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fix:del the chatcmpl.py useless logic,and in the modelscopechatcmpl.py Non-streaming add and del <think> logic,and fix the ppiochatcmpl.py stream logic and the giteeaichatcmpl.py inherit ppiochatcmpl.py

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This commit is contained in:
Dong_master
2025-08-10 00:16:13 +08:00
parent 7c59bc1ce5
commit 8c5cb71812
4 changed files with 78 additions and 236 deletions
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5
pkg/provider/modelmgr/requesters/chatcmpl.py
View File

@@ -160,7 +160,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
thinking_started = False
thinking_ended = False
role = 'assistant' # 默认角色
accumulated_reasoning = '' # 仅用于判断何时结束思维链
# accumulated_reasoning = '' # 仅用于判断何时结束思维链
async for chunk in self._req_stream(args, extra_body=extra_args):
# 解析 chunk 数据
@@ -182,7 +182,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
# 处理 reasoning_content
if reasoning_content:
accumulated_reasoning += reasoning_content
# accumulated_reasoning += reasoning_content
# 如果设置了 remove_think，跳过 reasoning_content
if remove_think:
chunk_idx += 1
@@ -289,6 +289,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
# 发送请求
resp = await self._req(args, extra_body=extra_args)
print(resp)
# 处理请求结果
message = await self._make_msg(resp, remove_think)

182
pkg/provider/modelmgr/requesters/giteeaichatcmpl.py
View File

@@ -3,7 +3,7 @@ from __future__ import annotations
import typing
from . import chatcmpl
from . import ppiochatcmpl
from .. import requester
from ....core import entities as core_entities
from ... import entities as llm_entities
@@ -12,7 +12,7 @@ import re
import openai.types.chat.chat_completion as chat_completion
class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
class GiteeAIChatCompletions(ppiochatcmpl.PPIOChatCompletions):
"""Gitee AI ChatCompletions API 请求器"""
default_config: dict[str, typing.Any] = {
@@ -20,181 +20,3 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
'timeout': 120,
}
async def _closure(
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] = {},
remove_think: bool = False,
) -> llm_entities.Message:
self.client.api_key = use_model.token_mgr.get_token()
args = {}
args['model'] = use_model.model_entity.name
if use_funcs:
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
if tools:
args['tools'] = tools
# gitee 不支持多模态，把content都转换成纯文字
for m in req_messages:
if 'content' in m and isinstance(m['content'], list):
m['content'] = ' '.join([c['text'] for c in m['content']])
args['messages'] = req_messages
resp = await self._req(args, extra_body=extra_args)
message = await self._make_msg(resp, remove_think)
return message
async def _make_msg(
self,
chat_completion: chat_completion.ChatCompletion,
remove_think: bool,
) -> llm_entities.Message:
chatcmpl_message = chat_completion.choices[0].message.model_dump()
# print(chatcmpl_message.keys(), chatcmpl_message.values())
# 确保 role 字段存在且不为 None
if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
chatcmpl_message['role'] = 'assistant'
reasoning_content = chatcmpl_message['reasoning_content'] if 'reasoning_content' in chatcmpl_message else None
# deepseek的reasoner模型
if remove_think:
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']
)
message = llm_entities.Message(**chatcmpl_message)
return message
async def _make_msg_chunk(
self,
delta: dict[str, typing.Any],
idx: int,
) -> llm_entities.MessageChunk:
# 处理流式chunk和完整响应的差异
# print(chat_completion.choices[0])
# 确保 role 字段存在且不为 None
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']
# print(reasoning_content)
# deepseek的reasoner模型
if reasoning_content is not None:
delta['content'] += reasoning_content
message = llm_entities.MessageChunk(**delta)
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,
extra_args: dict[str, typing.Any] = {},
remove_think: bool = False,
) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
self.client.api_key = use_model.token_mgr.get_token()
args = {}
args['model'] = use_model.model_entity.name
if use_funcs:
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
if tools:
args['tools'] = tools
# 设置此次请求中的messages
messages = req_messages.copy()
# 检查vision
for msg in messages:
if 'content' in msg and isinstance(msg['content'], list):
for me in msg['content']:
if me['type'] == 'image_base64':
me['image_url'] = {'url': me['image_base64']}
me['type'] = 'image_url'
del me['image_base64']
args['messages'] = messages
current_content = ''
args['stream'] = True
chunk_idx = 0
is_think = False
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
async for chunk in self._req_stream(args, extra_body=extra_args):
# 处理流式消息
if hasattr(chunk, 'choices'):
# 完整响应模式
if chunk.choices:
choice = chunk.choices[0]
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else choice.message.model_dump()
else:
continue
else:
# 流式chunk模式
delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {}
if remove_think:
print(delta)
if delta['content'] == '<think>':
is_think = True
continue
elif delta['content'] == r'</think>':
is_think = False
continue
elif is_think or delta['content'] == '\n\n':
continue
delta_message = await self._make_msg_chunk(delta, 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
yield delta_message

