41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
from typing import Iterable, Optional, Union
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.stdout import StdOutCallbackHandler
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain_community.llms import VLLM as BaseVLLM
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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@register_deserializable
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class VLLM(BaseLlm):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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super().__init__(config=config)
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if self.config.model is None:
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self.config.model = "mosaicml/mpt-7b"
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def get_llm_model_answer(self, prompt):
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return self._get_answer(prompt=prompt, config=self.config)
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@staticmethod
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def _get_answer(prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
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callback_manager = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
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# Prepare the arguments for BaseVLLM
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llm_args = {
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"model": config.model,
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"temperature": config.temperature,
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"top_p": config.top_p,
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"callback_manager": CallbackManager(callback_manager),
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}
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# Add model_kwargs if they are not None
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if config.model_kwargs is not None:
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llm_args.update(config.model_kwargs)
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llm = BaseVLLM(**llm_args)
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return llm.invoke(prompt)
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