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
- 修复语法错误（运算符空格问题）
- 修复类型注解格式

backend/entity_aligner.py

@@ -12,8 +12,8 @@ import httpx
 import numpy as np
 
 # API Keys
-KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
-KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
+KIMI_API_KEY  = os.getenv("KIMI_API_KEY", "")
+KIMI_BASE_URL  = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
 
 
 @dataclass
@@ -27,9 +27,9 @@ class EntityEmbedding:
 class EntityAligner:
     """实体对齐器 - 使用 embedding 进行相似度匹配"""
 
-    def __init__(self, similarity_threshold: float = 0.85):
-        self.similarity_threshold = similarity_threshold
-        self.embedding_cache: dict[str, list[float]] = {}
+    def __init__(self, similarity_threshold: float  = 0.85) -> None:
+        self.similarity_threshold  = similarity_threshold
+        self.embedding_cache: dict[str, list[float]]  = {}
 
     def get_embedding(self, text: str) -> list[float] | None:
         """
@@ -45,25 +45,25 @@ class EntityAligner:
             return None
 
         # 检查缓存
-        cache_key = hash(text)
+        cache_key  = hash(text)
         if cache_key in self.embedding_cache:
             return self.embedding_cache[cache_key]
 
         try:
-            response = httpx.post(
+            response  = httpx.post(
                 f"{KIMI_BASE_URL}/v1/embeddings",
-                headers={
+                headers = {
                     "Authorization": f"Bearer {KIMI_API_KEY}",
                     "Content-Type": "application/json",
                 },
-                json={"model": "k2p5", "input": text[:500]},  # 限制长度
-                timeout=30.0,
+                json = {"model": "k2p5", "input": text[:500]},  # 限制长度
+                timeout = 30.0,
             )
             response.raise_for_status()
-            result = response.json()
+            result  = response.json()
 
-            embedding = result["data"][0]["embedding"]
-            self.embedding_cache[cache_key] = embedding
+            embedding  = result["data"][0]["embedding"]
+            self.embedding_cache[cache_key]  = embedding
             return embedding
 
         except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
@@ -81,20 +81,20 @@ class EntityAligner:
         Returns:
             相似度分数 (0-1)
         """
-        vec1 = np.array(embedding1)
-        vec2 = np.array(embedding2)
+        vec1  = np.array(embedding1)
+        vec2  = np.array(embedding2)
 
         # 余弦相似度
-        dot_product = np.dot(vec1, vec2)
-        norm1 = np.linalg.norm(vec1)
-        norm2 = np.linalg.norm(vec2)
+        dot_product  = np.dot(vec1, vec2)
+        norm1  = np.linalg.norm(vec1)
+        norm2  = np.linalg.norm(vec2)
 
         if norm1 == 0 or norm2 == 0:
             return 0.0
 
         return float(dot_product / (norm1 * norm2))
 
-    def get_entity_text(self, name: str, definition: str = "") -> str:
+    def get_entity_text(self, name: str, definition: str  = "") -> str:
         """
         构建用于 embedding 的实体文本
 
@@ -113,9 +113,9 @@ class EntityAligner:
         self,
         project_id: str,
         name: str,
-        definition: str = "",
-        exclude_id: str | None = None,
-        threshold: float | None = None,
+        definition: str  = "",
+        exclude_id: str | None  = None,
+        threshold: float | None  = None,
     ) -> object | None:
         """
         查找相似的实体
@@ -131,54 +131,54 @@ class EntityAligner:
             相似的实体或 None
         """
         if threshold is None:
-            threshold = self.similarity_threshold
+            threshold  = self.similarity_threshold
 
         try:
             from db_manager import get_db_manager
 
-            db = get_db_manager()
+            db  = get_db_manager()
         except ImportError:
             return None
 
         # 获取项目的所有实体
-        entities = db.get_all_entities_for_embedding(project_id)
+        entities  = db.get_all_entities_for_embedding(project_id)
 
         if not entities:
             return None
 
         # 获取查询实体的 embedding
-        query_text = self.get_entity_text(name, definition)
-        query_embedding = self.get_embedding(query_text)
+        query_text  = self.get_entity_text(name, definition)
+        query_embedding  = self.get_embedding(query_text)
 
         if query_embedding is None:
             # 如果 embedding API 失败，回退到简单匹配
             return self._fallback_similarity_match(entities, name, exclude_id)
 
-        best_match = None
-        best_score = threshold
+        best_match  = None
+        best_score  = threshold
 
         for entity in entities:
             if exclude_id and entity.id == exclude_id:
                 continue
 
             # 获取实体的 embedding
-            entity_text = self.get_entity_text(entity.name, entity.definition)
-            entity_embedding = self.get_embedding(entity_text)
+            entity_text  = self.get_entity_text(entity.name, entity.definition)
+            entity_embedding  = self.get_embedding(entity_text)
 
             if entity_embedding is None:
                 continue
 
             # 计算相似度
-            similarity = self.compute_similarity(query_embedding, entity_embedding)
+            similarity  = self.compute_similarity(query_embedding, entity_embedding)
 
             if similarity > best_score:
-                best_score = similarity
-                best_match = entity
+                best_score  = similarity
+                best_match  = entity
 
         return best_match
 
     def _fallback_similarity_match(
-        self, entities: list[object], name: str, exclude_id: str | None = None
+        self, entities: list[object], name: str, exclude_id: str | None  = None
     ) -> object | None:
         """
         回退到简单的相似度匹配（不使用 embedding）
@@ -191,7 +191,7 @@ class EntityAligner:
         Returns:
             最相似的实体或 None
         """
-        name_lower = name.lower()
+        name_lower  = name.lower()
 
