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693 lines
39 KiB
Python
693 lines
39 KiB
Python
import random
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import time
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import pytest
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from base.client_v2_base import TestMilvusClientV2Base
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from common import common_func as cf
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from common import common_type as ct
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from common.common_type import CaseLabel, CheckTasks
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from utils.util_pymilvus import DataType
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import numpy as np
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prefix = "add_field"
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default_vector_field_name = "vector"
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default_primary_key_field_name = "id"
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default_string_field_name = "varchar"
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default_float_field_name = "float"
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default_new_field_name = "field_new"
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default_dynamic_field_name = "field_new"
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exp_res = "exp_res"
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default_nb = 2000
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default_dim = 128
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default_limit = 10
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class TestMilvusClientAddFieldFeature(TestMilvusClientV2Base):
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"""Test cases for add field feature with CaseLabel.L0"""
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@pytest.mark.tags(CaseLabel.L0)
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def test_milvus_client_collection_add_field(self):
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"""
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target: test self create collection normal case about add field
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method: create collection with added field
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expected: create collection with default schema, index, and load successfully
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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dim = 128
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# 1. create collection
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schema = self.create_schema(client, enable_dynamic_field=False)[0]
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schema.add_field("id_string", DataType.VARCHAR, max_length=64, is_primary=True, auto_id=False)
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schema.add_field("embeddings", DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field("title", DataType.VARCHAR, max_length=64, is_partition_key=True)
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schema.add_field("nullable_field", DataType.INT64, nullable=True, default_value=10)
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schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
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max_length=64, nullable=True)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index("embeddings", metric_type="COSINE")
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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collections = self.list_collections(client)[0]
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assert collection_name in collections
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check_items = {"collection_name": collection_name,
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"dim": dim,
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"consistency_level": 0,
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"enable_dynamic_field": False,
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"num_partitions": 16,
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"id_name": "id_string",
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"vector_name": "embeddings"}
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self.add_collection_field(client, collection_name, field_name="field_new_int64", data_type=DataType.INT64,
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nullable=True, is_cluster_key=True, mmap_enabled=True)
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self.add_collection_field(client, collection_name, field_name="field_new_var", data_type=DataType.VARCHAR,
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nullable=True, default_vaule="field_new_var", max_length=64, mmap_enabled=True)
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check_items["add_fields"] = ["field_new_int64", "field_new_var"]
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self.describe_collection(client, collection_name,
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check_task=CheckTasks.check_describe_collection_property,
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check_items=check_items)
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index = self.list_indexes(client, collection_name)[0]
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assert index == ['embeddings']
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if self.has_collection(client, collection_name)[0]:
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L0)
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def test_milvus_client_compact_with_added_field(self):
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"""
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target: test clustering compaction with added field as cluster key
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method: create connection, collection, insert, add field, insert and compact
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expected: successfully
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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dim = 128
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# 1. create collection
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schema = self.create_schema(client, enable_dynamic_field=False)[0]
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schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(default_vector_field_name+"new", DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(default_vector_field_name, metric_type="COSINE")
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index_params.add_index(default_vector_field_name+"new", metric_type="L2")
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [
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{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_vector_field_name+"new": list(rng.random((1, default_dim))[0]),
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default_string_field_name: str(i)} for i in range(10*default_nb)]
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self.insert(client, collection_name, rows)
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self.add_collection_field(client, collection_name, field_name=default_new_field_name, data_type=DataType.INT64,
