fix(api): 修復 7 個 Lint 錯誤 (unused imports + zip strict + dict comprehension)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

apps/api/src/services/auto_approve.py

@@ -25,7 +25,6 @@ from typing import Any
 
 import structlog
 
-from src.models.approval import RiskLevel
 from src.models.playbook import Playbook
 from src.services.playbook_rag import PlaybookMatch
 from src.services.trust_engine import TrustScoreManager, get_trust_manager

apps/api/src/services/decision_manager.py

@@ -484,8 +484,8 @@ class DecisionManager:
         """
         try:
             # 延遲導入避免循環依賴
-            from src.services.approval_execution import ApprovalExecutionService
             from src.models.approval import ApprovalRequest, ApprovalStatus
+            from src.services.approval_execution import ApprovalExecutionService
 
             # 建立虛擬 ApprovalRequest
             approval = ApprovalRequest(

apps/api/src/services/learning_service.py

@@ -26,7 +26,7 @@ from typing import Any
 import structlog
 
 from src.models.approval import ApprovalRequest
-from src.models.incident import Incident, IncidentStatus
+from src.models.incident import IncidentStatus
 from src.services.trust_engine import get_trust_manager
 
 logger = structlog.get_logger(__name__)

apps/api/src/services/playbook_rag.py

@@ -96,7 +96,7 @@ def cosine_similarity(vec_a: list[float], vec_b: list[float]) -> float:
     if len(vec_a) != len(vec_b):
         return 0.0
 
-    dot_product = sum(a * b for a, b in zip(vec_a, vec_b))
+    dot_product = sum(a * b for a, b in zip(vec_a, vec_b, strict=True))
     norm_a = math.sqrt(sum(a * a for a in vec_a))
     norm_b = math.sqrt(sum(b * b for b in vec_b))
 
@@ -423,7 +423,7 @@ class PlaybookRAGService:
             vector_scores = {m.playbook_id: m.similarity_score for m in vector_matches}
 
         # 2. Jaccard 分數
-        jaccard_scores = {pid: score for pid, score in jaccard_results}
+        jaccard_scores = dict(jaccard_results)
 
         # 3. 合併所有 playbook_id
         all_ids = set(vector_scores.keys()) | set(jaccard_scores.keys())