langchain.llms.huggingface_endpoint.HuggingFaceEndpoint¶
class langchain.llms.huggingface_endpoint.HuggingFaceEndpoint[source]¶
Bases: 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
from langchain.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"
)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param endpoint_url: str = ''¶
Endpoint URL to use.
param huggingfacehub_api_token: Optional[str] = None¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Optional[dict] = None¶
Key word arguments to pass to the model.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param task: Optional[str] = None¶
Task to call the model with.
Should be a task that returns generated_text or summary_text.
param verbose: bool [Optional]¶
Whether to print out response text.