def test_search(self, node_embeddings: List[TextNode]) -> None:
        """Test end to end Couchbase vector search."""
        # Add nodes to the couchbase vector
        self.vector_store.add(node_embeddings)

        # Wait for the documents to be indexed
        time.sleep(SLEEP_DURATION)

        # similarity search
        q = VectorStoreQuery(
            query_embedding=text_to_embedding("foo"), similarity_top_k=1
        )

        result = self.vector_store.query(q)
        assert result.nodes is not None and len(result.nodes) == 1
        assert (
            result.nodes[0].get_content(metadata_mode=MetadataMode.NONE)
            == node_embeddings[0].text
        )
        assert result.similarities is not None
Test end to end Couchbase vector search.
llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/tests/test_vector_stores_couchbase.py