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92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
"""事件模型（对应设计文档中的 Event / Trajectory 概念）。
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Flux 把 Agent 的一切经历都抽象成"事件"，按时间顺序追加到轨迹（trajectory）里。
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本文件定义事件类型、事件对象，以及"事件紧急度"的判定逻辑——这是实验 6-2 里
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"批量处理 vs 立即打断"两种处理机制的分类依据。
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"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass, field
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from typing import Optional
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class EventType:
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"""事件类型常量（对应设计文档第 2 节 Inputs / Interrupts / Thinking / Actions）。"""
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USER_INPUT = "user.input" # 用户输入（非紧急，走"排队处理"）
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USER_INTERRUPT = "user.interrupt" # 用户打断（紧急，走"取消式处理"）
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AGENT_OUTPUT = "agent.output" # Agent 面向用户的最终回复
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AGENT_TOOL_CALL = "agent.tool_call" # Agent 发起的工具调用（Action）
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TOOL_RESULT = "tool.result" # 工具返回结果（同步工具 / 异步占位符）
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ASYNC_RESULT = "async.result" # 异步工具真正完成后注入的新事件
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SYSTEM_NOTE = "system.note" # 框架注入的系统提示（如取消回执）
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class Urgency:
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"""事件紧急度：决定采用哪种事件处理机制。"""
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INTERRUPT = "interrupt" # 取消式处理：立刻打断当前执行并取消异步工具
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IMMEDIATE = "immediate" # 立即处理：不打断后台异步任务，但马上回应（如用户提问）
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DEFERRED = "deferred" # 排队处理：累积到 pending 队列，任务完成时批量追加
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# 打断类关键词：命中即视为紧急打断
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_INTERRUPT_KEYWORDS = ["取消", "停止", "中止", "打住", "别做了", "stop", "cancel", "abort"]
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# 疑问类信号：命中即视为需要"立即回应"（而不是排队）
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_QUESTION_MARKS = ("?", "？")
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_QUESTION_KEYWORDS = ["几点", "多少", "怎么", "如何", "为什么", "是不是", "有没有",
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"吗", "呢", "what", "when", "how", "why", "which"]
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def classify_urgency(text: str) -> str:
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"""根据用户消息内容判定紧急度。
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规则（简单、可解释，便于书中讲清楚）：
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1. 含打断关键词（取消/停止/stop...） -> INTERRUPT（紧急，取消式处理）
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2. 是一个提问（带问号或疑问词） -> IMMEDIATE（立即回应，但不打断后台任务）
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3. 其它（补充性指令，如"用日语回复"）-> DEFERRED（排队，批量处理）
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"""
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low = text.lower()
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if any(kw in text or kw in low for kw in _INTERRUPT_KEYWORDS):
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return Urgency.INTERRUPT
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if text.strip().endswith(_QUESTION_MARKS) or any(kw in text or kw in low for kw in _QUESTION_KEYWORDS):
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return Urgency.IMMEDIATE
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return Urgency.DEFERRED
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@dataclass
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class Event:
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"""一条轨迹事件。
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message 字段保存"可直接喂给 LLM 的 OpenAI 消息字典"（保证上下文的高保真回放）；
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没有 message 的事件（若有）只用于日志。
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"""
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type: str
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message: Optional[dict] = None # OpenAI chat 格式消息，供构建 LLM 上下文
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label: str = "" # 人类可读的日志标签
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task_id: Optional[str] = None # 关联的异步任务 ID（若有）
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urgency: Optional[str] = None # 仅用户输入事件会带
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ts: float = field(default_factory=time.time)
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def to_dict(self) -> dict:
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"""序列化为纯 JSON 可写的字典（用于状态检查点持久化）。"""
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return {
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"type": self.type, "message": self.message, "label": self.label,
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"task_id": self.task_id, "urgency": self.urgency, "ts": self.ts,
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}
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@classmethod
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def from_dict(cls, d: dict) -> "Event":
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"""从检查点字典还原事件对象。"""
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raw_ts = d.get("ts")
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return cls(
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type=d["type"], message=d.get("message"),
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label=d.get("label") or "",
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task_id=d.get("task_id"), urgency=d.get("urgency"),
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ts=raw_ts if raw_ts is not None else time.time(),
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)
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