def test_cache_update_on_search(self):
        """
        The cache should update after search
        With single KNN field, doc should be found even if field is not in cache
        """
        self.add_documents(
            config=self.config, add_docs_params=AddDocsParams(index_name=self.index_name_1,
                docs=[{"some field": "Plane 1"}], auto_refresh=True, device="cpu"))
        time.sleep(2.5)
        self._simulate_externally_added_docs(
            self.index_name_1, [{"brand new field": "a line of text", "_id": "1234"}], "brand new field")
        assert "brand new field" not in index_meta_cache.get_cache()[self.index_name_1].properties
        result = tensor_search.search(
            index_name=self.index_name_1, config=self.config, text="a line of text",
            searchable_attributes=["brand new field"],
             search_method=SearchMethod.TENSOR)
        assert result["hits"][0]["_id"] == "1234"
        time.sleep(0.5)
        if self.config.cluster_is_remote:
            # Allow extra time if using a remote cluster
            time.sleep(3)
        result_2 = tensor_search.search(
            index_name=self.index_name_1, config=self.config, text="a line of text",
            searchable_attributes=["brand new field"],
             search_method=SearchMethod.TENSOR)
        assert "brand new field" in index_meta_cache.get_cache()[self.index_name_1].properties
        assert result_2["hits"][0]["_id"] == "1234"

        The cache should update after search
        With single KNN field, doc should be found even if field is not in cache
        
tests/integ_tests/tensor_search/test_index_meta_cache.py