241 lines
8.4 KiB
Python
241 lines
8.4 KiB
Python
from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass
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from .file_summary.workflow_trigger import (
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evaluate_attachment_reader_trigger,
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evaluate_file_summary_trigger,
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)
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from .application_form_fill.constants import FORM_FILL_TRIGGER_KEYWORDS, WORKFLOW_TYPE as FORM_FILL_WORKFLOW_TYPE
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from .llm import LLMConfigurationError, LLMRequestError, generate_completion
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from .models import Conversation, FileAttachment
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logger = logging.getLogger(__name__)
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ROUTE_ACTIONS = {"normal_chat", "attachment_reader", "file_summary"}
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ROUTE_ACTIONS.add("regulatory_review")
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ROUTE_ACTIONS.add(FORM_FILL_WORKFLOW_TYPE)
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@dataclass(frozen=True)
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class SkillRoute:
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action: str
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skill_name: str = ""
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workflow_type: str = ""
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confidence: float = 0.0
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reason: str = ""
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source: str = "llm"
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@property
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def uses_attachment_reader(self) -> bool:
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return self.action == "attachment_reader"
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@property
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def starts_file_summary(self) -> bool:
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return self.action == "file_summary"
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@property
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def starts_regulatory_review(self) -> bool:
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return self.action == "regulatory_review"
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@property
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def starts_application_form_fill(self) -> bool:
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return self.action == FORM_FILL_WORKFLOW_TYPE
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@property
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def is_normal_chat(self) -> bool:
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return self.action == "normal_chat"
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def route_message_intent(conversation: Conversation, content: str) -> SkillRoute:
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attachments = list(_active_attachments(conversation))
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try:
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route = _route_with_llm(conversation, content, attachments)
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logger.info(
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"LLM skill route selected",
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extra={
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"conversation_id": conversation.pk,
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"action": route.action,
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"skill_name": route.skill_name,
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"workflow_type": route.workflow_type,
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"confidence": route.confidence,
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"route_source": route.source,
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"reason": route.reason,
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},
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)
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return route
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except (LLMConfigurationError, LLMRequestError, ValueError, json.JSONDecodeError) as exc:
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logger.warning(
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"LLM skill route failed, fallback to rules",
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extra={"conversation_id": conversation.pk, "error": str(exc)},
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)
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return _route_with_rules(conversation, content)
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def _route_with_llm(
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conversation: Conversation,
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content: str,
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attachments: list[FileAttachment],
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) -> SkillRoute:
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raw = generate_completion(
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[
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{"role": "system", "content": _router_system_prompt()},
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{
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"role": "user",
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"content": _router_user_prompt(
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user_message=content,
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attachments=attachments,
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),
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},
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],
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temperature=0.0,
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)
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payload = _parse_json_object(raw)
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action = str(payload.get("action", "normal_chat")).strip()
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if action not in ROUTE_ACTIONS:
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raise ValueError(f"不支持的路由动作：{action}")
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if action in {"attachment_reader", "file_summary"} and not attachments:
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return SkillRoute(
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action=action,
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skill_name="attachment_reader" if action == "attachment_reader" else "",
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workflow_type="file_summary" if action == "file_summary" else "",
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confidence=_float_or_zero(payload.get("confidence")),
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reason=str(payload.get("reason") or "LLM 判断需要附件，但当前无附件。"),
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source="llm_missing_attachment",
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)
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return SkillRoute(
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action=action,
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skill_name="attachment_reader" if action == "attachment_reader" else "",
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workflow_type=action if action in {"file_summary", "regulatory_review", FORM_FILL_WORKFLOW_TYPE} else "",
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confidence=_float_or_zero(payload.get("confidence")),
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reason=str(payload.get("reason") or ""),
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source="llm",
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)
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def _route_with_rules(conversation: Conversation, content: str) -> SkillRoute:
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if _matches_application_form_fill(content):
