ruff，私聊视为提及了bot

src/common/data_models/__init__.py

@@ -6,6 +6,7 @@ class BaseDataModel:
     def deepcopy(self):
         return copy.deepcopy(self)
 
+
 def temporarily_transform_class_to_dict(obj: Any) -> Any:
     # sourcery skip: assign-if-exp, reintroduce-else
     """

src/common/data_models/bot_interest_data_model.py

@@ -2,6 +2,7 @@
 机器人兴趣标签数据模型
 定义机器人的兴趣标签和相关的embedding数据结构
 """
+
 from dataclasses import dataclass, field
 from typing import List, Dict, Optional, Any
 from datetime import datetime
@@ -12,6 +13,7 @@ from . import BaseDataModel
 @dataclass
 class BotInterestTag(BaseDataModel):
     """机器人兴趣标签"""
+
     tag_name: str
     weight: float = 1.0  # 权重，表示对这个兴趣的喜好程度 (0.0-1.0)
     embedding: Optional[List[float]] = None  # 标签的embedding向量
@@ -27,7 +29,7 @@ class BotInterestTag(BaseDataModel):
             "embedding": self.embedding,
             "created_at": self.created_at.isoformat(),
             "updated_at": self.updated_at.isoformat(),
-            "is_active": self.is_active
+            "is_active": self.is_active,
         }
 
     @classmethod
@@ -39,13 +41,14 @@ class BotInterestTag(BaseDataModel):
             embedding=data.get("embedding"),
             created_at=datetime.fromisoformat(data["created_at"]) if data.get("created_at") else datetime.now(),
             updated_at=datetime.fromisoformat(data["updated_at"]) if data.get("updated_at") else datetime.now(),
-            is_active=data.get("is_active", True)
+            is_active=data.get("is_active", True),
         )
 
 
 @dataclass
 class BotPersonalityInterests(BaseDataModel):
     """机器人人格化兴趣配置"""
+
     personality_id: str
     personality_description: str  # 人设描述文本
     interest_tags: List[BotInterestTag] = field(default_factory=list)
@@ -57,7 +60,6 @@ class BotPersonalityInterests(BaseDataModel):
         """获取活跃的兴趣标签"""
         return [tag for tag in self.interest_tags if tag.is_active]
 
-
     def to_dict(self) -> Dict[str, Any]:
         """转换为字典格式"""
         return {
@@ -66,7 +68,7 @@ class BotPersonalityInterests(BaseDataModel):
             "interest_tags": [tag.to_dict() for tag in self.interest_tags],
             "embedding_model": self.embedding_model,
             "last_updated": self.last_updated.isoformat(),
-            "version": self.version
+            "version": self.version,
         }
 
     @classmethod
@@ -78,13 +80,14 @@ class BotPersonalityInterests(BaseDataModel):
             interest_tags=[BotInterestTag.from_dict(tag_data) for tag_data in data.get("interest_tags", [])],
             embedding_model=data.get("embedding_model", "text-embedding-ada-002"),
             last_updated=datetime.fromisoformat(data["last_updated"]) if data.get("last_updated") else datetime.now(),
-            version=data.get("version", 1)
+            version=data.get("version", 1),
         )
 
 
 @dataclass
 class InterestMatchResult(BaseDataModel):
     """兴趣匹配结果"""
+
     message_id: str
     matched_tags: List[str] = field(default_factory=list)
     match_scores: Dict[str, float] = field(default_factory=dict)  # tag_name -> score
@@ -120,7 +123,9 @@ class InterestMatchResult(BaseDataModel):
         # 计算置信度（基于匹配标签数量和分数分布）
         if len(self.match_scores) > 0:
             avg_score = self.overall_score
-            score_variance = sum((score - avg_score) ** 2 for score in self.match_scores.values()) / len(self.match_scores)
+            score_variance = sum((score - avg_score) ** 2 for score in self.match_scores.values()) / len(
+                self.match_scores
+            )
             # 分数越集中，置信度越高
             self.confidence = max(0.0, 1.0 - score_variance)
         else:
@@ -129,4 +134,4 @@ class InterestMatchResult(BaseDataModel):
     def get_top_matches(self, top_n: int = 3) -> List[tuple]:
         """获取前N个最佳匹配"""
         sorted_matches = sorted(self.match_scores.items(), key=lambda x: x[1], reverse=True)
-        return sorted_matches[:top_n]
+        return sorted_matches[:top_n]

