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372 lines
14 KiB
Python
372 lines
14 KiB
Python
from __future__ import annotations
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import asyncio
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import typing
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import openai
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import openai.types.chat.chat_completion as chat_completion
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import httpx
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from .. import errors, requester
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from ....core import entities as core_entities, app
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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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class OpenAIChatCompletions(requester.ProviderAPIRequester):
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"""OpenAI ChatCompletion API 请求器"""
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client: openai.AsyncClient
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is_content:bool
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default_config: dict[str, typing.Any] = {
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'base_url': 'https://api.openai.com/v1',
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'timeout': 120,
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}
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async def initialize(self):
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self.client = openai.AsyncClient(
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api_key='',
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base_url=self.requester_cfg['base_url'].replace(' ', ''),
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timeout=self.requester_cfg['timeout'],
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http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
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)
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self.is_content = False
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async def _req(
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self,
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args: dict,
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extra_body: dict = {},
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) -> chat_completion.ChatCompletion:
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return await self.client.chat.completions.create(**args, extra_body=extra_body)
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async def _req_stream(
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self,
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args: dict,
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extra_body: dict = {},
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) -> chat_completion.ChatCompletion:
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async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
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yield chunk
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async def _make_msg(
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self,
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pipeline_config: dict[str, typing.Any],
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chat_completion: chat_completion.ChatCompletion,
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) -> llm_entities.Message:
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chatcmpl_message = chat_completion.choices[0].message.model_dump()
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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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print(pipeline_config['trigger'].get('misc', '').get('remove_think'))
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if pipeline_config['trigger'].get('misc', '').get('remove_think'):
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pass
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else:
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if reasoning_content is not None :
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chatcmpl_message['content'] = '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
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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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pipeline_config: dict[str, typing.Any],
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chat_completion: chat_completion.ChatCompletion,
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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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if hasattr(chat_completion, 'choices'):
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# 完整响应模式
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choice = chat_completion.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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# 流式chunk模式
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delta = chat_completion.delta.model_dump() if hasattr(chat_completion, 'delta') else {}
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# 确保 role 字段存在且不为 None
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# print(delta.values())
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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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# deepseek的reasoner模型
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if pipeline_config['trigger'].get('misc', '').get('remove_think'):
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if reasoning_content is not None :
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pass
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else:
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delta['content'] = delta['content']
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else:
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if reasoning_content is not None:
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delta['content'] += f'<think>\n{reasoning_content}'
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elif reasoning_content is None:
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if self.is_content:
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delta['content'] = delta['content']
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else:
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delta['content'] = f'\n<think>\n\n{delta["content"]}'
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self.is_content = True
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else:
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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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stream: bool = False,
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extra_args: dict[str, typing.Any] = {},
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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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if stream:
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current_content = ''
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args["stream"] = True
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chunk_idx = 0
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self.is_content = False
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tool_calls_map: dict[str, llm_entities.ToolCall] = {}
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pipeline_config = query.pipeline_config
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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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delta_message = await self._make_msg_chunk(pipeline_config,chunk)
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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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print(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 '',
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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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if chunk_idx % 64 == 0 or delta_message.is_final:
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yield delta_message
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# return
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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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stream: bool = False,
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extra_args: dict[str, typing.Any] = {},
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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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# 发送请求
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resp = await self._req(args, extra_body=extra_args)
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# 处理请求结果
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pipeline_config = query.pipeline_config
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message = await self._make_msg(pipeline_config,resp)
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return message
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async def invoke_llm(
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self,
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query: core_entities.Query,
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model: requester.RuntimeLLMModel,
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messages: typing.List[llm_entities.Message],
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funcs: typing.List[tools_entities.LLMFunction] = None,
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extra_args: dict[str, typing.Any] = {},
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) -> llm_entities.Message:
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req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
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for m in messages:
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msg_dict = m.dict(exclude_none=True)
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content = msg_dict.get('content')
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if isinstance(content, list):
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# 检查 content 列表中是否每个部分都是文本
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if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
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# 将所有文本部分合并为一个字符串
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msg_dict['content'] = '\n'.join(part['text'] for part in content)
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req_messages.append(msg_dict)
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try:
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msg = await self._closure(
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query=query,
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req_messages=req_messages,
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use_model=model,
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use_funcs=funcs,
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extra_args=extra_args,
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)
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return msg
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except asyncio.TimeoutError:
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raise errors.RequesterError('请求超时')
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except openai.BadRequestError as e:
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if 'context_length_exceeded' in e.message:
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raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
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else:
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raise errors.RequesterError(f'请求参数错误: {e.message}')
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except openai.AuthenticationError as e:
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raise errors.RequesterError(f'无效的 api-key: {e.message}')
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except openai.NotFoundError as e:
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raise errors.RequesterError(f'请求路径错误: {e.message}')
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except openai.RateLimitError as e:
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raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
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except openai.APIError as e:
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raise errors.RequesterError(f'请求错误: {e.message}')
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async def invoke_embedding(
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self,
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model: requester.RuntimeEmbeddingModel,
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input_text: list[str],
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extra_args: dict[str, typing.Any] = {},
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) -> list[list[float]]:
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"""调用 Embedding API"""
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self.client.api_key = model.token_mgr.get_token()
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args = {
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'model': model.model_entity.name,
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'input': input_text,
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}
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if model.model_entity.extra_args:
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args.update(model.model_entity.extra_args)
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args.update(extra_args)
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try:
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resp = await self.client.embeddings.create(**args)
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return [d.embedding for d in resp.data]
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except asyncio.TimeoutError:
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raise errors.RequesterError('请求超时')
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except openai.BadRequestError as e:
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raise errors.RequesterError(f'请求参数错误: {e.message}')
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async def invoke_llm_stream(
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self,
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query: core_entities.Query,
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model: requester.RuntimeLLMModel,
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messages: typing.List[llm_entities.Message],
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funcs: typing.List[tools_entities.LLMFunction] = None,
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stream: bool = False,
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extra_args: dict[str, typing.Any] = {},
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) -> llm_entities.MessageChunk:
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req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
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for m in messages:
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msg_dict = m.dict(exclude_none=True)
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content = msg_dict.get('content')
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if isinstance(content, list):
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# 检查 content 列表中是否每个部分都是文本
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if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
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# 将所有文本部分合并为一个字符串
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msg_dict['content'] = '\n'.join(part['text'] for part in content)
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req_messages.append(msg_dict)
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try:
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async for item in self._closure_stream(
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query=query,
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req_messages=req_messages,
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use_model=model,
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use_funcs=funcs,
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stream=stream,
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extra_args=extra_args,
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):
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yield item
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print(item)
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except asyncio.TimeoutError:
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raise errors.RequesterError('请求超时')
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except openai.BadRequestError as e:
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if 'context_length_exceeded' in e.message:
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raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
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else:
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raise errors.RequesterError(f'请求参数错误: {e.message}')
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except openai.AuthenticationError as e:
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raise errors.RequesterError(f'无效的 api-key: {e.message}')
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except openai.NotFoundError as e:
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raise errors.RequesterError(f'请求路径错误: {e.message}')
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except openai.RateLimitError as e:
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raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
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except openai.APIError as e:
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raise errors.RequesterError(f'请求错误: {e.message}')
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