LQAgentPlatform/core/base/workflow_manager.py

"""
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

    # ==================== LangGraph 任务链工作流（方案D）====================

    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"),
                "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