feat(engine): #873 dual-Claw event contract + SseAdapter/BufferAdapter + chat.py SSE delegate + im_llm EngineCore thin-shell (gray-gated)
- app/core/engine_event.py (#880): EngineEvent dataclass + make_* factories, single construction entry, byte-compatible wire with legacy ChatPage.tsx - app/core/claw_adapters.py (#884): SseAdapter (hot frames zero-metadata, terminal keeps trace_id) + BufferAdapter (assemble() preserves __media__) - app/api/chat.py (#881): /messages/stream now delegates to SseAdapter; wire bytes unchanged (46B delta / done+message_id) - app/api/im_llm.py (#883): IM_USE_ENGINE_CORE gray gate; _call_engine_core reuses EngineCore via BufferAdapter (default OFF, preserves media fast-path) - app/api/chat_service.py: capture 3 container-ahead fixes (stray import asyncio removed, logger.info format, LiteLLM master key)
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"""双 Claw 适配器（#873 · P0）。
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单引擎原则:
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能力只在 EngineCore(`_send_message_core`) 实现一次；
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Adapter 只做「引擎事件 → 通道」的搬运，不含任何业务判断。
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┌──────────────┐ on_event(EngineEvent) ┌──────────────┐
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│ EngineCore │ ────────────────────────► │ ClawAdapter │
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└──────────────┘ └──────┬───────┘
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│
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┌───────────────────────┴────────────────────────┐
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▼ ▼
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SseAdapter (Web Claw) BufferAdapter (IM Bot Claw)
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逐帧 `data: {...}\\n\\n` 缓冲装配 → 整条 str | 媒体 dict
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历史教训（#866）:
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IM 侧曾出现「第二份实现」，一次全量部署就把它冲掉、能力静默回退。
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因此两个 Claw 的差异必须、且只能收敛在本文件的两个 Adapter 里。
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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from typing import Any, Dict, List, Optional
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from app.core.engine_event import (
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CLAW_IM,
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CLAW_WEB,
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DELTA,
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DONE,
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ERROR,
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REASONING,
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STEP,
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Diagnosis,
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EngineEvent,
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from_wire,
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make_error,
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)
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logger = logging.getLogger(__name__)
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__all__ = ["BaseClawAdapter", "SseAdapter", "BufferAdapter"]
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# 单条回复的软上限：防止异常循环把内存/微信消息撑爆
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_MAX_ASSEMBLED_CHARS = 20000
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_MEDIA_RE = re.compile(r'<(img|video)[^>]*\ssrc="([^"]+)"', re.IGNORECASE)
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class BaseClawAdapter:
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"""Adapter 基类。实例可直接当作 `on_event` 回调传给 EngineCore。"""
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claw: str = ""
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def __init__(self, *, trace_id: str = "", tenant_id: str = ""):
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self.trace_id = trace_id or ""
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self.tenant_id = tenant_id or ""
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self._seq = 0
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self.counts: Dict[str, int] = {}
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# -- 供 EngineCore 直接 await adapter(event) -----------------------
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async def __call__(self, event: Any) -> None:
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await self.emit(event)
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async def emit(self, event: Any) -> None:
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ev = self._normalize(event)
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if ev is None:
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return
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self.counts[ev.type] = self.counts.get(ev.type, 0) + 1
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try:
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await self._handle(ev)
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except Exception as exc: # Adapter 永不把异常反噬引擎
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logger.warning("[claw:%s] emit failed: %s", self.claw, exc, exc_info=True)
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# -- 子类实现 -------------------------------------------------------
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async def _handle(self, ev: EngineEvent) -> None:
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raise NotImplementedError
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# -- 归一化 ---------------------------------------------------------
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def _normalize(self, event: Any) -> Optional[EngineEvent]:
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ev = from_wire(event)
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if ev is None:
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logger.warning("[claw:%s] drop malformed event: %r", self.claw, event)
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return None
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self._seq += 1
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if not ev.seq:
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ev.seq = self._seq
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if not ev.trace_id:
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ev.trace_id = self.trace_id
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if not ev.tenant_id:
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ev.tenant_id = self.tenant_id
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if not ev.claw:
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ev.claw = self.claw
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return ev
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# =====================================================================
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# Web Claw —— SSE 逐帧
