Source code for langchain_community.embeddings.nlpcloud

from typing import Any, Dict, List

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.utils import get_from_dict_or_env

[docs]class NLPCloudEmbeddings(BaseModel, Embeddings): """NLP Cloud embedding models. To use, you should have the nlpcloud python package installed Example: .. code-block:: python from langchain_community.embeddings import NLPCloudEmbeddings embeddings = NLPCloudEmbeddings() """ model_name: str # Define model_name as a class attribute gpu: bool # Define gpu as a class attribute client: Any #: :meta private: def __init__( self, model_name: str = "paraphrase-multilingual-mpnet-base-v2", gpu: bool = False, **kwargs: Any, ) -> None: super().__init__(model_name=model_name, gpu=gpu, **kwargs) @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" nlpcloud_api_key = get_from_dict_or_env( values, "nlpcloud_api_key", "NLPCLOUD_API_KEY" ) try: import nlpcloud values["client"] = nlpcloud.Client( values["model_name"], nlpcloud_api_key, gpu=values["gpu"], lang="en" ) except ImportError: raise ImportError( "Could not import nlpcloud python package. " "Please install it with `pip install nlpcloud`." ) return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of documents using NLP Cloud. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ return self.client.embeddings(texts)["embeddings"]
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using NLP Cloud. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.client.embeddings([text])["embeddings"][0]