mirror of https://github.com/milvus-io/milvus.git
571 lines
28 KiB
Python
571 lines
28 KiB
Python
import pytest
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from base.client_v2_base import TestMilvusClientV2Base
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from utils.util_log import test_log as log
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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 *
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from common.constants import *
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from pymilvus import DataType
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prefix = "client_search"
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epsilon = ct.epsilon
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default_nb = ct.default_nb
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default_nb_medium = ct.default_nb_medium
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default_nq = ct.default_nq
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default_dim = ct.default_dim
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default_limit = ct.default_limit
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default_search_exp = "id >= 0"
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exp_res = "exp_res"
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default_search_string_exp = "varchar >= \"0\""
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default_search_mix_exp = "int64 >= 0 && varchar >= \"0\""
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default_invaild_string_exp = "varchar >= 0"
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default_json_search_exp = "json_field[\"number\"] >= 0"
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perfix_expr = 'varchar like "0%"'
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default_search_field = ct.default_float_vec_field_name
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default_search_params = ct.default_search_params
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default_primary_key_field_name = "id"
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default_vector_field_name = "vector"
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default_float_field_name = ct.default_float_field_name
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default_bool_field_name = ct.default_bool_field_name
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default_string_field_name = ct.default_string_field_name
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default_int32_array_field_name = ct.default_int32_array_field_name
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default_string_array_field_name = ct.default_string_array_field_name
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class TestMilvusClientSearchInvalid(TestMilvusClientV2Base):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=[False, True])
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def auto_id(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["COSINE", "L2"])
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def metric_type(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are invalid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.xfail(reason="pymilvus issue 1554")
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def test_milvus_client_collection_invalid_primary_field(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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error = {ct.err_code: 1, ct.err_msg: f"Param id_type must be int or string"}
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self.create_collection(client, collection_name, default_dim, id_type="invalid",
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_collection_string_auto_id(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with auto id on string primary key
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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error = {ct.err_code: 65535, ct.err_msg: f"type param(max_length) should be specified for varChar "
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f"field of collection {collection_name}"}
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self.create_collection(client, collection_name, default_dim, id_type="string", auto_id=True,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_create_same_collection_different_params(self):
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"""
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target: test high level api: client.create_collection
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method: create
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expected: 1. Successfully to create collection with same params
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2. Report errors for creating collection with same name and different params
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. create collection with same params
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self.create_collection(client, collection_name, default_dim)
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# 3. create collection with same name and different params
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error = {ct.err_code: 1, ct.err_msg: f"create duplicate collection with different parameters, "
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f"collection: {collection_name}"}
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self.create_collection(client, collection_name, default_dim + 1,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_collection_invalid_metric_type(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with auto id on string primary key
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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error = {ct.err_code: 1100,
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ct.err_msg: "float vector index does not support metric type: invalid: "
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"invalid parameter[expected=valid index params][actual=invalid index params]"}
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self.create_collection(client, collection_name, default_dim, metric_type="invalid",
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip("https://github.com/milvus-io/milvus/issues/29880")
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def test_milvus_client_search_not_consistent_metric_type(self, metric_type):
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"""
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target: test search with inconsistent metric type (default is IP) with that of index
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method: create connection, collection, insert and search with not consistent metric type
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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search_params = {"metric_type": metric_type}
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error = {ct.err_code: 1100,
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ct.err_msg: f"metric type not match: invalid parameter[expected=IP][actual={metric_type}]"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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search_params=search_params,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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class TestMilvusClientSearchValid(TestMilvusClientV2Base):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=[False, True])
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def auto_id(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["COSINE", "L2"])
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def metric_type(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are valid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L0)
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def test_milvus_client_search_query_default(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 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_unique_str(prefix)
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self.using_database(client, "default")
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
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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
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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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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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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# assert self.num_entities(client, collection_name)[0] == default_nb
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# 3. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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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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"limit": default_limit})
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# 4. query
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self.query(client, collection_name, filter=default_search_exp,
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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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"primary_field": 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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@pytest.mark.skip(reason="issue #36484")
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@pytest.mark.parametrize("nullable", [True, False])
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def test_milvus_client_search_query_self_creation_default(self, nullable):
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"""
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target: test fast create collection normal case
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method: create collection
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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_unique_str(prefix)
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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.VARCHAR, max_length=64, is_primary=True,
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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("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(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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rng = np.random.default_rng(seed=19530)
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rows = [
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{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_string_field_name: str(i), "nullable_field": None, "array_field": None} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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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.L1)
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def test_milvus_client_rename_search_query_default(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 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_unique_str(prefix)
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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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pk_name = 'pk_varchar'
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schema.add_field(pk_name, DataType.VARCHAR, max_length=64, is_primary=True,
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auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
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schema.add_field(default_float_field_name, DataType.FLOAT)
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self.create_collection(client, collection_name, schema=schema, consistency_level="Bounded")
