def test_search_with_filter(self, node_embeddings: List[TextNode]) -> None:
        """Test end to end Couchbase vector search with filter."""
        # 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("baz"),
            similarity_top_k=1,
            filters=MetadataFilters(
                filters=[
                    MetadataFilter(key="genre", value="Thriller", operator="=="),
                    MetadataFilter(key="pages", value=10, operator=">"),
                ]
            ),
        )

        result = self.vector_store.query(q)
        assert result.nodes is not None and len(result.nodes) == 1
        assert (
            result.nodes[0].metadata.get("genre") == "Thriller"
            and result.nodes[0].metadata.get("pages") == 20
        )
Test end to end Couchbase vector search with filter.
llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/tests/test_vector_stores_couchbase.py