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/entity_aligner.py

@@ -15,6 +15,7 @@ import numpy as np
 KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
 KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
 
+
 @dataclass
 class EntityEmbedding:
     entity_id: str
@@ -22,6 +23,7 @@ class EntityEmbedding:
     definition: str
     embedding: list[float]
 
+
 class EntityAligner:
     """实体对齐器 - 使用 embedding 进行相似度匹配"""
 
@@ -50,7 +52,10 @@ class EntityAligner:
         try:
             response = httpx.post(
                 f"{KIMI_BASE_URL}/v1/embeddings",
-                headers={"Authorization": f"Bearer {KIMI_API_KEY}", "Content-Type": "application/json"},
+                headers={
+                    "Authorization": f"Bearer {KIMI_API_KEY}",
+                    "Content-Type": "application/json",
+                },
                 json={"model": "k2p5", "input": text[:500]},  # 限制长度
                 timeout=30.0,
             )
@@ -230,7 +235,12 @@ class EntityAligner:
                 project_id, new_ent["name"], new_ent.get("definition", ""), threshold=threshold
             )
 
-            result = {"new_entity": new_ent, "matched_entity": None, "similarity": 0.0, "should_merge": False}
+            result = {
+                "new_entity": new_ent,
+                "matched_entity": None,
+                "similarity": 0.0,
+                "should_merge": False,
+            }
 
             if matched:
                 # 计算相似度
@@ -282,8 +292,15 @@ class EntityAligner:
         try:
             response = httpx.post(
                 f"{KIMI_BASE_URL}/v1/chat/completions",
-                headers={"Authorization": f"Bearer {KIMI_API_KEY}", "Content-Type": "application/json"},
-                json={"model": "k2p5", "messages": [{"role": "user", "content": prompt}], "temperature": 0.3},
+                headers={
+                    "Authorization": f"Bearer {KIMI_API_KEY}",
+                    "Content-Type": "application/json",
+                },
+                json={
+                    "model": "k2p5",
+                    "messages": [{"role": "user", "content": prompt}],
+                    "temperature": 0.3,
+                },
                 timeout=30.0,
             )
             response.raise_for_status()
@@ -301,6 +318,7 @@ class EntityAligner:
 
         return []
 
+
 # 简单的字符串相似度计算（不使用 embedding）
 def simple_similarity(str1: str, str2: str) -> float:
     """
@@ -332,6 +350,7 @@ def simple_similarity(str1: str, str2: str) -> float:
 
     return SequenceMatcher(None, s1, s2).ratio()
 
+
 if __name__ == "__main__":
     # 测试
     aligner = EntityAligner()