Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            search_k: inspect up to search_k nodes which defaults
                to n_trees * n if not provided
        Returns:
            List of Documents most similar to the query and score for each
        """
        embedding = self.embedding_function(query)
        docs = self.similarity_search_with_score_by_vector(embedding, k, search_k)
        return docs
[docs]    def similarity_search_by_vector(
        self, embedding: List[float], k: int = 4, search_k: int = -1, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            search_k: inspect up to search_k nodes which defaults
                to n_trees * n if not provided
        Returns:
            List of Documents most similar to the embedding.
        """
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding, k, search_k
        )
        return [doc for doc, _ in docs_and_scores]
[docs]    def similarity_search_by_index(
        self, docstore_index: int, k: int = 4, search_k: int = -1, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to docstore_index.
        Args:
            docstore_index: Index of document in docstore
            k: Number of Documents to return. Defaults to 4.
            search_k: inspect up to search_k nodes which defaults
                to n_trees * n if not provided
        Returns: