import json from typing import Any, Dict, List, Optional from langchain_core._api import deprecated from langchain_core.embeddings import Embeddings from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator from langchain_core.utils import get_from_dict_or_env DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2" VALID_TASKS = ("feature-extraction",) @deprecated( since="0.2.2", removal="0.3.0", alternative_import="langchain_huggingface.HuggingFaceEndpointEmbeddings", ) class HuggingFaceHubEmbeddings(BaseModel, Embeddings): """HuggingFaceHub embedding 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. Example: .. code-block:: python from langchain_community.embeddings import HuggingFaceHubEmbeddings model = "sentence-transformers/all-mpnet-base-v2" hf = HuggingFaceHubEmbeddings( model=model, task="feature-extraction", huggingfacehub_api_token="my-api-key", ) """ client: Any #: :meta private: async_client: Any #: :meta private: model: Optional[str] = None """Model name to use.""" repo_id: Optional[str] = None """Huggingfacehub repository id, for backward compatibility.""" task: Optional[str] = "feature-extraction" """Task to call the model with.""" 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(pre=True) 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 import AsyncInferenceClient, InferenceClient if values.get("model"): values["repo_id"] = values["model"] elif values.get("repo_id"): values["model"] = values["repo_id"] else: values["model"] = DEFAULT_MODEL values["repo_id"] = DEFAULT_MODEL client = InferenceClient( model=values["model"], token=huggingfacehub_api_token, ) async_client = AsyncInferenceClient( model=values["model"], token=huggingfacehub_api_token, ) values["client"] = client values["async_client"] = async_client except ImportError: raise ImportError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) return values @root_validator(pre=False, skip_on_failure=True) def post_init(cls, values: Dict) -> Dict: """Post init validation for the class.""" if values["task"] not in VALID_TASKS: raise ValueError( f"Got invalid task {values['task']}, " f"currently only {VALID_TASKS} are supported" ) return values def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to HuggingFaceHub's embedding endpoint for embedding search docs. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ # replace newlines, which can negatively affect performance. texts = [text.replace("\n", " ") for text in texts] _model_kwargs = self.model_kwargs or {} responses = self.client.post( json={"inputs": texts, "parameters": _model_kwargs}, task=self.task ) return json.loads(responses.decode()) async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """Async Call to HuggingFaceHub's embedding endpoint for embedding search docs. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ # replace newlines, which can negatively affect performance. texts = [text.replace("\n", " ") for text in texts] _model_kwargs = self.model_kwargs or {} responses = await self.async_client.post( json={"inputs": texts, "parameters": _model_kwargs}, task=self.task ) return json.loads(responses.decode()) def embed_query(self, text: str) -> List[float]: """Call out to HuggingFaceHub's embedding endpoint for embedding query text. Args: text: The text to embed. Returns: Embeddings for the text. """ response = self.embed_documents([text])[0] return response async def aembed_query(self, text: str) -> List[float]: """Async Call to HuggingFaceHub's embedding endpoint for embedding query text. Args: text: The text to embed. Returns: Embeddings for the text. """ response = (await self.aembed_documents([text]))[0] return response