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/knowledge_reasoner.py

@@ -4,13 +4,13 @@ InsightFlow Knowledge Reasoning - Phase 5
 知识推理与问答增强模块
 """
 
-import os
 import json
-import httpx
-from typing import List, Dict
+import os
 from dataclasses import dataclass
 from enum import Enum
 
+import httpx
+
 KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
 KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
 
@@ -32,9 +32,9 @@ class ReasoningResult:
     answer: str
     reasoning_type: ReasoningType
     confidence: float
-    evidence: List[Dict]  # 支撑证据
-    related_entities: List[str]  # 相关实体
-    gaps: List[str]  # 知识缺口
+    evidence: list[dict]  # 支撑证据
+    related_entities: list[str]  # 相关实体
+    gaps: list[str]  # 知识缺口
 
 
 @dataclass
@@ -43,7 +43,7 @@ class InferencePath:
 
     start_entity: str
     end_entity: str
-    path: List[Dict]  # 路径上的节点和关系
+    path: list[dict]  # 路径上的节点和关系
     strength: float  # 路径强度
 
 
@@ -71,7 +71,7 @@ class KnowledgeReasoner:
             return result["choices"][0]["message"]["content"]
 
     async def enhanced_qa(
-        self, query: str, project_context: Dict, graph_data: Dict, reasoning_depth: str = "medium"
+        self, query: str, project_context: dict, graph_data: dict, reasoning_depth: str = "medium"
     ) -> ReasoningResult:
         """
         增强问答 - 结合图谱推理的问答
@@ -95,7 +95,7 @@ class KnowledgeReasoner:
         else:
             return await self._associative_reasoning(query, project_context, graph_data)
 
-    async def _analyze_question(self, query: str) -> Dict:
+    async def _analyze_question(self, query: str) -> dict:
         """分析问题类型和意图"""
         prompt = f"""分析以下问题的类型和意图：
 
@@ -129,7 +129,7 @@ class KnowledgeReasoner:
 
         return {"type": "factual", "entities": [], "intent": "general", "complexity": "simple"}
 
-    async def _causal_reasoning(self, query: str, project_context: Dict, graph_data: Dict) -> ReasoningResult:
+    async def _causal_reasoning(self, query: str, project_context: dict, graph_data: dict) -> ReasoningResult:
         """因果推理 - 分析原因和影响"""
 
         # 构建因果分析提示
@@ -190,7 +190,7 @@ class KnowledgeReasoner:
             gaps=["无法完成因果推理"],
         )
 
-    async def _comparative_reasoning(self, query: str, project_context: Dict, graph_data: Dict) -> ReasoningResult:
+    async def _comparative_reasoning(self, query: str, project_context: dict, graph_data: dict) -> ReasoningResult:
         """对比推理 - 比较实体间的异同"""
 
         prompt = f"""基于以下知识图谱进行对比分析：
@@ -244,7 +244,7 @@ class KnowledgeReasoner:
             gaps=[],
         )
 
-    async def _temporal_reasoning(self, query: str, project_context: Dict, graph_data: Dict) -> ReasoningResult:
+    async def _temporal_reasoning(self, query: str, project_context: dict, graph_data: dict) -> ReasoningResult:
         """时序推理 - 分析时间线和演变"""
 
         prompt = f"""基于以下知识图谱进行时序分析：
@@ -298,7 +298,7 @@ class KnowledgeReasoner:
             gaps=[],
         )
 
-    async def _associative_reasoning(self, query: str, project_context: Dict, graph_data: Dict) -> ReasoningResult:
+    async def _associative_reasoning(self, query: str, project_context: dict, graph_data: dict) -> ReasoningResult:
         """关联推理 - 发现实体间的隐含关联"""
 
         prompt = f"""基于以下知识图谱进行关联分析：
@@ -353,8 +353,8 @@ class KnowledgeReasoner:
         )
 
     def find_inference_paths(
-        self, start_entity: str, end_entity: str, graph_data: Dict, max_depth: int = 3
-    ) -> List[InferencePath]:
+        self, start_entity: str, end_entity: str, graph_data: dict, max_depth: int = 3
+    ) -> list[InferencePath]:
         """
         发现两个实体之间的推理路径
 
@@ -416,7 +416,7 @@ class KnowledgeReasoner:
         paths.sort(key=lambda p: p.strength, reverse=True)
         return paths
 
-    def _calculate_path_strength(self, path: List[Dict]) -> float:
+    def _calculate_path_strength(self, path: list[dict]) -> float:
         """计算路径强度"""
         if len(path) < 2:
             return 0.0
@@ -438,8 +438,8 @@ class KnowledgeReasoner:
         return length_factor * confidence_factor
 
     async def summarize_project(
-        self, project_context: Dict, graph_data: Dict, summary_type: str = "comprehensive"
-    ) -> Dict:
+        self, project_context: dict, graph_data: dict, summary_type: str = "comprehensive"
+    ) -> dict:
         """
         项目智能总结