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"""Wrappers around embedding modules."""
import logging
from typing import Any
from langchain.embeddings.cohere import CohereEmbeddings
from langchain.embeddings.fake import FakeEmbeddings
from langchain.embeddings.huggingface import (
HuggingFaceEmbeddings,
HuggingFaceInstructEmbeddings,
)
from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings.sagemaker_endpoint import SagemakerEndpointEmbeddings
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
from langchain.embeddings.self_hosted_hugging_face import (
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
logger = logging.getLogger(__name__)
__all__ = [
"OpenAIEmbeddings",
"HuggingFaceEmbeddings",
"CohereEmbeddings",
"HuggingFaceHubEmbeddings",
"TensorflowHubEmbeddings",
"SagemakerEndpointEmbeddings",
"HuggingFaceInstructEmbeddings",
"SelfHostedEmbeddings",
"SelfHostedHuggingFaceEmbeddings",
"SelfHostedHuggingFaceInstructEmbeddings",
"FakeEmbeddings",
]
# TODO: this is in here to maintain backwards compatibility
class HypotheticalDocumentEmbedder:
def __init__(self, *args: Any, **kwargs: Any):
logger.warning(
"Using a deprecated class. Please use "
"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
)
from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
return H(*args, **kwargs) # type: ignore
@classmethod
def from_llm(cls, *args: Any, **kwargs: Any) -> Any:
logger.warning(
"Using a deprecated class. Please use "
"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
)
from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
return H.from_llm(*args, **kwargs)
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