69
pkg/provider/modelmgr/requesters/modelscopechatcmpl.py
View File

@@ -36,6 +36,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
self,
args: dict,
extra_body: dict = {},
remove_think:bool = False,
) -> chat_completion.ChatCompletion:
args['stream'] = True
@@ -47,11 +48,35 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
resp_gen: openai.AsyncStream = await self.client.chat.completions.create(**args, extra_body=extra_body)
chunk_idx = 0
thinking_started = False
thinking_ended = False
async for chunk in resp_gen:
# print(chunk)
if not chunk or not chunk.id or not chunk.choices or not chunk.choices[0] or not chunk.choices[0].delta:
continue
reasoning_content = chunk.choices[0].delta.reasoning_content
# 处理 reasoning_content
if reasoning_content:
# accumulated_reasoning += reasoning_content
# 如果设置了 remove_think，跳过 reasoning_content
if remove_think:
chunk_idx += 1
continue
# 第一次出现 reasoning_content，添加 <think> 开始标签
if not thinking_started:
thinking_started = True
pending_content += '<think>\n' + reasoning_content
else:
# 继续输出 reasoning_content
pending_content += reasoning_content
elif thinking_started and not thinking_ended and chunk.choices[0].delta.content:
# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
thinking_ended = True
pending_content += '\n</think>\n' + chunk.choices[0].delta.content
if chunk.choices[0].delta.content is not None:
pending_content += chunk.choices[0].delta.content
@@ -130,6 +155,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
use_model: requester.RuntimeLLMModel,
use_funcs: list[tools_entities.LLMFunction] = None,
extra_args: dict[str, typing.Any] = {},
remove_think:bool = False,
) -> llm_entities.Message:
self.client.api_key = use_model.token_mgr.get_token()
@@ -157,7 +183,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
args['messages'] = messages
# 发送请求
resp = await self._req(args, extra_body=extra_args)
resp = await self._req(args, extra_body=extra_args, remove_think=remove_think)
# 处理请求结果
message = await self._make_msg(resp)
@@ -172,41 +198,6 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
yield chunk
async def _make_msg_chunk(self,
delta: dict[str, typing.Any],
idx: int,
is_content: bool,
is_think: bool,
) -> llm_entities.MessageChunk:
# 处理流式chunk和完整响应的差异
# print(chat_completion.choices[0])
if 'role' not in delta or delta['role'] is None:
delta['role'] = 'assistant'
reasoning_content = delta['reasoning_content']
delta['content'] = '' if delta['content'] is None else delta['content']
# print(reasoning_content)
# deepseek的reasoner模型
if reasoning_content is not None and idx == 0:
delta['content'] += f'<think>\n{reasoning_content}'
is_think = True
elif reasoning_content is None and idx != 0:
if is_content:
delta['content'] = delta['content']
elif is_think:
delta['content'] = f'\n<think>\n\n{delta["content"]}'
is_content = True
is_think = False
elif reasoning_content is not None:
delta['content'] = reasoning_content
message = llm_entities.MessageChunk(**delta)
return message, is_content, is_think
async def _closure_stream(
self,
@@ -250,7 +241,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
thinking_started = False
thinking_ended = False
role = 'assistant' # 默认角色
accumulated_reasoning = '' # 仅用于判断何时结束思维链
# accumulated_reasoning = '' # 仅用于判断何时结束思维链
async for chunk in self._req_stream(args, extra_body=extra_args):
# 解析 chunk 数据
@@ -272,7 +263,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
# 处理 reasoning_content
if reasoning_content:
accumulated_reasoning += reasoning_content
# accumulated_reasoning += reasoning_content
# 如果设置了 remove_think，跳过 reasoning_content
if remove_think:
chunk_idx += 1
@@ -365,7 +356,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
try:
return await self._closure(
query=query, req_messages=req_messages, use_model=model, use_funcs=funcs, extra_args=extra_args
query=query, req_messages=req_messages, use_model=model, use_funcs=funcs, extra_args=extra_args, remove_think=remove_think
)
except asyncio.TimeoutError:
raise errors.RequesterError('请求超时')

58
pkg/provider/modelmgr/requesters/ppiochatcmpl.py
View File

@@ -39,20 +39,45 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
reasoning_content = chatcmpl_message['reasoning_content'] if 'reasoning_content' in chatcmpl_message else None
# deepseek的reasoner模型
if remove_think:
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"] = await self._process_thinking_content(
chatcmpl_message['content'],reasoning_content,remove_think)
# 移除 reasoning_content 字段，避免传递给 Message
if 'reasoning_content' in chatcmpl_message:
del chatcmpl_message['reasoning_content']
message = llm_entities.Message(**chatcmpl_message)
return message
async def _process_thinking_content(
self,
content: str,
reasoning_content: str = None,
remove_think: bool = False,
) -> tuple[str, str]:
"""处理思维链内容
Args:
content: 原始内容
reasoning_content: reasoning_content 字段内容
remove_think: 是否移除思维链
Returns:
处理后的内容
"""
if remove_think:
content = re.sub(
r'<think>.*?</think>', '', content, flags=re.DOTALL
)
else:
if reasoning_content is not None:
content = (
'<think>\n' + reasoning_content + '\n</think>\n' + content
)
return content
async def _make_msg_chunk(
self,
delta: dict[str, typing.Any],
@@ -119,7 +144,6 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
thinking_started = False
thinking_ended = False
role = 'assistant' # 默认角色
accumulated_reasoning = '' # 仅用于判断何时结束思维链
async for chunk in self._req_stream(args, extra_body=extra_args):
# 解析 chunk 数据
if hasattr(chunk, 'choices') and chunk.choices:
@@ -140,14 +164,18 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
if remove_think:
if delta['content'] is not None:
if '<think>' in delta['content']:
is_think = True
if '<think>' in delta['content'] and not thinking_started and not thinking_ended:
thinking_started = True
continue
elif delta['content'] == r'</think>':
is_think = False
elif delta['content'] == r'</think>' and not thinking_ended:
thinking_ended = True
continue
elif is_think or delta['content'] == '\n\n':
elif thinking_ended and delta['content'] == '\n\n' and thinking_started:
thinking_started = False
continue
elif thinking_started and not thinking_ended:
continue
delta_tool_calls = None
if delta.get('tool_calls'):