eaada4bc57
- 新增 steward_intent_registry，IntentDescriptor 统一描述意图的识别关键词、动作步骤构建、字段白名单与副作用集合，替代分散的 if/else - 新增 steward_intent_bootstrap 注册 expense_application 等意图；新增 steward_query_executors 提供差旅标准查询的无副作用执行与城市/席别标签化输出 - action_contracts/action_executor/graph_planner/intent_agent/model_plan_builder/planner_extraction/fallback 适配注册表，识别与执行分发自动从注册表取数 - 新增 intent_registry/query_executors 测试，更新 intent_agent 测试
456 lines
20 KiB
Python
456 lines
20 KiB
Python
from __future__ import annotations
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import re
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import uuid
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from datetime import date
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from typing import Any
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from app.schemas.steward import (
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StewardCandidateFlow,
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StewardConfirmationAction,
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StewardPendingFlowConfirmation,
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StewardPlanRequest,
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StewardPlanResponse,
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StewardTask,
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StewardThinkingEvent,
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)
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from app.services.steward_planner_shared import (
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APPLICATION_SPLIT_PATTERN,
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BUSINESS_FIELD_LABELS,
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PlannedTaskDraft,
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REIMBURSEMENT_PATTERN,
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STEWARD_BUSINESS_SIGNAL_KEYWORDS,
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STEWARD_GREETING_KEYWORDS,
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STEWARD_OFF_TOPIC_SCENARIO_GREETING,
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STEWARD_OFF_TOPIC_SCENARIO_MEANINGLESS,
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STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS,
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)
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class StewardPlannerFallbackMixin:
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def _should_use_model_intent_recognition(
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self,
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message: str,
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base_date: date,
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request: StewardPlanRequest,
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) -> bool:
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return bool(message.strip())
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@staticmethod
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def _is_business_irrelevant_input(message: str, request: StewardPlanRequest) -> bool:
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"""判断输入是否与小财管家支持的财务事项完全无关（向后兼容包装）。
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判定规则：消息去除所有空白后不含任何业务信号关键词，且没有上传附件。
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实际判定逻辑由 _classify_irrelevant_input 负责，命中任何场景即视为业务无关。
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"""
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return StewardPlannerFallbackMixin._classify_irrelevant_input(message, request) is not None
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@staticmethod
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def _classify_irrelevant_input(message: str, request: StewardPlanRequest) -> str | None:
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"""把业务无关输入细分为三个场景，便于给出更贴切的引导。
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返回值：
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- "greeting"：礼貌问候（"你好"等），无业务关键词
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- "meaningless"：完全无意义内容（纯数字、纯标点、单字符重复、纯字母数字乱码）
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- "off_business"：有意义但与财务无关（问天气、聊生活等）
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- None：消息与业务相关，无需走 off_topic 路径
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"""
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if request.attachments:
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return None
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compact = re.sub(r"\s+", "", message)
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if not compact:
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return None
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if any(keyword in compact for keyword in STEWARD_BUSINESS_SIGNAL_KEYWORDS):
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return None
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# 补充注册表里各意图声明的信号词(如查询类"差旅标准"等),避免被判 off_topic
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from app.services.steward_intent_registry import all_signal_keywords
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if any(keyword in compact for keyword in all_signal_keywords()):
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return None
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if StewardPlannerFallbackMixin._looks_like_greeting(compact):
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return STEWARD_OFF_TOPIC_SCENARIO_GREETING
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if StewardPlannerFallbackMixin._looks_like_meaningless(compact):
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return STEWARD_OFF_TOPIC_SCENARIO_MEANINGLESS
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return STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS
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@staticmethod
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def _looks_like_greeting(compact_message: str) -> bool:
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"""判断消息是否只是礼貌问候（无其他有意义内容）。"""
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normalized = compact_message.lower()
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for keyword in STEWARD_GREETING_KEYWORDS:
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if normalized == keyword.lower() or normalized.startswith(keyword.lower()):
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# 整句只是问候词（允许少量标点）
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tail = normalized[len(keyword.lower()):]
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if not tail or re.fullmatch(r"[！!。.？?,，~\s]+", tail):
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return True
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return False
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@staticmethod
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def _looks_like_meaningless(compact_message: str) -> bool:
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"""判断消息是否完全没有语义价值（纯数字、纯标点、单字符重复等）。"""
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if re.fullmatch(r"\d+", compact_message):
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return True
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# 纯标点
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if re.fullmatch(r"[\W_]+", compact_message):
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return True
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# 单字符重复（例如 "啊啊啊啊啊"）
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if len(compact_message) >= 2 and len(set(compact_message)) == 1:
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return True
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# 短字母数字组合但没有任何业务意义，例如 "abc"、"test123"
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# 注意：必须排除已经被关键词命中的情况（前面的判定已保证不命中关键词）
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if re.fullmatch(r"[a-zA-Z0-9]+", compact_message) and len(compact_message) <= 12:
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return True
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return False
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def _build_off_topic_plan(
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self,
