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。
267 lines
11 KiB
Python
267 lines
11 KiB
Python
"""PAM 部署 Agent 的 action 级 LangGraph 运行器。"""
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from __future__ import annotations
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Literal
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from uuid import uuid4
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from .agent import PamDeployAgent
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from .logging_utils import json_for_log
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from .models import AgentState
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GraphFlow = Literal["global", "deploy"]
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logger = logging.getLogger(__name__)
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@dataclass(slots=True)
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class LangGraphRunResult:
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"""一次 LangGraph 执行或恢复后的结果摘要。"""
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state: AgentState | None = None
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report: str = ""
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confirmation: dict[str, Any] = field(default_factory=dict)
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interrupted: bool = False
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chunks: list[dict[str, Any]] = field(default_factory=list)
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class LangGraphDeploymentRuntime:
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"""用 LangGraph 节点调度部署 action。"""
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def __init__(
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self,
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*,
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agent: PamDeployAgent,
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thread_id: str | None = None,
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flow: GraphFlow = "deploy",
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) -> None:
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"""初始化图实例和会话线程 ID。"""
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self.agent = agent
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self.thread_id = thread_id or str(uuid4())
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self.flow = flow
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self._waiting_confirmation = False
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self._graph = build_deployment_graph(agent=self.agent, flow=self.flow)
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logger.info(
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"LangGraph runtime 初始化 thread_id=%s flow=%s agent=%s",
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self.thread_id,
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self.flow,
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type(self.agent).__name__,
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)
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@property
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def waiting_confirmation(self) -> bool:
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"""返回当前 LangGraph 会话是否停在旧版 interrupt 确认点。"""
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return self._waiting_confirmation
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def start(self, state: AgentState) -> LangGraphRunResult:
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"""从给定 AgentState 开始执行，直到结束或业务状态暂停。"""
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self._waiting_confirmation = False
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logger.info(
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"LangGraph start run_id=%s thread_id=%s flow=%s paused=%s pending=%s",
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state.run_id,
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self.thread_id,
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self.flow,
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state.paused,
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state.pending_confirmation,
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)
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return self._consume(self._graph.stream({"agent_state": state}, self._config()))
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def resume(self, *, approved: bool, note: str = "") -> LangGraphRunResult:
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"""兼容旧版 LangGraph interrupt 确认恢复；新流程通常不使用。"""
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try:
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from langgraph.types import Command
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except ImportError as exc: # pragma: no cover - 依赖缺失时由调用方降级
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raise RuntimeError("未安装 langgraph，无法恢复 interrupt。") from exc
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decision = {"approved": approved, "note": note}
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logger.info("LangGraph resume thread_id=%s decision=%s note_len=%s", self.thread_id, approved, len(note))
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return self._consume(self._graph.stream(Command(resume=decision), self._config()))
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def _config(self) -> dict[str, Any]:
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"""生成 LangGraph checkpointer 使用的线程配置。"""
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return {"configurable": {"thread_id": self.thread_id}}
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def _consume(self, chunks: Any) -> LangGraphRunResult:
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"""消费 LangGraph stream 输出，提取状态、报告和旧版 interrupt 请求。"""
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result = LangGraphRunResult()
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for chunk in chunks:
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result.chunks.append(chunk)
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logger.info("LangGraph chunk=%s", json_for_log(chunk, max_text_len=1600))
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if "__interrupt__" in chunk:
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result.interrupted = True
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result.confirmation = _extract_interrupt_value(chunk["__interrupt__"])
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logger.info("LangGraph interrupt thread_id=%s confirmation=%s", self.thread_id, json_for_log(result.confirmation))
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continue
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for value in chunk.values():
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if not isinstance(value, dict):
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continue
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if isinstance(value.get("agent_state"), AgentState):
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result.state = value["agent_state"]
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if isinstance(value.get("report"), str):
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result.report = value["report"]
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self._waiting_confirmation = result.interrupted
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logger.info(
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"LangGraph consume 完成 thread_id=%s interrupted=%s waiting=%s state_run_id=%s report_len=%s",
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self.thread_id,
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result.interrupted,
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self._waiting_confirmation,
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result.state.run_id if result.state else "",
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len(result.report),
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)
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return result
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def build_deployment_graph(*, agent: PamDeployAgent, flow: GraphFlow = "deploy"):
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"""构建 action 级别的 LangGraph 部署图。"""
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logger.info("开始构建 LangGraph 部署图 flow=%s", flow)
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try:
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from langgraph.checkpoint.memory import InMemorySaver
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from langgraph.graph import END, START, StateGraph
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except ImportError as exc: # pragma: no cover - 依赖缺失时由调用方降级
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raise RuntimeError("未安装 langgraph，无法启用部署图。") from exc
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def entry_node(state: dict[str, Any]) -> dict[str, Any]:
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"""保留入口节点，便于统一路由已有 state 或恢复 state。"""
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agent_state = state["agent_state"]
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logger.info("LangGraph entry_node run_id=%s pending=%s paused=%s", agent_state.run_id, agent_state.pending_confirmation, agent_state.paused)
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return {"agent_state": state["agent_state"]}
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def global_action_node(state: dict[str, Any]) -> dict[str, Any]:
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"""执行一个全局 action。"""
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agent_state = state["agent_state"]
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action = agent.next_global_action(agent_state)
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if action:
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logger.info("LangGraph global_action_node run_id=%s action=%s", agent_state.run_id, action)
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agent.run_global_action(agent_state, action)
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return {"agent_state": agent_state}
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def prepare_ip_node(state: dict[str, Any]) -> dict[str, Any]:
