Delete no model tests (#301)

Author: wanliAlex

tests/cloud_test_logic/cloud_test_index.py

@@ -11,9 +11,8 @@ class CloudTestIndex(str, Enum):
 
     1) unstructured_text: Text-only index using hf/e5-base-v2, 2 shards, 1 replica, CPU, balanced storage, for hybrid duplicates testing.
     2) unstructured_image: Image-compatible index using open_clip/ViT-B-32/laion2b_s34b_b79k, 1 shard, no replicas, CPU, basic storage.
-    3) unstructured_no_model: 512-dimension custom vectors, 1 shard, no replicas, CPU, basic storage.
-    4) structured_text: Structured text index with hf/e5-base-v2, lexical search, 2 shards, 1 replica, CPU, balanced storage.
-    5) structured_image: Structured image-text index with open_clip/ViT-B-32, 2 shards, 1 replica, CPU, balanced storage, with image preprocessing.
+    3) structured_text: Structured text index with hf/e5-base-v2, lexical search, 2 shards, 1 replica, CPU, balanced storage.
+    4) structured_image: Structured image-text index with open_clip/ViT-B-32, 2 shards, 1 replica, CPU, balanced storage, with image preprocessing.
     For more information on the settings of each index, please refer to index_name_to_settings_mappings.
 
     FOR CLOUD REPLICAS AND SHARDS:
@@ -28,7 +27,6 @@ class CloudTestIndex(str, Enum):
     unstructured_text = "pymarqo_unstr_txt"
     unstructured_image = "pymarqo_unstr_img"
     unstructured_text_custom_prepro = "pymarqo_unstr_txt_cstm_pre"
-    unstructured_no_model = "pymarqo_unstr_no_model"
 
     structured_image_prepro = "pymarqo_str_img_prepro"
     structured_image_custom = "pymarqo_str_img_custom"
@@ -57,21 +55,6 @@ class CloudTestIndex(str, Enum):
         "numberOfShards": 1,
         "numberOfReplicas": 0,
     },
-    CloudTestIndex.unstructured_no_model: {
-        "type": "unstructured",
-        "treatUrlsAndPointersAsImages": False,
-
-        "inferenceType": "marqo.CPU.small",
-        "storageClass": "marqo.basic",
-        "numberOfShards": 1,
-        "numberOfReplicas": 0,
-
-        "model": "no_model",
-        "modelProperties": {
-            "type": "no_model",
-            "dimensions": 512
-        },
-    },
     CloudTestIndex.structured_text: {
         "type": "structured",
         "model": "hf/e5-base-v2",

tests/marqo_test.py

@@ -206,7 +206,6 @@ def setUpClass(cls) -> None:
         cls.structured_index_name = "structured_index"
         cls.structured_image_index_name = "structured_image_index"
         cls.unstructured_image_index_name = "unstructured_image_index"
-        cls.unstructured_no_model_index_name = "unstructured_no_model_index"
         cls.structured_image_index_name_simple_preprocessing_method = \
             "structured_image_index_simple_preprocessing_method"
 
@@ -254,16 +253,6 @@ def setUpClass(cls) -> None:
                         "tensorFields": ["text_field_1", "text_field_2", "text_field_3", "image_field_1"],
                         "model": "open_clip/ViT-B-32/laion400m_e32",
                     },
-                    {
-                        "indexName": cls.unstructured_no_model_index_name,
-                        "type": "unstructured",
-                        "model": "no_model",
-                        "treatUrlsAndPointersAsImages": True,
-                        "modelProperties": {
-                            "type": "no_model",
-                            "dimensions": 512
-                        }
-                    },
                 ])
             except Exception as e:
                 print("Error creating indexes: ", e)

tests/v2_tests/test_add_documents.py

@@ -573,96 +573,6 @@ def test_custom_vector_doc(self):
              assert doc_res['_tensor_facets'][0]["my_custom_vector"] == "custom vector text"
              assert (np.linalg.norm(np.array(doc_res['_tensor_facets'][0]['_embedding'])) - 1) < 1e-6
 
-    def test_no_model_custom_vector_doc(self):
-        """
-        Tests the `no_model` index model and searching with no `q` parameter.
-        Executed on documents with custom_vector field type.
-
-        Ensures the following features work on this index:
-        1. lexical search
-        2. filter string search
-        3. tensor search
-        4. bulk search
-        5. get document
-
-        Note: `no_model` is not yet supported on Cloud.
-        """
-        self.test_cases = [(CloudTestIndex.unstructured_no_model, self.unstructured_no_model_index_name)]
-
-        for cloud_test_index_to_use, open_source_test_index_name in self.test_cases:
-            test_index_name = self.get_test_index_name(
-                cloud_test_index_to_use=cloud_test_index_to_use,
-                open_source_test_index_name=open_source_test_index_name
-            )
-
-            DIMENSION = 512
-            custom_vector_1 = np.random.rand(DIMENSION)
-            custom_vector_1 = (custom_vector_1 / np.linalg.norm(custom_vector_1)).tolist()
-            custom_vector_2 = np.random.rand(DIMENSION)
-            custom_vector_2 = (custom_vector_2 / np.linalg.norm(custom_vector_2)).tolist()
-            custom_vector_3 = np.random.rand(DIMENSION)
-            custom_vector_3 = (custom_vector_3 / np.linalg.norm(custom_vector_3)).tolist()
-
-            self.client.index(index_name=test_index_name).add_documents(
-                documents=[
-                    {
-                        "my_custom_vector": {
-                            "content": "custom vector text",
-                            "vector": custom_vector_1,
-                        },
-                        "_id": "doc1",
-                    },
-                    {
-                        "my_custom_vector": {
-                            "content": "second text",
-                            "vector": custom_vector_2,
-                        },
-                        "_id": "doc2",
-                    },
-                    {
-                        "my_custom_vector": {
-                            "content": "third text",
-                            "vector": custom_vector_3,
-                        },
-                        "_id": "doc3",
-                    },
-                ], mappings={
-                    "my_custom_vector": {
-                        "type": "custom_vector"
-                    }
-                },
-                tensor_fields=["my_custom_vector"])
-
-            # lexical search test
-            if self.IS_MULTI_INSTANCE:
-                self.warm_request(self.client.index(test_index_name).search,
-                                  "custom vector text", search_method="lexical")
-
-            lexical_res = self.client.index(test_index_name).search(
-                "custom vector text", search_method="lexical")
-            self.assertEqual("doc1", lexical_res["hits"][0]["_id"])
-
-            # filter string test
-            if self.IS_MULTI_INSTANCE:
-                self.warm_request(self.client.index(test_index_name).search,
-                                  context={"tensor": [{"vector": custom_vector_2, "weight": 1}]},
-                                  filter_string="my_custom_vector:(second text)")
-
-            filtering_res = self.client.index(test_index_name).search(
-                context={"tensor": [{"vector": custom_vector_2, "weight": 1}]},  # no text query
-                filter_string="my_custom_vector:(second text)")
-            self.assertEqual("doc2", filtering_res["hits"][0]["_id"])
-
-            # tensor search test
-            if self.IS_MULTI_INSTANCE:
-                self.warm_request(self.client.index(test_index_name).search,
-                                  context={"tensor": [{"vector": custom_vector_3, "weight": 1}]})
-
-            tensor_res = self.client.index(test_index_name).search(
-                context={"tensor": [{"vector": custom_vector_3, "weight": 1}]}  # no text query
-            )
-            self.assertEqual("doc3", tensor_res["hits"][0]["_id"])
-
     def test_add_docs_image_download_headers(self):
         mock__post = mock.MagicMock()