def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
        """Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        """
        # Prepare documents, replacing empty strings with a single space
        prepared_documents = [" " if doc == "" else doc for doc in documents]
        response = self.embedding_model.embed_documents(prepared_documents)
        return np.array(response)
Embed a list of n documents/words into an n-dimensional
        matrix of embeddings.

        Arguments:
            documents: A list of documents or words to be embedded
            verbose: Controls the verbosity of the process

        Returns:
            Document/words embeddings with shape (n, m) with `n` documents/words
            that each have an embeddings size of `m`
        
bertopic/backend/_langchain.py