def test_recommend_docsWithoutVectors_success(self):
        """
        Test that the recommender fails when one or more documents do not have embeddings
        """
        docs = [
            {
                "_id": "1",
                "title": "Anacamptis laxiflora",
            },
            {
                "_id": "2",
                "title": "Cephalanthera longifolia",
                "content": "Content"
            }
        ]
        index = self.unstructured_text_index
        self.add_documents(
            self.config,
            add_docs_params=AddDocsParams(
                index_name=index.name,
                docs=docs,
                tensor_fields=['content']
            )
        )

        with self.assertRaisesStrict(InvalidArgumentError):
            self.recommender.recommend(
                index_name=index.name,
                documents=["1", "2"],
            )

        Test that the recommender fails when one or more documents do not have embeddings
        
tests/integ_tests/core/search/test_recommender.py