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195 lines
7.2 KiB
Python
195 lines
7.2 KiB
Python
from __future__ import annotations
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import openai
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import typing
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from . import chatcmpl
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import openai.types.chat.chat_completion as chat_completion
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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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from ...tools import entities as tools_entities
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import re
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class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
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"""欧派云 ChatCompletion API 请求器"""
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client: openai.AsyncClient
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default_config: dict[str, typing.Any] = {
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'base_url': 'https://api.ppinfra.com/v3/openai',
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'timeout': 120,
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}
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is_think: bool = False
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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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args['stream'] = True
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tool_calls_map: dict[str, llm_entities.ToolCall] = {}
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chunk_idx = 0
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thinking_started = False
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thinking_ended = False
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role = 'assistant' # 默认角色
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accumulated_reasoning = '' # 仅用于判断何时结束思维链
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async for chunk in self._req_stream(args, extra_body=extra_args):
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# 解析 chunk 数据
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if hasattr(chunk, 'choices') and chunk.choices:
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choice = chunk.choices[0]
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delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
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finish_reason = getattr(choice, 'finish_reason', None)
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else:
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delta = {}
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finish_reason = None
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# 从第一个 chunk 获取 role，后续使用这个 role
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if 'role' in delta and delta['role']:
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role = delta['role']
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# 获取增量内容
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delta_content = delta.get('content', '')
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# reasoning_content = delta.get('reasoning_content', '')
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if remove_think:
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if delta['content'] is not None:
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if '<think>' in delta['content']:
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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_tool_calls = None
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if delta.get('tool_calls'):
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delta_tool_calls = []
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for tool_call in delta['tool_calls']:
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tc_id = tool_call.get('id')
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if tc_id:
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if tc_id not in tool_calls_map:
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# 新的工具调用
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tool_calls_map[tc_id] = llm_entities.ToolCall(
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id=tc_id,
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type=tool_call.get('type', 'function'),
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function=llm_entities.FunctionCall(
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name=tool_call.get('function', {}).get('name', ''),
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arguments=tool_call.get('function', {}).get('arguments', ''),
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),
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)
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delta_tool_calls.append(tool_calls_map[tc_id])
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else:
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# 追加函数参数
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func_args = tool_call.get('function', {}).get('arguments', '')
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if func_args:
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tool_calls_map[tc_id].function.arguments += func_args
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# 返回更新后的完整工具调用
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delta_tool_calls.append(tool_calls_map[tc_id])
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# 跳过空的第一个 chunk（只有 role 没有内容）
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if chunk_idx == 0 and not delta_content and not delta.get('tool_calls'):
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chunk_idx += 1
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continue
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# 构建 MessageChunk - 只包含增量内容
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chunk_data = {
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'role': role,
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'content': delta_content if delta_content else None,
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'tool_calls': delta_tool_calls if delta_tool_calls else None,
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'is_final': bool(finish_reason),
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}
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# 移除 None 值
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chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
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yield llm_entities.MessageChunk(**chunk_data)
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chunk_idx += 1
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