feat(sense): T-X3 路由决策缓存——live 模式 embed+线性头去重（采纳 cortiq 决策哈希缓存）
- gateway/sense/decision_cache.py：DecisionCache（sha256(scope+文本) 键、 LRU + TTL 60s、4096 条上限、时钟可注入），对齐 auth._AuthCache 进程内模式 - Grader：仅 live 模式缓存纯决策负载（probs/head_version/vec）； 命中跳过 embed + 线性头预测；tier 仍按当前 conformal 阈值即时重算； 观察落盘（observer.log）不因缓存命中跳过——校准数据完整性不受影响 - collect/shadow 模式不走缓存（校准必须全量）；invalidate() 同步清空缓存 （阈值/工件切换即时生效，TTL 仅兜底陈旧） pytest 455 passed（T-X2 后 447 + 8）
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"""路由决策缓存（T-X3，采纳 cortiq router_client 的决策哈希缓存思路）。
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- key = sha256(scope + 0x00 + 归一文本)；LRU + TTL（默认 60s / 4096 条，
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对齐 auth._AuthCache 的进程内模式，D-P9 单进程前提）。
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- 只缓存**纯决策负载**（如 probs/head_version/vec）；调用方的观察与审计
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落盘职责不因缓存命中而跳过。
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- 时钟可注入（now(monotonic)），供测试确定性。
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"""
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from __future__ import annotations
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import hashlib
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import threading
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import time
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from collections import OrderedDict
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from typing import Any, Callable, Optional
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_DEFAULT_TTL_S = 60.0
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_DEFAULT_MAX = 4096
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class DecisionCache:
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"""进程内决策缓存：LRU + TTL，键为 (scope, 归一文本) 的 sha256。"""
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def __init__(self, maxsize: int = _DEFAULT_MAX, ttl_s: float = _DEFAULT_TTL_S,
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now: Optional[Callable[[], float]] = None):
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self._max = max(1, int(maxsize))
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self._ttl = float(ttl_s)
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self._now = now or time.monotonic
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self._data: "OrderedDict[str, tuple[float, Any]]" = OrderedDict()
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self._lock = threading.Lock()
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self.hits = 0
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self.misses = 0
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@staticmethod
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def key(scope: str, text: str) -> str:
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"""缓存键：scope 隔离消费方/模式，文本归一由调用方完成。"""
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return hashlib.sha256(f"{scope}\x00{text}".encode("utf-8")).hexdigest()
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def get(self, scope: str, text: str) -> Optional[Any]:
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now = self._now()
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k = self.key(scope, text)
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with self._lock:
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item = self._data.get(k)
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if item is None:
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self.misses += 1
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return None
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ts, payload = item
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if now - ts > self._ttl:
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self._data.pop(k, None)
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self.misses += 1
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return None
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self._data.move_to_end(k)
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self.hits += 1
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return payload
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def put(self, scope: str, text: str, payload: Any) -> None:
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k = self.key(scope, text)
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with self._lock:
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self._data[k] = (self._now(), payload)
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self._data.move_to_end(k)
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while len(self._data) > self._max:
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self._data.popitem(last=False)
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def clear(self) -> None:
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"""清空条目（阈值/工件版本切换时由 Grader.invalidate 调用）。"""
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with self._lock:
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self._data.clear()
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def stats(self) -> dict:
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with self._lock:
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return {"entries": len(self._data), "hits": self.hits,
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"misses": self.misses,
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"hit_rate": round(self.hits / (self.hits + self.misses), 4)
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if (self.hits + self.misses) else 0.0}
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+35
-17
@@ -17,6 +17,7 @@ from typing import Any, Dict, List, Optional
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from gateway.sense.calibrate import load_active
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from gateway.sense.classifier import LinearHead
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from gateway.sense.config import SenseConfig
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from gateway.sense.decision_cache import DecisionCache
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from gateway.sense.embedder import embed
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from gateway.sense.errors import EmbedderDown
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from gateway.sense.features import gate
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@@ -53,6 +54,7 @@ class Grader:
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self._head: Optional[LinearHead] = None
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self._head_loaded = False
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self._thresholds: Optional[Dict[str, Any]] = None
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self._dcache = DecisionCache() # T-X3：live 模式决策缓存
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def _load_head(self):
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if not self._head_loaded:
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@@ -66,10 +68,11 @@ class Grader:
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return self._head
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def invalidate(self) -> None:
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"""工件 promote 后调用（重载 active 工件与阈值）。"""
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"""工件 promote 后调用（重载 active 工件与阈值；同步清空决策缓存）。"""
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self._head = None
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self._head_loaded = False
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self._thresholds = None
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self._dcache.clear()
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def _thresholds_cached(self) -> Dict[str, Any]:
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if self._thresholds is None:
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@@ -79,32 +82,47 @@ class Grader:
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async def decide(self, query_or_messages, consumer: str,
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domain: str = "", request_id: str = "",
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executed_tier: str = "") -> TierDecision:
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"""分级决策（§8 时序；全模式写观察）。"""
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"""分级决策（§8 时序；全模式写观察）。
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T-X3：live 模式下对 (consumer, 文本) 的纯决策负载（probs/head_version/vec）
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做 60s LRU 缓存，命中时跳过 embed + 线性头；观察/审计照常落盘。
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collect/shadow 模式不走缓存（校准数据必须全量产出）。
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"""
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ts = time.time()
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rid = request_id or ("rt" + uuid.uuid4().hex[:10])
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text = (query_or_messages if isinstance(query_or_messages, str)
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else "\n".join(str(m.get("content") or "")
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for m in query_or_messages))
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feats = gate(query_or_messages, consumer, self.cfg)
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probs: Dict[str, float] = {}
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head_version = ""
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fallback = False
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vec: Optional[List[int]] = None
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try:
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vec = await embed(feats and (query_or_messages
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if isinstance(query_or_messages, str)
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else "\n".join(
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str(m.get("content") or "")
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for m in query_or_messages)),
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self.cfg.embedder)
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except EmbedderDown:
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fallback = True
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cached = self._dcache.get(consumer, text) \
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if self.cfg.mode == "live" else None
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if cached is not None:
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probs = dict(cached["probs"])
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head_version = str(cached["head_version"])
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vec = cached["vec"]
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else:
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try:
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vec = await embed(text, self.cfg.embedder)
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except EmbedderDown:
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fallback = True
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head = None if fallback else self._load_head()
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if head is None:
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fallback = True
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head = None if fallback else self._load_head()
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if head is None:
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fallback = True
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if not fallback and vec is not None:
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probs = head.predict([float(v) for v in vec])
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head_version = head.version
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if not fallback and vec is not None:
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probs = head.predict([float(v) for v in vec])
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head_version = head.version
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if self.cfg.mode == "live":
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self._dcache.put(consumer, text,
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{"probs": dict(probs),
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"head_version": head_version,
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"vec": vec})
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th = self._thresholds_cached()
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th_version = str(th.get("version") or "")
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