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| """Wrapper around HuggingFace APIs.""" | |
| from typing import Any, Dict, List, Mapping, Optional | |
| from pydantic import BaseModel, Extra, root_validator | |
| from langchain.llms.base import LLM | |
| from langchain.llms.utils import enforce_stop_tokens | |
| from langchain.utils import get_from_dict_or_env | |
| DEFAULT_REPO_ID = "gpt2" | |
| VALID_TASKS = ("text2text-generation", "text-generation") | |
| class HuggingFaceHub(LLM, BaseModel): | |
| """Wrapper around 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` and `text2text-generation` for now. | |
| Example: | |
| .. code-block:: python | |
| from langchain.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`.""" | |
| model_kwargs: Optional[dict] = None | |
| """Key word arguments to pass to the model.""" | |
| huggingfacehub_api_token: Optional[str] = None | |
| class Config: | |
| """Configuration for this pydantic object.""" | |
| extra = Extra.forbid | |
| 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 it install it with `pip install huggingface_hub`." | |
| ) | |
| return values | |
| 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}, | |
| } | |
| def _llm_type(self) -> str: | |
| """Return type of llm.""" | |
| return "huggingface_hub" | |
| def _call(self, prompt: str, stop: Optional[List[str]] = None) -> 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 {} | |
| response = self.client(inputs=prompt, params=_model_kwargs) | |
| 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"] | |
| 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 | |