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fix: the bug in the "remove_think" function.

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This commit is contained in:
Dong_master
2025-08-06 23:00:57 +08:00
parent e88302f1b4
commit 3a82ae8da5
2 changed files with 26 additions and 18 deletions
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pkg/provider/modelmgr/requesters/chatcmpl.py
+12 -8
View File
@@ -85,16 +85,14 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
if 'role' not in delta or delta['role'] is None: if 'role' not in delta or delta['role'] is None:
delta['role'] = 'assistant' delta['role'] = 'assistant'
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None reasoning_content = delta['reasoning_content']
delta['content'] = '' if delta['content'] is None else delta['content'] delta['content'] = '' if delta['content'] is None else delta['content']
# print(reasoning_content)
# deepseek的reasoner模型 # deepseek的reasoner模型
if reasoning_content is not None and idx == 0: if reasoning_content is not None and idx == 0:
if reasoning_content != '': delta['content'] += f'<think>\n{reasoning_content}'
delta['content'] += f'<think>\n{reasoning_content}' is_think = True
is_think = True
elif reasoning_content is None and idx != 0: elif reasoning_content is None and idx != 0:
if is_content: if is_content:
delta['content'] = delta['content'] delta['content'] = delta['content']
@@ -102,7 +100,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
delta['content'] = f'\n<think>\n\n{delta["content"]}' delta['content'] = f'\n<think>\n\n{delta["content"]}'
is_content = True is_content = True
is_think = False is_think = False
else: elif reasoning_content is not None and reasoning_content != '':
delta['content'] = reasoning_content delta['content'] = reasoning_content
message = llm_entities.MessageChunk(**delta) message = llm_entities.MessageChunk(**delta)
@@ -157,10 +155,16 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
else: else:
# 流式chunk模式 # 流式chunk模式
delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {} delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {}
print(delta)
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
delta['reasoning_content'] = reasoning_content
if remove_think: if remove_think:
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None if delta['reasoning_content'] is not None:
if reasoning_content is not None:
continue continue
if ((delta['content'] == '' or delta.get('content',None) is None) and
(delta.get('reasoning_content',None) is None or delta['reasoning_content'] == '') and
chunk_idx == 0): # 此处将第一条空消息排除，大部分模型第一条消息携带的是role,但是在role直接处理为ass
continue
# 处理流式消息 # 处理流式消息
delta_message,is_content,is_think = await self._make_msg_chunk(delta, delta_message,is_content,is_think = await self._make_msg_chunk(delta,
chunk_idx, chunk_idx,
pkg/provider/modelmgr/requesters/modelscopechatcmpl.py
+14 -10
View File
@@ -184,24 +184,24 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
if 'role' not in delta or delta['role'] is None: if 'role' not in delta or delta['role'] is None:
delta['role'] = 'assistant' delta['role'] = 'assistant'
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None reasoning_content = delta['reasoning_content']
delta['content'] = '' if delta['content'] is None else delta['content'] delta['content'] = '' if delta['content'] is None else delta['content']
# print(reasoning_content) # print(reasoning_content)
# deepseek的reasoner模型 # deepseek的reasoner模型
if reasoning_content is not None and idx == 0: if reasoning_content is not None and idx == 0:
if reasoning_content != '': delta['content'] += f'<think>\n{reasoning_content}'
delta['content'] += f'<think>\n{reasoning_content}' is_think = True
is_think = True elif reasoning_content is None and idx != 0:
elif reasoning_content == '' and idx != 0:
if is_content: if is_content:
delta['content'] = delta['content'] delta['content'] = delta['content']
elif is_think: elif is_think:
delta['content'] = f'\n<think>\n\n{delta["content"]}' delta['content'] = f'\n<think>\n\n{delta["content"]}'
is_content = True is_content = True
is_think = False is_think = False
else: elif reasoning_content is not None:
delta['content'] = reasoning_content delta['content'] = reasoning_content
message = llm_entities.MessageChunk(**delta) message = llm_entities.MessageChunk(**delta)
@@ -256,12 +256,16 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
else: else:
# 流式chunk模式 # 流式chunk模式
delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {} delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {}
print(delta) reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
delta['reasoning_content'] = None if reasoning_content == '' else reasoning_content # 直接不管有没有思考消息，构造一个，方便去除思考判断
if remove_think: if remove_think:
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None if delta['reasoning_content'] is not None:
if reasoning_content != '':
continue continue
# 处理流式消息 if ((delta['content'] == '' or delta.get('content', None) is None) and
(delta.get('reasoning_content', None) is None or delta['reasoning_content'] == '') and
chunk_idx == 0): # 此处将第一条空消息排除，大部分模型第一条消息携带的是role,但是在role直接处理为ass
continue
# 处理流式消息
delta_message, is_content, is_think = await self._make_msg_chunk(delta, delta_message, is_content, is_think = await self._make_msg_chunk(delta,
chunk_idx, chunk_idx,
is_content, is_content,