[V10.4-C] 三 AI NLP 鏈修復：Hermes/NemoTron/OpenClaw 全線串通

修復 P9-2 確認的自然語言對話完全失效問題：

- services/ai_orchestrator.py:
    4 處裸字串 SQL 全部包裝 text()，修復 SQLAlchemy 2.x ArgumentError
- services/hermes_analyst_service.py:
    新增 handle_l1() async 方法（Ollama hermes3 意圖分析 + rule-based fallback）
    asyncio.get_event_loop() → get_running_loop()（Py3.12+ 相容）
- services/nemoton_dispatcher_service.py:
    新增 handle_l2() async 方法（純 Python routing，不消耗 NIM 配額）
- services/openclaw_strategist_service.py:
    新增 generate_strategy_response()（Gemini 2.0 Flash，無 key 時優雅降級）
- telegram_ai_integration.py:
    整合 OpenClaw 為第三層（complexity >= 0.7 或 dispatch_to == "openclaw"）
    _format_*_response 全改為繁體中文
    asyncio.get_event_loop() → get_running_loop()
    _extract_date_range "to" → "至"

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

services/ai_orchestrator.py

@@ -3,6 +3,8 @@ import asyncio
 import logging
 from typing import Any, Dict, Optional
 
+from sqlalchemy import text
+
 from services.hermes_analyst_service import HermesAnalystService
 from services.nemoton_dispatcher_service import NemotronDispatcher
 from database.manager import get_session
@@ -46,7 +48,7 @@ class AIOrchestrator:
         session = get_session()
         try:
             rows = session.execute(
-                "SELECT context_key, context_val FROM agent_context WHERE session_id = :sid",
+                text("SELECT context_key, context_val FROM agent_context WHERE session_id = :sid"),
                 {"sid": session_id},
             ).fetchall()
             return {r[0]: r[1] for r in rows}
@@ -57,16 +59,16 @@ class AIOrchestrator:
         session = get_session()
         try:
             session.execute(
-                "DELETE FROM agent_context WHERE session_id = :sid AND agent_name = :ag",
+                text("DELETE FROM agent_context WHERE session_id = :sid AND agent_name = :ag"),
                 {"sid": session_id, "ag": agent},
             )
             session.execute(
-                """
+                text("""
                 INSERT INTO agent_context
                     (session_id, agent_name, context_key, context_val, created_at, ttl_minutes)
                 VALUES
                     (:sid, :ag, :ck, :cv, NOW(), 60)
-                """,
+                """),
                 {
                     "sid": session_id,
                     "ag": agent,
@@ -86,12 +88,12 @@ class AIOrchestrator:
         session = get_session()
         try:
             session.execute(
-                """
+                text("""
                 INSERT INTO action_plans
                     (session_id, plan_type, sku, payload, status, created_by)
                 VALUES
                     (:sid, :pt, :sku, :pl, 'pending', 'nemotron')
-                """,
+                """),
                 {
                     "sid": plan.get("session_id"),
                     "pt": plan.get("plan_type"),

services/hermes_analyst_service.py

@@ -12,6 +12,7 @@ Hermes 3 競價情報分析服務 (Module 2)
   SQL漏斗 → [本服務] → NemotronDispatcher → Telegram 告警
 """
 
+import asyncio
 import json
 import logging
 import re
@@ -86,6 +87,131 @@ class HermesAnalystService:
     def __init__(self, engine=None):
         self.engine = engine  # SQLAlchemy engine，可外部注入
 
