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

backend/llm_client.py

@@ -4,12 +4,13 @@ InsightFlow LLM Client - Phase 4
 用于与 Kimi API 交互，支持 RAG 问答和 Agent 功能
 """
 
-import os
 import json
-import httpx
-from typing import List, Dict, AsyncGenerator
+import os
+from collections.abc import AsyncGenerator
 from dataclasses import dataclass
 
+import httpx
+
 KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
 KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
 
@@ -44,7 +45,7 @@ class LLMClient:
         self.base_url = base_url or KIMI_BASE_URL
         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")
@@ -64,7 +65,7 @@ class LLMClient:
             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")
@@ -96,7 +97,7 @@ class LLMClient:
 
     async def extract_entities_with_confidence(
         self, text: str
-    ) -> tuple[List[EntityExtractionResult], List[RelationExtractionResult]]:
+    ) -> 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：
 
@@ -152,7 +153,7 @@ class LLMClient:
             print(f"Parse extraction result failed: {e}")
             return [], []
 
-    async def rag_query(self, query: str, context: str, project_context: Dict) -> str:
+    async def rag_query(self, query: str, context: str, project_context: dict) -> str:
         """RAG 问答 - 基于项目上下文回答问题"""
         prompt = f"""你是一个专业的项目分析助手。基于以下项目信息回答问题：
 
@@ -174,7 +175,7 @@ class LLMClient:
 
         return await self.chat(messages, temperature=0.3)
 
-    async def agent_command(self, command: str, project_context: Dict) -> Dict:
+    async def agent_command(self, command: str, project_context: dict) -> dict:
         """Agent 指令解析 - 将自然语言指令转换为结构化操作"""
         prompt = f"""解析以下用户指令，转换为结构化操作：
 
@@ -214,7 +215,7 @@ class LLMClient:
         except BaseException:
             return {"intent": "unknown", "explanation": "解析失败"}
 
-    async def analyze_entity_evolution(self, entity_name: str, mentions: List[Dict]) -> str:
+    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