perf: ruff format & remove stream params in requester

libs/dingtalk_api/api.py

@@ -3,7 +3,6 @@ import json
 import time
 from typing import Callable
 import dingtalk_stream  # type: ignore
-from dingtalk_stream import  AckMessage, ChatbotHandler, CallbackHandler, CallbackMessage, ChatbotMessage, AICardReplier
 from .EchoHandler import EchoTextHandler
 from .dingtalkevent import DingTalkEvent
 import httpx
@@ -254,24 +253,23 @@ class DingTalkClient:
             await self.logger.error(f'failed to send proactive massage to group: {traceback.format_exc()}')
             raise Exception(f'failed to send proactive massage to group: {traceback.format_exc()}')
 
-    async def create_and_card(self, temp_card_id: str, incoming_message: dingtalk_stream.ChatbotMessage,quote_origin:bool=False):
-        content_key = "content"
-        card_data = {content_key: ""}
+    async def create_and_card(
+        self, temp_card_id: str, incoming_message: dingtalk_stream.ChatbotMessage, quote_origin: bool = False
+    ):
+        content_key = 'content'
+        card_data = {content_key: ''}
 
-        card_instance = dingtalk_stream.AICardReplier(
-            self.client, incoming_message
-        )
+        card_instance = dingtalk_stream.AICardReplier(self.client, incoming_message)
         # print(card_instance)
         # 先投放卡片: https://open.dingtalk.com/document/orgapp/create-and-deliver-cards
         card_instance_id = await card_instance.async_create_and_deliver_card(
-            temp_card_id, card_data,
+            temp_card_id,
+            card_data,
         )
-        return card_instance,card_instance_id
+        return card_instance, card_instance_id
 
-    async def send_card_message(self,
-                                card_instance,
-                                card_instance_id: str,content: str,is_final: bool):
-        content_key = "content"
+    async def send_card_message(self, card_instance, card_instance_id: str, content: str, is_final: bool):
+        content_key = 'content'
         try:
             await card_instance.async_streaming(
                 card_instance_id,
@@ -286,16 +284,12 @@ class DingTalkClient:
             await card_instance.async_streaming(
                 card_instance_id,
                 content_key=content_key,
-                content_value="",
+                content_value='',
                 append=False,
                 finished=is_final,
                 failed=True,
             )
 
-
-
-
-
     async def start(self):
         """启动 WebSocket 连接，监听消息"""
         await self.client.start()

pkg/api/http/controller/groups/pipelines/webchat.py

@@ -14,8 +14,9 @@ class WebChatDebugRouterGroup(group.RouterGroup):
 
             async def stream_generator(generator):
                 async for message in generator:
-                    yield f"data: {json.dumps({'message': message})}\n\n"
-                yield "data: {\"type\": \"end\"}\n\n"
+                    yield f'data: {json.dumps({"message": message})}\n\n'
+                yield 'data: {"type": "end"}\n\n'
+
             try:
                 data = await quart.request.get_json()
                 session_type = data.get('session_type', 'person')
@@ -34,18 +35,18 @@ class WebChatDebugRouterGroup(group.RouterGroup):
                     return self.http_status(404, -1, 'WebChat adapter not found')
 
                 if is_stream:
-
-                    generator = webchat_adapter.send_webchat_message(pipeline_uuid, session_type, message_chain_obj, is_stream)
-
-                    return quart.Response(
-                        stream_generator(generator),
-                        mimetype='text/event-stream'
+                    generator = webchat_adapter.send_webchat_message(
+                        pipeline_uuid, session_type, message_chain_obj, is_stream
                     )
 
+                    return quart.Response(stream_generator(generator), mimetype='text/event-stream')
+
                 else:
                     # result = await webchat_adapter.send_webchat_message(pipeline_uuid, session_type, message_chain_obj)
                     result = None
-                    async for message in webchat_adapter.send_webchat_message(pipeline_uuid, session_type, message_chain_obj):
+                    async for message in webchat_adapter.send_webchat_message(
+                        pipeline_uuid, session_type, message_chain_obj
+                    ):
                         result = message
                     if result is not None:
                         return self.success(
@@ -56,7 +57,6 @@ class WebChatDebugRouterGroup(group.RouterGroup):
                     else:
                         return self.http_status(400, -1, 'message is required')
 
-
             except Exception as e:
                 return self.http_status(500, -1, f'Internal server error: {str(e)}')
 

pkg/core/entities.py

@@ -87,7 +87,9 @@ class Query(pydantic.BaseModel):
     """使用的函数，由前置处理器阶段设置"""
 
     resp_messages: (
-        typing.Optional[list[llm_entities.Message]] | typing.Optional[list[platform_message.MessageChain]] | typing.Optional[list[llm_entities.MessageChunk]]
+        typing.Optional[list[llm_entities.Message]]
+        | typing.Optional[list[platform_message.MessageChain]]
+        | typing.Optional[list[llm_entities.MessageChunk]]
     ) = []
     """由Process阶段生成的回复消息对象列表"""
 

pkg/pipeline/cntfilter/cntfilter.py

@@ -67,7 +67,7 @@ class ContentFilterStage(stage.PipelineStage):
         if query.pipeline_config['safety']['content-filter']['scope'] == 'output-msg':
             return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
         if not message.strip():
-            return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)    
+            return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
         else:
             for filter in self.filter_chain:
                 if filter_entities.EnableStage.PRE in filter.enable_stages:

pkg/pipeline/process/handlers/chat.py

@@ -81,9 +81,7 @@ class ChatMessageHandler(handler.MessageHandler):
                             query.resp_message_chain.pop()
 
