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207 lines
7.5 KiB
Python
207 lines
7.5 KiB
Python
from __future__ import annotations
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import asyncio
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import typing
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import json
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import base64
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from typing import AsyncGenerator
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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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import aiohttp
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import async_lru
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from .. import entities, 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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from ....utils import image
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class ModelScopeChatCompletions(requester.LLMAPIRequester):
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"""ModelScope ChatCompletion API 请求器"""
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client: openai.AsyncClient
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requester_cfg: dict
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def __init__(self, ap: app.Application):
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self.ap = ap
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self.requester_cfg = self.ap.provider_cfg.data['requester']['modelscope-chat-completions']
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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(
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trust_env=True,
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timeout=self.requester_cfg['timeout']
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)
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)
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async def _req(
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self,
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args: dict,
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) -> chat_completion.ChatCompletion:
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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)
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async for chunk in resp_gen:
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# print(chunk)
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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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if chunk.choices[0].delta.content is not None:
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pending_content += chunk.choices[0].delta.content
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if chunk.choices[0].delta.tool_calls is not None:
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for tool_call in chunk.choices[0].delta.tool_calls:
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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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real_tool_calls = []
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for tc in tool_calls:
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function = chat_completion_message_tool_call.Function(
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name=tc.function.name,
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arguments=tc.function.arguments
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)
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real_tool_calls.append(chat_completion_message_tool_call.ChatCompletionMessageToolCall(
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id=tc.id,
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function=function,
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type="function"
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))
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return chat_completion.ChatCompletion(
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id=chunk.id,
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object="chat.completion",
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created=chunk.created,
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choices=[
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chat_completion.Choice(
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index=0,
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message=chat_completion.ChatCompletionMessage(
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role="assistant",
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content=pending_content,
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tool_calls=real_tool_calls if len(real_tool_calls) > 0 else None
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),
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finish_reason=chunk.choices[0].finish_reason if hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason is not None else 'stop',
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logprobs=chunk.choices[0].logprobs,
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)
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],
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model=chunk.model,
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service_tier=chunk.service_tier if hasattr(chunk, 'service_tier') else None,
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system_fingerprint=chunk.system_fingerprint if hasattr(chunk, 'system_fingerprint') else None,
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usage=chunk.usage if hasattr(chunk, 'usage') else None
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) if chunk else None
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return await self.client.chat.completions.create(**args)
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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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) -> llm_entities.Message:
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chatcmpl_message = chat_completion.choices[0].message.dict()
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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: entities.LLMModelInfo,
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use_funcs: list[tools_entities.LLMFunction] = None,
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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 = self.requester_cfg['args'].copy()
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args["model"] = use_model.name if use_model.model_name is None else use_model.model_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"] = {
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"url": me["image_base64"]
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}
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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)
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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 call(
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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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) -> 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(query=query, req_messages=req_messages, use_model=model, use_funcs=funcs)
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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}') |