```
refactor(Dockerfile): 使用requirements.txt替代硬编码依赖 将Dockerfile中的硬编码pip包列表替换为通过requirements.txt文件安装， 提高依赖管理的灵活性和可维护性。 feat(scheduling): 移除内置APScheduler，采用独立调度系统 移除app/core/background/scheduler.py中原来的APScheduler实现， 改为使用新的应用级调度系统app.application.scheduling。 refactor(task_queue): 调整任务队列模块结构和导入路径 将任务队列相关常量从app.core.background.task_queue.constants迁移至 app.tasks.constants，并更新所有相关导入路径和引用。 refactor(events): 将事件服务重构为独立的应用层组件 将事件分发逻辑从核心层迁移到应用层，使用app.application.events.EventDispatchService 替代原有的app.modules.events.services.EventService。 feat(ai_memory): 增强AI记忆自动写入的安全策略 新增ai_memory_blocked_content_terms配置项用于阻止敏感内容， 添加TTL过期机制控制自动写入条目的生命周期。 fix(security): 强化生产环境安全验证机制 增加model_validator确保生产环境中数据库连接、API密钥、CORS设置等 关键安全配置符合要求。 feat(risks): 优化风险事件操作动作的外键约束 为RiskEventAction模型的风险事件ID字段添加外键约束， 防止孤立记录并增强数据完整性。 refactor(audit): 优化审计服务方法命名和事务处理 将AuditService的log方法重命名为record以反映其阶段行为， 并调整事务提交时机以提高性能。 feat(events): 增强领域事件并发处理和响应模型 添加事件锁定机制防止重复处理，更新API响应模型以提供 更准确的数据类型定义。 ```
This commit is contained in:
192
app/application/feishu/handlers/rules.py
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192
app/application/feishu/handlers/rules.py
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import re
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from typing import Any
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from fastapi import HTTPException
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from sqlalchemy.orm import Session
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from app.application.feishu.delivery import send_text_if_configured
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from app.application.feishu.results import command_result
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from app.core.config import get_settings
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from app.modules.ai_memory.constants import AIMemoryStatus
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from app.modules.ai_memory.service import AIMemoryService
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from app.modules.feishu.constants import FeishuCommandName, FeishuReplyType
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from app.modules.feishu.service import FeishuService
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RULE_TITLE = "AI 学习规则"
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RULE_CREATE_PATTERN = re.compile(r"^学习规则(?:\s+(\d{1,3}))?\s*[：:]\s*(.*)$")
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MARKET_RULE_CREATE_PATTERN = re.compile(
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r"^学习市场规则(?:\s+(\d{1,3}))?\s*[：:]\s*(.*)$"
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)
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RULE_DISABLE_PATTERN = re.compile(r"^停用规则\s+(MEM-[A-Za-z0-9-]+)$", re.IGNORECASE)
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RULE_ENABLE_PATTERN = re.compile(r"^启用规则\s+(MEM-[A-Za-z0-9-]+)$", re.IGNORECASE)
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RULE_LIST_COMMANDS = {"查看规则", "规则列表", "查看市场规则"}
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RULE_COMMAND_PREFIXES = (
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"学习市场规则",
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"学习规则",
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"查看市场规则",
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"查看规则",
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"规则列表",
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"停用规则",
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"启用规则",
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)
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RULE_COMMAND_HELP = (
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"规则指令格式：\n"
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"学习规则：<规则内容>\n"
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"学习规则 80：<规则内容>\n"
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"学习市场规则 80：<仅用于市场分析的规则内容>\n"
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"查看规则\n"
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"停用规则 <规则编号>\n"
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"启用规则 <规则编号>"
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)
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def handle_rule_command(
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db: Session,
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feishu: FeishuService,
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command_text: str,
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chat_id: str | None,
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actor: str,
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auto_reply: bool,
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) -> dict[str, Any] | None:
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"""Handle persistent AI rule commands."""