                         query.resp_messages.append(result)
-                        self.ap.logger.info(
-                            f'对话({query.query_id})流式响应: {self.cut_str(result.readable_str())}'
-                        )
+                        self.ap.logger.info(f'对话({query.query_id})流式响应: {self.cut_str(result.readable_str())}')
 
                         if result.content is not None:
                             text_length += len(result.content)

pkg/pipeline/respback/respback.py

@@ -3,12 +3,10 @@ from __future__ import annotations
 import random
 import asyncio
 
-from typing_inspection.typing_objects import is_final
 
 from ...platform.types import events as platform_events
 from ...platform.types import message as platform_message
 
-from ...provider import entities as llm_entities
 
 from .. import stage, entities
 from ...core import entities as core_entities
@@ -56,6 +54,4 @@ class SendResponseBackStage(stage.PipelineStage):
                 quote_origin=quote_origin,
             )
 
-
-
         return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

pkg/platform/adapter.py

@@ -25,7 +25,6 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
 
     logger: EventLogger
 
-
     def __init__(self, config: dict, ap: app.Application, logger: EventLogger):
         """初始化适配器
 
@@ -80,12 +79,12 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
         """
         raise NotImplementedError
 
-    async def create_message_card(self, message_id:typing.Type[str,int], event:platform_events.MessageEvent) -> bool:
+    async def create_message_card(self, message_id: typing.Type[str, int], event: platform_events.MessageEvent) -> bool:
         """创建卡片消息
         Args:
             message_id (str): 消息ID
             event (platform_events.MessageEvent): 消息源事件
-            """
+        """
         return False
 
     async def is_muted(self, group_id: int) -> bool:
@@ -94,8 +93,8 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
 
     def register_listener(
         self,
-        event_type: typing.Type[platform_message.Event],
-        callback: typing.Callable[[platform_message.Event, MessagePlatformAdapter], None],
+        event_type: typing.Type[platform_events.Event],
+        callback: typing.Callable[[platform_events.Event, MessagePlatformAdapter], None],
     ):
         """注册事件监听器
 
@@ -107,8 +106,8 @@ class MessagePlatformAdapter(metaclass=abc.ABCMeta):
 
     def unregister_listener(
         self,
-        event_type: typing.Type[platform_message.Event],
-        callback: typing.Callable[[platform_message.Event, MessagePlatformAdapter], None],
+        event_type: typing.Type[platform_events.Event],
+        callback: typing.Callable[[platform_events.Event, MessagePlatformAdapter], None],
     ):
         """注销事件监听器
 
@@ -167,7 +166,7 @@ class EventConverter:
     """事件转换器基类"""
 
     @staticmethod
-    def yiri2target(event: typing.Type[platform_message.Event]):
+    def yiri2target(event: typing.Type[platform_events.Event]):
         """将源平台事件转换为目标平台事件
 
         Args:
@@ -179,7 +178,7 @@ class EventConverter:
         raise NotImplementedError
 
     @staticmethod
-    def target2yiri(event: typing.Any) -> platform_message.Event:
+    def target2yiri(event: typing.Any) -> platform_events.Event:
         """将目标平台事件的调用参数转换为源平台的事件参数对象
 
         Args:

pkg/platform/sources/dingtalk.py

@@ -149,10 +149,10 @@ class DingTalkAdapter(adapter.MessagePlatformAdapter):
         quote_origin: bool = False,
         is_final: bool = False,
     ):
-        event = await DingTalkEventConverter.yiri2target(
-            message_source,
-        )
-        incoming_message = event.incoming_message
+        # event = await DingTalkEventConverter.yiri2target(
+        #     message_source,
+        # )
+        # incoming_message = event.incoming_message
 
         # msg_id = incoming_message.message_id
 

pkg/platform/sources/discord.py

@@ -8,7 +8,6 @@ import base64
 import uuid
 import os
 import datetime
-import io
 import asyncio
 from enum import Enum
 

pkg/platform/sources/qqbotpy.py

@@ -501,7 +501,7 @@ class OfficialAdapter(adapter_model.MessagePlatformAdapter):
             for event_handler in event_handler_mapping[event_type]:
                 setattr(self.bot, event_handler, wrapper)
         except Exception as e:
-            self.logger.error(f"Error in qqbotpy callback: {traceback.format_exc()}")
+            self.logger.error(f'Error in qqbotpy callback: {traceback.format_exc()}')
             raise e
 
     def unregister_listener(

pkg/platform/sources/telegram.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import time
 
 import telegram
 import telegram.ext

pkg/platform/sources/webchat.py

@@ -133,7 +133,11 @@ class WebChatAdapter(msadapter.MessagePlatformAdapter):
         )
 
