mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-09-04 00:27:14 +00:00
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
This commit is contained in:
Dong_master
@@ -3,7 +3,7 @@ from __future__ import annotations
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import typing
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from . import chatcmpl
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from . import ppiochatcmpl
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from .. import requester
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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@@ -12,7 +12,7 @@ import re
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import openai.types.chat.chat_completion as chat_completion
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class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
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class GiteeAIChatCompletions(ppiochatcmpl.PPIOChatCompletions):
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"""Gitee AI ChatCompletions API 请求器"""
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default_config: dict[str, typing.Any] = {
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@@ -20,181 +20,3 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
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'timeout': 120,
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}
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async def _closure(
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self,
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query: core_entities.Query,
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req_messages: list[dict],
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use_model: requester.RuntimeLLMModel,
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use_funcs: list[tools_entities.LLMFunction] = None,
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extra_args: dict[str, typing.Any] = {},
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remove_think: bool = False,
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) -> llm_entities.Message:
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self.client.api_key = use_model.token_mgr.get_token()
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args = {}
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args['tools'] = tools
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# gitee 不支持多模态，把content都转换成纯文字
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for m in req_messages:
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if 'content' in m and isinstance(m['content'], list):
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m['content'] = ' '.join([c['text'] for c in m['content']])
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args['messages'] = req_messages
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resp = await self._req(args, extra_body=extra_args)
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message = await self._make_msg(resp, remove_think)
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return message
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async def _make_msg(
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self,
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chat_completion: chat_completion.ChatCompletion,
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remove_think: bool,
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) -> llm_entities.Message:
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chatcmpl_message = chat_completion.choices[0].message.model_dump()
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# print(chatcmpl_message.keys(), chatcmpl_message.values())
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# 确保 role 字段存在且不为 None
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if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
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chatcmpl_message['role'] = 'assistant'
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reasoning_content = chatcmpl_message['reasoning_content'] if 'reasoning_content' in chatcmpl_message else None
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# deepseek的reasoner模型
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if remove_think:
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chatcmpl_message['content'] = re.sub(
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r'<think>.*?</think>', '', chatcmpl_message['content'], flags=re.DOTALL
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)
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else:
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if reasoning_content is not None:
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chatcmpl_message['content'] = (
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'<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
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)
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message = llm_entities.Message(**chatcmpl_message)
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return message
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async def _make_msg_chunk(
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self,
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delta: dict[str, typing.Any],
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idx: int,
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) -> llm_entities.MessageChunk:
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# 处理流式chunk和完整响应的差异
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# print(chat_completion.choices[0])
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# 确保 role 字段存在且不为 None
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if 'role' not in delta or delta['role'] is None:
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delta['role'] = 'assistant'
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reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
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delta['content'] = '' if delta['content'] is None else delta['content']
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# print(reasoning_content)
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# deepseek的reasoner模型
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if reasoning_content is not None:
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delta['content'] += reasoning_content
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message = llm_entities.MessageChunk(**delta)
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return message
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async def _closure_stream(
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self,
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query: core_entities.Query,
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req_messages: list[dict],
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use_model: requester.RuntimeLLMModel,
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use_funcs: list[tools_entities.LLMFunction] = None,
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extra_args: dict[str, typing.Any] = {},
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remove_think: bool = False,
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) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
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self.client.api_key = use_model.token_mgr.get_token()
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args = {}
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args['tools'] = tools
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# 设置此次请求中的messages
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messages = req_messages.copy()
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# 检查vision
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for msg in messages:
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if 'content' in msg and isinstance(msg['content'], list):
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for me in msg['content']:
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if me['type'] == 'image_base64':
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me['image_url'] = {'url': me['image_base64']}
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me['type'] = 'image_url'
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del me['image_base64']
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args['messages'] = messages
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current_content = ''
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args['stream'] = True
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chunk_idx = 0
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is_think = False
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tool_calls_map: dict[str, llm_entities.ToolCall] = {}
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async for chunk in self._req_stream(args, extra_body=extra_args):
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# 处理流式消息
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if hasattr(chunk, 'choices'):
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# 完整响应模式
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if chunk.choices:
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choice = chunk.choices[0]
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delta = choice.delta.model_dump() if hasattr(choice, 'delta') else choice.message.model_dump()
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else:
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continue
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else:
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# 流式chunk模式
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delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {}
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if remove_think:
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print(delta)
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if delta['content'] == '<think>':
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is_think = True
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continue
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elif delta['content'] == r'</think>':
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is_think = False
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continue
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elif is_think or delta['content'] == '\n\n':
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continue
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delta_message = await self._make_msg_chunk(delta, chunk_idx)
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if delta_message.content:
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current_content += delta_message.content
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delta_message.content = current_content
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# delta_message.all_content = current_content
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if delta_message.tool_calls:
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for tool_call in delta_message.tool_calls:
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if tool_call.id not in tool_calls_map:
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tool_calls_map[tool_call.id] = llm_entities.ToolCall(
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id=tool_call.id,
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type=tool_call.type,
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function=llm_entities.FunctionCall(
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name=tool_call.function.name if tool_call.function else '', arguments=''
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),
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)
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if tool_call.function and tool_call.function.arguments:
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# 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
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tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
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chunk_idx += 1
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chunk_choices = getattr(chunk, 'choices', None)
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if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
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delta_message.is_final = True
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delta_message.content = current_content
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yield delta_message
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