Source code for langchain_huggingface.embeddings.huggingface_endpoint

import json
import os
from typing import Any, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.utils import from_env
from pydantic import BaseModel, ConfigDict, Field, model_validator
from typing_extensions import Self

DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
VALID_TASKS = ("feature-extraction",)


[docs] class HuggingFaceEndpointEmbeddings(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_huggingface import HuggingFaceEndpointEmbeddings model = "sentence-transformers/all-mpnet-base-v2" hf = HuggingFaceEndpointEmbeddings( model=model, task="feature-extraction", huggingfacehub_api_token="my-api-key", ) """ client: Any = None #: :meta private: async_client: Any = None #: :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] = Field( default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None) ) model_config = ConfigDict( extra="forbid", protected_namespaces=(), ) @model_validator(mode="after") def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv( "HF_TOKEN" ) try: from huggingface_hub import ( # type: ignore[import] AsyncInferenceClient, InferenceClient, ) if self.model: self.repo_id = self.model elif self.repo_id: self.model = self.repo_id else: self.model = DEFAULT_MODEL self.repo_id = DEFAULT_MODEL client = InferenceClient( model=self.model, token=huggingfacehub_api_token, ) async_client = AsyncInferenceClient( model=self.model, token=huggingfacehub_api_token, ) if self.task not in VALID_TASKS: raise ValueError( f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) self.client = client self.async_client = async_client except ImportError: raise ImportError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) return self
[docs] 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 {} # api doc: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/embed responses = self.client.post( json={"inputs": texts, **_model_kwargs}, task=self.task ) return json.loads(responses.decode())
[docs] 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())
[docs] 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
[docs] 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