feat: ai-first intent detection with keyword fallback

utils/intent_analyzer.py

@@ -5,7 +5,8 @@
 """
 import os
 import logging
-from typing import Optional, Tuple
+from dataclasses import dataclass
+from typing import Optional
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +32,13 @@ EMOTION_TEMPLATES = {
 
 _template_embeddings: dict = {}
 
+
+@dataclass
+class IntentDecision:
+    intent: str = ""
+    source: str = "none"  # embedding / keyword / none
+    score: float = 0.0
+
 def _get_embedding(text: str, cache_key: str = None) -> Optional[list]:
     """调用 embedding API，失败返回 None。cache_key 用于缓存模板向量"""
     model = os.getenv("EMBEDDING_MODEL", "")
@@ -66,10 +74,16 @@ def _cosine_sim(a: list, b: list) -> float:
 
 
 def detect_intent_embedding(msg: str) -> Optional[str]:
-    """用 embedding 检测意图，未配置或失败返回 None"""
+    """用 embedding 检测意图，未配置或失败返回 None。"""
+    decision = detect_intent_embedding_decision(msg)
+    return decision.intent or None
+
+
+def detect_intent_embedding_decision(msg: str) -> IntentDecision:
+    """返回 embedding 意图决策（含分值）。"""
     msg_emb = _get_embedding(msg)
     if not msg_emb:
-        return None
+        return IntentDecision()
     best_intent, best_score = "", 0.0
     for intent, template in INTENT_TEMPLATES.items():
         tpl_emb = _get_embedding(template, cache_key=f"intent_{intent}")
@@ -79,7 +93,9 @@ def detect_intent_embedding(msg: str) -> Optional[str]:
         if sim > best_score:
             best_score = sim
             best_intent = intent
-    return best_intent if best_score > 0.6 else None
+    if best_score > 0.6:
+        return IntentDecision(intent=best_intent, source="embedding", score=float(best_score))
+    return IntentDecision()
 
 
 def detect_emotion_embedding(msg: str) -> Optional[str]:
@@ -112,9 +128,32 @@ def detect_intent_keywords(msg: str) -> str:
         return "砍价"
     if any(k in m for k in ["改", "修改", "不满意"]):
         return "修改"
-    if any(k in m for k in ["多少钱", "价格", "报价", "多钱"]):
+    if any(k in m for k in ["多少钱", "价格", "报价", "多钱", "收费", "怎么收费", "咋收费"]):
         return "询价"
     if any(k in m for k in ["在吗", "你好", "有人"]):
         return "打招呼"
     return ""
 
+
+def detect_intent(msg: str) -> IntentDecision:
+    """
+    AI 意图判定 + 规则兜底：
+    1) 有 embedding 配置时先走 embedding。
+    2) 失败/低置信时回退关键词规则。
+    """
+    text = (msg or "").strip()
+    if not text:
+        return IntentDecision()
+
+    try:
+        emb_decision = detect_intent_embedding_decision(text)
+    except Exception:
+        emb_decision = IntentDecision()
+    if emb_decision.intent:
+        return emb_decision
+
+    kw_intent = detect_intent_keywords(text)
+    if kw_intent:
+        return IntentDecision(intent=kw_intent, source="keyword", score=0.0)
+    return IntentDecision()
+