198 lines
9.3 KiB
Python
198 lines
9.3 KiB
Python
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from __future__ import annotations
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import json
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from typing import Any
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from app.schemas.steward import (
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StewardRuntimeDecisionRequest,
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StewardRuntimeDecisionResponse,
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)
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from app.services.runtime_chat import RuntimeChatService
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STEWARD_RUNTIME_DECISION_FUNCTION_NAME = "submit_steward_runtime_decision"
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RUNTIME_NEXT_ACTIONS = {
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"plan_new_tasks",
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"submit_current_application",
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"continue_next_task",
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"fill_current_slot",
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"ask_user",
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"cancel_current_action",
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"no_op",
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}
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class StewardRuntimeDecisionAgent:
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"""用小财管家运行时上下文判断用户当前输入应落到哪个等待动作。"""
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def __init__(self, runtime_chat_service: RuntimeChatService) -> None:
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self.runtime_chat_service = runtime_chat_service
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def decide(self, request: StewardRuntimeDecisionRequest) -> StewardRuntimeDecisionResponse:
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normalized_request = self._normalize_request(request)
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result = self.runtime_chat_service.complete_with_tool_call(
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self._build_messages(normalized_request),
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tools=[self._build_tool_schema()],
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tool_choice={
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"type": "function",
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"function": {"name": STEWARD_RUNTIME_DECISION_FUNCTION_NAME},
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},
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max_tokens=1000,
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temperature=0.05,
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timeout_seconds=30,
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max_attempts=1,
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)
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traces = result.calls_as_dicts()
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if result.tool_call is not None and result.tool_call.name == STEWARD_RUNTIME_DECISION_FUNCTION_NAME:
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response = self._build_response_from_model_payload(result.tool_call.arguments, normalized_request, traces)
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if response is not None:
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return response
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return self._build_rule_fallback(normalized_request, traces)
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@staticmethod
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def _normalize_request(request: StewardRuntimeDecisionRequest) -> StewardRuntimeDecisionRequest:
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return StewardRuntimeDecisionRequest(
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user_message=str(request.user_message or "").strip(),
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session_type=str(request.session_type or "steward").strip() or "steward",
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runtime_state=request.runtime_state if isinstance(request.runtime_state, dict) else {},
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context_json=request.context_json if isinstance(request.context_json, dict) else {},
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)
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@staticmethod
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def _build_messages(request: StewardRuntimeDecisionRequest) -> list[dict[str, Any]]:
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payload = {
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"user_message": request.user_message,
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"session_type": request.session_type,
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"runtime_state": request.runtime_state,
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"context_json": request.context_json,
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}
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return [
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{
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"role": "system",
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"content": (
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"你是 X-Financial 小财管家的运行时决策智能体。"
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"你必须基于 runtime_state 判断用户当前输入对应哪个等待动作，不能把每次输入都当成全新任务。"
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"runtime_state 会包含 current_task、remaining_tasks、completed_tasks、pending_application、"
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"pending_steward_action、waiting_for、recent_structured_result 等上下文。"
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"如果用户是在确认当前申请核对表无误，应返回 submit_current_application；"
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"如果用户是在确认继续下一项，应返回 continue_next_task；"
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"如果用户补充了当前等待字段，应返回 fill_current_slot；"
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"如果当前结构化结果仍缺字段，应返回 ask_user；"
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"只有当前没有可匹配上下文，且用户输入明显是新财务事项时，才返回 plan_new_tasks。"
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"提交、入库、绑定、审批等高风险动作只返回结构化意图，实际执行由系统安全校验完成。"
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"rationale 和 response_text 必须面向用户，不暴露内部推理链。"
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),
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},
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{"role": "user", "content": json.dumps(payload, ensure_ascii=False)},
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]
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@staticmethod
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def _build_tool_schema() -> dict[str, Any]:
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return {
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"type": "function",
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"function": {
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"name": STEWARD_RUNTIME_DECISION_FUNCTION_NAME,
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"description": "提交小财管家基于运行时上下文的下一步动作决策。",
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"parameters": {
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"type": "object",
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"properties": {
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"next_action": {
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"type": "string",
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"enum": sorted(RUNTIME_NEXT_ACTIONS),
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},
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"target_task_id": {"type": "string"},