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return SkillRoute(
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action=FORM_FILL_WORKFLOW_TYPE,
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workflow_type=FORM_FILL_WORKFLOW_TYPE,
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confidence=0.7,
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reason="命中申报文件自动填表关键词。",
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source="rule_fallback",
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)
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if _matches_regulatory_review(content):
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return SkillRoute(
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action="regulatory_review",
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workflow_type="regulatory_review",
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confidence=0.7,
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reason="命中法规核查关键词。",
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source="rule_fallback",
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)
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file_summary = evaluate_file_summary_trigger(conversation, content)
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if file_summary.should_start or file_summary.reason == "missing_attachment":
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return SkillRoute(
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action="file_summary",
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workflow_type="file_summary",
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confidence=0.5,
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reason=file_summary.reason,
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source="rule_fallback",
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)
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attachment_reader = evaluate_attachment_reader_trigger(conversation, content)
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if attachment_reader.should_start or attachment_reader.reason == "missing_attachment":
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return SkillRoute(
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action="attachment_reader",
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skill_name="attachment_reader",
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confidence=0.5,
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reason=attachment_reader.reason,
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source="rule_fallback",
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)
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return SkillRoute(
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action="normal_chat",
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confidence=0.5,
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reason="未匹配到需要调用 Skill 或工作流的意图。",
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source="rule_fallback",
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)
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def _active_attachments(conversation: Conversation):
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return (
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FileAttachment.objects.filter(conversation=conversation, is_active=True)
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.exclude(upload_status=FileAttachment.UploadStatus.DELETED)
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.order_by("original_name", "-version_no")
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)
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def _router_system_prompt() -> str:
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return (
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"你是审核智能体的工具路由器，只判断是否需要调用工具，不直接回答用户。"
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"你必须只输出 JSON 对象，不要输出 Markdown。"
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"可选 action：normal_chat、attachment_reader、file_summary、regulatory_review、application_form_fill。"
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"attachment_reader 用于用户要求阅读、提取、分析、总结、查看上传附件内容。"
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"file_summary 用于用户要求自动汇总文件目录、页数、清单或生成目录页数报告。"
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"regulatory_review 用于用户要求法规核查、NMPA核查、完整性核查、章节一致性核查、风险预警或整改建议。"
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"application_form_fill 用于用户要求填注册证、生成申报模板、填写对应表格、安全和性能基本原则清单或自动填表。"
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"normal_chat 用于不需要读取附件或执行工作流的一般问答。"
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"输出字段：action、confidence、reason。"
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)
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def _router_user_prompt(*, user_message: str, attachments: list[FileAttachment]) -> str:
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attachment_lines = [
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f"- id={attachment.pk}, name={attachment.original_name}, active={attachment.is_active}, status={attachment.upload_status}"
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for attachment in attachments
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]
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attachment_text = "\n".join(attachment_lines) if attachment_lines else "无 active 附件"
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return (
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f"用户消息：{user_message}\n\n"
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f"当前 active 附件：\n{attachment_text}\n\n"
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"请判断应调用哪个 action。只输出 JSON。"
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)
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def _parse_json_object(raw: str) -> dict:
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text = (raw or "").strip()
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if text.startswith("```"):
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text = text.strip("`").strip()
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if text.lower().startswith("json"):
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text = text[4:].strip()
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start = text.find("{")
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end = text.rfind("}")
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if start == -1 or end == -1 or end < start:
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raise json.JSONDecodeError("未找到 JSON 对象", text, 0)
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return json.loads(text[start : end + 1])
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def _float_or_zero(value) -> float:
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try:
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return float(value)
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except (TypeError, ValueError):
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return 0.0
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def _matches_regulatory_review(content: str) -> bool:
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normalized = content.lower()
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keywords = [
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"法规核查",
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"nmpa核查",
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"nmpa 核查",
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"完整性核查",
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"风险预警",
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"整改建议",
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"章节核查",
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"一致性核查",
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]
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return any(keyword in normalized for keyword in keywords)
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def _matches_application_form_fill(content: str) -> bool:
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normalized = content.lower()
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return any(keyword.lower() in normalized for keyword in FORM_FILL_TRIGGER_KEYWORDS)
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