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request: StewardPlanRequest,
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*,
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scenario: str,
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model_call_traces: list[dict[str, Any]] | None = None,
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fallback_reason: str = "",
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) -> StewardPlanResponse:
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"""业务无关输入的兜底计划：根据场景给出对应引导，off_business 场景可由 LLM 增强。"""
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base_summary = self._default_off_topic_summary(scenario)
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thinking_event = self._build_off_topic_thinking_event(scenario)
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suggested_prompts = self._off_topic_suggested_prompts(scenario)
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traces = list(model_call_traces or [])
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# 仅对 off_business 场景尝试让 LLM 生成多样化引导；问候/无意义场景用规则模板即可。
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if (
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scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS
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and self.off_topic_agent is not None
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):
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try:
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llm_result = self.off_topic_agent.generate(request, scenario=scenario)
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if llm_result is not None and llm_result.response_text:
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base_summary = llm_result.response_text
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traces = llm_result.model_call_traces
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except Exception:
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# 失败时静默回退到规则模板
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pass
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thinking_events = [thinking_event]
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if fallback_reason:
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thinking_events.insert(
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0,
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StewardThinkingEvent(
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event_id="intent_agent_rule_fallback",
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stage="rule_fallback",
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title="意图识别智能体进入兜底模式",
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content=fallback_reason,
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),
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)
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return StewardPlanResponse(
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plan_id=f"steward_plan_{uuid.uuid4().hex[:12]}",
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plan_status="off_topic",
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planning_source="rule_fallback",
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next_action="none",
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summary=base_summary,
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thinking_events=thinking_events,
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tasks=[],
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attachment_groups=[],
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confirmation_groups=[],
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candidate_flows=[],
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suggested_prompts=suggested_prompts,
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model_call_traces=traces,
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)
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@staticmethod
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def _default_off_topic_summary(scenario: str) -> str:
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"""off_topic 场景的默认引导文案；LLM 不可用时使用。"""
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if scenario == STEWARD_OFF_TOPIC_SCENARIO_GREETING:
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return (
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"### 您好主人，很高兴为您服务\n\n"
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"请问您今天要办理什么业务？目前小财管家能帮您整理"
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"**费用申请**和**费用报销**这两类事项。\n\n"
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"要不您换种说法告诉我："
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)
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if scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS:
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return (
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"### 抱歉主人，这句话我暂时帮不上忙\n\n"
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"我看了您刚才说的这句话，里面聊的不是财务事项。"
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"小财管家目前只能帮您整理**费用申请**和**费用报销**这两类业务。\n\n"
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"要不您换种说法告诉我："
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)
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# meaningless
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return (
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"### 这句话我暂时没识别到财务事项\n\n"
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"很抱歉主人，目前小财管家只能帮您整理**费用申请**和**费用报销**这两类事项。\n\n"
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"要不您换种说法告诉我："
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)
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@staticmethod
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def _build_off_topic_thinking_event(scenario: str) -> StewardThinkingEvent:
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"""off_topic 场景下向用户展示的思考过程摘要。"""
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if scenario == STEWARD_OFF_TOPIC_SCENARIO_GREETING:
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return StewardThinkingEvent(
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event_id="intent_agent_off_topic_greeting",
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stage="off_topic",
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title="先回应主人的问候",
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content="主人向我打了个招呼，我先礼貌回应一下，再引导他/她说出具体想办什么业务。",
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)
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if scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS:
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return StewardThinkingEvent(
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event_id="intent_agent_off_topic_non_business",
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stage="off_topic",
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title="这句话不在服务范围内",
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content="我看了您刚才说的这句话，里面聊的不是财务事项。小财管家目前只能帮您整理费用申请和费用报销。",
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)
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return StewardThinkingEvent(
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event_id="intent_agent_off_topic_meaningless",
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stage="off_topic",
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title="未识别到财务事项",
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content=(
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"我仔细看了看您刚才说的这句话，里面好像没有出现"
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"费用申请、报销、出差、交通、招待这些财务关键词。"
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),
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)
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@staticmethod
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def _off_topic_suggested_prompts(scenario: str) -> list[str]:
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"""off_topic 场景下展示给用户的推荐话术。"""
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if scenario == STEWARD_OFF_TOPIC_SCENARIO_GREETING:
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return [
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"我想要申请明天去北京出差3天，支撑客户现场实施",
