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Operation Ollama-First v5.0 / Phase 11+ 收尾（ADR-032/033 落地） services/learning_pipeline.py 新增 2 個 worker 函數： - process_pending_episodes(batch=50) — 批次處理 pending → can_promote → promote/reject/await 純規則引擎，不跑 LLM（Distiller 純 Hermes 規則） - push_awaiting_reviews_to_telegram(batch=5) — 推 Stage 4 awaiting_review 到 Telegram TELEGRAM_ADMIN_CHAT_ID 未設則跳過（fail-safe） 訊息含 episode_id + weight + quality + 600 字截斷文，附 promotion_review_keyboard 👍/👎 run_scheduler.py 加 3 個 cron + 對應 task wrapper： - 每 5 分鐘 → run_promotion_gate_worker - 每 30 分鐘 → run_awaiting_review_push - 每 4 小時 → run_expire_stale_reviews（24h 無回應 → weight=0.5） 設計安全保證： - RAG_ENABLED=false 時 learning_episodes 為空，3 個 worker 跑空 loop（無害） - 所有 worker 例外完全吞掉，僅 log error，不影響其他排程 - promote 成功才回 stats['promoted']++，DB 失敗計 errors 完整 RAG 自主學習迴圈閉環： LLM 結果 → Distiller → learning_episodes (pending) ↓ 每 5 分鐘 worker PromotionGate 4 階段 ↓ approved → 寫 ai_insights → RAG 可檢索 ↓ awaiting_review → 每 30 分鐘推 Telegram ↓ 24h 無回應 → 每 4h expire → weight=0.5 ↓ 👍 callback → promote → ai_insights ↓ 👎 callback → rejected_human → 永不晉升 仍待 Phase 12+ 完成： - learning_episodes.embedding 寫入路徑（Stage 3 dedup 解鎖） - RAG_ENABLED=true 灰度啟用條件（需 100+ episodes + ANTHROPIC_API_KEY） regression: 70 unit tests 全綠 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
908 lines
39 KiB
Python
908 lines
39 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/learning_pipeline.py
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Operation Ollama-First v5.0 / Phase 11 — 自主學習管線
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兩大核心：
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1. Distiller — LLM/MCP 結果 → learning_episodes（pending）
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2. PromotionGate — learning_episodes → ai_insights 4 階段晉升閘
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+ expire_stale_reviews() 24h 自動降級
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Owen 強調的 v5.0 護欄 #1（ADR-033）：
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反饋按鈕從「選配」升級為「強制晉升門檻」
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Stage 1: quality_score >= 0.7
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Stage 2: 規則引擎幻覺檢測
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Stage 3: 與既有 insight cosine < 0.95（去重）
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Stage 4: weight >= 0.8 必經 Telegram 👍/👎 人工驗收（24h 無回應降級 0.5）
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對應:
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- migrations/028_create_learning_episodes.sql
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- docs/adr/ADR-033 (Promotion Gate)
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"""
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from __future__ import annotations
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import hashlib
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import json
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import logging
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import os
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import re
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple
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logger = logging.getLogger(__name__)
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# ─────────────────────────────────────────────────────────────────────────────
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# 常數（避免 magic number）
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# ─────────────────────────────────────────────────────────────────────────────
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STAGE_1_AUTO_QUALITY = 0.7 # quality_score 下限
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STAGE_3_DEDUP_THRESHOLD = 0.95 # cosine similarity 視為重複
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STAGE_4_HUMAN_REVIEW_WEIGHT = 0.8 # weight >= 此值強制人工驗收
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HUMAN_REVIEW_TIMEOUT_HOURS = 24 # awaiting_review 過期門檻
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EXPIRED_FALLBACK_WEIGHT = 0.5 # 過期降級後的 weight
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DISTILLED_TEXT_MAX_BYTES = 16384 # 與 028 CHECK octet_length<=16384 對齊
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# 蒸餾品質規則
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_MCP_MIN_LEN = 200 # MCP 結果最小長度
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_LLM_FREE_TEXT_MIN = 500 # LLM 自由文本最小長度
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_LLM_KEYWORDS_HINT = ['結論', '建議', '分析', '預測', '趨勢', '威脅', '機會']
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# 幻覺規則（Stage 2）
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_HALLUCINATION_HEDGE_WORDS = ['我猜', '可能是', '也許', '大概是', '應該是', '我猜測']
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# 「具體數字」啟發式：阿拉伯數字 ≥ 1 個
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_NUMBER_PATTERN = re.compile(r'\d')
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# 「自相矛盾」啟發式：簡化判斷 — 同一句裡同一主詞同時被指派不同值（v5.0 不深做語意，rule-only）
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# user_feedback 蒸餾後預設 weight（高權重，必入 Stage 4 人工驗收）
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_USER_FEEDBACK_DEFAULT_WEIGHT = 0.9
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_MANUAL_CURATED_DEFAULT_WEIGHT = 1.0
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# ─────────────────────────────────────────────────────────────────────────────
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# 容器
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# ─────────────────────────────────────────────────────────────────────────────
