badcce5d2d
用户修复外部问题后输入 resume，会从失败 action 重新执行，而不是结束整个流程。 回滚从 workflow 中拆出，新增显式命令： chat：rollback [IP] CLI：rollback --checkpoint ... [--ip ...] [--stop-first|--no-stop-first] 旧 confirm approve/reject 只保留为旧 checkpoint 兼容入口，新流程不再推荐使用。 LangGraph workflow 已移除回滚确认 interrupt 节点，失败暂停和续跑走业务 checkpoint。 README、打包 README、run.sh --help、流程图、todo、提示词基线和测试都已同步。
262 lines
11 KiB
Python
262 lines
11 KiB
Python
"""PAM 部署 Agent 的命令行入口。"""
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from __future__ import annotations
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import argparse
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import json
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import logging
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from dataclasses import asdict
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from .agent import PamDeployAgent
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from .checkpoint_store import load_agent_state, redact_mapping
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from .interactive import run_interactive_chat
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from .langgraph_runtime import LangGraphDeploymentRuntime, LangGraphRunResult
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from .llm import build_llm_client
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from .logging_utils import configure_logging, json_for_log
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from .mcp_factory import build_mcp_runner_from_config
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from .params_loader import load_params_file
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logger = logging.getLogger(__name__)
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def add_llm_args(parser: argparse.ArgumentParser) -> None:
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"""为子命令追加真实 LLM 配置参数。"""
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parser.add_argument("--llm-base-url")
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parser.add_argument("--llm-api-key")
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parser.add_argument("--llm-model")
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parser.add_argument("--llm-action-analysis-prompt-file")
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def add_mcp_args(parser: argparse.ArgumentParser) -> None:
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"""为需要执行 MCP action 的子命令追加 MCP 配置参数。"""
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parser.add_argument("--mcp-config", help="MCP client JSON 配置文件路径")
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def add_action_analysis_arg(parser: argparse.ArgumentParser) -> None:
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"""为执行类子命令追加 action 后诊断开关。"""
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parser.add_argument("--analyze-actions", action="store_true", help="每个 action 后追加 LLM/规则诊断建议")
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def require_confirm(args: argparse.Namespace) -> None:
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"""真实执行前强制要求命令行显式传入 --confirm。"""
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if not getattr(args, "confirm", False):
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raise SystemExit("Refusing to execute actions without --confirm.")
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def print_pause_payload(agent: PamDeployAgent, state) -> None:
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"""输出 checkpoint 和待确认信息，便于用户续跑或确认。"""
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if state.pending_confirmation:
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print(json.dumps({"confirmation": agent.build_confirmation_request(state)}, ensure_ascii=False, indent=2))
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if state.checkpoint_path:
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print(json.dumps({"checkpoint": state.checkpoint_path}, ensure_ascii=False, indent=2))
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def run_graph_once(agent: PamDeployAgent, state, *, flow: str = "deploy") -> LangGraphRunResult:
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"""用 LangGraph runtime 执行一次状态，返回图执行结果。"""
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runtime = LangGraphDeploymentRuntime(agent=agent, flow=flow) # type: ignore[arg-type]
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return runtime.start(state)
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def print_graph_result(agent: PamDeployAgent, result: LangGraphRunResult) -> None:
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"""输出 LangGraph 执行结果、报告和暂停信息。"""
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state = result.state
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if result.report:
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print(result.report)
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elif state is not None:
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print(agent.render_report(state))
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if result.interrupted and result.confirmation:
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print(json.dumps({"confirmation": result.confirmation}, ensure_ascii=False, indent=2))
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if state is not None:
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print_pause_payload(agent, state)
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def main() -> None:
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"""解析 CLI 参数并分发到对应命令。"""
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parser = argparse.ArgumentParser(prog="pam-deploy-agent")
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sub = parser.add_subparsers(dest="command", required=True)
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preview = sub.add_parser("preview")
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preview.add_argument("--config", required=True)
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preview.add_argument("--strategy", default="hybrid_node_mcp", choices=["hybrid_node_mcp", "script_only", "fake"])
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analyze = sub.add_parser("analyze")
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analyze.add_argument("--text", required=True)
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analyze.add_argument("--config")
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add_llm_args(analyze)
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chat = sub.add_parser("chat")
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chat.add_argument("--config", required=True)
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chat.add_argument("--strategy", default="fake", choices=["hybrid_node_mcp", "script_only", "fake"])
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chat.add_argument("--target-ip", action="append", default=[])
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chat.add_argument("--checkpoint")
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add_llm_args(chat)