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nullable=True, is_clustering_key=True)
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# 3. insert new field after add field
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rows_new = [
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{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_vector_field_name+"new": list(rng.random((1, default_dim))[0]),default_string_field_name: str(i),
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default_new_field_name: random.randint(1, 1000)} for i in range(10*default_nb, 11*default_nb)]
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self.insert(client, collection_name, rows_new)
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self.flush(client, collection_name)
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# 4. compact
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compact_id = self.compact(client, collection_name, is_clustering=True)[0]
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cost = 180
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start = time.time()
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while True:
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time.sleep(1)
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res = self.get_compaction_state(client, compact_id, is_clustering=True)[0]
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if res == "Completed":
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break
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if time.time() - start > cost:
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raise Exception(1, f"Compact after index cost more than {cost}s")
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_insert_with_old_and_added_field(self):
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"""
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target: test search (high level api) normal case
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method: create connection, collection, insert, add field, insert old/new field and search
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expected: search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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dim = 8
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# 1. create collection
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schema = self.create_schema(client, enable_dynamic_field=False)[0]
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schema.add_field(default_primary_key_field_name, DataType.INT64, max_length=64, is_primary=True, auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
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schema.add_field(default_float_field_name, DataType.FLOAT, nullable=True)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(default_vector_field_name, metric_type="COSINE")
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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# 2. insert before add field
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vectors = cf.gen_vectors(default_nb * 3, dim, vector_data_type=DataType.FLOAT_VECTOR)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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results = self.insert(client, collection_name, rows)[0]
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assert results['insert_count'] == default_nb
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# 3. add new field
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self.add_collection_field(client, collection_name, field_name=default_new_field_name, data_type=DataType.VARCHAR,
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nullable=True, max_length=64)
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vectors_to_search = [vectors[0]]
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insert_ids = [i for i in range(default_nb)]
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# 4. check old dynamic data search is not impacted after add new field
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self.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"pk_name": default_primary_key_field_name,
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"limit": default_limit})
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# 5. insert data(old field)
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rows_old = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
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default_float_field_name: i * 1.0,
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default_string_field_name: str(i)} for i in range(default_nb, default_nb * 2)]
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results = self.insert(client, collection_name, rows_old)[0]
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assert results['insert_count'] == default_nb
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insert_ids_with_old_field = [i for i in range(default_nb, default_nb * 2)]
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# 6. insert data(new field)
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rows_new = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
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default_float_field_name: i * 1.0, default_string_field_name: str(i),
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default_new_field_name: default_new_field_name} for i in range(default_nb * 2, default_nb * 3)]
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results = self.insert(client, collection_name, rows_new)[0]
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assert results['insert_count'] == default_nb
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insert_ids_with_new_field = [i for i in range(default_nb * 2, default_nb * 3)]
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# 7. search filtered with the new field
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self.search(client, collection_name, vectors_to_search,
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filter=f'field_new is null',
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids + insert_ids_with_old_field,
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"pk_name": default_primary_key_field_name,
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"limit": default_limit})
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self.search(client, collection_name, vectors_to_search,
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filter=f"field_new=='field_new'",
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids_with_new_field,
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"pk_name": default_primary_key_field_name,
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"limit": default_limit})
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self.release_collection(client, collection_name)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_with_added_field(self):
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"""
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target: test upsert (high level api) normal case
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method: create connection, collection, insert, add field, upsert and search
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expected: upsert/search successfully