src/common/data_models/database_data_model.py

@@ -208,6 +208,7 @@ class DatabaseMessages(BaseDataModel):
             "chat_info_user_cardname": self.chat_info.user_info.user_cardname,
         }
 
+
 @dataclass(init=False)
 class DatabaseActionRecords(BaseDataModel):
     def __init__(
@@ -235,4 +236,4 @@ class DatabaseActionRecords(BaseDataModel):
         self.action_prompt_display = action_prompt_display
         self.chat_id = chat_id
         self.chat_info_stream_id = chat_info_stream_id
-        self.chat_info_platform = chat_info_platform
+        self.chat_info_platform = chat_info_platform

src/common/data_models/info_data_model.py

@@ -28,6 +28,7 @@ class ActionPlannerInfo(BaseDataModel):
 @dataclass
 class InterestScore(BaseDataModel):
     """兴趣度评分结果"""
+
     message_id: str
     total_score: float
     interest_match_score: float
@@ -41,6 +42,7 @@ class Plan(BaseDataModel):
     """
     统一规划数据模型
     """
+
     chat_id: str
     mode: "ChatMode"
 

src/common/data_models/llm_data_model.py

@@ -2,9 +2,11 @@ from dataclasses import dataclass
 from typing import Optional, List, Tuple, TYPE_CHECKING, Any
 
 from . import BaseDataModel
+
 if TYPE_CHECKING:
     from src.llm_models.payload_content.tool_option import ToolCall
 
+
 @dataclass
 class LLMGenerationDataModel(BaseDataModel):
     content: Optional[str] = None
@@ -13,4 +15,4 @@ class LLMGenerationDataModel(BaseDataModel):
     tool_calls: Optional[List["ToolCall"]] = None
     prompt: Optional[str] = None
     selected_expressions: Optional[List[int]] = None
-    reply_set: Optional[List[Tuple[str, Any]]] = None
+    reply_set: Optional[List[Tuple[str, Any]]] = None

src/common/data_models/message_manager_data_model.py

@@ -2,6 +2,7 @@
 消息管理模块数据模型
 定义消息管理器使用的数据结构
 """
+
 import asyncio
 import time
 from dataclasses import dataclass, field
@@ -16,14 +17,16 @@ if TYPE_CHECKING:
 
 class MessageStatus(Enum):
     """消息状态枚举"""
-    UNREAD = "unread"    # 未读消息
-    READ = "read"        # 已读消息
+
+    UNREAD = "unread"  # 未读消息
+    READ = "read"  # 已读消息
     PROCESSING = "processing"  # 处理中
 
 
 @dataclass
 class StreamContext(BaseDataModel):
     """聊天流上下文信息"""
+
     stream_id: str
     unread_messages: List["DatabaseMessages"] = field(default_factory=list)
     history_messages: List["DatabaseMessages"] = field(default_factory=list)
@@ -59,6 +62,7 @@ class StreamContext(BaseDataModel):
 @dataclass
 class MessageManagerStats(BaseDataModel):
     """消息管理器统计信息"""
+
     total_streams: int = 0
     active_streams: int = 0
     total_unread_messages: int = 0
@@ -74,9 +78,10 @@ class MessageManagerStats(BaseDataModel):
 @dataclass
 class StreamStats(BaseDataModel):
     """聊天流统计信息"""
+
     stream_id: str
     is_active: bool
     unread_count: int
     history_count: int
     last_check_time: float
-    has_active_task: bool
+    has_active_task: bool