Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

src/chat/heart_flow/heartflow_message_processor.py

@@ -112,7 +112,7 @@ class HeartFCMessageReceiver:
 
             # subheartflow.add_message_to_normal_chat_cache(message, interested_rate, is_mentioned)
 
-            chat_mood = mood_manager.get_mood_by_chat_id(subheartflow.chat_id)  # type: ignore
+            chat_mood = mood_manager.get_mood_by_chat_id(subheartflow.chat_id)
             asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
 
             # 3. 日志记录

src/chat/message_receive/message.py

@@ -497,10 +497,10 @@ class MessageSending(MessageProcessBase):
     # ) -> "MessageSending":
     #     """从思考状态消息创建发送状态消息"""
     #     return cls(
-    #         message_id=thinking.message_info.message_id,  # type: ignore
+    #         message_id=thinking.message_info.message_id,
     #         chat_stream=thinking.chat_stream,
     #         message_segment=message_segment,
-    #         bot_user_info=thinking.message_info.user_info,  # type: ignore
+    #         bot_user_info=thinking.message_info.user_info,
     #         reply=thinking.reply,
     #         is_head=is_head,
     #         is_emoji=is_emoji,

src/chat/utils/statistic.py

@@ -2348,7 +2348,7 @@ class AsyncStatisticOutputTask(AsyncTask):
 
     @staticmethod
     def _format_model_classified_stat(stats: Dict[str, Any]) -> str:
-        return StatisticOutputTask._format_model_classified_stat(stats)  # type: ignore
+        return StatisticOutputTask._format_model_classified_stat(stats)
 
     def _format_chat_stat(self, stats: Dict[str, Any]) -> str:
         return StatisticOutputTask._format_chat_stat(self, stats)  # type: ignore

src/chat/utils/utils.py

@@ -285,7 +285,7 @@ def random_remove_punctuation(text: str) -> str:
                 continue
         elif char == "，":
             rand = random.random()
-            if rand < 0.25:  # 5%概率删除逗号
+            if rand < 0.05:  # 5%概率删除逗号
                 continue
             elif rand < 0.25:  # 20%概率把逗号变成空格
                 result += " "
@@ -628,7 +628,7 @@ def get_chat_type_and_target_info(chat_id: str) -> Tuple[bool, Optional[Dict]]:
             elif chat_stream.user_info:  # It's a private chat
                 is_group_chat = False
                 user_info = chat_stream.user_info
-                platform: str = chat_stream.platform  # type: ignore
+                platform: str = chat_stream.platform
                 user_id: str = user_info.user_id  # type: ignore
 
                 # Initialize target_info with basic info

src/chat/willing/willing_manager.py

@@ -94,7 +94,7 @@ class BaseWillingManager(ABC):
 
     def setup(self, message: dict, chat: ChatStream):
         person_id = PersonInfoManager.get_person_id(chat.platform, chat.user_info.user_id)  # type: ignore
-        self.ongoing_messages[message.get("message_id", "")] = WillingInfo(  # type: ignore
+        self.ongoing_messages[message.get("message_id", "")] = WillingInfo(
             message=message,
             chat=chat,
             person_info_manager=get_person_info_manager(),

src/common/database/database_model.py

@@ -65,7 +65,7 @@ class ChatStreams(BaseModel):
     # user_cardname 可能为空字符串或不存在，设置 null=True 更具灵活性。
     user_cardname = TextField(null=True)
 
-    class Meta:  # type: ignore
+    class Meta:
         # 如果 BaseModel.Meta.database 已设置，则此模型将继承该数据库配置。
         # 如果不使用带有数据库实例的 BaseModel，或者想覆盖它，
         # 请取消注释并在下面设置数据库实例：
@@ -89,7 +89,7 @@ class LLMUsage(BaseModel):
     status = TextField()
     timestamp = DateTimeField(index=True)  # 更改为 DateTimeField 并添加索引
 
-    class Meta:  # type: ignore
+    class Meta:
         # 如果 BaseModel.Meta.database 已设置，则此模型将继承该数据库配置。
         # database = db
         table_name = "llm_usage"
@@ -112,7 +112,7 @@ class Emoji(BaseModel):
     usage_count = IntegerField(default=0)  # 使用次数（被使用的次数）
     last_used_time = FloatField(null=True)  # 上次使用时间
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "emoji"
 