         # notify waiter
-        session = (self.webchat_group_session if isinstance(message_source, platform_events.GroupMessage) else self.webchat_person_session)
+        session = (
+            self.webchat_group_session
+            if isinstance(message_source, platform_events.GroupMessage)
+            else self.webchat_person_session
+        )
         if message_source.message_chain.message_id not in session.resp_waiters:
             # session.resp_waiters[message_source.message_chain.message_id] = asyncio.Queue()
             queue = session.resp_queues[message_source.message_chain.message_id]
@@ -147,10 +151,8 @@ class WebChatAdapter(msadapter.MessagePlatformAdapter):
         # print(message_data)
         await queue.put(message_data)
 
-
-
         return message_data.model_dump()
-    
+
     async def is_stream_output_supported(self) -> bool:
         return self.is_stream
 
@@ -186,7 +188,10 @@ class WebChatAdapter(msadapter.MessagePlatformAdapter):
         await self.logger.info('WebChat调试适配器正在停止')
 
     async def send_webchat_message(
-        self, pipeline_uuid: str, session_type: str, message_chain_obj: typing.List[dict],
+        self,
+        pipeline_uuid: str,
+        session_type: str,
+        message_chain_obj: typing.List[dict],
         is_stream: bool = False,
     ) -> dict:
         self.is_stream = is_stream
@@ -202,7 +207,7 @@ class WebChatAdapter(msadapter.MessagePlatformAdapter):
 
         if is_stream:
             use_session.resp_queues[message_id] = asyncio.Queue()
-            logger.debug(f"Initialized queue for message_id: {message_id}")
+            logger.debug(f'Initialized queue for message_id: {message_id}')
 
         use_session.get_message_list(pipeline_uuid).append(
             WebChatMessage(

pkg/platform/sources/wechatpad.py

@@ -241,8 +241,8 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
         #         self.logger.info("_handler_compound_quote", ET.tostring(xml_data, encoding='unicode'))
         appmsg_data = xml_data.find('.//appmsg')
         quote_data = ''  # 引用原文
-        quote_id = None  # 引用消息的原发送者
-        tousername = None  # 接收方: 所属微信的wxid
+        # quote_id = None  # 引用消息的原发送者
+        # tousername = None  # 接收方: 所属微信的wxid
         user_data = ''  # 用户消息
         sender_id = xml_data.findtext('.//fromusername')  # 发送方：单聊用户/群member
 
@@ -250,13 +250,10 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
         if appmsg_data:
             user_data = appmsg_data.findtext('.//title') or ''
             quote_data = appmsg_data.find('.//refermsg').findtext('.//content')
-            quote_id = appmsg_data.find('.//refermsg').findtext('.//chatusr')
+            # quote_id = appmsg_data.find('.//refermsg').findtext('.//chatusr')
             message_list.append(platform_message.WeChatAppMsg(app_msg=ET.tostring(appmsg_data, encoding='unicode')))
-        if message:
-            tousername = message['to_user_name']['str']
-
-        _ = quote_id
-        _ = tousername
+        # if message:
+        #     tousername = message['to_user_name']['str']
 
         if quote_data:
             quote_data_message_list = platform_message.MessageChain()

pkg/platform/types/message.py

@@ -812,12 +812,14 @@ class File(MessageComponent):
     def __str__(self):
         return f'[文件]{self.name}'
 
+
 class Face(MessageComponent):
     """系统表情
     此处将超级表情骰子/划拳，一同归类于face
     当face_type为rps(划拳)时 face_id 对应的是手势
     当face_type为dice(骰子)时 face_id 对应的是点数
     """
+
     type: str = 'Face'
     """表情类型"""
     face_type: str = 'face'
@@ -834,15 +836,15 @@ class Face(MessageComponent):
         elif self.face_type == 'rps':
             return f'[表情]{self.face_name}({self.rps_data(self.face_id)})'
 
-
-    def rps_data(self,face_id):
-        rps_dict ={
-            1 : "布",
-            2 : "剪刀",
-            3 : "石头",
+    def rps_data(self, face_id):
+        rps_dict = {
+            1: '布',
+            2: '剪刀',
+            3: '石头',
         }
         return rps_dict[face_id]
 
+
 # ================ 个人微信专用组件 ================
 
 
@@ -971,5 +973,6 @@ class WeChatFile(MessageComponent):
     """文件地址"""
     file_base64: str = ''
     """base64"""
+
     def __str__(self):
-        return f'[文件]{self.file_name}'
+        return f'[文件]{self.file_name}'

pkg/provider/entities.py

@@ -127,6 +127,7 @@ class Message(pydantic.BaseModel):
 
 class MessageChunk(pydantic.BaseModel):
     """消息"""
+
     resp_message_id: typing.Optional[str] = None
     """消息id"""
 
@@ -148,7 +149,7 @@ class MessageChunk(pydantic.BaseModel):
     tool_call_id: typing.Optional[str] = None
 