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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collections = self.list_collections(client)[0]
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assert collection_name in collections
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self.describe_collection(client, collection_name,
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check_task=CheckTasks.check_describe_collection_property,
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check_items={"collection_name": collection_name,
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"dim": default_dim,
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"consistency_level": 0})
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# 2. insert before add field
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vectors = cf.gen_vectors(default_nb * 3, default_dim, vector_data_type=DataType.FLOAT_VECTOR)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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results = self.insert(client, collection_name, rows)[0]
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assert results['insert_count'] == default_nb
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# 3. add new field
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self.add_collection_field(client, collection_name, field_name=default_new_field_name, data_type=DataType.VARCHAR,
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nullable=True, max_length=64)
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half_default_nb = int (default_nb/2)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
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default_float_field_name: i * 1.0, default_string_field_name: str(i),
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default_new_field_name: "default"} for i in range(half_default_nb)]
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results = self.upsert(client, collection_name, rows)[0]
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assert results['upsert_count'] == half_default_nb
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vectors_to_search = [vectors[0]]
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insert_ids = [i for i in range(half_default_nb)]
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insert_ids_with_new_field = [i for i in range(half_default_nb, default_nb)]
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# 4. search filtered with the new field
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self.search(client, collection_name, vectors_to_search,
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filter=f'field_new is null',
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids_with_new_field,
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"pk_name": default_primary_key_field_name,
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"limit": default_limit})
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self.search(client, collection_name, vectors_to_search,
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filter=f"field_new=='default'",
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"pk_name": default_primary_key_field_name,
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"limit": default_limit})
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self.release_collection(client, collection_name)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("new_field_name", [default_dynamic_field_name, "new_field"])
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def test_milvus_client_search_query_enable_dynamic_and_add_field(self, new_field_name):
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"""
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target: test search (high level api) normal case
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method: create connection, collection, insert, add field(same as dynamic and different as dynamic) and search
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expected: search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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dim = 8
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# 1. create collection
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schema = self.create_schema(client, enable_dynamic_field=True)[0]
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schema.add_field(default_primary_key_field_name, DataType.INT64, max_length=64, is_primary=True, auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
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schema.add_field(default_float_field_name, DataType.FLOAT, nullable=True)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(default_vector_field_name, metric_type="COSINE")
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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# 2. insert
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vectors = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
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default_float_field_name: i * 1.0, default_string_field_name: str(i),
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default_dynamic_field_name: 1} for i in range(default_nb)]
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results = self.insert(client, collection_name, rows)[0]
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assert results['insert_count'] == default_nb
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# 3. add new field same as dynamic field name
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default_value = 1
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self.add_collection_field(client, collection_name, field_name=new_field_name, data_type=DataType.INT64,
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nullable=True, default_value=default_value)
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vectors_to_search = [vectors[0]]
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insert_ids = [i for i in range(default_nb)]
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# 4. check old dynamic data search is not impacted after add new field
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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filter=f'$meta["{default_dynamic_field_name}"] == 1',
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"limit": default_limit,
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"pk_name": default_primary_key_field_name})
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# 5. check old dynamic data query is not impacted after add new field
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for row in rows:
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row[new_field_name] = default_value
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self.query(client, collection_name, filter=f'$meta["{default_dynamic_field_name}"] == 1',
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows,
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"with_vec": True,
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"pk_name": default_primary_key_field_name,
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"vector_type": DataType.FLOAT_VECTOR})
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# 6. search filtered with the new field
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self.search(client, collection_name, vectors_to_search,