@@ -163,7 +163,7 @@ class Messages(BaseModel):
     is_picid = BooleanField(default=False)
     is_command = BooleanField(default=False)
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "messages"
 
@@ -187,7 +187,7 @@ class ActionRecords(BaseModel):
     chat_info_stream_id = TextField()
     chat_info_platform = TextField()
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "action_records"
 
@@ -207,7 +207,7 @@ class Images(BaseModel):
     type = TextField()  # 图像类型，例如 "emoji"
     vlm_processed = BooleanField(default=False)  # 是否已经过VLM处理
 
-    class Meta:  # type: ignore
+    class Meta:
         table_name = "images"
 
 
@@ -221,7 +221,7 @@ class ImageDescriptions(BaseModel):
     description = TextField()  # 图像的描述
     timestamp = FloatField()  # 时间戳
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "image_descriptions"
 
@@ -237,7 +237,7 @@ class OnlineTime(BaseModel):
     start_timestamp = DateTimeField(default=datetime.datetime.now)
     end_timestamp = DateTimeField(index=True)
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "online_time"
 
@@ -264,7 +264,7 @@ class PersonInfo(BaseModel):
     last_know = FloatField(null=True)  # 最后一次印象总结时间
     attitude = IntegerField(null=True, default=50)  # 态度，0-100，从非常厌恶到十分喜欢
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "person_info"
 
@@ -277,7 +277,7 @@ class Memory(BaseModel):
     create_time = FloatField(null=True)
     last_view_time = FloatField(null=True)
 
-    class Meta:  # type: ignore
+    class Meta:
         table_name = "memory"
 
 
@@ -290,7 +290,7 @@ class Knowledges(BaseModel):
     embedding = TextField()  # 知识内容的嵌入向量，存储为 JSON 字符串的浮点数列表
     # 可以添加其他元数据字段，如 source, create_time 等
 
-    class Meta:  # type: ignore
+    class Meta:
         # database = db # 继承自 BaseModel
         table_name = "knowledges"
 
@@ -307,7 +307,7 @@ class Expression(BaseModel):
     chat_id = TextField(index=True)
     type = TextField()
 
-    class Meta:  # type: ignore
+    class Meta:
         table_name = "expression"
 
 
@@ -331,7 +331,7 @@ class ThinkingLog(BaseModel):
     # And: import datetime
     created_at = DateTimeField(default=datetime.datetime.now)
 
-    class Meta:  # type: ignore
+    class Meta:
         table_name = "thinking_logs"
 
 
@@ -346,7 +346,7 @@ class GraphNodes(BaseModel):
     created_time = FloatField()  # 创建时间戳
     last_modified = FloatField()  # 最后修改时间戳
 
-    class Meta:  # type: ignore
+    class Meta:
         table_name = "graph_nodes"
 
 
@@ -362,7 +362,7 @@ class GraphEdges(BaseModel):
     created_time = FloatField()  # 创建时间戳
     last_modified = FloatField()  # 最后修改时间戳
 
-    class Meta:  # type: ignore
+    class Meta:
         table_name = "graph_edges"
 
 

src/llm_models/utils_model.py

@@ -2,7 +2,7 @@ import asyncio
 import json
 import re
 from datetime import datetime
-from typing import Tuple, Union, Dict, Any
+from typing import Tuple, Union, Dict, Any, Callable
 import aiohttp
 from aiohttp.client import ClientResponse
 from src.common.logger import get_logger
@@ -300,7 +300,7 @@ class LLMRequest:
         file_format: str = None,
         payload: dict = None,
         retry_policy: dict = None,
-        response_handler: callable = None,
+        response_handler: Callable = None,
         user_id: str = "system",
         request_type: str = None,
     ):
@@ -336,19 +336,17 @@ class LLMRequest:
                     headers["Accept"] = "text/event-stream"
                 async with aiohttp.ClientSession(connector=await get_tcp_connector()) as session:
                     post_kwargs = {"headers": headers}
-                    #form-data数据上传方式不同
+                    # form-data数据上传方式不同
                     if file_bytes:
                         post_kwargs["data"] = request_content["payload"]
                     else:
                         post_kwargs["json"] = request_content["payload"]
 