+    # ──────────────────────────────────────────────
+    # L1 意圖分析介面（給 EventRouter / Telegram NLP 使用）
+    # ──────────────────────────────────────────────
+    async def handle_l1(self, event: dict, ctx: dict) -> dict:
+        """L1 語意理解：輸入 event（含 message），輸出 intent / complexity_score / requires_data_fetch。
+
+        Contract:
+            event: {"message": str, "user_id": int, "chat_id": int, ...}
+            ctx:   既有 agent_context（可為空 dict）
+        Returns:
+            {
+                "intent": str,                # greeting / help / query_sales / analyze_competitor / report / unknown
+                "confidence": float,
+                "complexity_score": float,    # 0.0~1.0
+                "requires_data_fetch": bool,
+                "preliminary_answer": str,
+                "metadata": dict,
+            }
+
+        實作策略：
+          1) 先用 Hermes3 LLM 做意圖分類（timeout 短、temp 低）
+          2) Ollama 掛掉時降級為關鍵字規則引擎，保證永遠回傳結構化結果
+          3) 任何例外都被 graceful 吞掉，不會讓 handle_message 整個炸
+        """
+        message = (event or {}).get("message", "") or ""
+        llm_result = None
+        try:
+            llm_result = await asyncio.get_running_loop().run_in_executor(
+                None, self._call_hermes_intent, message
+            )
+        except Exception as e:
+            logger.warning(f"[Hermes.handle_l1] LLM 意圖分析失敗（降級規則引擎）: {e}")
+
+        if llm_result:
+            return llm_result
+        return self._rule_based_intent(message)
+
+    def _call_hermes_intent(self, message: str) -> Optional[dict]:
+        """呼叫 Hermes3 做輕量意圖分類（非批量分析）。失敗回 None 由上層降級。"""
+        if not message.strip():
+            return self._rule_based_intent(message)
+
+        system = (
+            "你是一位繁體中文意圖分類器。讀取使用者訊息，輸出單行 JSON，"
+            "嚴禁任何解釋文字與 markdown 標記。欄位：\n"
+            '{"intent": "greeting|help|query_sales|analyze_competitor|'
+            'report|product_info|unknown", "confidence": 0.0~1.0, '
+            '"complexity_score": 0.0~1.0, "requires_data_fetch": true|false, '
+            '"preliminary_answer": "若為 greeting/help 請回一句繁中問候，否則空字串"}\n'
+            "規則：greeting/help 類 complexity_score<=0.3；涉及數據、報告、日期、"
+            "品牌、競品對比者 complexity_score>=0.7 且 requires_data_fetch=true。"
+        )
+        payload = {
+            "model": HERMES_MODEL,
+            "system": system,
+            "prompt": f"使用者訊息：{message}\n輸出 JSON：",
+            "stream": False,
+            "options": {"temperature": 0.1},
+        }
+        try:
+            resp = requests.post(
+                f"{HERMES_URL}/api/generate",
+                json=payload,
+                timeout=15,  # 意圖分類不必等 120s
+            )
+            resp.raise_for_status()
+            raw = (resp.json().get("response", "") or "").strip()
+            if raw.startswith("```"):
+                raw = re.sub(r"^```(?:json)?\s*", "", raw, flags=re.MULTILINE)
+                raw = re.sub(r"\s*```\s*$", "", raw.strip(), flags=re.MULTILINE).strip()
+            data = json.loads(raw)
+            return {
+                "intent": data.get("intent", "unknown"),
+                "confidence": float(data.get("confidence", 0.5)),
+                "complexity_score": float(data.get("complexity_score", 0.5)),
+                "requires_data_fetch": bool(data.get("requires_data_fetch", False)),
+                "preliminary_answer": data.get("preliminary_answer", "") or "",
+                "metadata": {"source": "hermes_llm"},
+            }
+        except Exception as e:
+            logger.info(f"[Hermes.intent] 降級規則引擎（{type(e).__name__}: {e}）")
+            return None
+
+    def _rule_based_intent(self, message: str) -> dict:
+        """Ollama 掛掉時的規則引擎 fallback — 永遠返回結構化結果。"""
+        msg = (message or "").lower().strip()
+        if not msg:
+            return {
+                "intent": "unknown", "confidence": 0.3, "complexity_score": 0.1,
+                "requires_data_fetch": False, "preliminary_answer": "",
+                "metadata": {"source": "rule_empty"},
+            }
+        greetings = ("hi", "hello", "哈囉", "你好", "嗨", "早安", "午安", "晚安")
+        helps = ("help", "幫助", "說明", "怎麼用", "功能")
+        data_keywords = (
+            "業績", "銷量", "銷售", "營收", "排名", "威脅", "競品", "報表",
+            "週報", "月報", "日報", "2024", "2025", "2026",
+            "ppt", "report", "compare", "vs", "分析", "趨勢",
+        )
+        if any(g in msg for g in greetings):
+            return {
+                "intent": "greeting", "confidence": 0.9, "complexity_score": 0.1,
+                "requires_data_fetch": False,
+                "preliminary_answer": "您好！我是 MOMO Pro 智能助理，請問需要什麼協助？",
+                "metadata": {"source": "rule"},
+            }
+        if any(h in msg for h in helps):
+            return {
+                "intent": "help", "confidence": 0.85, "complexity_score": 0.2,
+                "requires_data_fetch": False,
+                "preliminary_answer": "我可以協助您查詢業績、商品、競品情報與產出報告。",
+                "metadata": {"source": "rule"},
+            }
+        if any(k in msg for k in data_keywords):
+            return {
+                "intent": "query_sales", "confidence": 0.7, "complexity_score": 0.8,
+                "requires_data_fetch": True, "preliminary_answer": "",
+                "metadata": {"source": "rule"},
+            }
+        return {
+            "intent": "unknown", "confidence": 0.4, "complexity_score": 0.5,
+            "requires_data_fetch": False, "preliminary_answer": "",
+            "metadata": {"source": "rule"},
+        }
+
     # ──────────────────────────────────────────────
     # Step 1：SQL 漏斗 — 從 226萬筆壓縮到 ~300 筆候選
     # ──────────────────────────────────────────────