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"我要报销昨天的交通费",
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"我上周出差去上海的费用需要报销",
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]
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if scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS:
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return [
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"我想要申请明天去北京出差3天，支撑客户现场实施",
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"我要报销昨天的交通费",
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"我需要整理上周出差的发票",
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]
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# meaningless
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return [
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"我想要申请明天去北京出差3天，支撑客户现场实施",
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"我要报销昨天的交通费",
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"我上周出差去上海的费用需要报销",
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]
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def _build_rule_fallback_plan(
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self,
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request: StewardPlanRequest,
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*,
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base_date: date,
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model_call_traces: list[dict[str, Any]] | None = None,
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fallback_reason: str = "",
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) -> StewardPlanResponse:
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message = self._clean_text(request.message)
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if self._looks_like_ambiguous_travel_flow(message, base_date, request):
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return self._build_pending_flow_fallback_plan(
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request,
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base_date=base_date,
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model_call_traces=model_call_traces,
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fallback_reason=fallback_reason,
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)
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task_drafts = self._extract_task_drafts(message)
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tasks = [self._build_task(draft, base_date, request) for draft in task_drafts]
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if not tasks:
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tasks = [self._build_fallback_task(message, base_date, request)]
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attachment_groups = self._build_attachment_groups(request.attachments, tasks)
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confirmation_groups = self._build_confirmation_actions(tasks, attachment_groups)
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thinking_events = self._build_thinking_events(tasks, attachment_groups, request.attachments)
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if fallback_reason:
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thinking_events.insert(
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0,
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StewardThinkingEvent(
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event_id="intent_agent_rule_fallback",
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stage="rule_fallback",
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title="意图识别智能体进入兜底模式",
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content=fallback_reason,
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),
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)
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plan_id = f"steward_plan_{uuid.uuid4().hex[:12]}"
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return StewardPlanResponse(
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plan_id=plan_id,
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plan_status="needs_confirmation" if confirmation_groups else "ready_to_delegate",
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planning_source="rule_fallback",
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next_action="confirm_task" if confirmation_groups else "delegate_task",
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summary=self._build_summary(tasks, attachment_groups),
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thinking_events=thinking_events,
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tasks=tasks,
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attachment_groups=attachment_groups,
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confirmation_groups=confirmation_groups,
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model_call_traces=model_call_traces or [],
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)
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def _build_pending_flow_fallback_plan(
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self,
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request: StewardPlanRequest,
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*,
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base_date: date,
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model_call_traces: list[dict[str, Any]] | None = None,
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fallback_reason: str = "",
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planning_source: str = "rule_fallback",
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) -> StewardPlanResponse:
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candidates = self._build_rule_candidate_flows(request, base_date)
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gate = self._resolve_required_application_gate(request, "travel")
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pending_reason = self._build_pending_flow_reason(gate)
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pending = StewardPendingFlowConfirmation(
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status="pending",
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source_message=request.message,
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reason=pending_reason,
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candidate_flows=candidates,
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)
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thinking_events = []
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if fallback_reason:
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thinking_events.append(
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StewardThinkingEvent(
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event_id="intent_agent_rule_fallback",
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stage="rule_fallback",
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title="意图识别智能体进入兜底模式",
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content=fallback_reason,
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)
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)
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thinking_events.append(
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StewardThinkingEvent(
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event_id="intent_pending_flow_confirmation",
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stage="flow_confirmation",
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title="需要确认流程方向",
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content=pending_reason,
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)
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)
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return StewardPlanResponse(
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plan_id=f"steward_plan_{uuid.uuid4().hex[:12]}",
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plan_status="needs_flow_confirmation",
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planning_source=planning_source, # type: ignore[arg-type]
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next_action="confirm_flow",
