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121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
"""Wrapper around HuggingFace APIs."""
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from typing import Any, Dict, List, Mapping, Optional
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from pydantic import BaseModel, Extra, root_validator
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.utils import get_from_dict_or_env
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DEFAULT_REPO_ID = "gpt2"
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VALID_TASKS = ("text2text-generation", "text-generation")
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class HuggingFaceHub(LLM, BaseModel):
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"""Wrapper around HuggingFaceHub models.
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To use, you should have the ``huggingface_hub`` python package installed, and the
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environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
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it as a named parameter to the constructor.
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Only supports `text-generation` and `text2text-generation` for now.
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Example:
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.. code-block:: python
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from langchain.llms import HuggingFaceHub
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hf = HuggingFaceHub(repo_id="gpt2", huggingfacehub_api_token="my-api-key")
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"""
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client: Any #: :meta private:
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repo_id: str = DEFAULT_REPO_ID
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"""Model name to use."""
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task: Optional[str] = None
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"""Task to call the model with. Should be a task that returns `generated_text`."""
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model_kwargs: Optional[dict] = None
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"""Key word arguments to pass to the model."""
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huggingfacehub_api_token: Optional[str] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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huggingfacehub_api_token = get_from_dict_or_env(
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values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
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)
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try:
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from huggingface_hub.inference_api import InferenceApi
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repo_id = values["repo_id"]
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client = InferenceApi(
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repo_id=repo_id,
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token=huggingfacehub_api_token,
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task=values.get("task"),
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)
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if client.task not in VALID_TASKS:
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raise ValueError(
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f"Got invalid task {client.task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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values["client"] = client
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except ImportError:
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raise ValueError(
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"Could not import huggingface_hub python package. "
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"Please it install it with `pip install huggingface_hub`."
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)
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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_model_kwargs = self.model_kwargs or {}
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return {
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**{"repo_id": self.repo_id, "task": self.task},
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**{"model_kwargs": _model_kwargs},
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "huggingface_hub"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to HuggingFace Hub's inference endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = hf("Tell me a joke.")
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"""
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_model_kwargs = self.model_kwargs or {}
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response = self.client(inputs=prompt, params=_model_kwargs)
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if "error" in response:
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raise ValueError(f"Error raised by inference API: {response['error']}")
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if self.client.task == "text-generation":
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# Text generation return includes the starter text.
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text = response[0]["generated_text"][len(prompt) :]
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elif self.client.task == "text2text-generation":
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text = response[0]["generated_text"]
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else:
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raise ValueError(
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f"Got invalid task {self.client.task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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if stop is not None:
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# This is a bit hacky, but I can't figure out a better way to enforce
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# stop tokens when making calls to huggingface_hub.
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text = enforce_stop_tokens(text, stop)
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return text
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