信号 if await self.check_terminate_signal(state["callback_task_id"]): logger.warning(f"报告生成阶段检测到终止信号: {state['callback_task_id']}") return { "current_stage": "report_generation", "overall_task_status": "terminated", "stage_status": {**state["stage_status"], "report": "terminated"}, "messages": [AIMessage(content="报告生成阶段检测到终止信号")] } # 获取AI审查结果 ai_review_result = state.get("ai_review_result") if not ai_review_result: raise ValueError("AI审查结果缺失，无法生成报告") # 获取 TaskFileInfo 实例 task_file_info = state["task_file_info"] # 创建报告生成工作流实例 report_workflow = ReportWorkflow( file_id=state["file_id"], file_name=state["file_name"], callback_task_id=state["callback_task_id"], user_id=state["user_id"], ai_review_results=ai_review_result, progress_manager=state["progress_manager"] ) # 执行报告生成 report_result = await report_workflow.execute() logger.info(f"报告生成完成: {state['callback_task_id']}") # 保存完整结果（包含文档处理、AI审查、报告生成） await self._save_complete_results(state, report_result) return { "current_stage": "report_generation", "overall_task_status": "processing", "stage_status": {**state["stage_status"], "report": "completed"}, "report_result": report_result, "messages": [AIMessage(content="报告生成完成")] } except Exception as e: logger.error(f"报告生成失败: {str(e)}", exc_info=True) return { "current_stage": "report_generation", "overall_task_status": "failed", "stage_status": {**state["stage_status"], "report": "failed"}, "error_message": f"报告生成失败: {str(e)}", "messages": [AIMessage(content=f"报告生成失败: {str(e)}")] } async def _complete_chain_node(self, state: TaskChainState) -> TaskChainState: """ 任务链完成节点 Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态，标记整体任务已完成 Note: 只有在所有阶段（文档处理、AI审查、报告生成）都完成后才标记 overall_task_status="completed" 这解决了原有的状态语义混乱问题（P0-1） """ logger.info(f"任务链工作流完成: {state['callback_task_id']}") # 标记整体任务完成 if state["progress_manager"]: await state["progress_manager"].complete_task( state["callback_task_id"], state["user_id"], {"overall_task_status": "completed", "message": "所有阶段已完成"} ) # 清理 Redis 缓存 try: from foundation.utils.redis_utils import delete_file_info await delete_file_info(state["file_id"]) logger.info(f"已清理 Redis 文件缓存: {state['file_id']}") except Exception as e: logger.warning(f"清理 Redis 文件缓存失败: {str(e)}") return { "current_stage": "complete", "overall_task_status": "completed", # ⚠️ 关键：只有到这里才标记整体完成 "messages": [AIMessage(content="任务链工作流完成")] } async def _error_handler_chain_node(self, state: TaskChainState) -> TaskChainState: """ 任务链错误处理节点 Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态，标记为失败 """ logger.error(f"任务链工作流错误: {state['callback_task_id']}, 错误: {state.get('error_message', '未知错误')}") # 通知失败 if state["progress_manager"]: error_data = { "overall_task_status": "failed", "error": state.get("error_message", "未知错误"), "status": "failed", "timestamp": datetime.now().isoformat() } await state["progress_manager"].complete_task( state["callback_task_id"], state["user_id"], error_data ) # 清理 Redis 缓存（即使失败也清理） try: from foundation.utils.redis_utils import delete_file_info await delete_file_info(state["file_id"]) logger.info(f"已清理 Redis 文件缓存: {state['file_id']}") except Exception as e: logger.warning(f"清理 Redis 文件缓存失败: {str(e)}") return { "current_stage": "error_handler", "overall_task_status": "failed", "messages": [AIMessage(content=f"任务链错误: {state.get('error_message', '未知错误')}")] } async def _terminate_chain_node(self, state: TaskChainState) -> TaskChainState: """ 任务链终止节点 Args: state: 任务链状态 Returns: TaskChainState: 更新后的状态，标记为已终止 """ logger.warning(f"任务链工作流已终止: {state['callback_task_id']}") # 通知终止 if state["progress_manager"]: await state["progress_manager"].complete_task( state["callback_task_id"], state["user_id"], {"overall_task_status": "terminated", "message": "任务已被用户终止"} ) # 清理 Redis 终止信号 await self.clear_terminate_signal(state["callback_task_id"]) # 清理 Redis 文件缓存 try: from foundation.utils.redis_utils import delete_file_info await delete_file_info(state["file_id"]) logger.info(f"已清理 Redis 文件缓存: {state['file_id']}") except Exception as e: logger.warning(f"清理 Redis 文件缓存失败: {str(e)}") return { "current_stage": "terminated", "overall_task_status": "terminated", "messages": [AIMessage(content="任务链已被终止")] } def _should_terminate_or_error_chain(self, state: TaskChainState) -> str: """ 检查任务链是否应该终止或发生错误 Args: state: 任务链状态 Returns: str: "terminate", "error", 或 "continue" Note: 这是条件边判断方法，用于决定工作流的下一步走向 1. 优先检查终止信号 2. 检查是否有错误 3. 都没有则继续执行 """ # 检查终止状态 if state.get("overall_task_status") == "terminated": return "terminate" # 检查错误状态 if state.get("overall_task_status") == "failed" or state.get("error_message"): return "error" # 默认继续执行 return "continue" async def _save_complete_results(self, state: TaskChainState, report_result: Dict[str, Any]): """ 保存完整结果（方案A-方式1：一次性保存） Args: state: 任务链状态 report_result: 报告生成结果 Note: 根据决策2（方案A-方式1），在报告工作流完成后一次性保存完整结果 包含：文档处理结果 + AI审查结果 + 报告生成结果 """ try: import json import os logger.info(f"开始保存完整结果: {state['callback_task_id']}") # 创建 temp 目录 temp_dir = "temp" os.makedirs(temp_dir, exist_ok=True) # 构建完整结果 complete_results = { "callback_task_id": state["callback_task_id"], "file_id": state["file_id"], "file_name": state["file_name"], "user_id": state["user_id"], "overall_task_status": "processing", # 此时还在处理中，complete节点才标记completed "stage_status": state["stage_status"], "document_result": state.get("document_result"), "ai_review_result": state.get("ai_review_result"), "issues": state.get("ai_review_result").get("review_results"), "report_result": report_result, "timestamp": datetime.now().isoformat() } # 保存到文件 file_path = os.path.join(temp_dir, f"{state['callback_task_id']}.json") with open(file_path, 'w', encoding='utf-8') as f: json.dump(complete_results, f, ensure_ascii=False, indent=2) logger.info(f"完整结果已保存到: {file_path}") except Exception as e: logger.error(f"保存完整结果失败: {str(e)}", exc_info=True) raise