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"target_message_id": {"type": "string"},
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"field_key": {"type": "string"},
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"field_value": {"type": "string"},
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"confirmation_required": {"type": "boolean"},
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"question": {"type": "string"},
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"response_text": {"type": "string"},
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"rationale": {"type": "string"},
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},
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"required": [
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"next_action",
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"target_task_id",
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"target_message_id",
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"field_key",
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"field_value",
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"confirmation_required",
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"question",
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"response_text",
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"rationale",
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],
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},
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},
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}
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def _build_response_from_model_payload(
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self,
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payload: dict[str, Any],
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request: StewardRuntimeDecisionRequest,
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traces: list[dict[str, Any]],
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) -> StewardRuntimeDecisionResponse | None:
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next_action = str(payload.get("next_action") or "").strip()
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if next_action not in RUNTIME_NEXT_ACTIONS:
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return None
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return StewardRuntimeDecisionResponse(
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decision_source="llm_function_call",
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next_action=next_action, # type: ignore[arg-type]
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target_task_id=self._clean_text(payload.get("target_task_id")),
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target_message_id=self._clean_text(payload.get("target_message_id")),
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field_key=self._clean_text(payload.get("field_key")),
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field_value=self._clean_text(payload.get("field_value")),
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confirmation_required=bool(payload.get("confirmation_required")),
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question=self._clean_text(payload.get("question")),
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response_text=self._clean_text(payload.get("response_text")),
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rationale=self._clean_text(payload.get("rationale")),
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model_call_traces=traces,
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)
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def _build_rule_fallback(
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self,
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request: StewardRuntimeDecisionRequest,
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traces: list[dict[str, Any]],
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) -> StewardRuntimeDecisionResponse:
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state = request.runtime_state
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pending_application = state.get("pending_application") if isinstance(state.get("pending_application"), dict) else {}
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pending_steward_action = state.get("pending_steward_action") if isinstance(state.get("pending_steward_action"), dict) else {}
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waiting_for = str(state.get("waiting_for") or "").strip()
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message = request.user_message.replace(" ", "")
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confirmation_text = message in {"确认", "确定", "无误", "确认提交", "可以提交", "提交", "没问题"}
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if confirmation_text and pending_application.get("ready_to_submit"):
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return StewardRuntimeDecisionResponse(
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decision_source="rule_fallback",
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next_action="submit_current_application",
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target_message_id=str(pending_application.get("message_id") or ""),
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target_task_id=str(pending_application.get("task_id") or ""),
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rationale="模型运行时决策暂不可用，我先按当前待提交申请单上下文处理你的确认。",
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model_call_traces=traces,
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)
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if confirmation_text and pending_steward_action:
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return StewardRuntimeDecisionResponse(
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decision_source="rule_fallback",
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next_action="continue_next_task",
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target_message_id=str(pending_steward_action.get("message_id") or ""),
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target_task_id=str(pending_steward_action.get("target_task_id") or ""),
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rationale="模型运行时决策暂不可用，我先按当前待确认的下一项任务继续处理。",
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model_call_traces=traces,
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)
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if waiting_for:
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return StewardRuntimeDecisionResponse(
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decision_source="rule_fallback",
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next_action="ask_user",
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question="我需要先确认当前等待事项，请补充或选择当前问题对应的信息。",
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rationale="模型运行时决策暂不可用，当前仍存在等待用户补充的信息。",
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model_call_traces=traces,
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)
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return StewardRuntimeDecisionResponse(
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decision_source="rule_fallback",
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next_action="plan_new_tasks",
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rationale="模型运行时决策暂不可用，当前没有可安全匹配的等待动作，回到任务规划。",
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model_call_traces=traces,
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)
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@staticmethod
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def _clean_text(value: Any) -> str:
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return str(value or "").strip()
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