wehub-resource-sync
498b235461
Build and test / Build and test AMD64 Ubuntu 22.04 (push) Failing after 0s
Publish Builder / amazonlinux2023 (push) Failing after 1s
Build and test / UT for Go (push) Has been skipped
Publish KRTE Images / KRTE (push) Failing after 1s
Build and test / Integration Test (push) Has been skipped
Build and test / Upload Code Coverage (push) Has been skipped
Publish Builder / rockylinux9 (push) Failing after 1s
Publish Builder / ubuntu22.04 (push) Failing after 0s
Publish Builder / ubuntu24.04 (push) Failing after 0s
Publish Gpu Builder / publish-gpu-builder (push) Failing after 1s
Publish Test Images / PyTest (push) Failing after 0s
Build and test / UT for Cpp (push) Has been cancelled
8078 lines
405 KiB
Python
8078 lines
405 KiB
Python
# ruff: noqa: E712,E731,F401,F403,F405,F541,F841,I001,UP031,UP032,W291,W292,W293
|
||
# fmt: off
|
||
import math
|
||
import time
|
||
import os
|
||
import json
|
||
import requests
|
||
import random
|
||
import numpy as np
|
||
|
||
import pytest
|
||
from faker import Faker
|
||
|
||
from base.client_v2_base import TestMilvusClientV2Base
|
||
from utils.util_log import test_log as log
|
||
from common import common_func as cf
|
||
from common import common_type as ct
|
||
from common.common_type import CaseLabel, CheckTasks
|
||
from utils.util_pymilvus import * # noqa
|
||
from common.constants import * # noqa
|
||
from pymilvus import DataType, Function, FunctionType, AnnSearchRequest
|
||
|
||
fake = Faker()
|
||
|
||
prefix = "client_search"
|
||
partition_prefix = "client_partition"
|
||
epsilon = ct.epsilon
|
||
default_nb = ct.default_nb
|
||
default_nb_medium = ct.default_nb_medium
|
||
default_nq = ct.default_nq
|
||
default_dim = ct.default_dim
|
||
default_limit = ct.default_limit
|
||
default_search_exp = "id >= 0"
|
||
exp_res = "exp_res"
|
||
default_search_string_exp = "varchar >= \"0\""
|
||
default_search_mix_exp = "int64 >= 0 && varchar >= \"0\""
|
||
default_invaild_string_exp = "varchar >= 0"
|
||
default_json_search_exp = "json_field[\"number\"] >= 0"
|
||
perfix_expr = 'varchar like "0%"'
|
||
default_search_field = ct.default_float_vec_field_name
|
||
default_search_params = ct.default_search_params
|
||
default_primary_key_field_name = "id"
|
||
default_vector_field_name = "vector"
|
||
default_dynamic_field_name = "field_new"
|
||
default_float_field_name = ct.default_float_field_name
|
||
default_bool_field_name = ct.default_bool_field_name
|
||
default_string_field_name = ct.default_string_field_name
|
||
default_int32_array_field_name = ct.default_int32_array_field_name
|
||
default_string_array_field_name = ct.default_string_array_field_name
|
||
|
||
# v2-migrated TestSearchInvalid{Shared,Independent} classes create collections with
|
||
# ct.default_int64_field_name as the PK field, so their filter expression must match
|
||
# that field name (distinct from v1's default_search_exp = 'id >= 0').
|
||
v2_invalid_search_exp = f"{ct.default_int64_field_name} >= 0"
