fix: auto-fix code issues (cron)

- 修复重复导入/字段
- 修复异常处理
- 修复PEP8格式问题
- 添加类型注解
- 修复缺失的urllib.parse导入

backend/llm_client.py

@@ -15,11 +15,13 @@ import httpx
 KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
 KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
 
+
 @dataclass
 class ChatMessage:
     role: str
     content: str
 
+
 @dataclass
 class EntityExtractionResult:
     name: str
@@ -27,6 +29,7 @@ class EntityExtractionResult:
     definition: str
     confidence: float
 
+
 @dataclass
 class RelationExtractionResult:
     source: str
@@ -34,15 +37,21 @@ class RelationExtractionResult:
     type: str
     confidence: float
 
+
 class LLMClient:
     """Kimi API 客户端"""
 
     def __init__(self, api_key: str = None, base_url: str = None):
         self.api_key = api_key or KIMI_API_KEY
         self.base_url = base_url or KIMI_BASE_URL
-        self.headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
+        self.headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
 
-    async def chat(self, messages: list[ChatMessage], temperature: float = 0.3, stream: bool = False) -> str:
+    async def chat(
+        self, messages: list[ChatMessage], temperature: float = 0.3, stream: bool = False
+    ) -> str:
         """发送聊天请求"""
         if not self.api_key:
             raise ValueError("KIMI_API_KEY not set")
@@ -56,13 +65,18 @@ class LLMClient:
 
         async with httpx.AsyncClient() as client:
             response = await client.post(
-                f"{self.base_url}/v1/chat/completions", headers=self.headers, json=payload, timeout=120.0
+                f"{self.base_url}/v1/chat/completions",
+                headers=self.headers,
+                json=payload,
+                timeout=120.0,
             )
             response.raise_for_status()
             result = response.json()
             return result["choices"][0]["message"]["content"]
 
-    async def chat_stream(self, messages: list[ChatMessage], temperature: float = 0.3) -> AsyncGenerator[str, None]:
+    async def chat_stream(
+        self, messages: list[ChatMessage], temperature: float = 0.3
+    ) -> AsyncGenerator[str, None]:
         """流式聊天请求"""
         if not self.api_key:
             raise ValueError("KIMI_API_KEY not set")
@@ -76,7 +90,11 @@ class LLMClient:
 
         async with httpx.AsyncClient() as client:
             async with client.stream(
-                "POST", f"{self.base_url}/v1/chat/completions", headers=self.headers, json=payload, timeout=120.0
+                "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():
@@ -164,7 +182,9 @@ class LLMClient:
 请用中文回答，保持简洁专业。如果信息不足，请明确说明。"""
 
         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。"),
+            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。"
+            ),
             ChatMessage(role="user", content=prompt),
         ]
 
@@ -211,7 +231,10 @@ class LLMClient:
     async def analyze_entity_evolution(self, entity_name: str, mentions: list[dict]) -> str:
         """分析实体在项目中的演变/态度变化"""
         mentions_text = "\n".join(
-            [f"[{m.get('created_at', '未知时间')}] {m.get('text_snippet', '')}" for m in mentions[:20]]  # 限制数量
+            [
+                f"[{m.get('created_at', '未知时间')}] {m.get('text_snippet', '')}"
+                for m in mentions[:20]
+            ]  # 限制数量
         )
 
         prompt = f"""分析实体 "{entity_name}" 在项目中的演变和态度变化：
@@ -230,9 +253,11 @@ class LLMClient:
         messages = [ChatMessage(role="user", content=prompt)]
         return await self.chat(messages, temperature=0.3)
 
+
 # Singleton instance
 _llm_client = None
 
+
 def get_llm_client() -> LLMClient:
     global _llm_client
     if _llm_client is None: