🗑️ 标记MarshoTools为弃用

nonebot_plugin_marshoai/utils/processor.py

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+from nonebot.log import logger
+from openai import AsyncStream
+from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessage
+from openai.types.chat.chat_completion import Choice
+
+
+async def process_chat_stream(
+    stream: AsyncStream[ChatCompletionChunk],
+) -> 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:
+                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,
+        )