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summary=self._build_pending_flow_summary(gate),
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thinking_events=thinking_events,
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pending_flow_confirmation=pending,
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candidate_flows=candidates,
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model_call_traces=model_call_traces or [],
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)
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def _build_rule_candidate_flows(
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self,
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request: StewardPlanRequest,
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base_date: date,
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) -> list[StewardCandidateFlow]:
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application_fields = self._extract_ontology_fields(
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request.message,
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"expense_application",
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base_date,
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request,
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)
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reimbursement_fields = self._extract_ontology_fields(
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request.message,
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"reimbursement",
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base_date,
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request,
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)
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gate = self._resolve_required_application_gate(request, "travel")
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if gate.get("checked") and int(gate.get("candidate_count") or 0) <= 0:
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return [
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StewardCandidateFlow(
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flow_id="travel_application",
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label="先发起出差申请",
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confidence=0.86,
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reason="已先查询您名下可关联的差旅申请单，暂未查到可关联单据，因此应先申请单据。",
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ontology_fields=application_fields,
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missing_fields=self._resolve_missing_fields("expense_application", application_fields),
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)
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]
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reimbursement_label = "发起费用报销"
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reimbursement_reason = "用户描述的也可能是已发生出差事项，需要进入报销材料整理。"
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if gate.get("checked"):
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candidate_count = int(gate.get("candidate_count") or 0)
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reimbursement_label = "关联已有申请单并发起报销"
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reimbursement_reason = f"已先查到 {candidate_count} 个可关联申请单，选择后会先请您关联具体单据。"
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return [
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StewardCandidateFlow(
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flow_id="travel_application",
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label="补办出差申请",
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confidence=0.52,
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reason="用户描述了出差时间、地点和事由，但没有明确说要报销。",
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ontology_fields=application_fields,
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missing_fields=self._resolve_missing_fields("expense_application", application_fields),
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),
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StewardCandidateFlow(
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flow_id="travel_reimbursement",
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label=reimbursement_label,
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confidence=0.48,
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reason=reimbursement_reason,
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ontology_fields=reimbursement_fields,
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missing_fields=self._resolve_missing_fields("reimbursement", reimbursement_fields),
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),
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]
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@staticmethod
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def _resolve_required_application_gate(
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request: StewardPlanRequest,
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expense_type: str,
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) -> dict[str, Any]:
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context = request.context_json if isinstance(request.context_json, dict) else {}
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gates = context.get("required_application_gate")
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if not isinstance(gates, dict):
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return {}
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gate = gates.get(expense_type)
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if not isinstance(gate, dict) or not gate.get("checked"):
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return {}
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try:
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candidate_count = max(0, int(gate.get("candidate_count") or 0))
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except (TypeError, ValueError):
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candidate_count = 0
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return {
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**gate,
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"candidate_count": candidate_count,
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"checked": True,
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}
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|
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@staticmethod
|
||
def _build_pending_flow_reason(gate: dict[str, Any]) -> str:
|
||
if gate.get("checked") and int(gate.get("candidate_count") or 0) <= 0:
|
||
return "我已先查询您名下可关联的差旅申请单，未查到可关联单据，所以当前应先申请单据。"
|
||
if gate.get("checked"):
|
||
candidate_count = int(gate.get("candidate_count") or 0)
|
||
return f"我已先查询您名下的差旅申请单，查到 {candidate_count} 个可关联申请单，需要您确认是否关联单据后发起报销。"
|
||
return "当前话术描述了出差事项，但没有明确说明要补办申请还是发起报销。"
|
||
|
||
@staticmethod
|
||
def _build_pending_flow_summary(gate: dict[str, Any]) -> str:
|
||
if gate.get("checked") and int(gate.get("candidate_count") or 0) <= 0:
|
||
return "我已先查询可关联申请单，暂未查到可关联单据；这次应先申请单据，再进入后续报销。"
|
||
if gate.get("checked"):
|
||
candidate_count = int(gate.get("candidate_count") or 0)
|
||
return (
|
||
f"我已先查询可关联申请单，查到 {candidate_count} 个可关联申请单；"
|
||
"您可以选择关联已有申请单发起报销，也可以改为补办新的出差申请。"
|
||
)
|
||
return (
|
||
"我识别到这是一次出差事项，但还不能确定您要做的是"
|
||
"**补办出差申请**还是**发起费用报销**。请先选择一个方向。"
|
||
)
|
||
|
||
def _extract_task_drafts(self, message: str) -> list[PlannedTaskDraft]:
|
||
drafts: list[PlannedTaskDraft] = []
|
||
first_reimbursement = self._find_first_reimbursement_index(message)
|
||
application_source = message[:first_reimbursement] if first_reimbursement >= 0 else message
|
||
if self._looks_like_application(application_source) or self._looks_like_future_travel_application(application_source):
|
||
drafts.append(
|
||
PlannedTaskDraft(
|
||
task_type="expense_application",
|
||
segment=application_source.strip("，,。；; "),
|
||
index=len(drafts) + 1,
|
||
)
|
||
)
|
||
|
||
for match in REIMBURSEMENT_PATTERN.finditer(message):
|
||
segment = f"报销{match.group(1)}"
|
||
drafts.append(
|
||
PlannedTaskDraft(
|
||
task_type="reimbursement",
|
||
segment=segment.strip("，,。；; "),
|
||
index=len(drafts) + 1,
|
||
)
|
||
)
|
||
|
||
return drafts
|