fix: auto-fix code issues (cron)

- 修复隐式 Optional 类型注解 (RUF013)
- 修复不必要的赋值后返回 (RET504)
- 优化列表推导式 (PERF401)
- 修复未使用的参数 (ARG002)
- 清理重复导入
- 优化异常处理

backend/llm_client.py

@@ -43,7 +43,7 @@ class RelationExtractionResult:
 class LLMClient:
     """Kimi API 客户端"""
 
-    def __init__(self, api_key: str = None, base_url: str = None) -> None:
+    def __init__(self, api_key: str | None = None, base_url: str = None) -> None:
         self.api_key = api_key or KIMI_API_KEY
         self.base_url = base_url or KIMI_BASE_URL
         self.headers = {
@@ -52,7 +52,10 @@ class LLMClient:
         }
 
     async def chat(
-        self, messages: list[ChatMessage], temperature: float = 0.3, stream: bool = False,
+        self,
+        messages: list[ChatMessage],
+        temperature: float = 0.3,
+        stream: bool = False,
     ) -> str:
         """发送聊天请求"""
         if not self.api_key:
@@ -77,7 +80,9 @@ class LLMClient:
             return result["choices"][0]["message"]["content"]
 
     async def chat_stream(
-        self, messages: list[ChatMessage], temperature: float = 0.3,
+        self,
+        messages: list[ChatMessage],
+        temperature: float = 0.3,
     ) -> AsyncGenerator[str, None]:
         """流式聊天请求"""
         if not self.api_key:
@@ -90,13 +95,16 @@ class LLMClient:
             "stream": True,
         }
 
-        async with httpx.AsyncClient() as client, client.stream(
-            "POST",
-            f"{self.base_url}/v1/chat/completions",
-            headers=self.headers,
-            json=payload,
-            timeout=120.0,
-        ) as response:
+        async with (
+            httpx.AsyncClient() as client,
+            client.stream(
+                "POST",
+                f"{self.base_url}/v1/chat/completions",
+                headers=self.headers,
+                json=payload,
+                timeout=120.0,
+            ) as response,
+        ):
             response.raise_for_status()
             async for line in response.aiter_lines():
                 if line.startswith("data: "):
@@ -112,7 +120,8 @@ class LLMClient:
                         pass
 
     async def extract_entities_with_confidence(
-        self, text: str,
+        self,
+        text: str,
     ) -> tuple[list[EntityExtractionResult], list[RelationExtractionResult]]:
         """提取实体和关系，带置信度分数"""
         prompt = f"""从以下会议文本中提取关键实体和它们之间的关系，以 JSON 格式返回：
@@ -189,7 +198,8 @@ class LLMClient:
 
         messages = [
             ChatMessage(
-                role="system", content="你是一个专业的项目分析助手，擅长从会议记录中提取洞察。",
+                role="system",
+                content="你是一个专业的项目分析助手，擅长从会议记录中提取洞察。",
             ),
             ChatMessage(role="user", content=prompt),
         ]