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 (E302, E305, E501)
- 修复行长度超过100字符的问题
- 修复F821未定义名称错误

backend/ai_manager.py

@@ -27,6 +27,7 @@ import httpx
 # Database path
 DB_PATH = os.path.join(os.path.dirname(__file__), "insightflow.db")
 
+
 class ModelType(StrEnum):
     """模型类型"""
 
@@ -35,6 +36,7 @@ class ModelType(StrEnum):
     SUMMARIZATION = "summarization"  # 摘要
     PREDICTION = "prediction"  # 预测
 
+
 class ModelStatus(StrEnum):
     """模型状态"""
 
@@ -44,6 +46,7 @@ class ModelStatus(StrEnum):
     FAILED = "failed"
     ARCHIVED = "archived"
 
+
 class MultimodalProvider(StrEnum):
     """多模态模型提供商"""
 
@@ -52,6 +55,7 @@ class MultimodalProvider(StrEnum):
     GEMINI = "gemini-pro-vision"
     KIMI_VL = "kimi-vl"
 
+
 class PredictionType(StrEnum):
     """预测类型"""
 
@@ -60,6 +64,7 @@ class PredictionType(StrEnum):
     ENTITY_GROWTH = "entity_growth"  # 实体增长预测
     RELATION_EVOLUTION = "relation_evolution"  # 关系演变预测
 
+
 @dataclass
 class CustomModel:
     """自定义模型"""
@@ -79,6 +84,7 @@ class CustomModel:
     trained_at: str | None
     created_by: str
 
+
 @dataclass
 class TrainingSample:
     """训练样本"""
@@ -90,6 +96,7 @@ class TrainingSample:
     metadata: dict
     created_at: str
 
+
 @dataclass
 class MultimodalAnalysis:
     """多模态分析结果"""
@@ -106,6 +113,7 @@ class MultimodalAnalysis:
     cost: float
     created_at: str
 
+
 @dataclass
 class KnowledgeGraphRAG:
     """基于知识图谱的 RAG 配置"""
@@ -122,6 +130,7 @@ class KnowledgeGraphRAG:
     created_at: str
     updated_at: str
 
+
 @dataclass
 class RAGQuery:
     """RAG 查询记录"""
@@ -137,6 +146,7 @@ class RAGQuery:
     latency_ms: int
     created_at: str
 
+
 @dataclass
 class PredictionModel:
     """预测模型"""
@@ -156,6 +166,7 @@ class PredictionModel:
     created_at: str
     updated_at: str
 
+
 @dataclass
 class PredictionResult:
     """预测结果"""
@@ -171,6 +182,7 @@ class PredictionResult:
     is_correct: bool | None
     created_at: str
 
+
 @dataclass
 class SmartSummary:
     """智能摘要"""
@@ -188,6 +200,7 @@ class SmartSummary:
     tokens_used: int
     created_at: str
 
+
 class AIManager:
     """AI 能力管理主类"""
 
@@ -242,7 +255,8 @@ class AIManager:
                 """
                 INSERT INTO custom_models
                 (id, tenant_id, name, description, model_type, status, training_data,
-                 hyperparameters, metrics, model_path, created_at, updated_at, trained_at, created_by)
+                hyperparameters, metrics, model_path, created_at, updated_at,
+                trained_at, created_by)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
                 (
@@ -846,7 +860,8 @@ class AIManager:
             conn.execute(
                 """
                 INSERT INTO rag_queries
-                (id, rag_id, query, context, answer, sources, confidence, tokens_used, latency_ms, created_at)
+                (id, rag_id, query, context, answer, sources, confidence,
+                tokens_used, latency_ms, created_at)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
                 (
@@ -1077,8 +1092,9 @@ class AIManager:
             conn.execute(
                 """
                 INSERT INTO prediction_models
-                (id, tenant_id, project_id, name, prediction_type, target_entity_type, features,
-                 model_config, accuracy, last_trained_at, prediction_count, is_active, created_at, updated_at)
+                (id, tenant_id, project_id, name, prediction_type, target_entity_type,
+                features, model_config, accuracy, last_trained_at, prediction_count,
+                is_active, created_at, updated_at)
                 VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
                 (
@@ -1487,9 +1503,11 @@ class AIManager:
             created_at=row["created_at"],
         )
 
+
 # Singleton instance
 _ai_manager = None
 
+
 def get_ai_manager() -> AIManager:
     global _ai_manager
     if _ai_manager is None: