"""Compute doc embeddings using a TensorflowHub embedding model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        embeddings = self.embed(texts).numpy()
        return embeddings.tolist()
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a TensorflowHub embedding model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embedding = self.embed([text]).numpy()[0]
        return embedding.tolist()
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/tensorflow_hub.html