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ba862ef91d
1. modify test case: test_search_repeatedly_ivf_index_different_limit 2. update pymilvus version from 2.4.0rc19 to 2.4.0rc24 3. Before, insert will return a pk list. In the latest milvus client, insert will return a number that is inserted successfully 4. In the latest milvus client, flush and num_entities have been removed 5. Before, the default consistency level of a new collection is strong. In the latest milvus client, it becomes bounded. So related cases have been modified correspondingly, or immediate search after insert will return no results. 6. In the latest pymilvus, new data type FLOAT16_VECTOR and BFLOAT16_VECTOR have been added. Signed-off-by: nico <cheng.yuan@zilliz.com>
350 lines
17 KiB
Python
350 lines
17 KiB
Python
import multiprocessing
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import numbers
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import random
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import numpy
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import threading
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import pytest
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import pandas as pd
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import decimal
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from decimal import Decimal, getcontext
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from time import sleep
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import heapq
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from base.client_base import TestcaseBase
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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.orm.types import CONSISTENCY_STRONG, CONSISTENCY_BOUNDED, CONSISTENCY_SESSION, CONSISTENCY_EVENTUALLY
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from base.high_level_api_wrapper import HighLevelApiWrapper
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client_w = HighLevelApiWrapper()
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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(TestcaseBase):
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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._connect(enable_high_level_api=True)
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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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client_w.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._connect(enable_high_level_api=True)
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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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client_w.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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. create collection with same params
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client_w.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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client_w.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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client_w.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._connect(enable_high_level_api=True)
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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: "metric type not found or not supported, supported: [L2 IP COSINE HAMMING JACCARD]"}
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client_w.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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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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.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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client_w.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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client_w.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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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collections = client_w.list_collections(client)[0]
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assert collection_name in collections
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client_w.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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client_w.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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client_w.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_high_level_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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client_w.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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client_w.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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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collections = client_w.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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client_w.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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client_w.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_high_level_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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.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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client_w.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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"auto_id": auto_id})
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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: 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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client_w.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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client_w.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_high_level_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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client_w.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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client_w.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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.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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client_w.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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client_w.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_high_level_api": True,
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"nq": len(vectors_to_search),
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"limit": default_limit})
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client_w.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._connect(enable_high_level_api=True)
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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client_w.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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client_w.insert(client, collection_name, rows)[0]
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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 = client_w.get(client, collection_name, pks[0:1])
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# 4. delete
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delete_num = 3
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client_w.delete(client, collection_name, 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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client_w.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_high_level_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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client_w.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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client_w.drop_collection(client, collection_name)
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