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@dataclass
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class DistillResult:
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"""Distiller 產出的候選筆（尚未寫入 DB）。caller 拿到後可再調整或直接 enqueue。"""
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episode_type: str # mcp_result / llm_response / user_feedback / manual_curated
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distilled_text: str
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quality_score: float
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weight: float
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source_table: Optional[str] = None
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source_id: Optional[int] = None
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@dataclass
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class GateDecision:
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"""PromotionGate 4 階段決策。"""
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can_promote: bool
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reason: str # approved / awaiting_review / rejected_*
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detail: Optional[str] = None # 失敗時的人類可讀原因
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similar_insight_id: Optional[int] = None # Stage 3 命中的相似 insight id
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# ─────────────────────────────────────────────────────────────────────────────
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# Distiller
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# ─────────────────────────────────────────────────────────────────────────────
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class Distiller:
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"""LLM/MCP 結果蒸餾器。
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純 Hermes 規則引擎（不再呼叫 LLM 避免循環燒錢）。
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輸出 DistillResult，由 caller 決定是否 enqueue。
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"""
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def distill(
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self,
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episode_type: str,
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raw_content: str,
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source_table: Optional[str] = None,
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source_id: Optional[int] = None,
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user_feedback_score: Optional[int] = None,
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) -> Optional[DistillResult]:
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"""主入口。
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Args:
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episode_type: mcp_result / llm_response / user_feedback / manual_curated
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raw_content: 原始文本（MCP 結果 / LLM response / 用戶留言）
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source_table: ai_calls / mcp_calls / None
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source_id: 對應 source_table.id
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user_feedback_score: 1-5（episode_type=user_feedback 時帶入；
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5 → quality 1.0；1 → 0.0 負樣本不晉升）
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Returns:
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DistillResult 或 None（不可蒸餾，例如空字串）。
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"""
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if not raw_content or not raw_content.strip():
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return None
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cleaned = raw_content.strip()
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# 截長度（與 028 CHECK octet_length<=16384 對齊）
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encoded = cleaned.encode('utf-8', errors='replace')
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if len(encoded) > DISTILLED_TEXT_MAX_BYTES:
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cleaned = encoded[:DISTILLED_TEXT_MAX_BYTES].decode('utf-8', errors='ignore')
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et = (episode_type or '').strip()
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if et not in ('mcp_result', 'llm_response', 'user_feedback', 'manual_curated'):
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logger.warning("[Distiller] unknown episode_type=%s", et)
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return None
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# ── 各類型品質規則 ──
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if et == 'manual_curated':
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quality, weight = 1.0, _MANUAL_CURATED_DEFAULT_WEIGHT
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elif et == 'user_feedback':
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score = max(1, min(int(user_feedback_score or 3), 5))
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# 5 → 1.0 高品質；1 → 0.0 負樣本
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quality = round((score - 1) / 4.0, 3)
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weight = _USER_FEEDBACK_DEFAULT_WEIGHT # 用戶直陳的事實，需人工驗收
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elif et == 'mcp_result':
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# MCP：>200 字 + 含 2+ 關鍵字 → 0.8；否則 0.5
