)
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        distance_func: Literal[
            "sqrt_euclid", "neg_dot_prod", "ned_cos"
        ] = "sqrt_euclid",
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to query."""
        query_vector = self._embedding.embed_query(query)
        return [
            doc
            for doc, score in self.similarity_search_with_score_by_vector(
                query_vector, k, distance_func, **kwargs
            )
        ]