def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]:
        """
        Add nodes to the vector store.

        Args:
            nodes: List[BaseNode]: list of nodes with embeddings

        """
        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models

        self._initialize()
        ids = []
        rows: List[gpdb_20160503_models.UpsertCollectionDataRequestRows] = []
        for node in nodes:
            ids.append(node.node_id)
            node_metadata_dict = node_to_metadata_dict(
                node,
                remove_text=True,
                flat_metadata=self.flat_metadata,
            )
            metadata = {
                "node_id": node.node_id,
                "ref_doc_id": node.ref_doc_id,
                "content": node.get_content(metadata_mode=MetadataMode.NONE),
                "metadata_": json.dumps(node_metadata_dict),
            }
            rows.append(
                gpdb_20160503_models.UpsertCollectionDataRequestRows(
                    vector=node.get_embedding(),
                    metadata=metadata,
                )
            )
        _logger.debug("adding nodes to vector store...")
        request = gpdb_20160503_models.UpsertCollectionDataRequest(
            dbinstance_id=self.instance_id,
            region_id=self.region_id,
            namespace=self.namespace,
            namespace_password=self.namespace_password,
            collection=self.collection,
            rows=rows,
        )
        response = self._client.upsert_collection_data(request)
        _logger.info(
            f"successfully adding nodes to vector store, size: {len(nodes)},"
            f"response body:{response.body}"
        )
        return ids

        Add nodes to the vector store.

        Args:
            nodes: List[BaseNode]: list of nodes with embeddings

        
llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/llama_index/vector_stores/analyticdb/base.py