fix: backend improvements (customer health, tests, middleware, corpus)

backend/app/services/corpus_trainer.py

@@ -1,5 +1,5 @@
 from typing import Dict, Any, Optional, List
-from sqlalchemy import select, func, and_
+from sqlalchemy import select, func, and_, String
 from sqlalchemy.ext.asyncio import AsyncSession
 from datetime import datetime, timedelta
 import logging
@@ -58,7 +58,7 @@ class CorpusTrainer:
             select(
                 CorpusEntry.source_text,
                 CorpusEntry.task_type,
-                func.min(CorpusEntry.id).label("keep_id"),
+                func.min(CorpusEntry.id.cast(String)).label("keep_id"),
             )
             .group_by(CorpusEntry.source_text, CorpusEntry.task_type)
             .having(func.count(CorpusEntry.id) > 1)
@@ -70,7 +70,7 @@ class CorpusTrainer:
                 and_(
                     CorpusEntry.source_text == subquery.c.source_text,
                     CorpusEntry.task_type == subquery.c.task_type,
-                    CorpusEntry.id != subquery.c.keep_id,
+                    CorpusEntry.id.cast(String) != subquery.c.keep_id,
                 )
             )
         )
@@ -170,7 +170,7 @@ class CorpusTrainer:
             from app.config import settings
             import httpx
 
-            if settings.OPENAI_API_KEY:
+            if getattr(settings, 'OPENAI_API_KEY', None):
                 async with httpx.AsyncClient() as client:
                     resp = await client.post(
                         "https://api.openai.com/v1/embeddings",

backend/app/services/customer_health.py

@@ -233,6 +233,12 @@ class CustomerHealthService:
             score = max(0, min(100, 80 - recent_hours * 3))
             return {"score": round(score), "recent_avg_hours": round(recent_hours, 1), "trend": "declining"}
 
+    @staticmethod
+    def _words_in_text(words, text_words):
+        """Check if all words in the phrase appear in the text word set.
+        Handles multi-word phrases where word order differs."""
+        return all(w in text_words for w in words)
+
     @staticmethod
     def calc_sentiment_score(messages: List[str]) -> Dict[str, Any]:
         if not messages:
@@ -240,11 +246,22 @@ class CustomerHealthService:
         positive = 0
         negative = 0
         for msg in messages:
-            lower = msg.lower()
-            if any(w in lower for w in POSITIVE_WORDS):
-                positive += 1
-            if any(w in lower for w in NEGATIVE_WORDS):
-                negative += 1
+            words = msg.lower().split()
+            words_set = set(words)
+            for w in POSITIVE_WORDS:
+                parts = w.split()
+                if len(parts) > 1:
+                    if CustomerHealthService._words_in_text(parts, words_set):
+                        positive += 1
+                elif w in words_set:
+                    positive += 1
+            for w in NEGATIVE_WORDS:
+                parts = w.split()
+                if len(parts) > 1:
+                    if CustomerHealthService._words_in_text(parts, words_set):
+                        negative += 1
+                elif w in words_set:
+                    negative += 1
         if positive > negative:
             return {"score": 80, "label": "positive", "last_messages": messages}
         elif negative > positive:
@@ -311,6 +328,24 @@ class CustomerHealthService:
                 return f"客户已沉默{silence_days}天，建议立即跟进，提供优惠或新产品信息"
             return f"客户已沉默{silence_days}天，建议重新激活"
 
+    @staticmethod
+    def _calculate_overview_static(rows) -> Dict[str, Any]:
+        total = len(rows)
+        active = 0
+        watch = 0
+        critical = 0
+        for row in rows:
+            score = CustomerHealthService.calculate_silence_score(row.last_contact_at)
+            status_weight = CustomerHealthService.status_weight(row.status)
+            combined = score * 0.7 + status_weight * 0.3
+            if combined >= 70:
+                active += 1
+            elif combined >= 40:
+                watch += 1
+            else:
+                critical += 1
+        return {"total": total, "active": active, "watch": watch, "critical": critical}
+
     def _calculate_silence_score(self, last_contact_at: Optional[datetime]) -> float:
         return self.calculate_silence_score(last_contact_at)