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"""选择下一个 IP action，并写入图状态。"""
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agent_state = state["agent_state"]
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work = agent.next_ip_action(agent_state)
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if work is None:
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logger.info("LangGraph prepare_ip_node 无待执行 IP action run_id=%s", agent_state.run_id)
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return {"agent_state": agent_state, "current_ip": "", "current_ip_action": ""}
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ip, action = work
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logger.info("LangGraph prepare_ip_node run_id=%s ip=%s action=%s", agent_state.run_id, ip, action)
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return {"agent_state": agent_state, "current_ip": ip, "current_ip_action": action}
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def ip_action_node(state: dict[str, Any]) -> dict[str, Any]:
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"""执行一个单 IP action。"""
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agent_state = state["agent_state"]
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ip = str(state.get("current_ip", ""))
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action = str(state.get("current_ip_action", ""))
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if ip and action:
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logger.info("LangGraph ip_action_node run_id=%s ip=%s action=%s", agent_state.run_id, ip, action)
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agent.run_ip_action(agent_state, ip, action)
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return {"agent_state": agent_state, "current_ip": "", "current_ip_action": ""}
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def report_node(state: dict[str, Any]) -> dict[str, Any]:
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"""渲染当前状态报告。"""
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agent_state = state["agent_state"]
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logger.info("LangGraph report_node run_id=%s pending=%s paused=%s", agent_state.run_id, agent_state.pending_confirmation, agent_state.paused)
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return {
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"agent_state": state["agent_state"],
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"report": agent.render_report(state["agent_state"]),
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}
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def route_entry(state: dict[str, Any]) -> str:
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"""从入口决定进入全局、IP 或报告节点。"""
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agent_state = state["agent_state"]
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if agent_state.pending_confirmation:
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logger.info("LangGraph route_entry -> report legacy_pending run_id=%s", agent_state.run_id)
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return "report"
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if agent.next_global_action(agent_state):
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logger.info("LangGraph route_entry -> global_action run_id=%s", agent_state.run_id)
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return "global_action"
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if flow == "global":
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logger.info("LangGraph route_entry -> report run_id=%s", agent_state.run_id)
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return "report"
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logger.info("LangGraph route_entry -> prepare_ip run_id=%s", agent_state.run_id)
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return "prepare_ip"
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def route_after_global(state: dict[str, Any]) -> str:
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"""全局 action 后继续全局循环或进入 IP 阶段。"""
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agent_state = state["agent_state"]
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if agent.next_global_action(agent_state):
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logger.info("LangGraph route_after_global -> global_action run_id=%s", agent_state.run_id)
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return "global_action"
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if flow == "global":
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logger.info("LangGraph route_after_global -> report run_id=%s", agent_state.run_id)
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return "report"
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logger.info("LangGraph route_after_global -> prepare_ip run_id=%s", agent_state.run_id)
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return "prepare_ip"
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def route_after_prepare_ip(state: dict[str, Any]) -> str:
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"""IP 准备节点后进入单 IP action 或报告。"""
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agent_state = state["agent_state"]
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if agent_state.pending_confirmation:
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logger.info("LangGraph route_after_prepare_ip -> report legacy_pending run_id=%s", agent_state.run_id)
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return "report"
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if state.get("current_ip_action"):
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logger.info("LangGraph route_after_prepare_ip -> ip_action run_id=%s ip=%s action=%s", agent_state.run_id, state.get("current_ip"), state.get("current_ip_action"))
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return "ip_action"
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logger.info("LangGraph route_after_prepare_ip -> report run_id=%s", agent_state.run_id)
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return "report"
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graph = StateGraph(dict)
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graph.add_node("entry", entry_node)
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graph.add_node("global_action", global_action_node)
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graph.add_node("prepare_ip", prepare_ip_node)
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graph.add_node("ip_action", ip_action_node)
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graph.add_node("report", report_node)
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graph.add_edge(START, "entry")
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graph.add_conditional_edges(
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"entry",
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route_entry,
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{
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"global_action": "global_action",
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"prepare_ip": "prepare_ip",
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"report": "report",
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},
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)
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graph.add_conditional_edges(
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"global_action",
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route_after_global,
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{
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"global_action": "global_action",
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"prepare_ip": "prepare_ip",
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"report": "report",
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},
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)
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graph.add_conditional_edges(
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"prepare_ip",
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route_after_prepare_ip,
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{"ip_action": "ip_action", "report": "report"},
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)
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graph.add_edge("ip_action", "prepare_ip")
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graph.add_edge("report", END)
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compiled = graph.compile(checkpointer=InMemorySaver())
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logger.info("LangGraph 部署图构建完成 flow=%s", flow)
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return compiled
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def _extract_interrupt_value(interrupts: Any) -> dict[str, Any]:
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"""从 LangGraph interrupt 对象中提取确认请求字典。"""
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if not interrupts:
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return {}
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first = interrupts[0]
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value = getattr(first, "value", first)
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return value if isinstance(value, dict) else {"value": value}
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def _parse_confirmation_decision(value: Any) -> tuple[bool, str]:
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"""把 interrupt resume 值解析为 approved/note。"""
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if isinstance(value, dict):
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return bool(value.get("approved", False)), str(value.get("note", ""))
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if isinstance(value, bool):
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return value, ""
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if isinstance(value, str):
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normalized = value.strip().lower()
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return normalized in ("approve", "approved", "yes", "y", "true"), value
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return False, str(value)
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