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filter=f"{new_field_name} == 1",
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"pk_name": default_primary_key_field_name,
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"limit": default_limit})
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self.search(client, collection_name, vectors_to_search,
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filter=f"{new_field_name} is null",
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"pk_name": default_primary_key_field_name,
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"limit": 0})
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# 7. query filtered with the new field
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self.query(client, collection_name, filter=f"{new_field_name} == 1",
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows,
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"with_vec": True,
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"pk_name": default_primary_key_field_name})
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self.query(client, collection_name, filter=f"{new_field_name} is null",
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: [],
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"pk_name": default_primary_key_field_name})
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self.release_collection(client, collection_name)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_add_field_with_analyzer(self):
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"""
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target: test add field with analyzer configuration
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method: create collection, add field with standard analyzer,
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insert data and verify
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expected: successfully add field with analyzer and perform text search
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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dim = 8
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# 1. create collection with basic schema
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schema = self.create_schema(client, enable_dynamic_field=False)[0]
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schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(default_vector_field_name, metric_type="COSINE")
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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# 2. insert initial data before adding analyzer field
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schema_info = self.describe_collection(client, collection_name)[0]
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rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema_info)
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results = self.insert(client, collection_name, rows)[0]
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assert results['insert_count'] == default_nb
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# 3. add new field with standard analyzer
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analyzer_params = {
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"type": "standard",
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"stop_words": ["for", "the", "is", "a"]
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}
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self.add_collection_field(client, collection_name, field_name="text_content", data_type=DataType.VARCHAR,
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nullable=True, max_length=1000, enable_analyzer=True, analyzer_params=analyzer_params)
|
|
|
|
# 4. insert data with the new analyzer field
|
|
text_data = [
|
|
"The Milvus vector database is built for scale",
|
|
"This is a test document for analyzer",
|
|
"Vector search with text analysis capabilities",
|
|
"Database performance and scalability features"
|
|
]
|
|
rows_with_analyzer = []
|
|
vectors = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
|
|
for i in range(default_nb, default_nb + len(text_data)):
|
|
rows_with_analyzer.append({
|
|
default_primary_key_field_name: i,
|
|
default_vector_field_name: vectors[i - default_nb],
|
|
default_string_field_name: str(i),
|
|
"text_content": text_data[i - default_nb]
|
|
})
|
|
results = self.insert(client, collection_name, rows_with_analyzer)[0]
|
|
assert results['insert_count'] == len(text_data)
|
|
|
|
# 5. verify the analyzer field was added correctly
|
|
collection_info = self.describe_collection(client, collection_name)[0]
|
|
field_names = [field["name"] for field in collection_info["fields"]]
|
|
assert "text_content" in field_names
|
|
|
|
# 6. test text search using the analyzer field
|
|
vectors_to_search = [vectors[0]]
|
|
# Simple search without filter to verify basic functionality
|
|
search_results = self.search(
|
|
client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={
|
|
"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"limit": 10, # Adjust limit to match actual results
|
|
"pk_name": default_primary_key_field_name
|
|
}
|
|
)
|
|
# Verify search returned some results
|
|
assert len(search_results[0]) > 0
|
|
|
|
# 7. test query with analyzer field - use simpler condition
|
|
query_results = self.query(
|
|
client, collection_name,
|
|
filter="text_content is not null",
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={
|
|
"pk_name": default_primary_key_field_name,
|
|
"exp_limit": 4 # We expect 4 documents with text_content
|
|
}
|
|
)
|
|
# Verify that we get results for documents with text_content
|
|
assert len(query_results[0]) > 0
|
|
|
|
# 8. test run_analyzer to verify the analyzer configuration
|
|
sample_text = "The Milvus vector database is built for scale"
|
|
analyzer_result = client.run_analyzer(sample_text, analyzer_params)
|
|
# Verify analyzer produces tokens
|
|
# (should remove stop words like "the", "is", "a")
|
|
tokens = analyzer_result.tokens
|
|
assert len(tokens) > 0
|
|
# Handle different token formats - tokens might be strings or dictionaries
|
|
if isinstance(tokens[0], str):
|
|
token_texts = tokens
|
|
else:
|
|
token_texts = [token["token"] for token in tokens]
|
|
# Check that stop words are filtered out
|
|
assert "the" not in token_texts
|
|
assert "is" not in token_texts
|
|
assert "a" not in token_texts
|
|
|
|
# 9. cleanup
|
|
self.release_collection(client, collection_name)
|
|
self.drop_collection(client, collection_name)
|
|
|
|
|
|
class TestMilvusClientAddFieldFeatureInvalid(TestMilvusClientV2Base):
|
|
"""Test invalid cases for add field feature"""
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_vector_field(self):
|
|
"""
|
|
target: test fast create collection with add vector field
|
|
method: create collection name with add vector field
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"not support to add vector field, "
|
|
f"field name = {field_name}: invalid parameter"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.FLOAT_VECTOR,
|
|
nullable=True, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_varchar_field_without_max_length(self):
|
|
"""
|
|
target: test fast create collection with add varchar field without maxlength
|
|
method: create collection name with add varchar field without maxlength
