"""
        embedding = self._embedding.embed_query(query)
        documents = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k
        )
        return documents
[docs]    def similarity_search_by_vector(
        self, embedding: List[float], k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Perform a similarity search with Yellowbrick by vectors
        Args:
            embedding (List[float]): query embedding
            k (int, optional): Top K neighbors to retrieve. Defaults to 4.
            NOTE: Please do not let end-user fill this and always be aware
                  of SQL injection.
        Returns:
            List[Document]: List of documents
        """
        documents = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k
        )
        return [doc for doc, _ in documents]