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if not command_text.startswith(RULE_COMMAND_PREFIXES):
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return None
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command = _command_name(command_text)
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if command in {
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FeishuCommandName.RULE_CREATE,
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FeishuCommandName.RULE_DISABLE,
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FeishuCommandName.RULE_ENABLE,
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} and get_settings().read_only_mode:
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return _result(
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feishu,
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command,
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"当前为只读模式，不能新增或修改学习规则。请由管理员启用操作后重试。",
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chat_id,
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actor,
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auto_reply,
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)
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content = RULE_COMMAND_HELP
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try:
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command, content = _execute(db, command_text, command, actor)
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except HTTPException as exc:
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detail = str(exc.detail)
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if "secret-like" in detail:
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content = "规则疑似包含密码、令牌或其他密钥信息，已拒绝学习。"
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elif exc.status_code == 404:
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content = "没有找到该规则，请先发送“查看规则”确认规则编号。"
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elif "priority" in detail:
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content = "规则优先级必须在 1 到 100 之间。"
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else:
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content = "规则未保存，请检查指令内容后重试。"
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return _result(feishu, command, content, chat_id, actor, auto_reply)
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def _execute(
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db: Session,
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command_text: str,
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command: FeishuCommandName,
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actor: str,
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) -> tuple[FeishuCommandName, str]:
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market_create_match = MARKET_RULE_CREATE_PATTERN.fullmatch(command_text)
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create_match = market_create_match or RULE_CREATE_PATTERN.fullmatch(command_text)
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disable_match = RULE_DISABLE_PATTERN.fullmatch(command_text)
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enable_match = RULE_ENABLE_PATTERN.fullmatch(command_text)
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memory = AIMemoryService(db)
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if create_match:
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return command, _create_rule(memory, create_match, market_create_match is not None, actor)
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if command_text in RULE_LIST_COMMANDS:
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return FeishuCommandName.RULE_LIST, _list_rules(memory, command_text)
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if disable_match or enable_match:
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enabled = enable_match is not None
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match = enable_match or disable_match
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rule = memory.update_rule(
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code=match.group(1),
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content=None,
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priority=None,
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tags=None,
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enabled=enabled,
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actor=actor,
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)
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state = "已启用" if enabled else "已停用"
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return (
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FeishuCommandName.RULE_ENABLE if enabled else FeishuCommandName.RULE_DISABLE,
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f"规则{state}。\n"
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f"编号：{rule['code']}\n"
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f"优先级：{rule['importance']}\n"
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f"范围：{rule['scope']} / {rule['subject']}\n"
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f"状态：{state}",
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)
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return command, RULE_COMMAND_HELP
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def _create_rule(
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memory: AIMemoryService,
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match: re.Match[str],
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market_rule: bool,
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actor: str,
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) -> str:
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priority = int(match.group(1) or 50)
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content = match.group(2).strip()
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if not content:
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return f"规则内容不能为空。\n\n{RULE_COMMAND_HELP}"
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if not 1 <= priority <= 100:
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return "规则优先级必须在 1 到 100 之间。"
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rule = memory.create_rule(
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content=content,
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scope="market" if market_rule else "global",
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subject="market" if market_rule else "company",
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priority=priority,
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tags=["feishu", *(["market"] if market_rule else [])],
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actor=actor,
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)
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return (
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"规则已学习。\n"
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f"编号：{rule['code']}\n"
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f"优先级：{rule['importance']}\n"
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f"范围：{rule['scope']} / {rule['subject']}\n"
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"状态：已启用"
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)
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def _list_rules(memory: AIMemoryService, command_text: str) -> str:
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rules = memory.list_rules(
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scope="market" if command_text == "查看市场规则" else None,
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status_filter=AIMemoryStatus.ACTIVE,
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limit=20,
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)
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if not rules:
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return "当前没有已启用的学习规则。"
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lines = ["当前已启用的学习规则："]
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for rule in rules:
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rule_text = str(rule["content"])
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if len(rule_text) > 80:
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rule_text = f"{rule_text[:80]}…"
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lines.append(
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f"{rule['code']}｜优先级 {rule['importance']}｜"
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f"{rule['scope']}/{rule['subject']}\n{rule_text}"
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)
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return "\n\n".join(lines)
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def _command_name(command_text: str) -> FeishuCommandName:
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if command_text.startswith("停用规则"):
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return FeishuCommandName.RULE_DISABLE
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if command_text.startswith("启用规则"):
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return FeishuCommandName.RULE_ENABLE
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if command_text.startswith(("查看市场规则", "查看规则", "规则列表")):
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return FeishuCommandName.RULE_LIST
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return FeishuCommandName.RULE_CREATE
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def _result(
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feishu: FeishuService,
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command: FeishuCommandName,
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content: str,
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chat_id: str | None,
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actor: str,
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auto_reply: bool,
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) -> dict[str, Any]:
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response = send_text_if_configured(feishu, chat_id, content, actor) if auto_reply else None
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return command_result(command, FeishuReplyType.TEXT, RULE_TITLE, content, response)
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