X-Financial/server/src/app/services/steward_planner_fallback.py

from __future__ import annotations

import re
import uuid
from datetime import date
from typing import Any

from app.schemas.steward import (
    StewardCandidateFlow,
    StewardConfirmationAction,
    StewardPendingFlowConfirmation,
    StewardPlanRequest,
    StewardPlanResponse,
    StewardTask,
    StewardThinkingEvent,
)
from app.services.steward_planner_shared import (
    APPLICATION_SPLIT_PATTERN,
    BUSINESS_FIELD_LABELS,
    PlannedTaskDraft,
    REIMBURSEMENT_PATTERN,
    STEWARD_BUSINESS_SIGNAL_KEYWORDS,
    STEWARD_GREETING_KEYWORDS,
    STEWARD_OFF_TOPIC_SCENARIO_GREETING,
    STEWARD_OFF_TOPIC_SCENARIO_MEANINGLESS,
    STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS,
)


class StewardPlannerFallbackMixin:
    def _should_use_model_intent_recognition(
        self,
        message: str,
        base_date: date,
        request: StewardPlanRequest,
    ) -> bool:
        return bool(message.strip())


    @staticmethod
    def _is_business_irrelevant_input(message: str, request: StewardPlanRequest) -> bool:
        """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）。

        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。
        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。
        """
        return StewardPlannerFallbackMixin._classify_irrelevant_input(message, request) is not None

    @staticmethod
    def _classify_irrelevant_input(message: str, request: StewardPlanRequest) -> str | None:
        """把业务无关输入细分为三个场景，便于给出更贴切的引导。

        返回值：
        - "greeting"：礼貌问候（"你好"等），无业务关键词
        - "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）
        - "off_business"：有意义但与财务无关（问天气、聊生活等）
        - None：消息与业务相关，无需走 off_topic 路径
        """
        if request.attachments:
            return None
        compact = re.sub(r"\s+", "", message)
        if not compact:
            return None
        if any(keyword in compact for keyword in STEWARD_BUSINESS_SIGNAL_KEYWORDS):
            return None

        if StewardPlannerFallbackMixin._looks_like_greeting(compact):
            return STEWARD_OFF_TOPIC_SCENARIO_GREETING
        if StewardPlannerFallbackMixin._looks_like_meaningless(compact):
            return STEWARD_OFF_TOPIC_SCENARIO_MEANINGLESS
        return STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS

    @staticmethod
    def _looks_like_greeting(compact_message: str) -> bool:
        """判断消息是否只是礼貌问候（无其他有意义内容）。"""
        normalized = compact_message.lower()
        for keyword in STEWARD_GREETING_KEYWORDS:
            if normalized == keyword.lower() or normalized.startswith(keyword.lower()):
                # 整句只是问候词（允许少量标点）
                tail = normalized[len(keyword.lower()):]
                if not tail or re.fullmatch(r"[！!。.？?,，~\s]+", tail):
                    return True
        return False

    @staticmethod
    def _looks_like_meaningless(compact_message: str) -> bool:
        """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）。"""
        if re.fullmatch(r"\d+", compact_message):
            return True
        # 纯标点
        if re.fullmatch(r"[\W_]+", compact_message):
            return True
        # 单字符重复（例如 "啊啊啊啊啊"）
        if len(compact_message) >= 2 and len(set(compact_message)) == 1:
            return True
        # 短字母数字组合但没有任何业务意义，例如 "abc"、"test123"
        # 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）
        if re.fullmatch(r"[a-zA-Z0-9]+", compact_message) and len(compact_message) <= 12:
            return True
        return False

    def _build_off_topic_plan(
        self,
        request: StewardPlanRequest,
        *,
        scenario: str,
        model_call_traces: list[dict[str, Any]] | None = None,
        fallback_reason: str = "",
    ) -> StewardPlanResponse:
        """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。"""
        base_summary = self._default_off_topic_summary(scenario)
        thinking_event = self._build_off_topic_thinking_event(scenario)
        suggested_prompts = self._off_topic_suggested_prompts(scenario)
        traces = list(model_call_traces or [])