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add_mcp_args(chat)
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add_action_analysis_arg(chat)
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run = sub.add_parser("run-global")
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run.add_argument("--config", required=True)
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run.add_argument("--strategy", default="fake", choices=["hybrid_node_mcp", "script_only", "fake"])
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run.add_argument("--checkpoint")
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run.add_argument("--confirm", action="store_true")
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add_llm_args(run)
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add_mcp_args(run)
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add_action_analysis_arg(run)
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deploy = sub.add_parser("run-deploy")
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deploy.add_argument("--config", required=True)
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deploy.add_argument("--strategy", default="fake", choices=["hybrid_node_mcp", "script_only", "fake"])
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deploy.add_argument("--target-ip", action="append", default=[])
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deploy.add_argument("--checkpoint")
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deploy.add_argument("--confirm", action="store_true")
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add_llm_args(deploy)
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add_mcp_args(deploy)
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add_action_analysis_arg(deploy)
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resume = sub.add_parser("resume")
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resume.add_argument("--checkpoint", required=True)
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resume.add_argument("--confirm", action="store_true")
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add_llm_args(resume)
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add_mcp_args(resume)
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add_action_analysis_arg(resume)
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confirm = sub.add_parser("confirm")
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confirm.add_argument("--checkpoint", required=True)
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confirm.add_argument("--decision", required=True, choices=["approve", "reject"])
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confirm.add_argument("--note", default="")
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confirm.add_argument("--confirm", action="store_true")
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add_llm_args(confirm)
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add_mcp_args(confirm)
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add_action_analysis_arg(confirm)
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rollback = sub.add_parser("rollback")
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rollback.add_argument("--checkpoint", required=True)
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rollback.add_argument("--ip", help="要回滚的 IP；不传时使用当前失败 IP")
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rollback.add_argument("--stop-first", dest="stop_first", action="store_true", default=None, help="回滚前先停机")
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rollback.add_argument("--no-stop-first", dest="stop_first", action="store_false", default=None, help="回滚前不先停机")
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rollback.add_argument("--note", default="")
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rollback.add_argument("--confirm", action="store_true")
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add_llm_args(rollback)
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add_mcp_args(rollback)
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add_action_analysis_arg(rollback)
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args = parser.parse_args()
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log_path = configure_logging()
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logger.info("CLI 启动 command=%s args=%s log_path=%s", args.command, json_for_log(vars(args)), log_path)
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params = load_params_file(args.config) if getattr(args, "config", None) else {}
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if getattr(args, "config", None):
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logger.info("参数文件已加载 command=%s config=%s params=%s", args.command, args.config, json_for_log(params))
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llm_client = None
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if args.command != "preview":
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llm_client = build_llm_client(
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base_url=getattr(args, "llm_base_url", None),
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api_key=getattr(args, "llm_api_key", None),
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model=getattr(args, "llm_model", None),
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action_analysis_prompt_path=getattr(args, "llm_action_analysis_prompt_file", None),
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)
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mcp_runner = None
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if getattr(args, "mcp_config", None):
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logger.info("开始加载 MCP 配置 path=%s", args.mcp_config)
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mcp_runner = build_mcp_runner_from_config(args.mcp_config)
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logger.info("MCP 配置加载完成 path=%s runner=%s", args.mcp_config, type(mcp_runner).__name__)
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agent = PamDeployAgent(
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llm_client=llm_client,
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mcp_runner=mcp_runner,
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action_analysis_enabled=bool(getattr(args, "analyze_actions", False)),
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)
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if args.command == "analyze":
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logger.info("开始执行 analyze text_len=%s", len(args.text))
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result = agent.analyze_request(args.text, params)
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payload = redact_mapping({key: asdict(value) for key, value in result.items()})
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logger.info("analyze 完成 result=%s", json_for_log(payload))
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print(json.dumps(payload, ensure_ascii=False, indent=2))
