fix(auto-repair): preserve exact playbook candidates

apps/api/src/services/playbook_service.py

@@ -14,6 +14,7 @@ Phase 3 ADR-030: RAG 向量搜尋整合
 - 封裝所有業務邏輯
 """
 
+import re as _re
 from typing import Protocol
 
 import structlog
@@ -32,13 +33,11 @@ from src.models.playbook import (
 )
 from src.repositories.interfaces import IPlaybookRepository
 from src.repositories.playbook_repository import get_playbook_repository
-from src.services.playbook_rag import get_playbook_rag_service
+from src.services.playbook_rag import PlaybookMatch, get_playbook_rag_service
 from src.utils.timezone import now_taipei
 
 logger = structlog.get_logger(__name__)
 
-import re as _re
-
 
 def _parse_ssh_command(ssh_cmd: str) -> tuple[str, str]:
     """
@@ -275,16 +274,16 @@ class PlaybookService:
 
         payload = KMWritePayload(
             path_type="playbook_extract",
-            entry_create_kwargs=dict(
-                title=f"[Playbook] {playbook.name}",
-                content=body,
-                entry_type=EntryType.INCIDENT_CASE,
-                category="auto_repair",
-                tags=[*playbook.tags, "playbook", "auto_extracted", playbook.status.value],
-                source=EntrySource.AI_EXTRACTED,
-                related_incident_id=incident.incident_id,
-                created_by="playbook_service",
-            ),
+            entry_create_kwargs={
+                "title": f"[Playbook] {playbook.name}",
+                "content": body,
+                "entry_type": EntryType.INCIDENT_CASE,
+                "category": "auto_repair",
+                "tags": [*playbook.tags, "playbook", "auto_extracted", playbook.status.value],
+                "source": EntrySource.AI_EXTRACTED,
+                "related_incident_id": incident.incident_id,
+                "created_by": "playbook_service",
+            },
             incident_id=incident.incident_id,
         )
         result = await km_write_with_flag(payload)
@@ -348,6 +347,17 @@ class PlaybookService:
                     vector_weight=0.6,
                     jaccard_weight=0.4,
                 )
+                hybrid_by_id = {match.playbook_id: match for match in hybrid_matches}
+                for playbook_id, jaccard_score in jaccard_results:
+                    if playbook_id in hybrid_by_id:
+                        continue
+                    hybrid_matches.append(
+                        PlaybookMatch(
+                            playbook_id=playbook_id,
+                            similarity_score=jaccard_score,
+                            match_type="jaccard",
+                        )
+                    )
 
                 # 補充 playbook_map (RAG 可能找到 Jaccard 沒找到的)
                 for match in hybrid_matches:
@@ -404,9 +414,9 @@ class PlaybookService:
                 )
             )
 
-        # Step 4: 按綜合分數排序 (similarity * success_rate)
+        # Step 4: 先保住 exact signal，避免精準 Playbook 被語意近似項擠掉。
         recommendations.sort(
-            key=lambda r: r.similarity_score * (0.5 + 0.5 * r.playbook.success_rate),
+            key=lambda r: self._recommendation_priority(r, symptoms),
             reverse=True,
         )
 
@@ -821,6 +831,25 @@ class PlaybookService:
 
         return matched
 
+    @staticmethod
+    def _normalized_overlap(left: list[str], right: list[str]) -> bool:
+        left_values = {value.casefold() for value in left if value}
+        right_values = {value.casefold() for value in right if value}
+        return bool(left_values & right_values)
+
+    def _recommendation_priority(
+        self,
+        recommendation: PlaybookRecommendation,
+        symptoms: SymptomPattern,
+    ) -> tuple[bool, bool, float]:
+        pattern = recommendation.playbook.symptom_pattern
+        alert_exact = self._normalized_overlap(symptoms.alert_names, pattern.alert_names)
+        service_exact = self._normalized_overlap(symptoms.affected_services, pattern.affected_services)
+        quality_score = recommendation.similarity_score * (
+            0.5 + 0.5 * recommendation.playbook.success_rate
+        )
+        return (alert_exact, service_exact, quality_score)
+
     def _generate_recommendation_reason(
         self,
         playbook: Playbook,