fix(api): lint errors in Rate Limiter + RAG services

- Remove unused imports (settings, uuid)
- Add 'from e' to exception raises (B904)
- Add strict=True to zip() (B905)
- Remove unused variable

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

apps/api/src/services/ai_rate_limiter.py

@@ -17,8 +17,6 @@ AI Rate Limiter - Gemini API 用量閥值控制
 
 import structlog
 
-from src.core.config import settings
-
 logger = structlog.get_logger(__name__)
 
 

apps/api/src/services/embedding_service.py

@@ -143,19 +143,19 @@ class OllamaEmbeddingService:
 
             return embedding
 
-        except httpx.TimeoutException:
+        except httpx.TimeoutException as e:
             logger.error("embedding_timeout", model=self._model, text_len=len(text))
-            raise EmbeddingError(f"Embedding timeout after {self._timeout}s")
+            raise EmbeddingError(f"Embedding timeout after {self._timeout}s") from e
         except httpx.HTTPStatusError as e:
             logger.error(
                 "embedding_http_error",
                 status=e.response.status_code,
                 model=self._model,
             )
-            raise EmbeddingError(f"Ollama API error: {e.response.status_code}")
+            raise EmbeddingError(f"Ollama API error: {e.response.status_code}") from e
         except Exception as e:
             logger.error("embedding_error", error=str(e), model=self._model)
-            raise EmbeddingError(f"Embedding failed: {e}")
+            raise EmbeddingError(f"Embedding failed: {e}") from e
 
     async def embed_batch(
         self,

apps/api/src/services/rag_service.py

@@ -16,14 +16,12 @@ Phase 13.2 #84 - Runbook RAG Tool
 
 import hashlib
 import struct
-import uuid
 from pathlib import Path
 from typing import Protocol
 
 import redis.asyncio as redis
 import structlog
 
-from src.core.config import settings
 from src.services.embedding_service import IEmbeddingService, get_embedding_service
 
 logger = structlog.get_logger(__name__)
@@ -311,7 +309,7 @@ class RAGService:
         embeddings = await embedding_service.embed_batch(texts, concurrency=3)
 
         # 儲存到 Redis
-        for chunk, embedding in zip(all_chunks, embeddings):
+        for chunk, embedding in zip(all_chunks, embeddings, strict=True):
             await self._store_chunk(chunk, embedding)
             total_chunks += 1
 
@@ -373,7 +371,7 @@ class RAGService:
         parsed = []
         i = 1
         while i < len(results):
-            key = results[i]
+            # results[i] is the Redis key, results[i+1] is the fields
             fields = results[i + 1] if i + 1 < len(results) else []
 
             # 將 fields list 轉為 dict