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"type param(max_length) should be specified for "
|
|
f"the field({field_name}) of collection {collection_name}"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.VARCHAR,
|
|
nullable=True, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_as_auto_id(self):
|
|
"""
|
|
target: test fast create collection with add new field as auto id
|
|
method: create collection name with add new field as auto id
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1, ct.err_msg: f"The auto_id can only be specified on the primary key field"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.INT64,
|
|
nullable=True, auto_id=True, check_task=CheckTasks.err_res,
|
|
check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_with_disable_nullable(self):
|
|
"""
|
|
target: test fast create collection with add new field as nullable false
|
|
method: create collection name with add new field as nullable false
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"added field must be nullable, please check it, "
|
|
f"field name = {field_name}: invalid parameter"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.INT64,
|
|
nullable=False, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_as_partition_ley(self):
|
|
"""
|
|
target: test fast create collection with add new field as partition key
|
|
method: create collection name with add new field as partition key
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"not support to add partition key field, "
|
|
f"field name = {field_name}: invalid parameter"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.INT64,
|
|
nullable=True, is_partition_key=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_exceed_max_length(self):
|
|
"""
|
|
target: test fast create collection with add new field with exceed max length
|
|
method: create collection name with add new field with exceed max length
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"the maximum length specified for the field({field_name}) "
|
|
f"should be in (0, 65535], but got 65536 instead: invalid parameter"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.VARCHAR,
|
|
nullable=True, max_length=65536, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_as_cluster_key(self):
|
|
"""
|
|
target: test fast create collection with add new field as cluster key
|
|
method: create collection with add new field as cluster key(already has cluster key)
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
field_name = default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"already has another clutering key field, "
|
|
f"field name: {field_name}: invalid parameter"}
|
|
schema = self.create_schema(client)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_clustering_key=True)
|
|
|
|
self.create_collection(client, collection_name, schema=schema)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.INT64,
|
|
nullable=True, is_clustering_key=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_same_other_name(self):
|
|
"""
|
|
target: test fast create collection with add new field as other same name
|
|
method: create collection with add new field as other same name
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
error = {ct.err_code: 1100, ct.err_msg: f"duplicate field name: {default_string_field_name}: invalid parameter"}
|
|
schema = self.create_schema(client)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_clustering_key=True)
|
|
|
|
self.create_collection(client, collection_name, schema=schema)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.add_collection_field(client, collection_name, field_name=default_string_field_name,
|
|
data_type=DataType.VARCHAR, nullable=True, max_length=64,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_collection_add_field_exceed_max_field_number(self):
|
|
"""
|
|
target: test fast create collection with add new field with exceed max field number
|
|
method: create collection name with add new field with exceed max field number
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
dim, field_name = 8, default_new_field_name
|
|
error = {ct.err_code: 1100, ct.err_msg: f"The number of fields has reached the maximum value 64: "
|
|
f"invalid parameter"}
|
|
self.create_collection(client, collection_name, dim)
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
for i in range(62):
|
|
self.add_collection_field(client, collection_name, field_name=f"{field_name}_{i}",
|
|
data_type=DataType.VARCHAR, nullable=True, max_length=64)
|
|
self.add_collection_field(client, collection_name, field_name=field_name, data_type=DataType.VARCHAR,
|
|
nullable=True, max_length=64, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_add_field_with_reranker_unsupported(self):
|
|
"""
|
|
target: test that add_collection_field and decay ranker combination is not supported
|
|
method: create collection without reranker field, add nullable reranker field via add_collection_field,
|
|
then try to use it with decay ranker
|
|
expected: raise exception because decay ranker requires non-nullable fields but add_collection_field
|
|
only supports nullable fields, creating a technical limitation
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 8
|
|
|
|
# 1. create collection WITHOUT reranker field initially
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
|
|
# Note: NO reranker field here - we'll try to add it later via add_collection_field
|
|
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
|
|
# 2. insert initial data WITHOUT reranker field
|
|
vectors = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = self.insert(client, collection_name, rows)[0]
|
|
assert results['insert_count'] == default_nb
|
|
|
|
# 3. Try to add nullable reranker field via add_collection_field (nullable must be True)
|
|
# This will succeed in adding the field, but then we'll test if it can work with decay reranker
|
|
# The conflict: add_collection_field only supports nullable fields, but decay reranker needs non-nullable fields
|
|
self.add_collection_field(client, collection_name, field_name=ct.default_reranker_field_name,
|
|
data_type=DataType.INT64, nullable=True, default_value=0)
|
|
|
|
# 4. Insert data with the newly added reranker field
|
|
# Generate new vectors for the second batch of data
|
|
vectors_batch2 = cf.gen_vectors(default_nb, dim, vector_data_type=DataType.FLOAT_VECTOR)
|
|
rows_with_reranker = [{default_primary_key_field_name: i, default_vector_field_name: vectors_batch2[i - default_nb],
|
|
default_string_field_name: str(i), ct.default_reranker_field_name: i}
|
|
for i in range(default_nb, default_nb * 2)]
|
|
results = self.insert(client, collection_name, rows_with_reranker)[0]
|
|
assert results['insert_count'] == default_nb
|
|
|
|
# 5. Try to use the nullable reranker field with decay reranker
|
|
# This should fail because decay reranker requires non-nullable fields for proper functionality
|
|
from pymilvus import Function, FunctionType
|
|
my_rerank_fn = Function(
|
|
name="my_reranker",
|
|
input_field_names=[ct.default_reranker_field_name],
|
|
function_type=FunctionType.RERANK,
|
|
params={
|
|
"reranker": "decay",
|
|
"function": "gauss",
|
|
"origin": 0,
|
|
"offset": 0,
|
|
"decay": 0.5,
|
|
"scale": 100
|
|
}
|
|
)
|
|
|
|
error = {ct.err_code: 65535, ct.err_msg: "Function input field cannot be nullable: field reranker_field"}
|
|
self.search(client, collection_name, [vectors[0]], ranker=my_rerank_fn,
|
|
check_task=CheckTasks.err_res, check_items=error)
|