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keyword_hits = sum(1 for kw in _LLM_KEYWORDS_HINT if kw in cleaned)
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if len(cleaned) >= _MCP_MIN_LEN and keyword_hits >= 2:
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quality = 0.8
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elif len(cleaned) >= _MCP_MIN_LEN:
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quality = 0.65
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else:
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quality = 0.5
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weight = 0.6 # MCP 事實但非用戶確認，中等權重
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else: # llm_response
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# LLM 結構化 JSON + status='ok' → 0.9；自由文本 >500 字 → 0.6
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quality, weight = self._distill_llm_response(cleaned)
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return DistillResult(
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episode_type=et,
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distilled_text=cleaned,
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quality_score=round(quality, 3),
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weight=round(weight, 3),
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source_table=source_table,
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source_id=source_id,
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)
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@staticmethod
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def _distill_llm_response(text: str) -> Tuple[float, float]:
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"""LLM 回應蒸餾：JSON 結構化 vs 自由文本兩種路徑。"""
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# 路徑 1：嘗試解析為 JSON（結構化 ⇒ 高品質）
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stripped = text.strip()
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if stripped.startswith('{') or stripped.startswith('['):
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try:
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obj = json.loads(stripped)
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# status == 'ok' 或非空 dict/list → 0.9
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status_ok = (
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isinstance(obj, dict) and obj.get('status') == 'ok'
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) or (
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isinstance(obj, list) and len(obj) > 0
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) or (
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isinstance(obj, dict) and len(obj) > 0
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)
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if status_ok:
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return 0.9, 0.7
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return 0.7, 0.6
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except (json.JSONDecodeError, ValueError):
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pass
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# 路徑 2：自由文本 — 長度 + 繁中數字判斷
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if len(text) >= _LLM_FREE_TEXT_MIN:
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# >500 字 + 含具體數字 → 0.65；無數字 → 0.55
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if _NUMBER_PATTERN.search(text):
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return 0.65, 0.55
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return 0.55, 0.5
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# 太短的自由文本 → 0.4 不會過 Stage 1
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return 0.4, 0.5
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# ─────────────────────────────────────────────────────────────────────────────
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# Learning Pipeline 主入口（enqueue + 整合 Distiller）
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# ─────────────────────────────────────────────────────────────────────────────
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class LearningPipeline:
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"""蒸餾 + enqueue 統一入口。
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使用範例:
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from services.learning_pipeline import learning_pipeline
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learning_pipeline.enqueue(
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episode_type='llm_response',
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raw_content=response_text,
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source_table='ai_calls', source_id=ai_call_id,
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)
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"""
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def __init__(self):
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self.distiller = Distiller()
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def enqueue(
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self,
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episode_type: str,
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raw_content: str,
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source_table: Optional[str] = None,
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source_id: Optional[int] = None,
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user_feedback_score: Optional[int] = None,