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return
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if args.command == "chat":
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logger.info("进入 chat 模式 strategy=%s checkpoint=%s target_ips=%s", args.strategy, args.checkpoint, args.target_ip)
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run_interactive_chat(
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agent=agent,
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params=params,
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strategy=args.strategy,
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checkpoint_path=args.checkpoint,
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target_ips=args.target_ip,
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)
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return
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if args.command == "preview":
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logger.info("执行 preview strategy=%s", args.strategy)
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print(agent.preview(params, args.strategy))
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return
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require_confirm(args)
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if args.command == "run-global":
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logger.info("开始 run-global strategy=%s checkpoint=%s", args.strategy, args.checkpoint)
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state = agent.create_state(
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params=params,
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execution_strategy=args.strategy,
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checkpoint_path=args.checkpoint,
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)
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result = run_graph_once(agent, state, flow="global")
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if result.state is not None:
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print(json.dumps({"events": result.state.events}, ensure_ascii=False, indent=2))
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print_pause_payload(agent, result.state)
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return
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if args.command == "resume":
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logger.info("开始 resume checkpoint=%s", args.checkpoint)
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state = load_agent_state(args.checkpoint)
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state.checkpoint_path = state.checkpoint_path or args.checkpoint
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if state.paused:
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state = agent.resume_state(state)
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result = run_graph_once(agent, state, flow="deploy")
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print_graph_result(agent, result)
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return
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if args.command == "confirm":
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logger.info("开始 confirm checkpoint=%s decision=%s note_len=%s", args.checkpoint, args.decision, len(args.note))
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state = load_agent_state(args.checkpoint)
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state.checkpoint_path = state.checkpoint_path or args.checkpoint
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if not state.pending_confirmation:
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raise SystemExit("当前 checkpoint 没有待确认事项；新流程请使用 resume 重试，或 rollback 显式回滚。")
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state = agent.confirm_pending(state, approved=args.decision == "approve", operator_note=args.note)
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print(agent.render_report(state))
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print_pause_payload(agent, state)
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return
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if args.command == "rollback":
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logger.info("开始 rollback checkpoint=%s ip=%s stop_first=%s note_len=%s", args.checkpoint, args.ip, args.stop_first, len(args.note))
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state = load_agent_state(args.checkpoint)
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state.checkpoint_path = state.checkpoint_path or args.checkpoint
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ip = args.ip or _find_current_failed_ip(state)
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if not ip:
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raise SystemExit("未找到当前失败 IP，请传入 --ip。")
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state = agent.rollback_ip(state, ip, stop_first=args.stop_first, operator_note=args.note)
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print(agent.render_report(state))
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print_pause_payload(agent, state)
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return
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logger.info("开始 run-deploy strategy=%s checkpoint=%s target_ips=%s", args.strategy, args.checkpoint, args.target_ip)
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state = agent.create_state(
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params=params,
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execution_strategy=args.strategy,
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checkpoint_path=args.checkpoint,
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target_ips=args.target_ip,
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)
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result = run_graph_once(agent, state, flow="deploy")
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print_graph_result(agent, result)
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def _find_current_failed_ip(state) -> str:
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"""从 checkpoint state 中找一个适合显式回滚的失败 IP。"""
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context_ip = str((state.review_context or {}).get("ip", ""))
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if context_ip and context_ip in state.ip_states:
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return context_ip
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for ip, ip_state in state.ip_states.items():
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if ip_state.get("status") == "FAILED":
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return ip
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return ""
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if __name__ == "__main__":
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main()
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