"""Return docs most similar to embedding vector.
        Args:
            embedding (List[float]): Embedding to look up documents similar to.
            k (int): Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query vector.
        """
        results = self.__query_collection(
            query_embeddings=embedding,
            n_results=k,
            where=filter,
            where_document=where_document,
        )
        return _results_to_docs(results)
[docs]    def similarity_search_by_vector_with_relevance_scores(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        filter: Optional[Dict[str, str]] = None,
        where_document: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """
        Return docs most similar to embedding vector and similarity score.
        Args:
            embedding (List[float]): Embedding to look up documents similar to.
            k (int): Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List[Tuple[Document, float]]: List of documents most similar to
            the query text and cosine distance in float for each.
            Lower score represents more similarity.
        """
        results = self.__query_collection(
            query_embeddings=embedding,
            n_results=k,
            where=filter,
            where_document=where_document,
        )
        return _results_to_docs_and_scores(results)
[docs]    def similarity_search_with_score(