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# =====================================================================
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class SseAdapter(BaseClawAdapter):
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"""把引擎事件序列化为 SSE 帧并入队，由路由的 event_gen 逐帧 yield。
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行为与改造前的 chat.py 内联实现逐字节一致：
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`data: ` + json.dumps(wire, ensure_ascii=False) + `\\n\\n`
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"""
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claw = CLAW_WEB
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_SENTINEL = {"type": "__end__"}
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# 仅用于内部可观测(#876)，默认不上线 —— 见 _thin()
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_META_KEYS = ("seq", "trace_id", "tenant_id", "claw")
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def __init__(self, queue: "Optional[asyncio.Queue]" = None, *, wire_meta: bool = False, **kw):
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super().__init__(**kw)
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self.queue: asyncio.Queue = queue if queue is not None else asyncio.Queue()
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self.wire_meta = wire_meta
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self._closed = False
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def _thin(self, ev: EngineEvent, wire: Dict[str, Any]) -> Dict[str, Any]:
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"""裁剪线上元数据。
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why: delta 是最热的帧，一次长回复有上千个。实测带全量元数据会让
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单帧从 46B 膨胀到 117B(2.5x)，2000 帧即多传 ~140KB 纯噪音，
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而前端根本不消费这些键。故默认剥离，只在终帧留 trace_id ——
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单帧成本可忽略，却是线上排障唯一的抓手。
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"""
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if self.wire_meta:
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return wire
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for k in self._META_KEYS:
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wire.pop(k, None)
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if ev.is_terminal() and ev.trace_id:
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wire["trace_id"] = ev.trace_id
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return wire
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async def _handle(self, ev: EngineEvent) -> None:
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await self.queue.put(self._thin(ev, ev.to_wire()))
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async def emit_error(self, message: str, *, diagnosis: Any = None) -> None:
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await self.emit(make_error(message, diagnosis=diagnosis))
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async def close(self) -> None:
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"""通知 frames() 结束。幂等。"""
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if self._closed:
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return
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self._closed = True
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await self.queue.put(dict(self._SENTINEL))
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@staticmethod
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def frame(wire: Dict[str, Any]) -> str:
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return "data: " + json.dumps(wire, ensure_ascii=False) + "\n\n"
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async def frames(self):
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"""异步生成 SSE 帧文本，直到收到哨兵。"""
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while True:
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wire = await self.queue.get()
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if not isinstance(wire, dict):
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continue
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if wire.get("type") == "__end__":
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break
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yield self.frame(wire)
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# =====================================================================
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# IM Bot Claw —— 缓冲装配
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# =====================================================================
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class BufferAdapter(BaseClawAdapter):
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"""累积引擎事件，最后一次性装配为可直接推送给 IM 的回复。
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IM 通道（微信/iLink）没有流式能力，也没有卡片 UI：
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* delta 累积成正文；
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* reasoning 默认丢弃（思维链不该刷屏微信），可用 keep_reasoning 打开；
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* step 里的 error 级 diagnosis 降级成文本尾注，用户至少知道「哪一步炸了」；
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* done 的 content 优先于累积的 delta（引擎已做最终裁剪/落库）。
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"""
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claw = CLAW_IM
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def __init__(self, *, keep_reasoning: bool = False, keep_diagnosis: bool = True, **kw):
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super().__init__(**kw)
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self.keep_reasoning = keep_reasoning
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self.keep_diagnosis = keep_diagnosis
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self._delta_parts: List[str] = []
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self._reasoning_parts: List[str] = []
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self.steps: List[Dict[str, Any]] = []
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self.done: Optional[Dict[str, Any]] = None
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self.error: Optional[str] = None
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self.error_diagnosis: Optional[Diagnosis] = None
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self.used_skills: List[Any] = []
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self.used_mcp: List[Any] = []
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self.generating: bool = False
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async def _handle(self, ev: EngineEvent) -> None:
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if ev.type == DELTA:
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self._delta_parts.append(ev.content)
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elif ev.type == REASONING:
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if self.keep_reasoning:
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self._reasoning_parts.append(ev.content)
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elif ev.type == STEP:
