解决类型问题

nonebot_plugin_marshoai/handler.py

@@ -17,7 +17,7 @@ from nonebot.matcher import (
     current_event,
     current_matcher,
 )
-from nonebot_plugin_alconna.uniseg import UniMessage, UniMsg
+from nonebot_plugin_alconna.uniseg import UniMessage, UniMsg, get_message_id, get_target
 from openai import AsyncOpenAI, AsyncStream
 from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage
 
@@ -50,8 +50,8 @@ class MarshoHandler:
         self.event: Event = current_event.get()
         # self.state: T_State = current_handler.get().state
         self.matcher: Matcher = current_matcher.get()
-        self.message_id: str = UniMessage.get_message_id(self.event)
-        self.target = UniMessage.get_target(self.event)
+        self.message_id: str = get_message_id(self.event)
+        self.target = get_target(self.event)
 
     async def process_user_input(
         self, user_input: UniMsg, model_name: str
@@ -117,7 +117,7 @@ class MarshoHandler:
 
     async def handle_function_call(
         self,
-        completion: Union[ChatCompletion, AsyncStream[ChatCompletionChunk]],
+        completion: Union[ChatCompletion],
         user_message: Union[str, list],
         model_name: str,
         tools_list: list | None = None,
@@ -259,7 +259,7 @@ class MarshoHandler:
         model_name: str,
         tools_list: list | None = None,
         tools_message: Optional[list] = None,
-    ) -> Union[ChatCompletion, None]:
+    ) -> ChatCompletion:
         """
         处理流式请求
         """
@@ -274,5 +274,4 @@ class MarshoHandler:
         if isinstance(response, AsyncStream):
             return await process_chat_stream(response)
         else:
-            logger.error("Unexpected response type for stream request")
-            return None
+            raise TypeError("Unexpected response type for stream request")

nonebot_plugin_marshoai/marsho.py

@@ -299,14 +299,16 @@ with contextlib.suppress(ImportError):  # 优化先不做（）
             if config.marshoai_poke_suffix != "":
                 logger.info(f"收到戳一戳，用户昵称：{user_nickname}")
 
-                response = await make_chat_openai(
+                pre_response = await make_chat_openai(
                     client=client,
                     model_name=model_name,
                     msg=usermsg,
                     stream=config.marshoai_stream,
                 )
-                if isinstance(response, AsyncStream):
-                    response = await process_chat_stream(response)
+                if isinstance(pre_response, AsyncStream):
+                    response = await process_chat_stream(pre_response)
+                else:
+                    response = pre_response
                 choice = response.choices[0]  # type: ignore
                 if choice.finish_reason == CompletionsFinishReason.STOPPED:
                     content = extract_content_and_think(choice.message)[0]

nonebot_plugin_marshoai/utils/request.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from nonebot.log import logger
 from openai import AsyncStream
 from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage
@@ -8,58 +6,66 @@ from openai.types.chat.chat_completion import Choice
 
 async def process_chat_stream(
     stream: AsyncStream[ChatCompletionChunk],
-) -> Optional[ChatCompletion]:
-    if isinstance(stream, AsyncStream):
-        reasoning_contents = ""
-        answer_contents = ""
-        last_chunk = None
-        is_first_token_appeared = False
-        is_answering = False
-        async for chunk in stream:
-            last_chunk = chunk
-            # print(chunk)
-            if not is_first_token_appeared:
-                logger.debug(f"{chunk.id}: 第一个 token 已出现")
-                is_first_token_appeared = True
-            if not chunk.choices:
-                logger.info("Usage:", chunk.usage)
+) -> ChatCompletion:
+    reasoning_contents = ""
+    answer_contents = ""
+    last_chunk = None
+    is_first_token_appeared = False
+    is_answering = False
+    async for chunk in stream:
+        last_chunk = chunk
+        # print(chunk)
+        if not is_first_token_appeared:
+            logger.debug(f"{chunk.id}: 第一个 token 已出现")
+            is_first_token_appeared = True
+        if not chunk.choices:
+            logger.info("Usage:", chunk.usage)
+        else:
+            delta = chunk.choices[0].delta
+            if (
+                hasattr(delta, "reasoning_content")
+                and delta.reasoning_content is not None
+            ):
+                reasoning_contents += delta.reasoning_content
             else:
-                delta = chunk.choices[0].delta
-                if (
-                    hasattr(delta, "reasoning_content")
-                    and delta.reasoning_content is not None
-                ):
-                    reasoning_contents += delta.reasoning_content
-                else:
-                    if not is_answering:
-                        logger.debug(
-                            f"{chunk.id}: 思维链已输出完毕或无 reasoning_content 字段输出"
-                        )
-                        is_answering = True
-                    if delta.content is not None:
-                        answer_contents += delta.content
-        # print(last_chunk)
-        # 创建新的 ChatCompletion 对象
-        if last_chunk and last_chunk.choices:
-            message = ChatCompletionMessage(
-                content=answer_contents,
-                role="assistant",
-                tool_calls=last_chunk.choices[0].delta.tool_calls,  # type: ignore
-            )
-            if reasoning_contents != "":
-                setattr(message, "reasoning_content", reasoning_contents)
-            choice = Choice(
-                finish_reason=last_chunk.choices[0].finish_reason,  # type: ignore
-                index=last_chunk.choices[0].index,
-                message=message,
-            )
-            return ChatCompletion(
-                id=last_chunk.id,
-                choices=[choice],
-                created=last_chunk.created,
-                model=last_chunk.model,
-                system_fingerprint=last_chunk.system_fingerprint,
-                object="chat.completion",
-                usage=last_chunk.usage,
-            )
-    return None
+                if not is_answering:
+                    logger.debug(
+                        f"{chunk.id}: 思维链已输出完毕或无 reasoning_content 字段输出"
+                    )
+                    is_answering = True
+                if delta.content is not None:
+                    answer_contents += delta.content
+    # print(last_chunk)
+    # 创建新的 ChatCompletion 对象
+    if last_chunk and last_chunk.choices:
+        message = ChatCompletionMessage(
+            content=answer_contents,
+            role="assistant",
+            tool_calls=last_chunk.choices[0].delta.tool_calls,  # type: ignore
+        )
+        if reasoning_contents != "":
+            setattr(message, "reasoning_content", reasoning_contents)
+        choice = Choice(
+            finish_reason=last_chunk.choices[0].finish_reason,  # type: ignore
+            index=last_chunk.choices[0].index,
+            message=message,
+        )
+        return ChatCompletion(
+            id=last_chunk.id,
+            choices=[choice],
+            created=last_chunk.created,
+            model=last_chunk.model,
+            system_fingerprint=last_chunk.system_fingerprint,
+            object="chat.completion",
+            usage=last_chunk.usage,
+        )
+    else:
+        return ChatCompletion(
+            id="",
+            choices=[],
+            created=0,
+            model="",
+            system_fingerprint="",
+            object="chat.completion",
+            usage=None,
+        )