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ThearchyHelios
dcba9a63e0
思考流上线时漏了把它登记进 ephemeral 集合，导致每个流式增量都 落库——一条思考链在执行轨迹里被拆成几十条 llm.thinking 事件。 拆成两种事件：llm.thinking.delta 是流式增量，只推给画布做实时 滚动、不落库（与 llm.token 同类）；llm.thinking 是调用收尾时落的 单条汇总，携带完整思考内容（截断 6000 字符，完整版在节点输出 工件里）。顺带修好刷新回放：此前回放重现不了思考（增量被隐藏、 汇总不存在），现在一条汇总事件直接恢复全文，时间线上也可展开查看。 验证：确定性假思考模型过完整管道（3 条增量实时推、汇总恰一条且 内容完整）；真模型跑模板⑤，轨迹 29 条降到 18 条，llm.thinking 恰好 1 条。adaptive thinking 对简单任务自主跳过思考时两类事件 都不产生，已从工件核实是模型行为而非管道丢失。
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
from __future__ import annotations
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import time
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import uuid
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from enum import StrEnum
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from typing import Any
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from pydantic import BaseModel, Field
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class EventType(StrEnum):
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"""前端与后端之间的实时协议。画布高亮、时间线、token 流全部基于这套事件。"""
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# run 级
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RUN_STARTED = "run.started"
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RUN_FINISHED = "run.finished"
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RUN_FAILED = "run.failed"
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RUN_CANCELLED = "run.cancelled"
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RUN_INTERRUPTED = "run.interrupted" # 等待人工介入
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RUN_RESUMED = "run.resumed"
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# 节点级
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NODE_STARTED = "node.started"
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NODE_FINISHED = "node.finished"
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NODE_FAILED = "node.failed"
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NODE_SKIPPED = "node.skipped"
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EDGE_TAKEN = "edge.taken" # 条件分支实际走了哪条边
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# 模型级
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LLM_START = "llm.start"
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LLM_TOKEN = "llm.token"
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# 思考的流式增量：只推给画布做实时显示，不落库
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LLM_THINKING_DELTA = "llm.thinking.delta"
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# 一次完整思考：调用结束时落一条，这才是进轨迹的事件
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LLM_THINKING = "llm.thinking"
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LLM_END = "llm.end"
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# 工具 / 沙箱
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TOOL_START = "tool.start"
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TOOL_END = "tool.end"
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TOOL_ERROR = "tool.error"
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SANDBOX_START = "sandbox.start"
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SANDBOX_END = "sandbox.end"
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# 多 agent 协作中的单个 agent 步骤
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AGENT_STEP_START = "agent.step.start"
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AGENT_STEP_END = "agent.step.end"
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# 人工介入
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HUMAN_REQUESTED = "human.requested"
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HUMAN_RESOLVED = "human.resolved"
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# 出具与口径治理
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ISSUANCE = "issuance" # 出具契约的判定结果（档位 / 缺数据 / 未回指数字）
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CALIBER_UPGRADE = "caliber.upgrade" # 钉住的方法卡有新版本，按声明的策略处置
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# 其他
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LOG = "log"
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USAGE = "usage" # token / 成本累计
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class RunEventModel(BaseModel):
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id: str = Field(default_factory=lambda: uuid.uuid4().hex)
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run_id: str
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seq: int = 0
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type: EventType
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node_id: str | None = None
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ts: float = Field(default_factory=time.time)
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data: dict[str, Any] = Field(default_factory=dict)
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def to_wire(self) -> dict[str, Any]:
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return self.model_dump(mode="json")
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