fix(ai): 價格調整轉人工覆核

CONSTITUTION.md

@@ -2,7 +2,7 @@
 
 > 本文件定義專案開發的核心準則與不可違反的規範
 > **建立日期**: 2026-01-12
-> **當前版本**: V10.50 (Vendor stockout query service extraction)
+> **當前版本**: V10.51 (Price adjustment actions require human review)
 > **最後更新**: 2026-05-01
 
 ---

app.py

@@ -95,8 +95,8 @@ except Exception as e:
     sys_log.error(f"無法檢測磁碟空間: {e}")
 
 # 🚩 系統版本定義 (備份與顯示用)
-# 🚩 2026-05-01 V10.50: Vendor stockout query service extraction
-SYSTEM_VERSION = "V10.50"
+# 🚩 2026-05-01 V10.51: Price adjustment actions require human review
+SYSTEM_VERSION = "V10.51"
 
 # ==========================================
 # 🔒 SQL Injection 防護函數

docs/AI_INTELLIGENCE_MODULE_SOT.md

@@ -66,6 +66,7 @@ SQL漏斗(~300筆)
 - `momo-db` / `momo-postgres` 不可被 AI 自動 restart / stop / recreate。
 - raw `ai_insights` insert 必須接 `enqueue_insight_embedding()` 或可被 backfill。
 - ElephantAlpha 只做編排與 bridge，不可繞過 ADR-011 / ADR-012 / ADR-013。
+- ElephantAlpha / NemoTron 不可直接執行商品價格調整；`execute_price_adjustment`、`adjust_price` 等動作必須攔截並寫入 `human_review`，等待人工核准。
 
 可觀測性：
 

services/elephant_alpha_autonomous_engine.py

@@ -103,10 +103,20 @@ _ACTION_ZH = {
     "generate_market_analysis":    "市場分析",
     "generate_pricing_strategy":   "定價策略建議",
     "generate_meta_analysis":      "AI 系統自我審視",
+    "execute_price_adjustment":    "價格調整覆核",
     "adjust_price":                "調整定價",
     "send_alert":                  "發送告警",
 }
 
+_PRICE_ADJUSTMENT_REVIEW_ACTIONS = frozenset({
+    "execute_price_adjustment",
+    "adjust_price",
+    "apply_price_change",
+    "update_price",
+    "dispatch_price_update",
+    "dispatch_price_updates",
+})
+
 
 def _zh_trigger(trigger_type: str) -> str:
     return _TRIGGER_ZH.get(trigger_type, trigger_type)
@@ -559,7 +569,7 @@ class ElephantAlphaAutonomousEngine:
     async def _execute_step(self, step: Dict[str, Any]) -> None:
         agent_type = step.get("agent", "").lower()
         action = step.get("action", "")
-        params = step.get("parameters", {})
+        params = step.get("parameters") or step.get("params") or {}
 
         if agent_type == "hermes" and action == "analyze_price_competition":
             return await self._run_with_timeout(
@@ -579,7 +589,10 @@ class ElephantAlphaAutonomousEngine:
             return
 
         if agent_type == "openclaw" and action in (
-            "generate_strategic_analysis", "generate_weekly_strategy", "generate_market_analysis"
+            "generate_strategic_analysis",
+            "generate_weekly_strategy",
+            "generate_market_analysis",
+            "generate_pricing_strategy",
         ):
             return await self._run_with_timeout(
                 self._generate_strategy_report,
@@ -608,6 +621,13 @@ class ElephantAlphaAutonomousEngine:
                 timeout=SSH_COMMAND_TIMEOUT,
             )
 
+        if action in _PRICE_ADJUSTMENT_REVIEW_ACTIONS:
+            return await self._run_with_timeout(
+                self._record_price_adjustment_review,
+                step,
+                timeout=SSH_COMMAND_TIMEOUT,
+            )
+
         raise ValueError(f"Unrecognized step: agent={agent_type} action={action}")
 
     # ---- Sub-services ----
@@ -638,6 +658,64 @@ class ElephantAlphaAutonomousEngine:
         payload.setdefault("error_type", error_type)
         return auto_heal_service.handle_exception(error_type=error_type, context=payload)
 