     # tool_calls: typing.Optional[list[ToolCallChunk]] = None
-    
+
     is_final: bool = False
 
     def readable_str(self) -> str:
@@ -210,6 +211,7 @@ class ToolCallChunk(pydantic.BaseModel):
     function: FunctionCall
     """函数调用"""
 
+
 class Prompt(pydantic.BaseModel):
     """供AI使用的Prompt"""
 

pkg/provider/modelmgr/requester.py

@@ -94,19 +94,18 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
             extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
 
         Returns:
-            llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk]: 返回消息对象
+            llm_entities.Message: 返回消息对象
         """
         pass
 
     @abc.abstractmethod
     async def invoke_llm_stream(
-            self,
-            query: core_entities.Query,
-            model: RuntimeLLMModel,
-            messages: typing.List[llm_entities.Message],
-            funcs: typing.List[tools_entities.LLMFunction] = None,
-            stream: bool = False,
-            extra_args: dict[str, typing.Any] = {},
+        self,
+        query: core_entities.Query,
+        model: RuntimeLLMModel,
+        messages: typing.List[llm_entities.Message],
+        funcs: typing.List[tools_entities.LLMFunction] = None,
+        extra_args: dict[str, typing.Any] = {},
     ) -> llm_entities.MessageChunk:
         """调用API
 
@@ -117,7 +116,7 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
             extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
 
         Returns:
-            llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk]: 返回消息对象
+            typing.AsyncGenerator[llm_entities.MessageChunk]: 返回消息对象
         """
         pass
 

pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -8,7 +8,7 @@ import openai.types.chat.chat_completion as chat_completion
 import httpx
 
 from .. import errors, requester
-from ....core import entities as core_entities, app
+from ....core import entities as core_entities
 from ... import entities as llm_entities
 from ...tools import entities as tools_entities
 
@@ -129,12 +129,10 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
         req_messages: list[dict],
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[tools_entities.LLMFunction] = None,
-        stream: bool = False,
         extra_args: dict[str, typing.Any] = {},
-    ) ->llm_entities.MessageChunk:
+    ) -> llm_entities.MessageChunk:
         self.client.api_key = use_model.token_mgr.get_token()
 
-
         args = {}
         args['model'] = use_model.model_entity.name
 
@@ -158,43 +156,42 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
 
         args['messages'] = messages
 
-        if stream:
-            current_content = ''
-            args['stream'] = True
-            chunk_idx = 0
-            self.is_content = False
-            tool_calls_map: dict[str, llm_entities.ToolCall] = {}
-            pipeline_config = query.pipeline_config
-            async for chunk in self._req_stream(args, extra_body=extra_args):
-                # 处理流式消息
-                delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
-                if delta_message.content:
-                    current_content += delta_message.content
-                    delta_message.content = current_content
-                    # delta_message.all_content = current_content
-                if delta_message.tool_calls:
-                    for tool_call in delta_message.tool_calls:
-                        if tool_call.id not in tool_calls_map:
-                            tool_calls_map[tool_call.id] = llm_entities.ToolCall(
-                                id=tool_call.id,
-                                type=tool_call.type,
-                                function=llm_entities.FunctionCall(
-                                    name=tool_call.function.name if tool_call.function else '', arguments=''
-                                ),
-                            )
-                        if tool_call.function and tool_call.function.arguments:
-                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
-                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
+        current_content = ''
+        args['stream'] = True
+        chunk_idx = 0
+        self.is_content = False
+        tool_calls_map: dict[str, llm_entities.ToolCall] = {}
+        pipeline_config = query.pipeline_config
+        async for chunk in self._req_stream(args, extra_body=extra_args):
+            # 处理流式消息
+            delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
+            if delta_message.content:
+                current_content += delta_message.content
+                delta_message.content = current_content
+                # delta_message.all_content = current_content
+            if delta_message.tool_calls:
+                for tool_call in delta_message.tool_calls:
+                    if tool_call.id not in tool_calls_map:
+                        tool_calls_map[tool_call.id] = llm_entities.ToolCall(
+                            id=tool_call.id,
+                            type=tool_call.type,
+                            function=llm_entities.FunctionCall(
+                                name=tool_call.function.name if tool_call.function else '', arguments=''
+                            ),
+                        )
+                    if tool_call.function and tool_call.function.arguments:
+                        # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
+                        tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
-                chunk_idx += 1
-                chunk_choices = getattr(chunk, 'choices', None)
-                if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
-                    delta_message.is_final = True
-                    delta_message.content = current_content
+            chunk_idx += 1
+            chunk_choices = getattr(chunk, 'choices', None)
+            if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
+                delta_message.is_final = True
+                delta_message.content = current_content
 
