def test_add_documents_with_custom_vector_zero_vector_normalize_embeddings_true(self):
        """
        Test adding a document with a custom vector field where the vector is a zero vector,
        in an index where normalize_embeddings is set to True at the time of index creation.

        This test method verifies that when a document with a zero vector is added to both structured and
        unstructured indexes, the addition fails with an appropriate error message.

        The test covers the following scenarios:
        1. Adding a document with a zero vector to both structured and unstructured indexes.
        2. Ensuring that the addition fails with a "Zero magnitude vector detected, cannot normalize." error.
        """
        for index in self.indexes:
            mock_feed_batch = mock.MagicMock()
            mock_feed_batch.return_value = FeedBatchResponse(
                responses=[FeedBatchDocumentResponse(
                    status=200,
                    pathId='/document/v1/aa5ed6d56e6aa4a048d95b496b79659f9/aa5ed6d56e6aa4a048d95b496b79659f9/docid/0',
                    id='id:aa5ed6d56e6aa4a048d95b496b79659f9:aa5ed6d56e6aa4a048d95b496b79659f9::0', message=None)
                ],
                errors=False)

            add_documents_response = self.add_documents(
                    config=self.config, add_docs_params=AddDocsParams(
                        index_name=index.name,
                        docs=[{
                            "_id": "0",
                            "my_custom_vector": {
                                "content": "custom content is here!!",
                                "vector": self.zero_vector
                            }
                        }],
                        device="cpu",
                        mappings=self.mappings if isinstance(index, UnstructuredMarqoIndex) else None,
                        tensor_fields=["my_custom_vector"] if isinstance(index, UnstructuredMarqoIndex) else None
                    )
                )

            if isinstance(index, (StructuredMarqoIndex, SemiStructuredMarqoIndex)):
                self.assertEqual(add_documents_response.errors, True)
                self.assertIn("Field my_custom_vector has zero magnitude vector, cannot normalize.",
                              add_documents_response.items[0].message)
                self.assertEqual(add_documents_response.items[0].status, 400)
                self.assertEqual(add_documents_response.items[0].code, 'invalid_argument')
            elif isinstance(index, UnstructuredMarqoIndex):
                # legacy unstructured index will not have custom vector normalised
                self.assertEqual(add_documents_response.errors, False)

        Test adding a document with a custom vector field where the vector is a zero vector,
        in an index where normalize_embeddings is set to True at the time of index creation.

        This test method verifies that when a document with a zero vector is added to both structured and
        unstructured indexes, the addition fails with an appropriate error message.

        The test covers the following scenarios:
        1. Adding a document with a zero vector to both structured and unstructured indexes.
        2. Ensuring that the addition fails with a "Zero magnitude vector detected, cannot normalize." error.
        
tests/integ_tests/tensor_search/integ_tests/test_custom_vector_field.py