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) -> Optional[int]:
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"""蒸餾後寫入 learning_episodes（pending 狀態）。
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Returns:
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learning_episodes.id 或 None（蒸餾失敗 / DB 寫入失敗）。
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"""
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result = self.distiller.distill(
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episode_type=episode_type,
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raw_content=raw_content,
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source_table=source_table,
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source_id=source_id,
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user_feedback_score=user_feedback_score,
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)
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if not result:
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return None
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try:
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from sqlalchemy import text as sa_text
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from database.manager import get_session
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session = get_session()
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try:
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row = session.execute(
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sa_text("""
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INSERT INTO learning_episodes (
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episode_type, source_table, source_id,
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distilled_text, quality_score, weight,
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promotion_status
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) VALUES (
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:episode_type, :source_table, :source_id,
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:distilled_text, :quality_score, :weight,
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'pending'
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)
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RETURNING id
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"""),
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{
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'episode_type': result.episode_type,
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'source_table': result.source_table,
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'source_id': result.source_id,
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'distilled_text': result.distilled_text,
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'quality_score': result.quality_score,
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'weight': result.weight,
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},
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).fetchone()
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session.commit()
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return int(row[0]) if row else None
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except Exception:
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session.rollback()
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raise
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finally:
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session.close()
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except Exception as exc:
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logger.warning("[LearningPipeline] enqueue failed: %s", exc)
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return None
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# ─────────────────────────────────────────────────────────────────────────────
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# PromotionGate (v5.0 核心護欄 #1)
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# ─────────────────────────────────────────────────────────────────────────────
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class PromotionGate:
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"""learning_episodes → ai_insights 4 階段晉升閘。
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Owen 強調：高權重必經人工驗收，避免幻覺污染 RAG。
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使用範例:
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gate = PromotionGate()
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decision = gate.can_promote(episode_id)
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if decision.can_promote:
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insight_id = gate.promote(episode_id)
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elif decision.reason == 'awaiting_review':
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# 推 Telegram 等 👍/👎
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else:
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gate.reject(episode_id, decision.detail or decision.reason)
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"""
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# ──────────────────────────────────────────────────────────────────────
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# Stage 1: quality_score 自動門檻
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# ──────────────────────────────────────────────────────────────────────