-                if chunk_idx % 64 == 0 or delta_message.is_final:
-                    yield delta_message
-                # return
+            if chunk_idx % 64 == 0 or delta_message.is_final:
+                yield delta_message
+            # return
 
     async def _closure(
         self,
@@ -202,7 +199,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
         req_messages: list[dict],
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[tools_entities.LLMFunction] = None,
-        stream: bool = False,
         extra_args: dict[str, typing.Any] = {},
     ) -> llm_entities.Message:
         self.client.api_key = use_model.token_mgr.get_token()
@@ -317,7 +313,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
         model: requester.RuntimeLLMModel,
         messages: typing.List[llm_entities.Message],
         funcs: typing.List[tools_entities.LLMFunction] = None,
-        stream: bool = False,
         extra_args: dict[str, typing.Any] = {},
     ) -> llm_entities.MessageChunk:
         req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
@@ -337,7 +332,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
                 req_messages=req_messages,
                 use_model=model,
                 use_funcs=funcs,
-                stream=stream,
                 extra_args=extra_args,
             ):
                 yield item

pkg/provider/modelmgr/requesters/giteeaichatcmpl.py

@@ -12,7 +12,6 @@ import re
 import openai.types.chat.chat_completion as chat_completion
 
 
-
 class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
     """Gitee AI ChatCompletions API 请求器"""
 
@@ -20,7 +19,7 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
         'base_url': 'https://ai.gitee.com/v1',
         'timeout': 120,
     }
-    is_think:bool = False
+    is_think: bool = False
 
     async def _closure(
         self,
@@ -52,15 +51,14 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
 
         pipeline_config = query.pipeline_config
 
-        message = await self._make_msg(resp,pipeline_config)
+        message = await self._make_msg(resp, pipeline_config)
 
         return message
 
-
     async def _make_msg(
-            self,
-            chat_completion: chat_completion.ChatCompletion,
-            pipeline_config: dict[str, typing.Any] = {'trigger': {'misc': {'remove_think': False}}},
+        self,
+        chat_completion: chat_completion.ChatCompletion,
+        pipeline_config: dict[str, typing.Any] = {'trigger': {'misc': {'remove_think': False}}},
     ) -> llm_entities.Message:
         chatcmpl_message = chat_completion.choices[0].message.model_dump()
         # print(chatcmpl_message.keys(), chatcmpl_message.values())
@@ -73,23 +71,25 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
 
         # deepseek的reasoner模型
         if pipeline_config['trigger'].get('misc', '').get('remove_think'):
-            chatcmpl_message['content'] = re.sub(r'<think>.*?</think>', '', chatcmpl_message['content'], flags=re.DOTALL)
+            chatcmpl_message['content'] = re.sub(
+                r'<think>.*?</think>', '', chatcmpl_message['content'], flags=re.DOTALL
+            )
         else:
             if reasoning_content is not None:
-                chatcmpl_message['content'] = '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
+                chatcmpl_message['content'] = (
+                    '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
+                )
 
         message = llm_entities.Message(**chatcmpl_message)
 
         return message
 
-
     async def _make_msg_chunk(
         self,
         pipeline_config: dict[str, typing.Any],
         chat_completion: chat_completion.ChatCompletion,
         idx: int,
     ) -> llm_entities.MessageChunk:
-
         # 处理流式chunk和完整响应的差异
         # print(chat_completion.choices[0])
         if hasattr(chat_completion, 'choices'):
@@ -104,7 +104,6 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
         if 'role' not in delta or delta['role'] is None:
             delta['role'] = 'assistant'
 
-
         reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
 
         delta['content'] = '' if delta['content'] is None else delta['content']
@@ -115,7 +114,7 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
             if delta['content'] == '<think>':
                 self.is_think = True
                 delta['content'] = ''
-            if delta['content'] == rf'</think>':
+            if delta['content'] == r'</think>':
                 self.is_think = False
                 delta['content'] = ''
             if not self.is_think:
@@ -126,7 +125,6 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
             if reasoning_content is not None:
                 delta['content'] += reasoning_content
 
-
         message = llm_entities.MessageChunk(**delta)
 
         return message
@@ -137,7 +135,6 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
         req_messages: list[dict],
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[tools_entities.LLMFunction] = None,
-        stream: bool = False,
         extra_args: dict[str, typing.Any] = {},
     ) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
         self.client.api_key = use_model.token_mgr.get_token()
@@ -165,44 +162,38 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
 