-                    async with session.post(                            
+                    async with session.post(request_content["api_url"], **post_kwargs) as response:
-                        request_content["api_url"], **post_kwargs
-                    ) as response:
                         handled_result = await self._handle_response(
                             response, request_content, retry, response_handler, user_id, request_type, endpoint
                         )
-                        return handled_result             
+                        return handled_result
 
             except Exception as e:
                 handled_payload, count_delta = await self._handle_exception(e, retry, request_content)
@@ -366,11 +364,11 @@ class LLMRequest:
         response: ClientResponse,
         request_content: Dict[str, Any],
         retry_count: int,
-        response_handler: callable,
+        response_handler: Callable,
         user_id,
         request_type,
         endpoint,
-    ) -> Union[Dict[str, Any], None]:
+    ):
         policy = request_content["policy"]
         stream_mode = request_content["stream_mode"]
         if response.status in policy["retry_codes"] or response.status in policy["abort_codes"]:
@@ -477,9 +475,7 @@ class LLMRequest:
         }
         return result
 
-    async def _handle_error_response(
+    async def _handle_error_response(self, response: ClientResponse, retry_count: int, policy: Dict[str, Any]):
-        self, response: ClientResponse, retry_count: int, policy: Dict[str, Any]
-    ) -> Union[Dict[str, any]]:
         if response.status in policy["retry_codes"]:
             wait_time = policy["base_wait"] * (2**retry_count)
             logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
@@ -629,7 +625,9 @@ class LLMRequest:
                 )
                 # 安全地检查和记录请求详情
                 handled_payload = await _safely_record(request_content, payload)
-                logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {str(handled_payload)[:100]}")
+                logger.critical(
+                    f"请求头: {await self._build_headers(no_key=True)} 请求体: {str(handled_payload)[:100]}"
+                )
                 raise RuntimeError(
                     f"模型 {self.model_name} API请求失败: 状态码 {exception.status}, {exception.message}"
                 )
@@ -643,7 +641,9 @@ class LLMRequest:
                 logger.critical(f"模型 {self.model_name} 请求失败: {str(exception)}")
                 # 安全地检查和记录请求详情
                 handled_payload = await _safely_record(request_content, payload)
-                logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {str(handled_payload)[:100]}")
+                logger.critical(
+                    f"请求头: {await self._build_headers(no_key=True)} 请求体: {str(handled_payload)[:100]}"
+                )
                 raise RuntimeError(f"模型 {self.model_name} API请求失败: {str(exception)}")
 
     async def _transform_parameters(self, params: dict) -> dict:
@@ -682,15 +682,14 @@ class LLMRequest:
             logger.warning(f"暂不支持的文件类型: {file_format}")
 
         data.add_field(
-            "file",io.BytesIO(file_bytes),
+            "file",
+            io.BytesIO(file_bytes),
             filename=f"file.{file_format}",
-            content_type=f'{content_type}' # 根据实际文件类型设置
+            content_type=f"{content_type}",  # 根据实际文件类型设置
-        )
-        data.add_field(
-            "model", self.model_name
         )
+        data.add_field("model", self.model_name)
         return data
-    
+
     async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
         """构建请求体"""
         # 复制一份参数，避免直接修改 self.params
@@ -819,9 +818,11 @@ class LLMRequest:
 
     async def generate_response_for_voice(self, voice_bytes: bytes) -> Tuple:
         """根据输入的语音文件生成模型的异步响应"""
-        response = await self._execute_request(endpoint="/audio/transcriptions",file_bytes=voice_bytes, file_format='wav')
+        response = await self._execute_request(
+            endpoint="/audio/transcriptions", file_bytes=voice_bytes, file_format="wav"
+        )
         return response
-    
+
     async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
         """异步方式根据输入的提示生成模型的响应"""
         # 构建请求体，不硬编码max_tokens

src/mood/mood_manager.py

@@ -134,7 +134,7 @@ class ChatMood:
 
         self.mood_state = response
 
-        self.last_change_time = message_time  # type: ignore
+        self.last_change_time = message_time
 
     async def regress_mood(self):
         message_time = time.time()

src/tools/tool_can_use/rename_person_tool.py

@@ -71,7 +71,7 @@ class RenamePersonTool(BaseTool):
                 user_nickname=user_nickname,  # type: ignore
                 user_cardname=user_cardname,  # type: ignore
                 user_avatar=user_avatar,  # type: ignore
-                request=request_context,  # type: ignore
+                request=request_context,
             )
 
             # 3. 处理结果