def test_add_documents_use_existing_tensors_with_custom_vector_field(self):
        """
        Add a document with a custom vector field and use existing tensors:
        Will not actually use existing tensors, as custom vector pipeline
        doesn't chunk or vectorise anyway.
        """

        for index in self.indexes:
            with self.subTest(f"Index: {index.name}, type: {index.type}"):
                # If we change the custom vector, doc should change
                self.add_documents(
                    config=self.config, add_docs_params=AddDocsParams(
                        index_name=index.name,
                        docs=[{
                            "_id": "0",
                            "my_custom_vector": {
                                "content": "1 - custom content is here!!",
                                "vector": self.random_vector_1
                            }
                        }],
                        device="cpu",
                        mappings=self.mappings if isinstance(index, UnstructuredMarqoIndex) else None,
                        tensor_fields=["my_custom_vector"] if isinstance(index, UnstructuredMarqoIndex) else None
                    )
                )

                get_doc_1 = tensor_search.get_document_by_id(
                    config=self.config, index_name=index.name,
                    document_id="0", show_vectors=True)

                self.add_documents(
                    config=self.config, add_docs_params=AddDocsParams(
                        index_name=index.name,
                        docs=[{
                            "_id": "0",
                            "my_custom_vector": {
                                "content": "2 - custom content is here!!",
                                "vector": self.random_vector_2
                            }
                        }],
                        device="cpu",
                        mappings=self.mappings if isinstance(index, UnstructuredMarqoIndex) else None,
                        tensor_fields=["my_custom_vector"] if isinstance(index, UnstructuredMarqoIndex) else None,
                        use_existing_tensors=True
                    )
                )

                get_doc_2 = tensor_search.get_document_by_id(
                    config=self.config, index_name=index.name,
                    document_id="0", show_vectors=True)
                assert get_doc_1["my_custom_vector"] == "1 - custom content is here!!"
                assert get_doc_1[enums.TensorField.tensor_facets][0][enums.TensorField.embedding] == self.random_vector_1
                assert get_doc_2["my_custom_vector"] == "2 - custom content is here!!"
                assert get_doc_2[enums.TensorField.tensor_facets][0][enums.TensorField.embedding] == self.random_vector_2

                # If we do not, it should remain the same, no errors
                self.add_documents(
                    config=self.config, add_docs_params=AddDocsParams(
                        index_name=index.name,
                        docs=[{
                            "_id": "0",
                            "my_custom_vector": {
                                "content": "2 - custom content is here!!",
                                "vector": self.random_vector_2
                            }
                        }],
                        device="cpu",
                        mappings=self.mappings if isinstance(index, UnstructuredMarqoIndex) else None,
                        tensor_fields=["my_custom_vector"] if isinstance(index, UnstructuredMarqoIndex) else None,
                        use_existing_tensors=True
                    )
                )

                get_doc_3 = tensor_search.get_document_by_id(
                    config=self.config, index_name=index.name,
                    document_id="0", show_vectors=True)
                assert get_doc_2["my_custom_vector"] == "2 - custom content is here!!"
                assert get_doc_2[enums.TensorField.tensor_facets][0][enums.TensorField.embedding] == self.random_vector_2

        Add a document with a custom vector field and use existing tensors:
        Will not actually use existing tensors, as custom vector pipeline
        doesn't chunk or vectorise anyway.
        
tests/integ_tests/tensor_search/integ_tests/test_custom_vector_field.py