Source code for langchain_community.llms.huggingface_hub

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

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

DEFAULT_REPO_ID = "gpt2"
VALID_TASKS = ("text2text-generation", "text-generation", "summarization")


[docs]class HuggingFaceHub(LLM): """HuggingFaceHub models. 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. Only supports `text-generation`, `text2text-generation` and `summarization` for now. 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: str = DEFAULT_REPO_ID """Model name to use.""" task: Optional[str] = None """Task to call the model with. Should be a task that returns `generated_text` or `summary_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.inference_api import InferenceApi repo_id = values["repo_id"] client = InferenceApi( repo_id=repo_id, token=huggingfacehub_api_token, task=values.get("task"), ) if client.task not in VALID_TASKS: raise ValueError( f"Got invalid task {client.task}, " f"currently only {VALID_TASKS} are supported" ) values["client"] = client except ImportError: raise ValueError( "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 {} params = {**_model_kwargs, **kwargs} response = self.client(inputs=prompt, params=params) if "error" in response: raise ValueError(f"Error raised by inference API: {response['error']}") if self.client.task == "text-generation": # Text generation return includes the starter text. text = response[0]["generated_text"][len(prompt) :] elif self.client.task == "text2text-generation": text = response[0]["generated_text"] elif self.client.task == "summarization": text = response[0]["summary_text"] else: raise ValueError( f"Got invalid task {self.client.task}, " f"currently only {VALID_TASKS} are supported" ) 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