        # 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。
        if (
            scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS
            and self.off_topic_agent is not None
        ):
            try:
                llm_result = self.off_topic_agent.generate(request, scenario=scenario)
                if llm_result is not None and llm_result.response_text:
                    base_summary = llm_result.response_text
                    traces = llm_result.model_call_traces
            except Exception:
                # 失败时静默回退到规则模板
                pass

        thinking_events = [thinking_event]
        if fallback_reason:
            thinking_events.insert(
                0,
                StewardThinkingEvent(
                    event_id="intent_agent_rule_fallback",
                    stage="rule_fallback",
                    title="意图识别智能体进入兜底模式",
                    content=fallback_reason,
                ),
            )

        return StewardPlanResponse(
            plan_id=f"steward_plan_{uuid.uuid4().hex[:12]}",
            plan_status="off_topic",
            planning_source="rule_fallback",
            next_action="none",
            summary=base_summary,
            thinking_events=thinking_events,
            tasks=[],
            attachment_groups=[],
            confirmation_groups=[],
            candidate_flows=[],
            suggested_prompts=suggested_prompts,
            model_call_traces=traces,
        )

    @staticmethod
    def _default_off_topic_summary(scenario: str) -> str:
        """off_topic 场景的默认引导文案；LLM 不可用时使用。"""
        if scenario == STEWARD_OFF_TOPIC_SCENARIO_GREETING:
            return (
                "### 您好主人，很高兴为您服务\n\n"
                "请问您今天要办理什么业务？目前小财管家能帮您整理"
                "**费用申请**和**费用报销**这两类事项。\n\n"
                "要不您换种说法告诉我："
            )
        if scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS:
            return (
                "### 抱歉主人，这句话我暂时帮不上忙\n\n"
                "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**这两类业务。\n\n"
                "要不您换种说法告诉我："
            )
        # meaningless
        return (
            "### 这句话我暂时没识别到财务事项\n\n"
            "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"
            "要不您换种说法告诉我："
        )

    @staticmethod
    def _build_off_topic_thinking_event(scenario: str) -> StewardThinkingEvent:
        """off_topic 场景下向用户展示的思考过程摘要。"""
        if scenario == STEWARD_OFF_TOPIC_SCENARIO_GREETING:
            return StewardThinkingEvent(
                event_id="intent_agent_off_topic_greeting",
                stage="off_topic",
                title="先回应主人的问候",
                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。",
            )
        if scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS:
            return StewardThinkingEvent(
                event_id="intent_agent_off_topic_non_business",
                stage="off_topic",
                title="这句话不在服务范围内",
                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。",
            )
        return StewardThinkingEvent(
            event_id="intent_agent_off_topic_meaningless",
            stage="off_topic",
            title="未识别到财务事项",
            content=(
                "我仔细看了看您刚才说的这句话，里面好像没有出现"
                "费用申请、报销、出差、交通、招待这些财务关键词。"
            ),
        )

    @staticmethod
    def _off_topic_suggested_prompts(scenario: str) -> list[str]:
        """off_topic 场景下展示给用户的推荐话术。"""
        if scenario == STEWARD_OFF_TOPIC_SCENARIO_GREETING:
            return [
                "我想要申请明天去北京出差3天，支撑客户现场实施",
                "我要报销昨天的交通费",
                "我上周出差去上海的费用需要报销",
            ]
        if scenario == STEWARD_OFF_TOPIC_SCENARIO_OFF_BUSINESS:
            return [
                "我想要申请明天去北京出差3天，支撑客户现场实施",
                "我要报销昨天的交通费",
                "我需要整理上周出差的发票",
            ]
        # meaningless
        return [
            "我想要申请明天去北京出差3天，支撑客户现场实施",
            "我要报销昨天的交通费",
            "我上周出差去上海的费用需要报销",
        ]

    def _build_rule_fallback_plan(
        self,
        request: StewardPlanRequest,
        *,
        base_date: date,
        model_call_traces: list[dict[str, Any]] | None = None,
        fallback_reason: str = "",
    ) -> StewardPlanResponse:
        message = self._clean_text(request.message)
        if self._looks_like_ambiguous_travel_flow(message, base_date, request):
            return self._build_pending_flow_fallback_plan(
                request,
                base_date=base_date,
                model_call_traces=model_call_traces,
                fallback_reason=fallback_reason,
            )
        task_drafts = self._extract_task_drafts(message)
        tasks = [self._build_task(draft, base_date, request) for draft in task_drafts]
        if not tasks:
            tasks = [self._build_fallback_task(message, base_date, request)]

        attachment_groups = self._build_attachment_groups(request.attachments, tasks)
        confirmation_groups = self._build_confirmation_actions(tasks, attachment_groups)
        thinking_events = self._build_thinking_events(tasks, attachment_groups, request.attachments)
        if fallback_reason:
            thinking_events.insert(
                0,
                StewardThinkingEvent(
                    event_id="intent_agent_rule_fallback",
                    stage="rule_fallback",
                    title="意图识别智能体进入兜底模式",
                    content=fallback_reason,
                ),
            )
        plan_id = f"steward_plan_{uuid.uuid4().hex[:12]}"