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self._record_step(ev)
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elif ev.type == DONE:
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wire = ev.to_wire()
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self.done = wire
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self.used_skills = wire.get("used_skills") or []
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self.used_mcp = wire.get("used_mcp") or []
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self.generating = bool(wire.get("generating"))
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if wire.get("process_steps"):
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self.steps = list(wire["process_steps"])
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elif ev.type == ERROR:
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self.error = str(ev.extra.get("message") or ev.content or "未知错误")
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self.error_diagnosis = ev.diagnosis
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def _record_step(self, ev: EngineEvent) -> None:
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step = ev.extra.get("step")
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if not isinstance(step, dict):
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return
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idx = ev.extra.get("index")
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if isinstance(idx, int) and 0 <= idx < len(self.steps):
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self.steps[idx] = step
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else:
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self.steps.append(step)
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# -- 装配 -----------------------------------------------------------
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@property
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def text(self) -> str:
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"""当前累积正文（done 优先）。"""
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if self.done and isinstance(self.done.get("content"), str) and self.done["content"]:
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return self.done["content"]
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return "".join(self._delta_parts)
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def assemble(self) -> "str | dict":
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"""产出最终回复：媒体则给结构化 dict，否则给纯文本。
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返回的 dict 契约必须与 im_llm 媒体快路径一致：
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{"__media__": True, "media_type": "image"|"video", "url": "..."}
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—— im_bot_user.py 依赖它做微信内联发图/发视频。
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"""
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body = self.text
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media = self._extract_media(body)
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if media is not None:
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return media
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if not body:
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if self.error:
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return self._render_failure()
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if self.generating:
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return "（内容正在生成中，请稍后在平台「AI 聊天」查看结果）"
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return "（未生成有效回复，请换个说法再试一次）"
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notes = self._diagnosis_notes()
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if notes:
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body = body.rstrip() + "\n\n" + "\n".join(notes)
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if self.error:
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body = body.rstrip() + "\n\n" + self._render_failure()
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if self.generating:
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body = body.rstrip() + "\n\n（媒体正在后台生成，稍后可在平台「AI 聊天」查看）"
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if len(body) > _MAX_ASSEMBLED_CHARS:
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body = body[:_MAX_ASSEMBLED_CHARS] + "\n…（内容过长已截断）"
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return body
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# -- 内部 -----------------------------------------------------------
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@staticmethod
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def _extract_media(body: str) -> Optional[Dict[str, Any]]:
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s = (body or "").strip()
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if not (s.startswith("<img") or s.startswith("<video")):
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return None
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m = _MEDIA_RE.search(s)
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if not m:
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return None
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return {
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"__media__": True,
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"media_type": "image" if m.group(1).lower() == "img" else "video",
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"url": m.group(2),
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}
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def _diagnosis_notes(self) -> List[str]:
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"""把失败步骤的结构化诊断降级成文本尾注（IM 无卡片 UI）。"""
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if not self.keep_diagnosis:
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return []
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notes: List[str] = []
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for step in self.steps:
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if not isinstance(step, dict):
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continue
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diag = Diagnosis.from_dict(step.get("diagnosis"))
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if diag is not None and diag.severity == "error":
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notes.append(diag.to_text())
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# 去重保序
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return list(dict.fromkeys(notes))
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def _render_failure(self) -> str:
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if self.error_diagnosis is not None:
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return self.error_diagnosis.to_text()
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return f"⚠️ 生成中断：{self.error}"
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