def test_languagebind_model_inference_pipeline_vectorise_text(self):
        """Test that the pipeline returns the embeddings for the two texts without chunking or prefix."""
        text_inference_request = InferenceRequest(
            modality=Modality.TEXT,
            contents=["text", "very long long long long text"],
            device="cuda",
            model_config=ModelConfig(
                model_name=self.model_name,
                model_properties=self.model_properties,
                normalize_embeddings=True
            ),
            preprocessing_config=TextPreprocessingConfig(
                should_chunk=False
            )
        )

        results = self.inference.vectorise(text_inference_request)
        self.assertTrue(isinstance(results, InferenceResult))
        self.assertTrue(isinstance(results.result, list))
        self.assertTrue(len(results.result) == 2)

        results_1: list[tuple[str, ndarray]] = results.result[0]
        self.assertTrue(isinstance(results_1, list))
        self.assertTrue(len(results_1) == 1)
        self.assertTrue(isinstance(results_1[0], tuple))
        self.assertTrue(isinstance(results_1[0][0], str))
        self.assertTrue(isinstance(results_1[0][1], np.ndarray))
        self.assertEqual((768, ), results_1[0][1].shape)
        self.assertEqual("text", results_1[0][0])

        results_2: list[tuple[str, ndarray]] = results.result[1]
        self.assertTrue(isinstance(results_2, list))
        self.assertTrue(len(results_2) == 1)
        self.assertTrue(isinstance(results_2[0], tuple))
        self.assertTrue(isinstance(results_2[0][0], str))
        self.assertTrue(isinstance(results_2[0][1], np.ndarray))
        self.assertEqual((768, ), results_2[0][1].shape)
        self.assertEqual("very long long long long text", results_2[0][0])
Test that the pipeline returns the embeddings for the two texts without chunking or prefix.
tests/integ_tests/inference/native_inference/test_languagebind_model_inference_pipeline.py