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Operation Ollama-First v5.0 / Phase 11 / RAG 自主學習迴圈 services/rag_service.py (532 行) - RAGService.query() — bge-m3 embed + cosine 0.85 threshold + top_k=5 - get_embedding_signature() — v5.0 護欄 #3 一致性檢查 (SHA1[:12]) - fire-and-forget rag_query_log INSERT (不阻塞主流程) - feedback() — Telegram 👍/👎 寫回 feedback_score - RAG_ENABLED 預設 OFF(戰前行為不變) services/learning_pipeline.py (750 行) - Distiller — 純 Hermes 規則引擎,零 LLM 成本 Quality 規則:MCP >200 字 0.8 / LLM JSON ok 0.9 / TextRank 0.6 / 👍 1.0 / 👎 0.0 - PromotionGate — Owen v5.0 護欄 #1 鐵律 Stage 1: quality_score >= 0.7 Stage 2: 無幻覺檢測(規則引擎,零 LLM) Stage 3: 與既有 insight 相似度 < 0.95(Stage 3 在 episode embed 後啟用) Stage 4: weight >= 0.8 必經 Telegram 👍/👎 - expire_stale_reviews() — 24h 無回應自動降級 weight=0.5 - hash_human_approver — Telegram username SHA1[:8] PII 保護 services/hermes_analyst_service.py — 新增 analyze() RAG-first - RAG hit → return synthesize(不燒 LLM) - RAG miss → 既有 LLM 路徑 + enqueue learning_episodes services/openclaw_strategist_service.py — Q&A 入口接 RAG-first - 不動週/月/年報(敘事報告 RAG hit 機率低) services/telegram_templates.py - rag_feedback_keyboard() — 👍/👎 inline keyboard - promotion_review_keyboard() — Stage 4 人工驗收按鈕 routes/openclaw_bot_routes.py — 3 組 callback handler - rag_fb:{id}:{score} → rag_service.feedback() - pg_ok:{episode_id} → PromotionGate.promote() - pg_no:{episode_id} → PromotionGate.reject() 70 unit tests 全綠 + 全戰役 196 tests zero regression(4:17 跑完) 剩餘 limitations(Phase 12+ 補): 1. learning_episodes.embedding 寫入路徑(Stage 3 dedup 暫 skip) 2. PromotionGate worker cron 未掛 3. Telegram awaiting_review 推播未接(callback handler 已就位) 灰度開啟條件(建議 1 週後): - ANTHROPIC_API_KEY 設定 + RAG_ENABLED=true + threshold=0.90 保守 - feedback_score >= 4 比率 > 70% → threshold 降至 0.85 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>