|
||
|
||
# Module-level search vectors used across TestSearchInvalid{Shared,Independent} tests
|
||
# (migrated from test_milvus_client_search_invalid.py where these were
|
||
# referenced bare without `self.` — keep as module-level to preserve byte-identical bodies).
|
||
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
||
|
||
# Shared collection for TestMilvusClientSearchInvalidRerankerShared — unique suffix avoids
|
||
# conflicts across parallel workers; dim=5 matches the original per-test schema.
|
||
RERANKER_INVALID_SHARED_COLLECTION = "test_reranker_invalid_shared_" + cf.gen_unique_str("_")
|
||
RERANKER_INVALID_DIM = 5
|
||
|
||
# Shared collections for TestMilvusClientSearchDecayRerankShared — two collections
|
||
# provisioned to preserve flushed vs. growing segment coverage from Test #1's is_flush
|
||
# parametrization.
|
||
DECAY_RERANK_SHARED_COLLECTION_GROWING = "test_decay_rerank_shared_growing_" + cf.gen_unique_str("_")
|
||
DECAY_RERANK_SHARED_COLLECTION_FLUSHED = "test_decay_rerank_shared_flushed_" + cf.gen_unique_str("_")
|
||
DECAY_RERANK_SHARED_DIM = 5
|
||
|
||
|
||
class TestMilvusClientSearchInvalid(TestMilvusClientV2Base):
|
||
""" Test case of search interface """
|
||
|
||
@pytest.fixture(scope="function", params=[False, True])
|
||
def auto_id(self, request):
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
||
def metric_type(self, request):
|
||
yield request.param
|
||
|
||
"""
|
||
******************************************************************
|
||
# The following are invalid base cases
|
||
******************************************************************
|
||
"""
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("invalid_collection_name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
|
||
def test_milvus_client_search_invalid_collection_name_string(self, invalid_collection_name):
|
||
"""
|
||
target: test search with invalid collection name
|
||
method: create connection, collection, insert and search with invalid collection name
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
# collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
# self.create_collection(client, collection_name, default_dim)
|
||
# 2. search
|
||
rng = np.random.default_rng(seed=19530)
|
||
vectors_to_search = rng.random((1, 8))
|
||
error = {ct.err_code: 100,
|
||
ct.err_msg: f"collection not found[database=default][collection={invalid_collection_name}]"}
|
||
self.search(client, invalid_collection_name, vectors_to_search, limit=default_limit,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
# self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.skip(reason="pymilvus issue 2587")
|
||
@pytest.mark.parametrize("invalid_collection_name", [1])
|
||
def test_milvus_client_search_invalid_collection_name_non_string(self, invalid_collection_name):
|
||
"""
|
||
target: test search with invalid collection name
|
||
method: create connection, collection, insert and search with invalid collection name
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim)
|
||
# 2. search
|
||
rng = np.random.default_rng(seed=19530)
|
||
vectors_to_search = rng.random((1, 8))
|
||
error = {ct.err_code: 100,
|
||
ct.err_msg: f"collection not found[database=default][collection={invalid_collection_name}]"}
|
||
self.search(client, invalid_collection_name, vectors_to_search, limit=default_limit,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
# self.drop_collection(client, collection_name)
|
||
|
||
# self.drop_collection(client, collection_name)
|
||
|
||
# self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.skip(reason="pymilvus issue 2588")
|
||
@pytest.mark.parametrize("invalid_search_params", [1, "1"])
|
||
def test_milvus_client_search_invalid_search_params(self, invalid_search_params):
|
||
"""
|
||
target: test search with invalid data
|
||
method: create connection, collection, insert and search with invalid data
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim)
|
||
# 2. search
|
||
rng = np.random.default_rng(seed=19530)
|
||
vectors_to_search = rng.random((1, 8))
|
||
error = {ct.err_code: 1,
|
||
ct.err_msg: f"`search_params` value {invalid_search_params} is illegal"}
|
||
self.search(client, collection_name, vectors_to_search, limit=default_limit,
|
||
search_params=invalid_search_params,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
# self.drop_collection(client, collection_name)
|
||
|
||
# self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.skip(reason="pymilvus issue 1554")
|
||
def test_milvus_client_collection_invalid_primary_field(self):
|
||
"""
|
||
target: test high level api: client.create_collection
|
||
method: create collection with invalid primary field
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
error = {ct.err_code: 1, ct.err_msg: "Param id_type must be int or string"}
|
||
self.create_collection(client, collection_name, default_dim, id_type="invalid",
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_milvus_client_collection_string_auto_id(self):
|
||
"""
|
||
target: test high level api: client.create_collection
|
||
method: create collection with auto id on string primary key without mx length
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