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def _stage_1_quality(self, episode: Dict[str, Any]) -> Optional[GateDecision]:
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"""quality_score < 0.7 → rejected_quality。"""
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q = float(episode.get('quality_score') or 0)
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if q < STAGE_1_AUTO_QUALITY:
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return GateDecision(
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can_promote=False,
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reason='rejected_quality',
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detail=f'quality_score={q:.3f} < {STAGE_1_AUTO_QUALITY}',
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)
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return None
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# ──────────────────────────────────────────────────────────────────────
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# Stage 2: 幻覺檢測（規則引擎）
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# ──────────────────────────────────────────────────────────────────────
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def _stage_2_hallucination(self, episode: Dict[str, Any]) -> Optional[GateDecision]:
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"""規則：
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R1. 含 hedge words（'我猜' / '可能' / '也許'）+ 無具體數字 → suspect
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R2. 文本中含「A 是 X」又含「A 是 Y」（同主詞矛盾，rule-light） → suspect
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R3. （可擴充）引用 SKU 不在 product_master → suspect
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Returns: rejected_hallucination 或 None（通過）。
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"""
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text = (episode.get('distilled_text') or '').strip()
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if not text:
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return None
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# R1: hedge + 無數字
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hedge_hits = [w for w in _HALLUCINATION_HEDGE_WORDS if w in text]
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if hedge_hits and not _NUMBER_PATTERN.search(text):
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return GateDecision(
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can_promote=False,
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reason='rejected_hallucination',
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detail=f'hedge words {hedge_hits} 但缺具體數字',
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)
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# R2: 簡單矛盾偵測 — 找「X 是 A」與「X 是 B」（A != B）
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contradiction = _detect_simple_contradiction(text)
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if contradiction:
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return GateDecision(
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can_promote=False,
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reason='rejected_hallucination',
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detail=f'自相矛盾偵測: {contradiction}',
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)
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return None
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# ──────────────────────────────────────────────────────────────────────
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# Stage 3: 去重（cosine similarity vs ai_insights）
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# ──────────────────────────────────────────────────────────────────────
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def _stage_3_dedup(self, episode: Dict[str, Any]) -> Optional[GateDecision]:
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"""與既有 ai_insights cosine similarity >= 0.95 → rejected_duplicate。
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若 episode.embedding 為 NULL（蒸餾時尚未 embed）：略過此 stage（warning）。
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"""
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embedding = episode.get('embedding')
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if not embedding:
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logger.debug(
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"[PromotionGate] episode_id=%s embedding 為 NULL，略過 Stage 3 去重",
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episode.get('id'),
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)
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return None
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try:
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from sqlalchemy import text as sa_text
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from database.manager import get_session
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session = get_session()
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try:
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row = session.execute(
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sa_text("""
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SELECT id, content,
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1.0 - (embedding <=> CAST(:vec AS vector)) AS similarity
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FROM ai_insights
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WHERE embedding IS NOT NULL
