def test_determine_text_chunk_prefix(self):
        """
        Ensures proper priority order is followed when determining the chunk prefix.
        add docs request-level > index override-level > model default level
        """

        with self.subTest("All prefixes on (request level chosen)"):
            result = self.unstructured_index_with_override.model.get_text_chunk_prefix("request-level")
            self.assertEqual(result, "request-level")

        with self.subTest("Request and model default on (request level chosen)"):
            result = self.unstructured_index_with_model_default.model.get_text_chunk_prefix("request-level")
            self.assertEqual(result, "request-level")

        with self.subTest("Index override and model default on (index override chosen)"):
            result = self.unstructured_index_with_override.model.get_text_chunk_prefix(None)
            self.assertEqual(result, "index-override: ")

        with self.subTest("Only model default on (model default chosen)"):
            result = self.unstructured_index_with_model_default.model.get_text_chunk_prefix(None)
            self.assertEqual(result, "test passage: ")

        # doc_a should default to the override prefix
        self.add_documents(config=self.config, add_docs_params=AddDocsParams(
            index_name=self.unstructured_index_with_override.name, docs=[{"_id": "doc_a", "text": "hello"}], auto_refresh=True,
            tensor_fields=["text"] if isinstance(self.unstructured_index_with_override, UnstructuredMarqoIndex) else None
        ))

        # Get all documents (with vectors)
        res = tensor_search.get_documents_by_ids(
            config=self.config, index_name=self.unstructured_index_with_override.name, document_ids=["doc_a"],
            show_vectors=True
        ).dict(exclude_none=True, by_alias=True)

        # we hardcode the prefix into the text chunk and embed
        embed_res = embed(
            marqo_config=self.config, index_name=self.unstructured_index_with_override.name,
            embedding_request_dict={"content": ["index-override: hello"]},
            device="cpu"
        )

        # We assert that the embeddings are equal
        with self.subTest("Embeddings are equal between overriden doc and direct embed"):
            self.assertTrue(np.allclose(embed_res["embeddings"][0], res["results"][0]["_tensor_facets"][0]["_embedding"]))

        Ensures proper priority order is followed when determining the chunk prefix.
        add docs request-level > index override-level > model default level
        
tests/integ_tests/tensor_search/test_prefix.py