mirror of https://github.com/milvus-io/milvus.git
338 lines
16 KiB
Python
338 lines
16 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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prefix = "high_level_api"
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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 TestHighLevelApi(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_high_level_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, consistency_level="Strong",
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id_type="invalid", check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_high_level_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_high_level_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, consistency_level="Strong")
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# 2. create collection with same params
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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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, consistency_level="Strong",
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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_high_level_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: 65535,
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ct.err_msg: "float vector index does not support metric type: invalid: invalid parameter"}
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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_high_level_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, consistency_level="Strong")
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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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"""
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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.L1)
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def test_high_level_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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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, "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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# 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.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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def test_high_level_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_high_level_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",
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max_length=ct.default_length, consistency_level="Strong")
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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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# 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_high_level_search_different_metric_types(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,
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auto_id=auto_id, 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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search_params=search_params,
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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.L1)
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def test_high_level_delete(self):
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"""
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target: test delete (high level api)
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method: create connection, collection, insert delete, and search
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expected: search/query successfully without deleted data
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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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# 2. insert
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default_nb = 1000
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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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pks = [i for i in range(default_nb)]
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# 3. get first primary key
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first_pk_data = self.get(client, collection_name, ids=pks[0:1])
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# 4. delete
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delete_num = 3
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self.delete(client, collection_name, ids=pks[0:delete_num])
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# 5. 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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for insert_id in pks[0:delete_num]:
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if insert_id in insert_ids:
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insert_ids.remove(insert_id)
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limit = default_nb - delete_num
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self.search(client, collection_name, vectors_to_search, limit=default_nb,
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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": limit})
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# 6. 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[delete_num:],
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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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