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AND status IN ('approved','active','executed')
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ORDER BY embedding <=> CAST(:vec AS vector) ASC
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LIMIT 1
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"""),
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{'vec': str(embedding)},
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).fetchone()
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finally:
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session.close()
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if row and float(row.similarity or 0) >= STAGE_3_DEDUP_THRESHOLD:
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return GateDecision(
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can_promote=False,
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reason='rejected_duplicate',
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detail=f'similarity={row.similarity:.4f} >= {STAGE_3_DEDUP_THRESHOLD}',
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similar_insight_id=int(row.id),
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)
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return None
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except Exception as exc:
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logger.warning(
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"[PromotionGate] Stage 3 dedup query failed (episode_id=%s): %s — 視為通過",
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episode.get('id'), exc,
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)
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return None
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# ──────────────────────────────────────────────────────────────────────
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# Stage 4: 高權重強制人工驗收
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# ──────────────────────────────────────────────────────────────────────
|
||
def _stage_4_review(self, episode: Dict[str, Any]) -> GateDecision:
|
||
"""weight >= 0.8 → awaiting_review；否則自動晉升 approved。"""
|
||
weight = float(episode.get('weight') or 0)
|
||
if weight >= STAGE_4_HUMAN_REVIEW_WEIGHT:
|
||
return GateDecision(
|
||
can_promote=False,
|
||
reason='awaiting_review',
|
||
detail=f'weight={weight:.3f} >= {STAGE_4_HUMAN_REVIEW_WEIGHT} 強制人工驗收',
|
||
)
|
||
return GateDecision(can_promote=True, reason='approved')
|
||
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
# 主入口
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
def can_promote(self, episode_id: int) -> GateDecision:
|
||
"""執行 4 階段檢查，回 GateDecision。
|
||
|
||
- approved → 可呼叫 promote()
|
||
- awaiting_review → 不晉升，caller 負責推 Telegram 等 👍/👎
|
||
- rejected_* → 不晉升，caller 應呼叫 reject() 標狀態
|
||
"""
|
||
episode = self._load_episode(episode_id)
|
||
if not episode:
|
||
return GateDecision(
|
||
can_promote=False,
|
||
reason='rejected_quality',
|
||
detail=f'episode_id={episode_id} not found',
|
||
)
|
||
|
||
for stage_fn in (self._stage_1_quality, self._stage_2_hallucination, self._stage_3_dedup):
|
||
verdict = stage_fn(episode)
|
||
if verdict:
|
||
return verdict
|
||
|
||
return self._stage_4_review(episode)
|
||
|
||
def promote(self, episode_id: int) -> Optional[int]:
|
||
"""執行晉升：寫 ai_insights + 更新 learning_episodes.{insight_id, promotion_status}。
|
||
|
||
Returns:
|
||
ai_insights.id 或 None（晉升失敗）。
|
||
|
||
注意：呼叫前 caller 必須先 can_promote() 確認 reason='approved'。
|
||
本函式不重複跑 4 stage（避免 race condition + 雙重檢查浪費 query）。
|
||
"""
|
||
episode = self._load_episode(episode_id)
|
||
if not episode:
|
||
logger.warning("[PromotionGate] promote skipped: episode_id=%s not found", episode_id)
|
||
return None
|
||
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
session = get_session()
|
||
try:
|
||
# 1. 寫入 ai_insights（content + insight_type 從 episode 推斷）
|
||
inferred_type = _infer_insight_type(episode)
|
||
row = session.execute(
|
||
sa_text("""
|
||
INSERT INTO ai_insights (
|
||
insight_type, content, avg_quality, status,
|
||
confidence, created_by
|
||
) VALUES (
|
||
:insight_type, :content, :quality, 'approved',
|
||
:confidence, 'learning_pipeline'
|
||
)
|
||
RETURNING id
|
||
"""),
|
||
{
|
||
'insight_type': inferred_type,
|
||
'content': episode['distilled_text'],
|
||
'quality': float(episode.get('quality_score') or 0.5),
|
||
'confidence': float(episode.get('weight') or 0.5),
|
||
},
|
||
).fetchone()
|
||
insight_id = int(row[0]) if row else None
|
||
|
||
if not insight_id:
|
||
raise RuntimeError('ai_insights INSERT 未回 id')
|
||
|
||
# 2. 更新 learning_episodes（approved + insight_id 回填，CHECK chk_le_approved_consistent 強制一致）
|
||
session.execute(
|
||
sa_text("""
|
||
UPDATE learning_episodes
|
||
SET promotion_status = 'approved',
|
||
insight_id = :insight_id,
|
||
reviewed_at = NOW()
|
||
WHERE id = :id
|
||
"""),
|
||
{'insight_id': insight_id, 'id': episode_id},
|
||
)
|
||
session.commit()
|
||
logger.info(
|
||
"[PromotionGate] episode_id=%s promoted → insight_id=%s",
|
||
episode_id, insight_id,
|
||
)
|
||
return insight_id
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
logger.error("[PromotionGate] promote failed (episode_id=%s): %s", episode_id, exc)
|
||
return None
|
||
|
||
def reject(self, episode_id: int, reason: str, detail: Optional[str] = None) -> bool:
|
||
"""拒絕晉升：標 promotion_status='rejected_*' + rejected_reason。
|
||
|
||
Args:
|
||
reason: rejected_quality / rejected_hallucination / rejected_duplicate / rejected_human
|
||
detail: 補充說明（會與 reason 拼成 rejected_reason 文本）
|
||
"""
|
||
