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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
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
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"]
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]
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