- 'all' - query all replicas, and return values present in all replicas
            **kwargs:
                Any other named arguments to pass through to QdrantClient.search()
        Returns:
            List of Documents most similar to the query.
        """
        results = self.similarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))
[docs]    @sync_call_fallback
    async def asimilarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the