content: #  Copyright (c) ZenML GmbH 2023. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at:
  #
  #       https://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
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  """Implementation of the Langchain OpenAI embedding materializer."""
  
  from typing import Any, ClassVar, Tuple, Type
  
  from langchain_community.embeddings import (
      OpenAIEmbeddings,
  )
  
  from zenml.enums import ArtifactType
  from zenml.materializers.cloudpickle_materializer import (
      CloudpickleMaterializer,
  )
  
  
  class LangchainOpenaiEmbeddingMaterializer(CloudpickleMaterializer):
      """Materializer for Langchain OpenAI Embeddings."""
  
      ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.MODEL
      ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (OpenAIEmbeddings,)
  
      def save(self, embeddings: Any) -> None:
          """Saves the embeddings model after clearing non-picklable clients.
  
          Args:
              embeddings: The embeddings model to save.
          """
          # Clear the clients which will be recreated on load
          embeddings.client = None
          embeddings.async_client = None
  
          # Use the parent class's save implementation which uses cloudpickle
          super().save(embeddings)
  
      def load(self, data_type: Type[Any]) -> Any:
          """Loads the embeddings model and lets it recreate clients when needed.
  
          Args:
              data_type: The type of the data to load.
  
          Returns:
              The loaded embeddings model.
          """
          return super().load(data_type)
  