         args['messages'] = messages
 
-        if stream:
-            current_content = ''
-            args["stream"] = True
-            chunk_idx = 0
-            self.is_content = False
-            tool_calls_map: dict[str, llm_entities.ToolCall] = {}
-            pipeline_config = query.pipeline_config
-            async for chunk in self._req_stream(args, extra_body=extra_args):
-                # 处理流式消息
-                delta_message = await self._make_msg_chunk(pipeline_config,chunk,chunk_idx)
-                if delta_message.content:
-                    current_content += delta_message.content
-                    delta_message.content = current_content
-                    # delta_message.all_content = current_content
-                if delta_message.tool_calls:
-                    for tool_call in delta_message.tool_calls:
-                        if tool_call.id not in tool_calls_map:
-                            tool_calls_map[tool_call.id] = llm_entities.ToolCall(
-                                id=tool_call.id,
-                                type=tool_call.type,
-                                function=llm_entities.FunctionCall(
-                                    name=tool_call.function.name if tool_call.function else '',
-                                    arguments=''
-                                ),
-                            )
-                        if tool_call.function and tool_call.function.arguments:
-                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
-                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-
-
-                chunk_idx += 1
-                chunk_choices = getattr(chunk, 'choices', None)
-                if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
-                    delta_message.is_final = True
-                    delta_message.content = current_content
-
-                if chunk_idx % 64 == 0 or delta_message.is_final:
-
-                    yield delta_message
+        current_content = ''
+        args['stream'] = True
+        chunk_idx = 0
+        self.is_content = False
+        tool_calls_map: dict[str, llm_entities.ToolCall] = {}
+        pipeline_config = query.pipeline_config
+        async for chunk in self._req_stream(args, extra_body=extra_args):
+            # 处理流式消息
+            delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
+            if delta_message.content:
+                current_content += delta_message.content
+                delta_message.content = current_content
+                # delta_message.all_content = current_content
+            if delta_message.tool_calls:
+                for tool_call in delta_message.tool_calls:
+                    if tool_call.id not in tool_calls_map:
+                        tool_calls_map[tool_call.id] = llm_entities.ToolCall(
+                            id=tool_call.id,
+                            type=tool_call.type,
+                            function=llm_entities.FunctionCall(
+                                name=tool_call.function.name if tool_call.function else '', arguments=''
+                            ),
+                        )
+                    if tool_call.function and tool_call.function.arguments:
+                        # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
+                        tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
+            chunk_idx += 1
+            chunk_choices = getattr(chunk, 'choices', None)
+            if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
+                delta_message.is_final = True
+                delta_message.content = current_content
 
+            if chunk_idx % 64 == 0 or delta_message.is_final:
+                yield delta_message

pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -165,11 +165,10 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
         return message
 
     async def _req_stream(
-            self,
-            args: dict,
-            extra_body: dict = {},
+        self,
+        args: dict,
+        extra_body: dict = {},
     ) -> chat_completion.ChatCompletion:
-
         async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
             yield chunk
 
@@ -179,7 +178,6 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
         chat_completion: chat_completion.ChatCompletion,
         idx: int,
     ) -> llm_entities.MessageChunk:
-
         # 处理流式chunk和完整响应的差异
         # print(chat_completion.choices[0])
         if hasattr(chat_completion, 'choices'):
@@ -195,7 +193,6 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
         if 'role' not in delta or delta['role'] is None:
             delta['role'] = 'assistant'
 
-
         reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
 
         delta['content'] = '' if delta['content'] is None else delta['content']
@@ -203,13 +200,13 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
 
         # deepseek的reasoner模型
         if pipeline_config['trigger'].get('misc', '').get('remove_think'):
-            if reasoning_content is not None :
+            if reasoning_content is not None:
                 pass
             else:
                 delta['content'] = delta['content']
         else:
             if reasoning_content is not None and idx == 0:
-                delta['content']  += f'<think>\n{reasoning_content}'
+                delta['content'] += f'<think>\n{reasoning_content}'
             elif reasoning_content is None:
                 if self.is_content:
                     delta['content'] = delta['content']
@@ -219,7 +216,6 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
             else:
                 delta['content'] += reasoning_content
 
-
         message = llm_entities.MessageChunk(**delta)
 
         return message
@@ -230,7 +226,6 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
         req_messages: list[dict],
         use_model: requester.RuntimeLLMModel,
         use_funcs: list[tools_entities.LLMFunction] = None,
-        stream: bool = False,
         extra_args: dict[str, typing.Any] = {},
     ) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
         self.client.api_key = use_model.token_mgr.get_token()
@@ -258,48 +253,42 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
 
         args['messages'] = messages
 
-        if stream:
-            current_content = ''
-            args["stream"] = True
-            chunk_idx = 0
-            self.is_content = False
-            tool_calls_map: dict[str, llm_entities.ToolCall] = {}
-            pipeline_config = query.pipeline_config
-            async for chunk in self._req_stream(args, extra_body=extra_args):
-                # 处理流式消息
-                delta_message = await self._make_msg_chunk(pipeline_config,chunk,chunk_idx)
-                if delta_message.content:
-                    current_content += delta_message.content
-                    delta_message.content = current_content
-                    # delta_message.all_content = current_content
-                if delta_message.tool_calls:
-                    for tool_call in delta_message.tool_calls:
-                        if tool_call.id not in tool_calls_map:
-                            tool_calls_map[tool_call.id] = llm_entities.ToolCall(
-                                id=tool_call.id,
-                                type=tool_call.type,
-                                function=llm_entities.FunctionCall(
-                                    name=tool_call.function.name if tool_call.function else '',
-                                    arguments=''
-                                ),
-                            )
-                        if tool_call.function and tool_call.function.arguments:
-                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
-                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-
-
-                chunk_idx += 1
-                chunk_choices = getattr(chunk, 'choices', None)
-                if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
-                    delta_message.is_final = True
-                    delta_message.content = current_content
-
-                if chunk_idx % 64 == 0 or delta_message.is_final:
-
-                    yield delta_message
-                # return
+        current_content = ''
+        args['stream'] = True
+        chunk_idx = 0
+        self.is_content = False
+        tool_calls_map: dict[str, llm_entities.ToolCall] = {}
+        pipeline_config = query.pipeline_config
+        async for chunk in self._req_stream(args, extra_body=extra_args):
+            # 处理流式消息
+            delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
+            if delta_message.content:
+                current_content += delta_message.content
+                delta_message.content = current_content
+                # delta_message.all_content = current_content
+            if delta_message.tool_calls:
+                for tool_call in delta_message.tool_calls:
+                    if tool_call.id not in tool_calls_map:
+                        tool_calls_map[tool_call.id] = llm_entities.ToolCall(
+                            id=tool_call.id,
+                            type=tool_call.type,
+                            function=llm_entities.FunctionCall(
+                                name=tool_call.function.name if tool_call.function else '', arguments=''
+                            ),
+                        )
+                    if tool_call.function and tool_call.function.arguments:
+                        # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
+                        tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
+            chunk_idx += 1
+            chunk_choices = getattr(chunk, 'choices', None)
+            if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
+                delta_message.is_final = True
+                delta_message.content = current_content
 
