Source code for langchain_community.llms.huggingface_endpoint

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

import requests
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

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


class HuggingFaceEndpoint(LLM):
    """HuggingFace Endpoint 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` and `text2text-generation` for now.

    Example:
        .. code-block:: python

            from langchain_community.llms import HuggingFaceEndpoint
            endpoint_url = (
                "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
            )
            hf = HuggingFaceEndpoint(
                endpoint_url=endpoint_url,
                huggingfacehub_api_token="my-api-key"
            )
    """

    endpoint_url: str = ""
    """Endpoint URL 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.hf_api import HfApi

            try:
                HfApi(
                    endpoint="https://huggingface.co",  # Can be a Private Hub endpoint.
                    token=huggingfacehub_api_token,
                ).whoami()
            except Exception as e:
                raise ValueError(
                    "Could not authenticate with huggingface_hub. "
                    "Please check your API token."
                ) from e

        except ImportError:
            raise ImportError(
                "Could not import huggingface_hub python package. "
                "Please install it with `pip install huggingface_hub`."
            )
        values["huggingfacehub_api_token"] = huggingfacehub_api_token
        return values

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        _model_kwargs = self.model_kwargs or {}
        return {
            **{"endpoint_url": self.endpoint_url, "task": self.task},
            **{"model_kwargs": _model_kwargs},
        }

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "huggingface_endpoint"

    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 {}

        # payload samples
        params = {**_model_kwargs, **kwargs}
        parameter_payload = {"inputs": prompt, "parameters": params}

        # HTTP headers for authorization
        headers = {
            "Authorization": f"Bearer {self.huggingfacehub_api_token}",
            "Content-Type": "application/json",
        }

        # send request
        try:
            response = requests.post(
                self.endpoint_url, headers=headers, json=parameter_payload
            )
        except requests.exceptions.RequestException as e:  # This is the correct syntax
            raise ValueError(f"Error raised by inference endpoint: {e}")
        generated_text = response.json()
        if "error" in generated_text:
            raise ValueError(
                f"Error raised by inference API: {generated_text['error']}"
            )
        if self.task == "text-generation":
            text = generated_text[0]["generated_text"]
            # Remove prompt if included in generated text.
            if text.startswith(prompt):
                text = text[len(prompt) :]
        elif self.task == "text2text-generation":
            text = generated_text[0]["generated_text"]
        elif self.task == "summarization":
            text = generated_text[0]["summary_text"]
        else:
            raise ValueError(
                f"Got invalid task {self.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