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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, "primary_field": pk_name,
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"consistency_level": 0})
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old_name = collection_name
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new_name = collection_name + "new"
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self.rename_collection(client, old_name, new_name)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{pk_name: str(i),
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default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_string_field_name: str(i),
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default_float_field_name: i*1.0
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} for i in range(default_nb)]
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self.insert(client, new_name, rows)
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self.flush(client, new_name)
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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_index(client, new_name, index_params=index_params)
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self.load_collection(client, new_name)
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# assert self.num_entities(client, collection_name)[0] == default_nb
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# 3. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [str(i) for i in range(default_nb)]
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self.search(client, new_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, "primary_field": pk_name,
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"limit": default_limit})
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# 4. query
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filter = f"{default_float_field_name} >= 0"
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self.query(client, new_name, filter=filter,
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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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"primary_field": pk_name})
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self.release_collection(client, new_name)
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self.drop_collection(client, new_name)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_array_insert_search(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 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_unique_str(prefix)
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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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# 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,
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default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0,
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default_int32_array_field_name: [i, i + 1, i + 2],
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default_string_array_field_name: [str(i), str(i + 1), str(i + 2)]
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} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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# 3. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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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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"limit": default_limit})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="issue 25110")
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def test_milvus_client_search_query_string(self):
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"""
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target: test search (high level api) for string primary key
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method: create connection, collection, insert 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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, id_type="string", max_length=ct.default_length)
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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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# 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: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
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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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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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assert self.num_entities(client, collection_name)[0] == default_nb
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# 3. search
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vectors_to_search = rng.random((1, default_dim))
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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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"limit": default_limit})
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# 4. query
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self.query(client, collection_name, filter=default_search_exp,
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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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"primary_field": default_primary_key_field_name})
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_search_different_metric_types_not_specifying_in_search_params(self, metric_type, auto_id):
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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 and search
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expected: search successfully with limit(topK)
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, metric_type=metric_type, auto_id=auto_id,
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consistency_level="Strong")
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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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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if auto_id:
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for row in rows:
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row.pop(default_primary_key_field_name)
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self.insert(client, collection_name, rows)
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# 3. search
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vectors_to_search = rng.random((1, default_dim))
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# search_params = {"metric_type": metric_type}
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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output_fields=[default_primary_key_field_name],
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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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"limit": default_limit})
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip("pymilvus issue #1866")
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def test_milvus_client_search_different_metric_types_specifying_in_search_params(self, metric_type, auto_id):
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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 and search
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expected: search successfully with limit(topK)
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, metric_type=metric_type, auto_id=auto_id,
|
|
consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
if auto_id:
|
|
for row in rows:
|
|
row.pop(default_primary_key_field_name)
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
search_params = {"metric_type": metric_type}
|
|
self.search(client, collection_name, vectors_to_search, limit=default_limit,
|
|
search_params=search_params,
|
|
output_fields=[default_primary_key_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"limit": default_limit})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_delete_with_ids(self):
|
|
"""
|
|
target: test delete (high level api)
|
|
method: create connection, collection, insert delete, and search
|
|
expected: search/query successfully without deleted data
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
default_nb = 1000
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
pks = self.insert(client, collection_name, rows)[0]
|
|
# 3. delete
|
|
delete_num = 3
|
|
self.delete(client, collection_name, ids=[i for i in range(delete_num)])
|
|
# 4. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
for insert_id in range(delete_num):
|
|
if insert_id in insert_ids:
|
|
insert_ids.remove(insert_id)
|
|
limit = default_nb - delete_num
|
|
self.search(client, collection_name, vectors_to_search, limit=default_nb,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": limit})
|
|
# 5. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows[delete_num:],
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_delete_with_filters(self):
|
|
"""
|
|
target: test delete (high level api)
|
|
method: create connection, collection, insert delete, and search
|
|
expected: search/query successfully without deleted data
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
default_nb = 1000
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
pks = self.insert(client, collection_name, rows)[0]
|
|
# 3. delete
|
|
delete_num = 3
|
|
self.delete(client, collection_name, filter=f"id < {delete_num}")
|
|
# 4. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
for insert_id in range(delete_num):
|
|
if insert_id in insert_ids:
|
|
insert_ids.remove(insert_id)
|
|
limit = default_nb - delete_num
|
|
self.search(client, collection_name, vectors_to_search, limit=default_nb,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": limit})
|
|
# 5. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows[delete_num:],
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_client_search_with_iterative_filter(self):
|
|
"""
|
|
target: test search with iterative filter
|
|
method: create connection, collection, insert, search with iterative filter
|
|
expected: search successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
dim = 32
|
|
pk_field_name = 'id'
|
|
vector_field_name = 'embeddings'
|
|
str_field_name = 'title'
|
|
json_field_name = 'json_field'
|
|
max_length = 16
|
|
schema.add_field(pk_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(str_field_name, DataType.VARCHAR, max_length=max_length)
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=vector_field_name, metric_type="COSINE",
|
|
index_type="IVF_FLAT", params={"nlist": 128})
|
|
index_params.add_index(field_name=str_field_name)
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{
|
|
pk_field_name: i,
|
|
vector_field_name: list(rng.random((1, dim))[0]),
|
|
str_field_name: cf.gen_str_by_length(max_length),
|
|
json_field_name: {"number": i}
|
|
} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
self.load_collection(client, collection_name)
|
|
|
|
# 3. search
|
|
search_vector = list(rng.random((1, dim))[0])
|
|
search_params = {'hints': "iterative_filter",
|
|
'params': cf.get_search_params_params('IVF_FLAT')}
|
|
self.search(client, collection_name, data=[search_vector], filter='id >= 10',
|
|
search_params=search_params, limit=default_limit)
|
|
not_supported_hints = "not_supported_hints"
|
|
error = {ct.err_code: 0,
|
|
ct.err_msg: f"Create Plan by expr failed: => hints: {not_supported_hints} not supported"}
|
|
search_params = {'hints': not_supported_hints,
|
|
'params': cf.get_search_params_params('IVF_FLAT')}
|
|
self.search(client, collection_name, data=[search_vector], filter='id >= 10',
|
|
search_params=search_params, check_task=CheckTasks.err_res, check_items=error)
|