+            if chunk_idx % 64 == 0 or delta_message.is_final:
+                yield delta_message
+            # return
 
     async def invoke_llm(
         self,
@@ -340,16 +329,14 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
         except openai.APIError as e:
             raise errors.RequesterError(f'请求错误: {e.message}')
 
-
     async def invoke_llm_stream(
         self,
         query: core_entities.Query,
         model: requester.RuntimeLLMModel,
         messages: typing.List[llm_entities.Message],
         funcs: typing.List[tools_entities.LLMFunction] = None,
-        stream: bool = False,
         extra_args: dict[str, typing.Any] = {},
-    ) ->  llm_entities.MessageChunk:
+    ) -> llm_entities.MessageChunk:
         req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
         for m in messages:
             msg_dict = m.dict(exclude_none=True)
@@ -367,7 +354,6 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
                 req_messages=req_messages,
                 use_model=model,
                 use_funcs=funcs,
-                stream=stream,
                 extra_args=extra_args,
             ):
                 yield item
@@ -386,4 +372,4 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
         except openai.RateLimitError as e:
             raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
         except openai.APIError as e:
-            raise errors.RequesterError(f'请求错误: {e.message}')
+            raise errors.RequesterError(f'请求错误: {e.message}')

pkg/provider/modelmgr/requesters/ppiochatcmpl.py

@@ -5,8 +5,8 @@ import typing
 
 from . import chatcmpl
 import openai.types.chat.chat_completion as chat_completion
-from .. import errors, requester
-from ....core import entities as core_entities, app
+from .. import requester
+from ....core import entities as core_entities
 from ... import entities as llm_entities
 from ...tools import entities as tools_entities
 import re
@@ -25,9 +25,9 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
     is_think: bool = False
 
     async def _make_msg(
-            self,
-            chat_completion: chat_completion.ChatCompletion,
-            pipeline_config: dict[str, typing.Any] = {'trigger': {'misc': {'remove_think': False}}},
+        self,
+        chat_completion: chat_completion.ChatCompletion,
+        pipeline_config: dict[str, typing.Any] = {'trigger': {'misc': {'remove_think': False}}},
     ) -> llm_entities.Message:
         chatcmpl_message = chat_completion.choices[0].message.model_dump()
         # print(chatcmpl_message.keys(), chatcmpl_message.values())
@@ -40,21 +40,24 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
 
         # deepseek的reasoner模型
         if pipeline_config['trigger'].get('misc', '').get('remove_think'):
-            chatcmpl_message['content'] = re.sub(r'<think>.*?</think>', '', chatcmpl_message['content'], flags=re.DOTALL)
+            chatcmpl_message['content'] = re.sub(
+                r'<think>.*?</think>', '', chatcmpl_message['content'], flags=re.DOTALL
+            )
         else:
             if reasoning_content is not None:
-                chatcmpl_message['content'] = '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
+                chatcmpl_message['content'] = (
+                    '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
+                )
 
         message = llm_entities.Message(**chatcmpl_message)
 
         return message
 
-
     async def _make_msg_chunk(
-            self,
-            pipeline_config: dict[str, typing.Any],
-            chat_completion: chat_completion.ChatCompletion,
-            idx: int,
+        self,
+        pipeline_config: dict[str, typing.Any],
+        chat_completion: chat_completion.ChatCompletion,
+        idx: int,
     ) -> llm_entities.MessageChunk:
         # 处理流式chunk和完整响应的差异
         # print(chat_completion.choices[0])
@@ -80,7 +83,7 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
             if '<think>' in delta['content']:
                 self.is_think = True
                 delta['content'] = ''
-            if rf'</think>' in delta['content']:
+            if r'</think>' in delta['content']:
                 self.is_think = False
                 delta['content'] = ''
             if not self.is_think:
@@ -95,15 +98,13 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
 
         return message
 
-
     async def _closure_stream(
-            self,
-            query: core_entities.Query,
-            req_messages: list[dict],
-            use_model: requester.RuntimeLLMModel,
-            use_funcs: list[tools_entities.LLMFunction] = None,
-            stream: bool = False,
-            extra_args: dict[str, typing.Any] = {},
+        self,
+        query: core_entities.Query,
+        req_messages: list[dict],
+        use_model: requester.RuntimeLLMModel,
+        use_funcs: list[tools_entities.LLMFunction] = None,
+        extra_args: dict[str, typing.Any] = {},
     ) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
         self.client.api_key = use_model.token_mgr.get_token()
 
