uuid_time_filter=self.date_to_range_filter(**kwargs),
        )
        docs = [
            (
                Document(
                    page_content=result[client.SEARCH_RESULT_CONTENTS_IDX],
                    metadata=result[client.SEARCH_RESULT_METADATA_IDX],
                ),
                result[client.SEARCH_RESULT_DISTANCE_IDX],
            )
            for result in results
        ]
        return docs
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Union[dict, list]] = None,
        predicates: Optional[Predicates] = None,
        **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.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query vector.
        """
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding=embedding, k=k, filter=filter, predicates=predicates, **kwargs
        )
        return [doc for doc, _ in docs_and_scores]
[docs]    async def asimilarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Union[dict, list]] = None,
        predicates: Optional[Predicates] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding to look up documents similar to.