valid_statuses = (
|
||
'rejected_quality',
|
||
'rejected_hallucination',
|
||
'rejected_duplicate',
|
||
'rejected_human',
|
||
)
|
||
if reason not in valid_statuses:
|
||
logger.warning("[PromotionGate] invalid reject reason=%s", reason)
|
||
return False
|
||
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
session = get_session()
|
||
try:
|
||
full_reason = detail or reason
|
||
session.execute(
|
||
sa_text("""
|
||
UPDATE learning_episodes
|
||
SET promotion_status = :status,
|
||
rejected_reason = :rej_reason,
|
||
reviewed_at = NOW()
|
||
WHERE id = :id
|
||
"""),
|
||
{'status': reason, 'rej_reason': full_reason, 'id': episode_id},
|
||
)
|
||
session.commit()
|
||
return True
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"[PromotionGate] reject failed (episode_id=%s, reason=%s): %s",
|
||
episode_id, reason, exc,
|
||
)
|
||
return False
|
||
|
||
def mark_awaiting_review(self, episode_id: int) -> bool:
|
||
"""進入 awaiting_review 狀態（caller 推 Telegram 後呼叫，準備等 👍/👎）。"""
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
session = get_session()
|
||
try:
|
||
session.execute(
|
||
sa_text("""
|
||
UPDATE learning_episodes
|
||
SET promotion_status = 'awaiting_review'
|
||
WHERE id = :id
|
||
AND promotion_status = 'pending'
|
||
"""),
|
||
{'id': episode_id},
|
||
)
|
||
session.commit()
|
||
return True
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"[PromotionGate] mark_awaiting_review failed (episode_id=%s): %s",
|
||
episode_id, exc,
|
||
)
|
||
return False
|
||
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
# 內部
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
@staticmethod
|
||
def _load_episode(episode_id: int) -> Optional[Dict[str, Any]]:
|
||
"""讀取 learning_episodes 單筆（dict 化方便 stage 函式 unit test mock）。"""
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
session = get_session()
|
||
try:
|
||
row = session.execute(
|
||
sa_text("""
|
||
SELECT id, episode_type, distilled_text,
|
||
quality_score, weight, embedding,
|
||
promotion_status
|
||
FROM learning_episodes
|
||
WHERE id = :id
|
||
"""),
|
||
{'id': int(episode_id)},
|
||
).fetchone()
|
||
if not row:
|
||
return None
|
||
return {
|
||
'id': int(row.id),
|
||
'episode_type': row.episode_type,
|
||
'distilled_text': row.distilled_text,
|
||
'quality_score': float(row.quality_score or 0),
|
||
'weight': float(row.weight or 0),
|
||
'embedding': row.embedding,
|
||
'promotion_status': row.promotion_status,
|
||
}
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
logger.warning("[PromotionGate] load_episode failed (id=%s): %s", episode_id, exc)
|
||
return None
|
||
|
||
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
# 24h 自動降級
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
def expire_stale_reviews(hours: int = HUMAN_REVIEW_TIMEOUT_HOURS) -> int:
|
||
"""awaiting_review 超過 N 小時無 👍/👎 → 自動降級 weight=0.5 + status='expired'。
|
||
|
||
呼叫時機：scheduler 每 4 小時跑一次（建議與 ai_calls 90 天保留同排程）。
|
||
|
||
Returns:
|
||
被降級的筆數。
|
||
"""
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
session = get_session()
|
||
try:
|
||
result = session.execute(
|
||
sa_text("""
|
||
UPDATE learning_episodes
|
||
SET promotion_status = 'expired',
|
||
weight = :fallback_weight,
|
||
rejected_reason = COALESCE(rejected_reason, '')
|
||
|| '24h 無人工反饋自動降級',
|
||
reviewed_at = NOW()
|
||
WHERE promotion_status = 'awaiting_review'
|
||
AND created_at < NOW() - (:hours || ' hours')::INTERVAL
|
||
"""),
|
||
{
|
||
'fallback_weight': EXPIRED_FALLBACK_WEIGHT,
|
||
'hours': str(int(hours)),
|
||
},
|
||
)
|
||
session.commit()
|
||
count = result.rowcount or 0
|
||
if count:
|
||
logger.info(
|
||
"[PromotionGate] expire_stale_reviews: %d episodes 降級 weight=%.2f",
|
||
count, EXPIRED_FALLBACK_WEIGHT,
|
||
)
|
||
return count
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
logger.warning("[PromotionGate] expire_stale_reviews failed: %s", exc)
|
||
return 0
|
||
|
||
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
# 工具函式
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
def _detect_simple_contradiction(text: str) -> Optional[str]:
|
||
"""偵測「X 是 A」與「X 是 B」（A != B）的同主詞矛盾。
|
||
|
||
純規則 light 偵測；不深做語意，避免誤殺合理推論。
|
||
Returns: 矛盾描述 或 None。
|
||
"""
|
||
pattern = re.compile(r'([一-龥A-Za-z0-9]{1,8})\s*[是為]\s*([一-龥A-Za-z0-9]{1,12})')
|
||
matches = pattern.findall(text)
|
||
if len(matches) < 2:
|
||
return None
|
||
seen: Dict[str, str] = {}
|
||
for subject, value in matches:
|
||
if subject in seen and seen[subject] != value:
|
||
return f'"{subject}" 同時被指為 "{seen[subject]}" 與 "{value}"'
|
||
seen[subject] = value
|
||
return None
|
||
|
||
|
||
def _infer_insight_type(episode: Dict[str, Any]) -> str:
|
||
"""從 episode 推斷 ai_insights.insight_type。
|
||
|
||
規則：
|
||
- episode_type=user_feedback → 'human_review'
|
||
- episode_type=manual_curated → 'manual_curated'
|
||
- episode_type=mcp_result → 'mcp_grounding'
|
||
- episode_type=llm_response → 'llm_distilled'
|
||
"""
|
||
et = episode.get('episode_type') or ''
|
||
return {
|
||
'user_feedback': 'human_review',
|
||
'manual_curated': 'manual_curated',
|
||
'mcp_result': 'mcp_grounding',
|
||
'llm_response': 'llm_distilled',
|
||
}.get(et, 'llm_distilled')
|
||
|
||
|
||
def hash_human_approver(username: str) -> str:
|
||
"""Telegram username SHA1[:8]（與 028 schema 註解一致 — 避免 PII 落地）。"""
|
||
if not username:
|
||
return ''
|
||
return hashlib.sha1(username.encode('utf-8')).hexdigest()[:8]
|
||
|
||
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
# Worker 函數（給 run_scheduler.py 排程用）— Phase 11+ 收尾
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
||
|
||
# 預設批次大小：每次處理 N 筆 pending，避免 worker 一次跑太久阻塞排程
|
