)
            # Check if the vector configuration has the same dimensionality.
            if current_vector_config.size != vector_size:  # type: ignore[union-attr]
                raise QdrantException(
                    f"Existing Qdrant collection is configured for vectors with "
                    f"{current_vector_config.size} "  # type: ignore[union-attr]
                    f"dimensions. Selected embeddings are {vector_size}-dimensional. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            current_distance_func = (
                current_vector_config.distance.name.upper()  # type: ignore[union-attr]
            )
            if current_distance_func != distance_func:
                raise QdrantException(
                    f"Existing Qdrant collection is configured for "
                    f"{current_distance_func} similarity, but requested "
                    f"{distance_func}. Please set `distance_func` parameter to "
                    f"`{current_distance_func}` if you want to reuse it. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
        except (UnexpectedResponse, RpcError, ValueError):
            vectors_config = rest.VectorParams(
                size=vector_size,
                distance=rest.Distance[distance_func],
                on_disk=on_disk,
            )
            # If vector name was provided, we're going to use the named vectors feature
            # with just a single vector.
            if vector_name is not None:
                vectors_config = {  # type: ignore[assignment]
                    vector_name: vectors_config,
                }
            client.recreate_collection(
                collection_name=collection_name,
                vectors_config=vectors_config,