+    def _record_price_adjustment_review(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Price changes are business-critical. Elephant Alpha may recommend them,
+        but this system records the proposal for HITL review instead of applying it.
+        """
+        params = step.get("parameters") or step.get("params") or {}
+        sku = (
+            params.get("sku")
+            or params.get("product_sku")
+            or params.get("i_code")
+            or params.get("item_id")
+            or "unknown"
+        )
+        action = step.get("action", "price_adjustment")
+        content = (
+            f"[Elephant Alpha 價格調整覆核] AI 建議執行 {action}，"
+            f"商品 {sku} 已攔截直接執行並轉入人工審核。"
+        )
+
+        session = get_session()
+        try:
+            row = session.execute(
+                text("""
+                    INSERT INTO ai_insights
+                        (insight_type, content, confidence, created_by, status, metadata_json)
+                    VALUES (:type, :content, :confidence, :created_by, :status, :metadata)
+                    RETURNING id
+                """),
+                {
+                    "type": "human_review",
+                    "content": content,
+                    "confidence": 0.8,
+                    "created_by": "elephant_alpha",
+                    "status": "pending",
+                    "metadata": json.dumps({
+                        "source": "price_adjustment_review",
+                        "step": step,
+                        "sku": sku,
+                        "reason": "price_adjustment_requires_human_approval",
+                    }, ensure_ascii=False),
+                },
+            ).fetchone()
+            session.commit()
+            insight_id = row[0] if row else None
+            if insight_id:
+                try:
+                    from services.openclaw_learning_service import enqueue_insight_embedding
+                    enqueue_insight_embedding(insight_id, "human_review", content)
+                except Exception as embed_err:
+                    self._log.warning("Embedding enqueue failed for price adjustment review: %s", embed_err)
+            self._log.warning("Price adjustment intercepted for HITL review: action=%s sku=%s", action, sku)
+            return {"status": "pending_review", "insight_id": insight_id, "sku": sku, "action": action}
+        except Exception:
+            session.rollback()
+            raise
+        finally:
+            session.close()
+
     # ---- Notification ----
     async def _notify_telegram_executed(
         self,

services/elephant_alpha_orchestrator.py

@@ -146,6 +146,22 @@ DECISION FRAMEWORK:
 6. Create detailed execution plans
 7. Monitor and adapt based on outcomes
 
+ALLOWED EXECUTION ACTIONS:
+- hermes / analyze_price_competition
+- nemotron / dispatch_alert
+- openclaw / generate_strategic_analysis
+- openclaw / generate_weekly_strategy
+- openclaw / generate_market_analysis
+- openclaw / generate_pricing_strategy
+- openclaw / generate_meta_analysis
+- elephant_alpha / auto_heal
+- elephant_alpha / code_fix
+
+價格調整紅線：
+- 禁止輸出 execute_price_adjustment、adjust_price、apply_price_change、update_price。
+- 若需要調價，請改輸出 openclaw / generate_pricing_strategy，並在 description 說明「需要人工核准後才能調價」。
+- 本系統不得由 AI 直接修改商品價格，只能產生建議與人工覆核項目。
+
 BUSINESS CONTEXT:
 - E-commerce platform (momo-pro-system)
 - Real-time price competition monitoring

tests/test_elephant_alpha_engine.py

@@ -42,6 +42,27 @@ def test_execute_step_routes_code_fix_to_autoheal(monkeypatch):
     assert calls == [("python_exception", {"target_file": "services/example.py", "error_message": "Traceback"})]
 
 
+def test_execute_step_routes_price_adjustment_to_human_review(monkeypatch):
+    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
+
+    calls = []
+    engine = ElephantAlphaAutonomousEngine()
+    monkeypatch.setattr(
+        engine,
+        "_record_price_adjustment_review",
+        lambda step: calls.append(step) or {"status": "pending_review", "sku": "SKU-9"},
+    )
+
+    result = asyncio.run(engine._execute_step({
+        "agent": "nemotron",
+        "action": "execute_price_adjustment",
+        "parameters": {"sku": "SKU-9", "recommended_price": 1280},
+    }))
+
+    assert result == {"status": "pending_review", "sku": "SKU-9"}
+    assert calls[0]["parameters"]["recommended_price"] == 1280
+
+
 def test_autoheal_derives_python_exception_from_traceback():
     from services.auto_heal_service import AutoHealService