83 lines
2.9 KiB
Python
Executable File
83 lines
2.9 KiB
Python
Executable File
"""MiniMax Provider - 使用阿里云百炼平台"""
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import asyncio
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from typing import AsyncIterator, Iterator
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from openai import OpenAI
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from ..base import BaseLLMProvider, LLMMessage, LLMResponse
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class MiniMaxProvider(BaseLLMProvider):
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"""MiniMax模型Provider - 通过阿里云百炼平台调用
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支持的模型:
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- minimax-text-01: 文本模型，适合知识查询
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- minimax-speech-01: 语音模型（暂不支持）
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"""
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provider_name = "minimax"
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DASHSCOPE_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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# 百炼平台模型名称映射
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MODEL_MAPPING = {
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"minimax-text-01": "minimax-text-01",
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"abab6.5s-chat": "minimax-text-01",
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"abab6.5-chat": "minimax-text-01",
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}
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def __init__(
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self,
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api_key: str,
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model: str = "minimax-text-01",
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temperature: float = 0.7,
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max_tokens: int = 4096,
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):
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# 映射模型名称
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dashscope_model = self.MODEL_MAPPING.get(model, model)
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super().__init__(
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api_key=api_key,
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api_base=self.DASHSCOPE_BASE_URL,
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model=dashscope_model,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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self.client = OpenAI(api_key=api_key, base_url=self.DASHSCOPE_BASE_URL)
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def call(self, messages: list[LLMMessage], **kwargs) -> LLMResponse:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=self._build_messages(messages),
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temperature=kwargs.get("temperature", self.temperature),
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max_tokens=kwargs.get("max_tokens", self.max_tokens),
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)
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return LLMResponse(
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content=response.choices[0].message.content,
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model=self.model,
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provider=self.provider_name,
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usage={
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"prompt_tokens": response.usage.prompt_tokens,
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"completion_tokens": response.usage.completion_tokens,
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"total_tokens": response.usage.total_tokens,
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},
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finish_reason=response.choices[0].finish_reason,
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)
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async def acall(self, messages: list[LLMMessage], **kwargs) -> LLMResponse:
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return await asyncio.to_thread(self.call, messages, **kwargs)
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def stream(self, messages: list[LLMMessage], **kwargs) -> Iterator[str]:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=self._build_messages(messages),
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temperature=kwargs.get("temperature", self.temperature),
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max_tokens=kwargs.get("max_tokens", self.max_tokens),
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stream=True,
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)
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for chunk in response:
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if chunk.choices[0].delta.content:
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yield chunk.choices[0].delta.content
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async def astream(self, messages: list[LLMMessage], **kwargs) -> AsyncIterator[str]:
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for content in self.stream(messages, **kwargs):
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yield content
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