55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
import importlib
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import logging
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import os
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from typing import Optional
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from langchain.llms.huggingface_hub import HuggingFaceHub
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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 HuggingFaceLlm(BaseLlm):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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if "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
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raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable.")
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try:
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importlib.import_module("huggingface_hub")
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except ModuleNotFoundError:
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raise ModuleNotFoundError(
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"The required dependencies for HuggingFaceHub are not installed."
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'Please install with `pip install --upgrade "embedchain[huggingface-hub]"`'
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) from None
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super().__init__(config=config)
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def get_llm_model_answer(self, prompt):
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if self.config.system_prompt:
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raise ValueError("HuggingFaceLlm does not support `system_prompt`")
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return HuggingFaceLlm._get_answer(prompt=prompt, config=self.config)
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@staticmethod
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def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
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model_kwargs = {
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"temperature": config.temperature or 0.1,
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"max_new_tokens": config.max_tokens,
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}
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if config.top_p > 0.0 and config.top_p < 1.0:
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model_kwargs["top_p"] = config.top_p
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else:
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raise ValueError("`top_p` must be > 0.0 and < 1.0")
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model = config.model or "google/flan-t5-xxl"
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logging.info(f"Using HuggingFaceHub with model {model}")
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llm = HuggingFaceHub(
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huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
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repo_id=model,
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model_kwargs=model_kwargs,
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)
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return llm(prompt)
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