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