services/nemoton_dispatcher_service.py

@@ -916,6 +916,63 @@ class NemotronDispatcher:
     # ──────────────────────────────────────────────
     # 公開介面
     # ──────────────────────────────────────────────
+    async def handle_l2(self, event: dict, ctx: dict) -> dict:
+        """L2 行動規劃介面（給 EventRouter / Telegram NLP 使用）。
+
+        Contract:
+            event: {"message": str, "user_id": int, "chat_id": int, ...}
+            ctx:   包含 Hermes handle_l1 結果（在 "latest" key 或 flatten 後）
+        Returns:
+            {
+                "action_plan": [{"action": str, "params": dict}, ...],
+                "dispatch_to": "openclaw|direct_response|human_review",
+                "metadata": dict,
+            }
+
+        策略：
+          本方法不呼叫 NIM（批量 tool calling 專用），改以 L1 intent + 訊息關鍵字
+          決定下一步路由。複雜分析 → openclaw；簡單回覆 → direct_response。
+          任何例外都回 direct_response 保底。
+        """
+        try:
+            message = (event or {}).get("message", "") or ""
+            # ctx 可能是 {"latest": {...}} 或已攤平的 intent 結果
+            hermes = {}
+            if isinstance(ctx, dict):
+                hermes = ctx.get("latest") if isinstance(ctx.get("latest"), dict) else ctx
+
+            intent = (hermes or {}).get("intent", "unknown")
+            complexity = float((hermes or {}).get("complexity_score", 0.0) or 0.0)
+            needs_data = bool((hermes or {}).get("requires_data_fetch", False))
+
+            msg_lower = message.lower()
+            report_keywords = ("report", "ppt", "週報", "月報", "日報", "報表", "報告")
+            wants_report = any(k in msg_lower for k in report_keywords)
+
+            if wants_report or complexity >= 0.7 or needs_data or intent in (
+                "query_sales", "analyze_competitor", "report"
+            ):
+                dispatch_to = "openclaw"
+            else:
+                dispatch_to = "direct_response"
+
+            action_plan = [{
+                "action": "strategist_analyze" if dispatch_to == "openclaw" else "reply_simple",
+                "params": {"message": message, "intent": intent},
+            }]
+            return {
+                "action_plan": action_plan,
+                "dispatch_to": dispatch_to,
+                "metadata": {"complexity_score": complexity, "intent": intent},
+            }
+        except Exception as e:
+            logger.warning(f"[NemotronDispatcher.handle_l2] 規劃失敗 fallback direct_response: {e}")
+            return {
+                "action_plan": [{"action": "reply_simple", "params": {}}],
+                "dispatch_to": "direct_response",
+                "metadata": {"error": str(e)},
+            }
+
     def dispatch(self, threats: list, hermes_stats: Optional[dict] = None) -> dict:
         """
         主入口：接收 Hermes 威脅清單，透過 NIM 決策後執行語意化告警

services/openclaw_strategist_service.py

@@ -45,9 +45,60 @@ __all__ = [
     "generate_weekly_strategy_report",
     "generate_monthly_report",
     "generate_meta_analysis_report",
+    "generate_strategy_response",
 ]
 
 
+# ═══════════════════════════════════════════════════════════════════════════════
+# Telegram NLP 互動入口（輕量查詢，不走完整報告管線）
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def generate_strategy_response(query: str, context: Optional[Dict[str, Any]] = None) -> str:
+    """給 Telegram NLP 使用的輕量策略回覆。
+
+    Contract:
+        query:   使用者自然語言訊息（繁體中文）
+        context: 可選，{"intent": str, "user_id": int, ...}
+    Returns:
+        繁體中文回覆字串。GEMINI_API_KEY 未設或呼叫失敗時，回降級訊息
+        （永遠回字串，不拋例外，由呼叫端顯示於 Telegram）。
+    """
+    q = (query or "").strip()
+    if not q:
+        return "請輸入您的問題，例如：本週業績趨勢、競品價差分析、產出週報 PPT。"
+
+    if not GEMINI_API_KEY:
+        return (
+            "OpenClaw 策略師目前離線（未設定 GEMINI_API_KEY）。\n"
+            "您可直接輸入以下指令取得報告：\n"
+            "• /daily — 每日業績\n"
+            "• /weekly — 週報\n"
+            "• /threats — 最新競價威脅\n"
+            "• /help — 完整功能說明"
+        )
+
+    system_prompt = (
+        "你是 MOMO Pro 電商情報策略師。以繁體中文（台灣用語）回覆使用者。"
+        "嚴禁簡體字，嚴禁空洞套話。若使用者要求的資料需即時查詢，"
+        "請告知使用者相關可用指令（例如 /daily、/weekly、/threats）。"
+        "回覆長度控制在 500 字內，可用 Markdown 條列。"
+    )
+    user_prompt = f"使用者問題：{q}\n上下文：{json.dumps(context or {}, ensure_ascii=False)}"
+
+    try:
+        text_reply = _call_gemini(system_prompt, user_prompt, temperature=0.5)
+    except Exception as e:
+        logger.error("[OpenClaw] generate_strategy_response 例外：%s", e)
+        text_reply = None
+
+    if not text_reply:
+        return (
+            "策略師暫時無法回覆（模型呼叫逾時或失敗）。\n"
+            "您可改用：/daily、/weekly、/threats 取得結構化報告。"
+        )
+    return text_reply
+
+
 # ═══════════════════════════════════════════════════════════════════════════════
 # DB 數據讀取層
 # ═══════════════════════════════════════════════════════════════════════════════