||
PENDING_BATCH_SIZE = int(os.environ.get('PROMOTION_PENDING_BATCH_SIZE', '50'))
|
||
AWAITING_REVIEW_PUSH_BATCH = int(os.environ.get('AWAITING_REVIEW_PUSH_BATCH', '5'))
|
||
|
||
|
||
def process_pending_episodes(batch_size: int = PENDING_BATCH_SIZE) -> Dict[str, int]:
|
||
"""批次處理 learning_episodes pending → can_promote → promote/reject/await_review。
|
||
|
||
給 run_scheduler.py 每 5 分鐘跑一次。
|
||
依 ADR-032 PromotionGate 4 階段，每筆走完整檢查。
|
||
|
||
Returns:
|
||
{'pending_seen': N, 'promoted': X, 'rejected': Y, 'awaiting': Z, 'errors': E}
|
||
"""
|
||
stats = {'pending_seen': 0, 'promoted': 0, 'rejected': 0, 'awaiting': 0, 'errors': 0}
|
||
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
except Exception as exc:
|
||
logger.warning('[PromotionWorker] DB import failed: %s', exc)
|
||
return stats
|
||
|
||
session = get_session()
|
||
try:
|
||
rows = session.execute(
|
||
sa_text("""
|
||
SELECT id FROM learning_episodes
|
||
WHERE promotion_status = 'pending'
|
||
ORDER BY created_at ASC
|
||
LIMIT :n
|
||
"""),
|
||
{'n': batch_size},
|
||
).fetchall()
|
||
episode_ids = [int(r[0]) for r in rows]
|
||
except Exception as exc:
|
||
logger.error('[PromotionWorker] SELECT pending failed: %s', exc)
|
||
session.close()
|
||
return stats
|
||
finally:
|
||
session.close()
|
||
|
||
stats['pending_seen'] = len(episode_ids)
|
||
if not episode_ids:
|
||
return stats
|
||
|
||
for ep_id in episode_ids:
|
||
try:
|
||
decision = promotion_gate.can_promote(ep_id)
|
||
if decision.reason == 'approved':
|
||
if promotion_gate.promote(ep_id):
|
||
stats['promoted'] += 1
|
||
else:
|
||
stats['errors'] += 1
|
||
elif decision.reason == 'awaiting_review':
|
||
if promotion_gate.mark_awaiting_review(ep_id):
|
||
stats['awaiting'] += 1
|
||
else:
|
||
stats['errors'] += 1
|
||
elif decision.reason.startswith('rejected_'):
|
||
if promotion_gate.reject(ep_id, decision.reason, decision.detail):
|
||
stats['rejected'] += 1
|
||
else:
|
||
stats['errors'] += 1
|
||
except Exception as exc:
|
||
logger.warning('[PromotionWorker] episode_id=%s failed: %s', ep_id, exc)
|
||
stats['errors'] += 1
|
||
|
||
logger.info(
|
||
'[PromotionWorker] batch done: pending=%d promoted=%d rejected=%d awaiting=%d errors=%d',
|
||
stats['pending_seen'], stats['promoted'], stats['rejected'],
|
||
stats['awaiting'], stats['errors']
|
||
)
|
||
return stats
|
||
|
||
|
||
def push_awaiting_reviews_to_telegram(batch: int = AWAITING_REVIEW_PUSH_BATCH,
|
||
chat_id: Optional[str] = None) -> int:
|
||
"""找 awaiting_review 但尚未推送的 episode → 推 Telegram 帶 👍/👎 keyboard。
|
||
|
||
給 run_scheduler.py 每 30 分鐘跑（與 expire_stale_reviews 配合 24h timeout）。
|
||
|
||
判斷「未推送」：reviewed_at IS NULL（mark_awaiting_review 時不設 reviewed_at；
|
||
24h expired / human approve/reject 時才寫 reviewed_at）。
|
||
"""
|
||
pushed = 0
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
except Exception as exc:
|
||
logger.warning('[AwaitingReviewPush] DB import failed: %s', exc)
|
||
return 0
|
||
|
||
# 取 chat_id（預設 admin）
|
||
if chat_id is None:
|
||
chat_id = os.environ.get('TELEGRAM_ADMIN_CHAT_ID', '').strip() or None
|
||
if not chat_id:
|
||
logger.info('[AwaitingReviewPush] TELEGRAM_ADMIN_CHAT_ID 未設，跳過推送')
|
||
return 0
|
||
|
||
session = get_session()
|
||
try:
|
||
rows = session.execute(
|
||
sa_text("""
|
||
SELECT id, distilled_text, weight, quality_score
|
||
FROM learning_episodes
|
||
WHERE promotion_status = 'awaiting_review'
|
||
AND reviewed_at IS NULL
|
||
ORDER BY created_at ASC
|
||
LIMIT :n
|
||
"""),
|
||
{'n': batch},
|
||
).fetchall()
|
||
except Exception as exc:
|
||
logger.error('[AwaitingReviewPush] SELECT failed: %s', exc)
|
||
session.close()
|
||
return 0
|
||
finally:
|
||
session.close()
|
||
|
||
if not rows:
|
||
return 0
|
||
|
||
# 推送
|
||
try:
|
||
from services.telegram_templates import promotion_review_keyboard, _send_telegram_raw
|
||
except Exception as exc:
|
||
logger.warning('[AwaitingReviewPush] template import failed: %s', exc)
|
||
return 0
|
||
|
||
for r in rows:
|
||
ep_id, text_, weight, quality = r[0], r[1], float(r[2] or 0), float(r[3] or 0)
|
||
msg = (
|
||
f"🧠 <b>RAG 學習晉升審核</b>\n"
|
||
f"━━━━━━━━━━━━━━━━━━━━\n"
|
||
f"📋 episode #{ep_id} (weight={weight:.2f} quality={quality:.2f})\n\n"
|
||
f"{(text_ or '')[:600]}\n\n"
|
||
f"審核：通過 → 寫入 ai_insights 供 RAG 檢索；拒絕 → 永不晉升"
|
||
)
|
||
try:
|
||
_send_telegram_raw(msg, chat_id=chat_id, reply_markup=promotion_review_keyboard(ep_id))
|
||
pushed += 1
|
||
except Exception as exc:
|
||
logger.warning('[AwaitingReviewPush] episode_id=%s push failed: %s', ep_id, exc)
|
||
|
||
logger.info('[AwaitingReviewPush] pushed %d awaiting_review episodes to chat=%s',
|
||
pushed, chat_id)
|
||
return pushed
|
||
|
||
|
||
# ─────────────────────────────────────────────────────────────────────────────
|
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# 全域單例
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# ─────────────────────────────────────────────────────────────────────────────
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||
distiller = Distiller()
|
||
learning_pipeline = LearningPipeline()
|
||
promotion_gate = PromotionGate()
|
||
|
||
|
||
__all__ = [
|
||
'Distiller',
|
||
'DistillResult',
|
||
'GateDecision',
|
||
'LearningPipeline',
|
||
'PromotionGate',
|
||
'distiller',
|
||
'learning_pipeline',
|
||
'promotion_gate',
|
||
'expire_stale_reviews',
|
||
'process_pending_episodes',
|
||
'push_awaiting_reviews_to_telegram',
|
||
'hash_human_approver',
|
||
'STAGE_1_AUTO_QUALITY',
|
||
'STAGE_3_DEDUP_THRESHOLD',
|
||
'STAGE_4_HUMAN_REVIEW_WEIGHT',
|
||
'HUMAN_REVIEW_TIMEOUT_HOURS',
|
||
]
|