        return StewardPlanResponse(
            plan_id=plan_id,
            plan_status="needs_confirmation" if confirmation_groups else "ready_to_delegate",
            planning_source="rule_fallback",
            next_action="confirm_task" if confirmation_groups else "delegate_task",
            summary=self._build_summary(tasks, attachment_groups),
            thinking_events=thinking_events,
            tasks=tasks,
            attachment_groups=attachment_groups,
            confirmation_groups=confirmation_groups,
            model_call_traces=model_call_traces or [],
        )

    def _build_pending_flow_fallback_plan(
        self,
        request: StewardPlanRequest,
        *,
        base_date: date,
        model_call_traces: list[dict[str, Any]] | None = None,
        fallback_reason: str = "",
        planning_source: str = "rule_fallback",
    ) -> StewardPlanResponse:
        candidates = self._build_rule_candidate_flows(request, base_date)
        gate = self._resolve_required_application_gate(request, "travel")
        pending_reason = self._build_pending_flow_reason(gate)
        pending = StewardPendingFlowConfirmation(
            status="pending",
            source_message=request.message,
            reason=pending_reason,
            candidate_flows=candidates,
        )
        thinking_events = []
        if fallback_reason:
            thinking_events.append(
                StewardThinkingEvent(
                    event_id="intent_agent_rule_fallback",
                    stage="rule_fallback",
                    title="意图识别智能体进入兜底模式",
                    content=fallback_reason,
                )
            )
        thinking_events.append(
            StewardThinkingEvent(
                event_id="intent_pending_flow_confirmation",
                stage="flow_confirmation",
                title="需要确认流程方向",
                content=pending_reason,
            )
        )
        return StewardPlanResponse(
            plan_id=f"steward_plan_{uuid.uuid4().hex[:12]}",
            plan_status="needs_flow_confirmation",
            planning_source=planning_source,  # type: ignore[arg-type]
            next_action="confirm_flow",
            summary=self._build_pending_flow_summary(gate),
            thinking_events=thinking_events,
            pending_flow_confirmation=pending,
            candidate_flows=candidates,
            model_call_traces=model_call_traces or [],
        )

    def _build_rule_candidate_flows(
        self,
        request: StewardPlanRequest,
        base_date: date,
    ) -> list[StewardCandidateFlow]:
        application_fields = self._extract_ontology_fields(
            request.message,
            "expense_application",
            base_date,
            request,
        )
        reimbursement_fields = self._extract_ontology_fields(
            request.message,
            "reimbursement",
            base_date,
            request,
        )
        gate = self._resolve_required_application_gate(request, "travel")
        if gate.get("checked") and int(gate.get("candidate_count") or 0) <= 0:
            return [
                StewardCandidateFlow(
                    flow_id="travel_application",
                    label="先发起出差申请",
                    confidence=0.86,
                    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。",
                    ontology_fields=application_fields,
                    missing_fields=self._resolve_missing_fields("expense_application", application_fields),
                )
            ]
        reimbursement_label = "发起费用报销"
        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。"
        if gate.get("checked"):
            candidate_count = int(gate.get("candidate_count") or 0)
            reimbursement_label = "关联已有申请单并发起报销"
            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。"
        return [
            StewardCandidateFlow(
                flow_id="travel_application",
                label="补办出差申请",
                confidence=0.52,
                reason="用户描述了出差时间、地点和事由，但没有明确说要报销。",
                ontology_fields=application_fields,
                missing_fields=self._resolve_missing_fields("expense_application", application_fields),
            ),
            StewardCandidateFlow(
                flow_id="travel_reimbursement",
                label=reimbursement_label,
                confidence=0.48,
                reason=reimbursement_reason,
                ontology_fields=reimbursement_fields,
                missing_fields=self._resolve_missing_fields("reimbursement", reimbursement_fields),
            ),
        ]

    @staticmethod
    def _resolve_required_application_gate(
        request: StewardPlanRequest,
        expense_type: str,
    ) -> dict[str, Any]:
        context = request.context_json if isinstance(request.context_json, dict) else {}
        gates = context.get("required_application_gate")
        if not isinstance(gates, dict):
            return {}
        gate = gates.get(expense_type)
        if not isinstance(gate, dict) or not gate.get("checked"):
            return {}
        try:
            candidate_count = max(0, int(gate.get("candidate_count") or 0))
        except (TypeError, ValueError):
            candidate_count = 0
        return {
            **gate,
            "candidate_count": candidate_count,
            "checked": True,
        }

    @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