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/knowledge_reasoner.py

@@ -15,6 +15,7 @@ import httpx
 KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
 KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
 
+
 class ReasoningType(Enum):
     """推理类型"""
 
@@ -24,6 +25,7 @@ class ReasoningType(Enum):
     COMPARATIVE = "comparative"  # 对比推理
     SUMMARY = "summary"  # 总结推理
 
+
 @dataclass
 class ReasoningResult:
     """推理结果"""
@@ -35,6 +37,7 @@ class ReasoningResult:
     related_entities: list[str]  # 相关实体
     gaps: list[str]  # 知识缺口
 
+
 @dataclass
 class InferencePath:
     """推理路径"""
@@ -44,24 +47,35 @@ class InferencePath:
     path: list[dict]  # 路径上的节点和关系
     strength: float  # 路径强度
 
+
 class KnowledgeReasoner:
     """知识推理引擎"""
 
     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 _call_llm(self, prompt: str, temperature: float = 0.3) -> str:
         """调用 LLM"""
         if not self.api_key:
             raise ValueError("KIMI_API_KEY not set")
 
-        payload = {"model": "k2p5", "messages": [{"role": "user", "content": prompt}], "temperature": temperature}
+        payload = {
+            "model": "k2p5",
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": temperature,
+        }
 
         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()
@@ -124,7 +138,9 @@ 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
@@ -183,7 +199,9 @@ 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：
@@ -235,7 +253,9 @@ 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：
@@ -287,7 +307,9 @@ 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：
@@ -360,7 +382,9 @@ class KnowledgeReasoner:
                 adj[tgt] = []
             adj[src].append({"target": tgt, "relation": r.get("type", "related"), "data": r})
             # 无向图也添加反向
-            adj[tgt].append({"target": src, "relation": r.get("type", "related"), "data": r, "reverse": True})
+            adj[tgt].append(
+                {"target": src, "relation": r.get("type", "related"), "data": r, "reverse": True}
+            )
 
         # BFS 搜索路径
         from collections import deque
@@ -478,9 +502,11 @@ class KnowledgeReasoner:
             "confidence": 0.5,
         }
 
+
 # Singleton instance
 _reasoner = None
 
+
 def get_knowledge_reasoner() -> KnowledgeReasoner:
     global _reasoner
     if _reasoner is None: