feat(nemoton): 新增 route_to_km + mark_for_relearn 工具

- route_to_km: NIM 決策後靜默歸檔洞察到指定 KM 領域
  (price_competition / sales_anomaly / promotion_opportunity / market_trend)
- mark_for_relearn: 新數據推翻歷史洞察時，批次更新 ai_insights.status='relearn'
  + feedback_down+1，供品質分數重算批次感知
- TOOL_MAP 加入兩個新 handler，Python 獨裁層補 route_to_km threat 注入

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

services/nemoton_dispatcher_service.py

@@ -203,6 +203,57 @@ TOOLS = [
             },
         },
     },
+    {
+        "type": "function",
+        "function": {
+            "name": "route_to_km",
+            "description": (
+                "將商品競價洞察路由到知識庫（KM）的指定領域分類，"
+                "供未來 RAG 查詢與 OpenClaw 週報引用。"
+                "適用於：數據有參考價值但不需立即告警的情況。"
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "sku":       {"type": "string", "description": "商品 SKU 編號"},
+                    "name":      {"type": "string", "description": "商品名稱"},
+                    "km_domain": {
+                        "type": "string",
+                        "description": (
+                            "KM 領域分類，必須為以下之一："
+                            "price_competition（競價情報）、"
+                            "sales_anomaly（銷量異常）、"
+                            "promotion_opportunity（促銷機會）、"
+                            "market_trend（市場趨勢）"
+                        ),
+                    },
+                    "summary":    {"type": "string", "description": "此洞察的核心摘要（繁體中文，50 字內）"},
+                    "confidence": {"type": "number", "description": "AI 信心度 0.0~1.0"},
+                },
+                "required": ["sku", "name", "km_domain", "summary", "confidence"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "mark_for_relearn",
+            "description": (
+                "當新數據與 KM 既有洞察矛盾，或告警方向被推翻時，"
+                "將該商品的歷史洞察標記為需重新學習（relearn）。"
+                "適用於：此次分析結果與上次截然不同的情況。"
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "sku":    {"type": "string", "description": "商品 SKU 編號"},
+                    "name":   {"type": "string", "description": "商品名稱"},
+                    "reason": {"type": "string", "description": "標記原因說明（繁體中文）"},
+                },
+                "required": ["sku", "name", "reason"],
+            },
+        },
+    },
 ]
 
 
@@ -737,6 +788,73 @@ class NemotronDispatcher:
                       "momo_price": momo_price, "comp_price": comp_price},
         )
 
+    def _exec_route_to_km(
+        self,
+        sku: str, name: str, km_domain: str, summary: str, confidence: float,
+        footprint: str = "",
+        threat=None,
+    ):
+        """
+        將洞察路由到 KM 指定領域，sink 到 ai_insights 供 RAG 使用。
+        不送 Telegram 告警（靜默操作，僅 log）。
+        """
+        _KM_DOMAINS = {"price_competition", "sales_anomaly", "promotion_opportunity", "market_trend"}
+        domain = km_domain if km_domain in _KM_DOMAINS else "price_competition"
+        summary = _sanitize_text(summary, fallback="競價洞察已歸檔")
+
+        self._sink_insight_to_km(
+            insight_type=f"km_{domain}",
+            sku=sku, name=name,
+            content=f"[KM 路由 {domain}] {name}：{summary}",
+            metadata={
+                "km_domain":  domain,
+                "confidence": confidence,
+                "momo_price":   getattr(threat, "momo_price",         None) if threat else None,
+                "pchome_price": getattr(threat, "pchome_price",       None) if threat else None,
+                "gap_pct":      getattr(threat, "gap_pct",            None) if threat else None,
+                "sales_delta":  getattr(threat, "sales_7d_delta_pct", None) if threat else None,
+            },
+        )
+        logger.info(f"[Dispatcher] KM 路由 → {sku} domain={domain} confidence={confidence:.2f}")
+
+    def _exec_mark_for_relearn(
+        self,
+        sku: str, name: str, reason: str,
+        footprint: str = "",
+    ):
+        """
+        將該 SKU 的既有 ai_insights 標記 status='relearn' + feedback_down+1，
+        讓每日去重/品質分數重算批次可感知「此洞察已被推翻」。
+        不送 Telegram 告警（靜默操作，僅 log）。
+        """
+        reason = _sanitize_text(reason, fallback="新數據與歷史洞察矛盾，需重新學習")
+        try:
+            from database.manager import DatabaseManager
+            db = DatabaseManager()
+            with db.get_session() as session:
+                from sqlalchemy import text
+                result = session.execute(text("""
+                    UPDATE ai_insights
+                       SET status       = 'relearn',
+                           feedback_down = COALESCE(feedback_down, 0) + 1,
+                           updated_at    = CURRENT_TIMESTAMP
+                     WHERE product_sku = :sku
+                       AND status NOT IN ('relearn', 'archived')
+                """), {"sku": sku})
+                session.commit()
+                rows = result.rowcount
+            logger.info(f"[Dispatcher] mark_for_relearn → {sku} 共更新 {rows} 筆洞察；原因：{reason}")
+        except Exception as e:
+            logger.warning(f"[Dispatcher] mark_for_relearn DB 更新失敗 ({sku}): {e}")
+
+        # 同時寫入一筆 relearn 事件到 ai_insights 留存紀錄
+        self._sink_insight_to_km(
+            insight_type="relearn_event",
+            sku=sku, name=name,
+            content=f"[重新學習事件] {name}：{reason}",
+            metadata={"sku": sku, "trigger": "nemoton_dispatcher"},
+        )
+
     def _sink_insight_to_km(self, insight_type: str, sku: str, name: str,
                             content: str, metadata: dict = None):
         """
@@ -948,6 +1066,8 @@ class NemotronDispatcher:
             "trigger_price_alert":   self._exec_trigger_price_alert,
             "add_to_recommendation": self._exec_add_to_recommendation,
             "flag_for_human_review": self._exec_flag_for_human_review,
+            "route_to_km":           self._exec_route_to_km,
+            "mark_for_relearn":      self._exec_mark_for_relearn,
         }
 
         for tc in tool_calls:
@@ -979,6 +1099,9 @@ class NemotronDispatcher:
             elif tool_name == "add_to_recommendation":
                 args["footprint_data"] = footprint_data
                 args["threat"]         = t
+            elif tool_name == "route_to_km":
+                args["threat"] = t
+            # mark_for_relearn 無需注入客觀數字（僅寫 DB）
 
             try:
                 handler(**args)