tzt
51f1de2154
- T-G1 embedder.py：embed() 调 llama-server /v1/embeddings -> 对称 per-vector int8 量化（scale=127/max|v|，1024 维余弦扰动 ~1e-4 << 1e-2 验收）； 超时/连接/非200/空形状 -> EmbedderDown（D-G4 fail-closed）； /v1/embeddings OpenAI 兼容透传（双路由组：/sense 前缀 + /v1 无前缀， 不可用 503）；build_sense_routers 返回列表 - T-G2 observer.py：Observation + 批量缓冲 100ms 刷盘（asyncio.Queue + to_thread，D-P10）；三消费方共用；升级阶梯回写 outcome/executed_tier； D-G6 无 query 原文列（int8 BLOB + 特征 JSON） - 测试 +11（量化误差/范围/fail-closed×3/透传/单例/BLOB 持久化/批量刷盘/ 回写/无原文列），全量 381->386->381? 校正：386 passed - 待真机项：llama-server embedder 端点冒烟（需配置 embedder.base_url）
123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
"""观察埋点（T-G2）：批量缓冲 100ms 刷盘（to_thread），消费方统一入口。
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设计：
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- Observer 单例持后台事件循环队列；log() 仅入队（不阻塞调用方）；
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刷盘协程每 100ms 把缓冲批量交给 store（to_thread）。
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- D-G6：不落 query 原文——Observation 只含哈希可关联的 request_id + 特征 JSON
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+ int8 embedding BLOB。
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- 三消费方（pipeline/proxy/client）共用本模块；埋点 SDK = Observation dataclass
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+ log()（§7）。
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"""
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from __future__ import annotations
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import asyncio
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import threading
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import time
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from gateway.sense.store import SenseStore, json_dumps
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FLUSH_INTERVAL_S = 0.1
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@dataclass
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class Observation:
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"""一条分级观察（§4 tier_observations 行的内存形态）。"""
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request_id: str
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consumer: str # pipeline | proxy | client
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decided_tier: str # T1|T2|T3（collect/shadow 期照算）
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executed_tier: str # 实际执行的档位（现行为）
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probs: Dict[str, float] = field(default_factory=dict)
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policy_version: str = ""
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features: Dict[str, Any] = field(default_factory=dict)
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embedding: Optional[bytes] = None # int8 BLOB
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bucket: str = "default"
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domain: str = ""
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outcome: str = "" # ok|verified|escalated|failed|user_retry|timeout
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true_tier: str = ""
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human_override: str = ""
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ts: float = field(default_factory=time.time)
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def to_row(self) -> Dict[str, Any]:
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return {
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"ts": self.ts, "request_id": self.request_id, "consumer": self.consumer,
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"bucket": self.bucket, "domain": self.domain,
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"decided_tier": self.decided_tier, "executed_tier": self.executed_tier,
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"probs": json_dumps(self.probs), "policy_version": self.policy_version,
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"features": json_dumps(self.features), "embedding": self.embedding,
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"outcome": self.outcome, "true_tier": self.true_tier,
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"human_override": self.human_override,
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}
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class Observer:
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"""观察写入器：队列 + 100ms 批量刷盘（异步启动一次，随网关生命周期）。"""
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def __init__(self, store: SenseStore, flush_interval: float = FLUSH_INTERVAL_S):
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self.store = store
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self.flush_interval = flush_interval
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self._queue: "asyncio.Queue[Dict[str, Any]]" = asyncio.Queue()
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self._task: Optional[asyncio.Task] = None
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def log(self, obs: Observation) -> None:
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"""同步入口（消费方在事件循环内调用，非阻塞）。"""
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self._queue.put_nowait(obs.to_row())
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async def start(self) -> None:
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if self._task is None or self._task.done():
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self._task = asyncio.create_task(self._flush_loop())
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async def stop(self) -> None:
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if self._task is not None:
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self._task.cancel()
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try:
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await self._task
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except (asyncio.CancelledError, Exception):
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pass
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self._task = None
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async def _flush_loop(self) -> None:
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while True:
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await asyncio.sleep(self.flush_interval)
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await self.flush_once()
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async def flush_once(self) -> int:
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"""把当前缓冲批量交给 store（to_thread，D-P10）。"""
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rows: List[Dict[str, Any]] = []
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while not self._queue.empty():
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try:
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rows.append(self._queue.get_nowait())
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except asyncio.QueueEmpty:
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break
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if not rows:
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return 0
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await asyncio.to_thread(self._write_rows, rows)
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return len(rows)
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def _write_rows(self, rows: List[Dict[str, Any]]) -> None:
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for row in rows:
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self.store.insert_observation(row)
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_observer: Optional[Observer] = None
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_observer_loop: Optional[asyncio.AbstractEventLoop] = None
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def get_observer(store: Optional[SenseStore] = None) -> Observer:
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"""进程内单例（绑定创建时的事件循环——uvicorn 单循环，D-P9）。"""
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global _observer, _observer_loop
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if _observer is None:
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try:
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_observer_loop = asyncio.get_running_loop()
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except RuntimeError:
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_observer_loop = None
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_observer = Observer(store or SenseStore())
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return _observer
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def reset_observer() -> None:
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"""测试用。"""
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global _observer
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_observer = None
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