def test_embed_list_content(self):
        """Embeds a list with string and dict. Use add docs and get docs with tensor facets to ensure the vector is correct.
                                Checks the basic functionality and response structure"""
        test_cases = [self.structured_index_name, self.unstructured_index_name, self.unstructured_index_non_e5]

        for test_index_name in test_cases:
            with (self.subTest(test_index_name)):
                # Add document
                tensor_fields = ["text_field_1"] if "unstr" in test_index_name else None
                d1 = {
                    "_id": "doc1",
                    "text_field_1": "Jimmy Butler is the GOAT."
                }
                d2 = {
                    "_id": "doc2",
                    "text_field_1": "Alex Caruso is the GOAT."
                }
                res = self.client.index(test_index_name).add_documents([d1, d2], tensor_fields=tensor_fields)

                # Get doc with tensor facets (for reference vector)
                retrieved_docs = self.client.index(test_index_name).get_documents(
                    document_ids=["doc1", "doc2"], expose_facets=True)

                # Call embed
                if test_index_name == self.unstructured_index_non_e5:
                    embed_res = self.client.index(test_index_name).embed(
                        content=[{"Jimmy Butler is the GOAT.": 1}, "Alex Caruso is the GOAT."],
                    )
                else:
                    embed_res = self.client.index(test_index_name).embed(
                        content=[{"Jimmy Butler is the GOAT.": 1}, "Alex Caruso is the GOAT."],
                        content_type="document"
                    )

                self.assertIn("processingTimeMs", embed_res)
                self.assertEqual(embed_res["content"], [{"Jimmy Butler is the GOAT.": 1}, "Alex Caruso is the GOAT."])
                self.assertTrue(
                    np.allclose(embed_res["embeddings"][0], retrieved_docs["results"][0]["_tensor_facets"][0]["_embedding"], atol=1e-6))
                self.assertTrue(
                    np.allclose(embed_res["embeddings"][1], retrieved_docs["results"][1]["_tensor_facets"][0]["_embedding"], atol=1e-6))
Embeds a list with string and dict. Use add docs and get docs with tensor facets to ensure the vector is correct.
                                Checks the basic functionality and response structure
tests/api_tests/v1/tests/api_tests/test_embed.py