287 lines
12 KiB
Python
287 lines
12 KiB
Python
"""LLM 结构化输出的确定性规则 fallback。
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该类不是对真实模型的替代，只用于本地开发和测试时提供稳定输出。
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真实 LLM client 需要实现相同方法。
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"""
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from __future__ import annotations
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import re
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from typing import Any
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from pam_deploy_graph.constants import GLOBAL_ACTION_SEQUENCE, IP_ACTION_SEQUENCE, REQUIRED_PARAMS
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from pam_deploy_graph.models import (
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ActionResult,
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ExecutionStrategy,
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LlmActionAnalysis,
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LlmDeployPlan,
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LlmIntentResult,
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LlmModeDecision,
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LlmParamResult,
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)
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KEY_ALIASES = {
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"home_base_url": "HOME_BASE_URL",
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"HOME_BASE_URL": "HOME_BASE_URL",
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"client_id": "CLIENT_ID",
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"CLIENT_ID": "CLIENT_ID",
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"client_secret": "CLIENT_SECRET",
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"CLIENT_SECRET": "CLIENT_SECRET",
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"airportCode": "AIRPORT_CODE",
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"AIRPORT_CODE": "AIRPORT_CODE",
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"applicationName": "APP_NAME",
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"APP_NAME": "APP_NAME",
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"moduleName": "MODULE_NAME",
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"MODULE_NAME": "MODULE_NAME",
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"versionNumber": "VERSION_NUMBER",
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"VERSION_NUMBER": "VERSION_NUMBER",
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"zipFilePath": "ZIP_FILE_PATH",
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"ZIP_FILE_PATH": "ZIP_FILE_PATH",
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"actionType": "ACTION_TYPE",
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"ACTION_TYPE": "ACTION_TYPE",
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"timeOut": "TIMEOUT",
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"TIMEOUT": "TIMEOUT",
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"logName": "LOG_NAME",
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"LOG_NAME": "LOG_NAME",
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}
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class RuleBasedLlmClient:
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"""基于规则的轻量 LLM client fallback。"""
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def understand_request(self, text: str) -> LlmIntentResult:
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"""用关键词规则识别用户意图和执行策略偏好。"""
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lowered = text.lower()
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reasons: list[str] = []
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intent = "deploy"
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if any(word in lowered for word in ("用法", "怎么用", "生成脚本", "给我脚本", "usage")):
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intent = "show_usage"
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reasons.append("用户在询问脚本用法或脚本生成")
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elif any(word in lowered for word in ("预演", "计划", "不执行", "不要动环境", "dry-run", "preview")):
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intent = "preview"
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reasons.append("用户要求只预演或不触碰环境")
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elif any(word in lowered for word in ("在线ip", "在线 ip", "查询ip", "查询 ip", "node", "工作站")):
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intent = "query_node_ips"
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reasons.append("用户要求查询 Node 或在线工作站")
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elif any(word in lowered for word in ("回滚", "rollback")):
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intent = "rollback"
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reasons.append("用户要求回滚")
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else:
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reasons.append("默认识别为部署请求")
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mode_preference = "未指定"
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strategy_preference = "未指定"
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if any(word in lowered for word in ("mcp", "在线执行", "直接在线")):
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mode_preference = "MCP"
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strategy_preference = "hybrid_node_mcp"
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reasons.append("用户倾向 MCP；PAM_HOME 仍需脚本 action")
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if any(word in lowered for word in ("脚本", "离线", "script", "shell", "powershell")):
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mode_preference = "API脚本"
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strategy_preference = "script_only"
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reasons.append("用户倾向脚本或离线执行")
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if intent == "preview":
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strategy_preference = strategy_preference if strategy_preference != "未指定" else "hybrid_node_mcp"
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return LlmIntentResult(
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intent=intent, # type: ignore[arg-type]
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mode_preference=mode_preference, # type: ignore[arg-type]
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strategy_preference=strategy_preference, # type: ignore[arg-type]
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confidence=0.72 if intent != "deploy" else 0.6,
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reasons=reasons,
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)
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def extract_params(self, text: str, base_params: dict[str, Any] | None = None) -> LlmParamResult:
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"""从 key=value、中文短语和 IP 地址中抽取参数。"""
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params = dict(base_params or {})
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params.update(self._extract_key_values(text))
