self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to embedding vector.
        Args:
            embedding (str): Embedding to look up documents similar to.
            k (int): Number of Documents to return. Defaults to 4.
        Returns:
            List of (Document, score), the most similar to the query vector.
        """
        return [
            (doc, score)
            for (doc, score, docId) in self.similarity_search_with_score_id_by_vector(
                embedding=embedding,
                k=k,
                filter=filter,
            )
        ]
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        embedding_vector = self.embedding.embed_query(query)
        return self.similarity_search_by_vector(
            embedding_vector,
            k,
            filter=filter,
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        return [
            doc
            for doc, _ in self.similarity_search_with_score_by_vector(
                embedding,
                k,
                filter=filter,
            )
        ]
[docs]    def similarity_search_with_score(
        self,
        query: str,