@@ -130,40 +131,38 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
 
         args['messages'] = messages
 
-        if stream:
-            current_content = ''
-            args["stream"] = True
-            chunk_idx = 0
-            self.is_content = False
-            tool_calls_map: dict[str, llm_entities.ToolCall] = {}
-            pipeline_config = query.pipeline_config
-            async for chunk in self._req_stream(args, extra_body=extra_args):
-                # 处理流式消息
-                delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
-                if delta_message.content:
-                    current_content += delta_message.content
-                    delta_message.content = current_content
-                    # delta_message.all_content = current_content
-                if delta_message.tool_calls:
-                    for tool_call in delta_message.tool_calls:
-                        if tool_call.id not in tool_calls_map:
-                            tool_calls_map[tool_call.id] = llm_entities.ToolCall(
-                                id=tool_call.id,
-                                type=tool_call.type,
-                                function=llm_entities.FunctionCall(
-                                    name=tool_call.function.name if tool_call.function else '',
-                                    arguments=''
-                                ),
-                            )
-                        if tool_call.function and tool_call.function.arguments:
-                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
-                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
+        current_content = ''
+        args['stream'] = True
+        chunk_idx = 0
+        self.is_content = False
+        tool_calls_map: dict[str, llm_entities.ToolCall] = {}
+        pipeline_config = query.pipeline_config
+        async for chunk in self._req_stream(args, extra_body=extra_args):
+            # 处理流式消息
+            delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
+            if delta_message.content:
+                current_content += delta_message.content
+                delta_message.content = current_content
+                # delta_message.all_content = current_content
+            if delta_message.tool_calls:
+                for tool_call in delta_message.tool_calls:
+                    if tool_call.id not in tool_calls_map:
+                        tool_calls_map[tool_call.id] = llm_entities.ToolCall(
+                            id=tool_call.id,
+                            type=tool_call.type,
+                            function=llm_entities.FunctionCall(
+                                name=tool_call.function.name if tool_call.function else '', arguments=''
+                            ),
+                        )
+                    if tool_call.function and tool_call.function.arguments:
+                        # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
+                        tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
-                chunk_idx += 1
-                chunk_choices = getattr(chunk, 'choices', None)
-                if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
-                    delta_message.is_final = True
-                    delta_message.content = current_content
+            chunk_idx += 1
+            chunk_choices = getattr(chunk, 'choices', None)
+            if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
+                delta_message.is_final = True
+                delta_message.content = current_content
 
-                if chunk_idx % 64 == 0 or delta_message.is_final:
-                    yield delta_message
+            if chunk_idx % 64 == 0 or delta_message.is_final:
+                yield delta_message

pkg/provider/runners/difysvapi.py

@@ -348,7 +348,9 @@ class DifyServiceAPIRunner(runner.RequestRunner):
         except AttributeError:
             is_stream = False
 
-        batch_pending_index = 0
+        _ = is_stream
+
+        # batch_pending_index = 0
 
         plain_text, image_ids = await self._preprocess_user_message(query)
 

pkg/provider/runners/localagent.py

@@ -128,8 +128,7 @@ class LocalAgentRunner(runner.RequestRunner):
                                 id=tool_call.id,
                                 type=tool_call.type,
                                 function=llm_entities.FunctionCall(
-                                    name=tool_call.function.name if tool_call.function else '',
-                                    arguments=''
+                                    name=tool_call.function.name if tool_call.function else '', arguments=''
                                 ),
                             )
                         if tool_call.function and tool_call.function.arguments:

pkg/utils/image.py

@@ -204,9 +204,9 @@ async def get_slack_image_to_base64(pic_url: str, bot_token: str):
     try:
         async with aiohttp.ClientSession() as session:
             async with session.get(pic_url, headers=headers) as resp:
-                mime_type = resp.headers.get("Content-Type", "application/octet-stream")
+                mime_type = resp.headers.get('Content-Type', 'application/octet-stream')
                 file_bytes = await resp.read()
-                base64_str = base64.b64encode(file_bytes).decode("utf-8")
-            return f"data:{mime_type};base64,{base64_str}"
+                base64_str = base64.b64encode(file_bytes).decode('utf-8')
+            return f'data:{mime_type};base64,{base64_str}'
     except Exception as e:
-        raise (e)
+        raise (e)

pkg/utils/importutil.py

@@ -32,7 +32,7 @@ def import_dir(path: str):
             rel_path = full_path.replace(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), '')
             rel_path = rel_path[1:]
             rel_path = rel_path.replace('/', '.')[:-3]
-            rel_path = rel_path.replace("\\",".")
+            rel_path = rel_path.replace('\\', '.')
             importlib.import_module(rel_path)