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perf: ruff format & remove stream params in requester

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This commit is contained in:
Junyan Qin
2025-08-03 13:08:51 +08:00
parent 68906c43ff
commit 47ff883fc7
24 changed files with 263 additions and 299 deletions
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pkg/provider/modelmgr/requesters/modelscopechatcmpl.py
+41 -55
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@@ -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}')