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params.update(self._extract_chinese_patterns(text))
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control: dict[str, Any] = {}
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ips = re.findall(r"\b(?:\d{1,3}\.){3}\d{1,3}\b", text)
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if ips:
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control["user_specified_ips"] = ips
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missing = [key for key in REQUIRED_PARAMS if not params.get(key)]
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sensitive = [key for key in ("CLIENT_SECRET", "CLIENT_ID") if params.get(key)]
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return LlmParamResult(
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extracted_params=params,
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extracted_control=control,
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missing_required_params=missing,
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sensitive_fields_present=sensitive,
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)
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def generate_plan(
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self,
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*,
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params: dict[str, Any],
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intent: str,
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strategy: ExecutionStrategy,
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skill_policy: dict[str, Any],
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tool_summaries: list[dict[str, Any]],
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) -> LlmDeployPlan:
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"""生成确定性的部署计划和风险提示。"""
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if strategy == "hybrid_node_mcp":
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strategy_text = "PAM_HOME 使用脚本 action，PAM_NODE 使用 MCP"
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elif strategy == "script_only":
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strategy_text = "全部 action 使用脚本 action"
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else:
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strategy_text = "全部 action 使用 fake runner"
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summary = (
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f"计划处理 {params.get('AIRPORT_CODE', '-')}/"
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f"{params.get('APP_NAME', '-')}/"
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f"{params.get('MODULE_NAME', '-')}/"
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f"{params.get('VERSION_NUMBER', '-')}，执行策略为 {strategy_text}。"
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)
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risk_notes = [
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"真实部署前必须确认参数。",
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"发布版本、创建下载任务、升级和回滚属于高风险动作。",
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"回滚只能在用户确认后执行。",
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]
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if strategy == "hybrid_node_mcp":
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risk_notes.append("PAM_HOME 当前没有 MCP 能力，HOME 阶段仍会调用脚本 action。")
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if intent == "query_node_ips":
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planned_actions = ["get-token", "get-node-url", "get-online-ips"]
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elif intent == "rollback":
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planned_actions = ["rollback-ip", "verify-ip", "download-log"]
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elif intent == "deploy":
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planned_actions = [*GLOBAL_ACTION_SEQUENCE, *IP_ACTION_SEQUENCE]
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else:
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planned_actions = list(GLOBAL_ACTION_SEQUENCE)
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return LlmDeployPlan(
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summary=summary,
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risk_notes=risk_notes,
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planned_actions=planned_actions,
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requires_confirmation=intent in ("deploy", "query_node_ips", "rollback"),
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execution_strategy=strategy,
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)
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def decide_execution_mode(
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self,
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*,
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text: str,
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params: dict[str, Any],
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intent: str,
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strategy: ExecutionStrategy,
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allowed_modes: list[str],
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tool_summaries: list[dict[str, Any]],
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) -> LlmModeDecision:
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"""根据关键词规则决定进入固定 runtime 或 agentic skill。"""
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lowered = text.lower()
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requested_agentic = any(
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word in lowered for word in ("自主编排", "按 skill", "自动选择工具", "自动决策", "toolcall", "agentic")
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)
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diagnostic_intent = any(word in lowered for word in ("诊断", "排查", "分析异常", "帮我看看", "explore"))
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high_risk_intent = intent in ("deploy", "rollback") or any(word in lowered for word in ("批量", "升级", "回滚"))
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mode = "fixed_runtime"
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reason = "标准部署和高风险动作默认走固定 runtime。"
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risk_level = "high" if high_risk_intent else "medium"
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requires_confirmation = True
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if requested_agentic or diagnostic_intent:
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mode = "agentic_skill"
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reason = "用户明确要求按 skill 自主编排，或任务更偏探索/诊断。"
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risk_level = "medium"
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if mode not in allowed_modes:
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mode = allowed_modes[0] if allowed_modes else "fixed_runtime"
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reason = "原始模式不在 skill 允许集合内，已回退到允许模式。"
