Source code for langchain_community.llms.huggingface_hub

import json
from typing import Any, Dict, List, Mapping, Optional

from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_community.llms.utils import enforce_stop_tokens

# key: task
# value: key in the output dictionary
    "translation": "translation_text",
    "summarization": "summary_text",
    "conversational": "generated_text",
    "text-generation": "generated_text",
    "text2text-generation": "generated_text",

[docs]@deprecated("0.0.21", removal="0.3.0", alternative="HuggingFaceEndpoint") class HuggingFaceHub(LLM): """HuggingFaceHub models. ! This class is deprecated, you should use HuggingFaceEndpoint instead. To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Supports `text-generation`, `text2text-generation`, `conversational`, `translation`, and `summarization`. Example: .. code-block:: python from langchain_community.llms import HuggingFaceHub hf = HuggingFaceHub(repo_id="gpt2", huggingfacehub_api_token="my-api-key") """ client: Any #: :meta private: repo_id: Optional[str] = None """Model name to use. If not provided, the default model for the chosen task will be used.""" task: Optional[str] = None """Task to call the model with. Should be a task that returns `generated_text`, `summary_text`, or `translation_text`.""" model_kwargs: Optional[dict] = None """Keyword arguments to pass to the model.""" huggingfacehub_api_token: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = get_from_dict_or_env( values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" ) try: from huggingface_hub import HfApi, InferenceClient repo_id = values["repo_id"] client = InferenceClient( model=repo_id, token=huggingfacehub_api_token, ) if not values["task"]: if not repo_id: raise ValueError( "Must specify either `repo_id` or `task`, or both." ) # Use the recommended task for the chosen model model_info = HfApi(token=huggingfacehub_api_token).model_info( repo_id=repo_id ) values["task"] = model_info.pipeline_tag if values["task"] not in VALID_TASKS_DICT: raise ValueError( f"Got invalid task {values['task']}, " f"currently only {VALID_TASKS_DICT.keys()} are supported" ) values["client"] = client except ImportError: raise ImportError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"repo_id": self.repo_id, "task": self.task}, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "huggingface_hub" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to HuggingFace Hub's inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} parameters = {**_model_kwargs, **kwargs} response = json={"inputs": prompt, "parameters": parameters}, task=self.task ) response = json.loads(response.decode()) if "error" in response: raise ValueError(f"Error raised by inference API: {response['error']}") response_key = VALID_TASKS_DICT[self.task] # type: ignore if isinstance(response, list): text = response[0][response_key] else: text = response[response_key] if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text