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90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
"""Wrappers around embedding modules."""
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import logging
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from typing import Any
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from langchain.embeddings.aleph_alpha import (
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AlephAlphaAsymmetricSemanticEmbedding,
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AlephAlphaSymmetricSemanticEmbedding,
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)
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from langchain.embeddings.bedrock import BedrockEmbeddings
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from langchain.embeddings.cohere import CohereEmbeddings
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from langchain.embeddings.dashscope import DashScopeEmbeddings
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from langchain.embeddings.deepinfra import DeepInfraEmbeddings
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from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings
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from langchain.embeddings.embaas import EmbaasEmbeddings
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from langchain.embeddings.fake import FakeEmbeddings
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from langchain.embeddings.google_palm import GooglePalmEmbeddings
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from langchain.embeddings.huggingface import (
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HuggingFaceEmbeddings,
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HuggingFaceInstructEmbeddings,
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)
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from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
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from langchain.embeddings.jina import JinaEmbeddings
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from langchain.embeddings.llamacpp import LlamaCppEmbeddings
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from langchain.embeddings.minimax import MiniMaxEmbeddings
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from langchain.embeddings.modelscope_hub import ModelScopeEmbeddings
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from langchain.embeddings.mosaicml import MosaicMLInstructorEmbeddings
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.embeddings.sagemaker_endpoint import SagemakerEndpointEmbeddings
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from langchain.embeddings.self_hosted import SelfHostedEmbeddings
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from langchain.embeddings.self_hosted_hugging_face import (
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SelfHostedHuggingFaceEmbeddings,
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SelfHostedHuggingFaceInstructEmbeddings,
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)
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from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
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from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
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from langchain.embeddings.vertexai import VertexAIEmbeddings
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logger = logging.getLogger(__name__)
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__all__ = [
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"OpenAIEmbeddings",
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"HuggingFaceEmbeddings",
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"CohereEmbeddings",
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"ElasticsearchEmbeddings",
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"JinaEmbeddings",
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"LlamaCppEmbeddings",
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"HuggingFaceHubEmbeddings",
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"ModelScopeEmbeddings",
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"TensorflowHubEmbeddings",
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"SagemakerEndpointEmbeddings",
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"HuggingFaceInstructEmbeddings",
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"MosaicMLInstructorEmbeddings",
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"SelfHostedEmbeddings",
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"SelfHostedHuggingFaceEmbeddings",
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"SelfHostedHuggingFaceInstructEmbeddings",
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"FakeEmbeddings",
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"AlephAlphaAsymmetricSemanticEmbedding",
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"AlephAlphaSymmetricSemanticEmbedding",
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"SentenceTransformerEmbeddings",
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"GooglePalmEmbeddings",
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"MiniMaxEmbeddings",
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"VertexAIEmbeddings",
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"BedrockEmbeddings",
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"DeepInfraEmbeddings",
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"DashScopeEmbeddings",
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"EmbaasEmbeddings",
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]
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# TODO: this is in here to maintain backwards compatibility
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class HypotheticalDocumentEmbedder:
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def __init__(self, *args: Any, **kwargs: Any):
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logger.warning(
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"Using a deprecated class. Please use "
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"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
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)
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
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return H(*args, **kwargs) # type: ignore
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@classmethod
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def from_llm(cls, *args: Any, **kwargs: Any) -> Any:
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logger.warning(
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"Using a deprecated class. Please use "
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"`from langchain.chains import HypotheticalDocumentEmbedder` instead"
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)
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
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return H.from_llm(*args, **kwargs)
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