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return LlmModeDecision(
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mode=mode, # type: ignore[arg-type]
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reason=reason,
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risk_level=risk_level, # type: ignore[arg-type]
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requires_confirmation=requires_confirmation,
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)
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def analyze_action_result(
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self,
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*,
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action: str,
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result: ActionResult,
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state_summary: dict[str, Any],
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) -> LlmActionAnalysis:
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"""用本地规则分析 action 结果，作为真实 LLM 不可用时的兜底。"""
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notes: list[str] = []
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has_anomaly = not result.ok
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severity = "info"
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possible_reason = ""
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suggested_action = "继续观察。"
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requires_confirmation = False
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should_continue = True
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if not result.ok:
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severity = "medium"
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possible_reason = result.error_summary or "action 返回失败状态。"
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suggested_action = "查看 action stderr/raw_output，确认参数、网络和目标服务状态。"
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notes.append("硬规则检测到 action 执行失败。")
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should_continue = False
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if action == "verify-ip":
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success = result.values.get("SUCCESS")
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if success is not None and str(success).lower() not in ("true", "1", "yes"):
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has_anomaly = True
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severity = "high"
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possible_reason = result.values.get("MESSAGE", "") or "工作站健康检查未通过。"
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suggested_action = "先下载日志并人工确认是否执行回滚。"
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requires_confirmation = True
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notes.append("verify-ip SUCCESS 非成功值。")
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should_continue = False
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if action == "rollback-ip" and not result.ok:
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severity = "high"
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suggested_action = "保持待确认状态，人工排查回滚失败原因后重试或转人工处理。"
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requires_confirmation = True
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notes.append("rollback-ip 失败需要人工处理。")
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should_continue = False
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if result.values.get("PENDING_AGENT_CONFIRMATION"):
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has_anomaly = True
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severity = "high"
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possible_reason = str(result.values["PENDING_AGENT_CONFIRMATION"])
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suggested_action = "暂停自动流程，等待人工确认。"
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requires_confirmation = True
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notes.append("action 返回待人工确认标记。")
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should_continue = False
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return LlmActionAnalysis(
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action=action,
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has_anomaly=has_anomaly,
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severity=severity, # type: ignore[arg-type]
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possible_reason=possible_reason,
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suggested_action=suggested_action,
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requires_confirmation=requires_confirmation,
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should_continue=should_continue,
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notes=notes,
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)
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def _extract_key_values(self, text: str) -> dict[str, str]:
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"""抽取 KEY=VALUE 形式的参数。"""
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params: dict[str, str] = {}
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for match in re.finditer(r"([A-Za-z_][A-Za-z0-9_]*)\s*=\s*([^\s,;]+)", text):
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raw_key, value = match.groups()
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key = KEY_ALIASES.get(raw_key)
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if key:
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params[key] = value.strip()
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return params
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def _extract_chinese_patterns(self, text: str) -> dict[str, str]:
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"""抽取常见中文描述中的部署参数。"""
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patterns = {
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"AIRPORT_CODE": r"(?:机场|三字码)\s*[:：]?\s*([A-Z]{3})",
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"APP_NAME": r"(?:应用|应用名)\s*[:：]?\s*([A-Za-z0-9_.-]+)",
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"MODULE_NAME": r"(?:模块|模块名)\s*[:：]?\s*([A-Za-z0-9_.-]+)",
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"VERSION_NUMBER": r"(?:版本|版本号)\s*[:：]?\s*([A-Za-z0-9_.-]+)",
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"ZIP_FILE_PATH": r"(?:包|软件包|zip)\s*[:：]?\s*([A-Za-z]:[\\/][^\s,;]+|/[^\s,;]+)",
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}
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params: dict[str, str] = {}
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for key, pattern in patterns.items():
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match = re.search(pattern, text)
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if match:
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params[key] = match.group(1)
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return params
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