embeddings: List[List[float]],
        embedding: Embeddings,
        index_uri: str,
        *,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        metric: str = DEFAULT_METRIC,
        index_type: str = "FLAT",
        config: Optional[Mapping[str, Any]] = None,
        index_timestamp: int = 0,
        **kwargs: Any,
    ) -> TileDB:
        if metric not in INDEX_METRICS:
            raise ValueError(
                (
                    f"Unsupported distance metric: {metric}. "
                    f"Expected one of {list(INDEX_METRICS)}"
                )
            )
        tiledb_vs, tiledb = dependable_tiledb_import()
        input_vectors = np.array(embeddings).astype(np.float32)
        cls.create(
            index_uri=index_uri,
            index_type=index_type,
            dimensions=input_vectors.shape[1],
            vector_type=input_vectors.dtype,
            metadatas=metadatas is not None,
            config=config,
        )
        with tiledb.scope_ctx(ctx_or_config=config):
            if not embeddings:
                raise ValueError("embeddings must be provided to build a TileDB index")
            vector_index_uri = get_vector_index_uri(index_uri)
            docs_uri = get_documents_array_uri(index_uri)
            if ids is None:
                ids = [str(random.randint(0, MAX_UINT64 - 1)) for _ in texts]
            external_ids = np.array(ids).astype(np.uint64)
            tiledb_vs.ingestion.ingest(
                index_type=index_type,
                index_uri=vector_index_uri,
                input_vectors=input_vectors,
                external_ids=external_ids,