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387 lines
15 KiB
Python
387 lines
15 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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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 openai.types.chat.chat_completion_message_tool_call as chat_completion_message_tool_call
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import httpx
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from .. import entities, errors, 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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class ModelScopeChatCompletions(requester.ProviderAPIRequester):
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"""ModelScope 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-inference.modelscope.cn/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'],
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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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async def _req(
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self,
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query: core_entities.Query,
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args: dict,
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extra_body: dict = {},
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remove_think: bool = False,
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) -> list[dict[str, typing.Any]]:
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args['stream'] = True
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chunk = None
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pending_content = ''
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tool_calls = []
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resp_gen: openai.AsyncStream = await self.client.chat.completions.create(**args, extra_body=extra_body)
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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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tool_id = ''
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tool_name = ''
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message_delta = {}
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async for chunk in resp_gen:
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if not chunk or not chunk.id or not chunk.choices or not chunk.choices[0] or not chunk.choices[0].delta:
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continue
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delta = chunk.choices[0].delta.model_dump() if hasattr(chunk.choices[0], 'delta') else {}
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reasoning_content = delta.get('reasoning_content')
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# 处理 reasoning_content
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if reasoning_content:
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# accumulated_reasoning += reasoning_content
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# 如果设置了 remove_think，跳过 reasoning_content
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if remove_think:
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chunk_idx += 1
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continue
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# 第一次出现 reasoning_content，添加 <think> 开始标签
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if not thinking_started:
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thinking_started = True
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pending_content += '<think>\n' + reasoning_content
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else:
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# 继续输出 reasoning_content
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pending_content += reasoning_content
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elif thinking_started and not thinking_ended and delta.get('content'):
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# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
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thinking_ended = True
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pending_content += '\n</think>\n' + delta.get('content')
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if delta.get('content') is not None:
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pending_content += delta.get('content')
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if delta.get('tool_calls') is not None:
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for tool_call in delta.get('tool_calls'):
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if tool_call['id'] != '':
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tool_id = tool_call['id']
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if tool_call['function']['name'] is not None:
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tool_name = tool_call['function']['name']
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if tool_call['function']['arguments'] is None:
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continue
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tool_call['id'] = tool_id
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tool_call['name'] = tool_name
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for tc in tool_calls:
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if tc['index'] == tool_call['index']:
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tc['function']['arguments'] += tool_call['function']['arguments']
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break
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else:
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tool_calls.append(tool_call)
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if chunk.choices[0].finish_reason is not None:
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break
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message_delta['content'] = pending_content
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message_delta['role'] = 'assistant'
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message_delta['tool_calls'] = tool_calls if tool_calls else None
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return [message_delta]
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async def _make_msg(
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self,
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chat_completion: list[dict[str, typing.Any]],
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) -> llm_entities.Message:
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chatcmpl_message = chat_completion[0]
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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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message = llm_entities.Message(**chatcmpl_message)
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return message
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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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# 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(query, args, extra_body=extra_args, remove_think=remove_think)
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# 处理请求结果
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message = await self._make_msg(resp)
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return message
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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 _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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# 流式处理状态
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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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# 处理 reasoning_content
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if reasoning_content:
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# accumulated_reasoning += reasoning_content
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# 如果设置了 remove_think，跳过 reasoning_content
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if remove_think:
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chunk_idx += 1
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continue
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# 第一次出现 reasoning_content，添加 <think> 开始标签
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if not thinking_started:
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thinking_started = True
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delta_content = '<think>\n' + reasoning_content
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else:
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# 继续输出 reasoning_content
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delta_content = reasoning_content
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elif thinking_started and not thinking_ended and delta_content:
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# reasoning_content 结束，normal content 开始，添加 </think> 结束标签
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thinking_ended = True
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delta_content = '\n</think>\n' + delta_content
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# 处理 content 中已有的 <think> 标签（如果需要移除）
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# if delta_content and remove_think and '<think>' in delta_content:
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# import re
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#
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# # 移除 <think> 标签及其内容
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# delta_content = re.sub(r'<think>.*?</think>', '', delta_content, flags=re.DOTALL)
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# 处理工具调用增量
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if delta.get('tool_calls'):
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for tool_call in delta['tool_calls']:
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if tool_call['id'] != '':
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tool_id = tool_call['id']
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if tool_call['function']['name'] is not None:
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tool_name = tool_call['function']['name']
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if tool_call['type'] is None:
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tool_call['type'] = 'function'
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tool_call['id'] = tool_id
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tool_call['function']['name'] = tool_name
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tool_call['function']['arguments'] = "" if tool_call['function']['arguments'] is None else tool_call['function']['arguments']
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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 reasoning_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.get('tool_calls'),
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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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# return
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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: entities.LLMModelInfo,
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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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remove_think: bool = False,
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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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return await self._closure(
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query=query, req_messages=req_messages, use_model=model, use_funcs=funcs, extra_args=extra_args, remove_think=remove_think
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)
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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_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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extra_args: dict[str, typing.Any] = {},
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remove_think: bool = False,
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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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extra_args=extra_args,
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remove_think=remove_think,
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):
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yield 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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