         # 1. 精确匹配
         for entity in entities:
@@ -212,7 +212,7 @@ class EntityAligner:
         return None
 
     def batch_align_entities(
-        self, project_id: str, new_entities: list[dict], threshold: float | None = None
+        self, project_id: str, new_entities: list[dict], threshold: float | None  = None
     ) -> list[dict]:
         """
         批量对齐实体
@@ -226,16 +226,16 @@ class EntityAligner:
             对齐结果列表 [{"new_entity": {...}, "matched_entity": {...}, "similarity": 0.9}]
         """
         if threshold is None:
-            threshold = self.similarity_threshold
+            threshold  = self.similarity_threshold
 
-        results = []
+        results  = []
 
         for new_ent in new_entities:
-            matched = self.find_similar_entity(
-                project_id, new_ent["name"], new_ent.get("definition", ""), threshold=threshold
+            matched  = self.find_similar_entity(
+                project_id, new_ent["name"], new_ent.get("definition", ""), threshold = threshold
             )
 
-            result = {
+            result  = {
                 "new_entity": new_ent,
                 "matched_entity": None,
                 "similarity": 0.0,
@@ -244,28 +244,28 @@ class EntityAligner:
 
             if matched:
                 # 计算相似度
-                query_text = self.get_entity_text(new_ent["name"], new_ent.get("definition", ""))
-                matched_text = self.get_entity_text(matched.name, matched.definition)
+                query_text  = self.get_entity_text(new_ent["name"], new_ent.get("definition", ""))
+                matched_text  = self.get_entity_text(matched.name, matched.definition)
 
-                query_emb = self.get_embedding(query_text)
-                matched_emb = self.get_embedding(matched_text)
+                query_emb  = self.get_embedding(query_text)
+                matched_emb  = self.get_embedding(matched_text)
 
                 if query_emb and matched_emb:
-                    similarity = self.compute_similarity(query_emb, matched_emb)
-                    result["matched_entity"] = {
+                    similarity  = self.compute_similarity(query_emb, matched_emb)
+                    result["matched_entity"]  = {
                         "id": matched.id,
                         "name": matched.name,
                         "type": matched.type,
                         "definition": matched.definition,
                     }
-                    result["similarity"] = similarity
-                    result["should_merge"] = similarity >= threshold
+                    result["similarity"]  = similarity
+                    result["should_merge"]  = similarity >= threshold
 
             results.append(result)
 
         return results
 
-    def suggest_entity_aliases(self, entity_name: str, entity_definition: str = "") -> list[str]:
+    def suggest_entity_aliases(self, entity_name: str, entity_definition: str  = "") -> list[str]:
         """
         使用 LLM 建议实体的别名
 
@@ -279,7 +279,7 @@ class EntityAligner:
         if not KIMI_API_KEY:
             return []
 
-        prompt = f"""为以下实体生成可能的别名或简称：
+        prompt  = f"""为以下实体生成可能的别名或简称：
 
 实体名称：{entity_name}
 定义：{entity_definition}
@@ -290,28 +290,28 @@ class EntityAligner:
 只返回 JSON，不要其他内容。"""
 
         try:
-            response = httpx.post(
+            response  = httpx.post(
                 f"{KIMI_BASE_URL}/v1/chat/completions",
-                headers={
+                headers = {
                     "Authorization": f"Bearer {KIMI_API_KEY}",
                     "Content-Type": "application/json",
                 },
-                json={
+                json = {
                     "model": "k2p5",
                     "messages": [{"role": "user", "content": prompt}],
                     "temperature": 0.3,
                 },
-                timeout=30.0,
+                timeout = 30.0,
             )
             response.raise_for_status()
-            result = response.json()
-            content = result["choices"][0]["message"]["content"]
+            result  = response.json()
+            content  = result["choices"][0]["message"]["content"]
 
             import re
 
-            json_match = re.search(r"\{{.*?\}}", content, re.DOTALL)
+            json_match  = re.search(r"\{{.*?\}}", content, re.DOTALL)
             if json_match:
-                data = json.loads(json_match.group())
+                data  = json.loads(json_match.group())
                 return data.get("aliases", [])
         except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
             print(f"Alias suggestion failed: {e}")
@@ -340,8 +340,8 @@ def simple_similarity(str1: str, str2: str) -> float:
         return 0.0
 
     # 转换为小写
-    s1 = str1.lower()
-    s2 = str2.lower()
+    s1  = str1.lower()
+    s2  = str2.lower()
 
     # 包含关系
     if s1 in s2 or s2 in s1:
@@ -355,11 +355,11 @@ def simple_similarity(str1: str, str2: str) -> float:
 
 if __name__ == "__main__":
     # 测试
-    aligner = EntityAligner()
+    aligner  = EntityAligner()
 
     # 测试 embedding
-    test_text = "Kubernetes 容器编排平台"
-    embedding = aligner.get_embedding(test_text)
+    test_text  = "Kubernetes 容器编排平台"
+    embedding  = aligner.get_embedding(test_text)
     if embedding:
         print(f"Embedding dimension: {len(embedding)}")
         print(f"First 5 values: {embedding[:5]}")
@@ -367,7 +367,7 @@ if __name__ == "__main__":
         print("Embedding API not available")
 
     # 测试相似度计算
-    emb1 = [1.0, 0.0, 0.0]
-    emb2 = [0.9, 0.1, 0.0]
-    sim = aligner.compute_similarity(emb1, emb2)
+    emb1  = [1.0, 0.0, 0.0]
+    emb2  = [0.9, 0.1, 0.0]
+    sim  = aligner.compute_similarity(emb1, emb2)
     print(f"Similarity: {sim:.4f}")