error = {ct.err_code: 65535, ct.err_msg: f"type param(max_length) should be specified for the "
|
||
f"field({default_primary_key_field_name}) of collection {collection_name}"}
|
||
self.create_collection(client, collection_name, default_dim, id_type="string", auto_id=True,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_create_same_collection_different_params(self):
|
||
"""
|
||
target: test high level api: client.create_collection
|
||
method: create
|
||
expected: 1. Successfully to create collection with same params
|
||
2. Report errors for creating collection with same name and different params
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim)
|
||
# 2. create collection with same params
|
||
self.create_collection(client, collection_name, default_dim)
|
||
# 3. create collection with same name and different params
|
||
error = {ct.err_code: 1, ct.err_msg: f"create duplicate collection with different parameters, "
|
||
f"collection: {collection_name}"}
|
||
self.create_collection(client, collection_name, default_dim + 1,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_milvus_client_collection_invalid_metric_type(self):
|
||
"""
|
||
target: test high level api: client.create_collection
|
||
method: create collection with auto id on string primary key
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
error = {ct.err_code: 1100,
|
||
ct.err_msg: "float vector index does not support metric type"}
|
||
self.create_collection(client, collection_name, default_dim, metric_type="invalid",
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.skip("https://github.com/milvus-io/milvus/issues/29880")
|
||
def test_milvus_client_search_not_consistent_metric_type(self, metric_type):
|
||
"""
|
||
target: test search with inconsistent metric type (default is IP) with that of index
|
||
method: create connection, collection, insert and search with not consistent metric type
|
||
expected: Raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim)
|
||
# 2. search
|
||
rng = np.random.default_rng(seed=19530)
|
||
vectors_to_search = rng.random((1, 8))
|
||
search_params = {"metric_type": metric_type}
|
||
error = {ct.err_code: 1100,
|
||
ct.err_msg: f"metric type not match: invalid parameter[expected=IP][actual={metric_type}]"}
|
||
self.search(client, collection_name, vectors_to_search, limit=default_limit,
|
||
search_params=search_params,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_null_expr_vector_field(self):
|
||
"""
|
||
target: test search with null expression on vector field
|
||
method: create connection, collection, insert and search
|
||
expected: raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
vectors_to_search = rng.random((1, dim))
|
||
for null_expr_op in null_expr_ops:
|
||
null_expr = default_vector_field_name + " " + null_expr_op
|
||
error = {ct.err_code: 1100,
|
||
ct.err_msg: "IsNull/IsNotNull operations are not supported on vector fields"}
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_null_expr_not_exist_field(self):
|
||
"""
|
||
target: test search with null expression on vector field
|
||
method: create connection, collection, insert and search
|
||
expected: raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
not_exist_field_name = "not_exist_field"
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
null_expr = not_exist_field_name + " " + null_expr_op
|
||
error = {ct.err_code: 1100,
|
||
ct.err_msg: f"field {not_exist_field_name} not exist"}
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
def test_milvus_client_search_null_expr_json_key(self, nullable):
|
||
"""
|
||
target: test search with null expression on each key of json
|
||
method: create connection, collection, insert and search
|
||
expected: raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(nullable_field_name, DataType.JSON, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
vectors = cf.gen_vectors(default_nb, dim)
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
||
nullable_field_name: {'a': None}} for i in range(default_nb)]
|
||
null_expr = nullable_field_name + "['a']" + " " + null_expr_op
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
||
nullable_field_name: {'a': 1, 'b': None}} for i in range(default_nb)]
|
||
null_expr = nullable_field_name + "['b']" + " " + null_expr_op
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
self.search(client, collection_name, [vectors[0]],
|
||
filter=null_expr)
|
||
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
def test_milvus_client_search_null_expr_array_element(self, nullable):
|
||
"""
|
||
target: test search with null expression on each key of json
|
||
method: create connection, collection, insert and search
|
||
expected: raise exception
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(nullable_field_name, DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
||
max_length=64, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
vectors = cf.gen_vectors(default_nb, dim)
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
||
nullable_field_name: None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
||
nullable_field_name: [1, 2, 3]} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
null_expr = nullable_field_name + "[0]" + " " + null_expr_op
|
||
error = {
|
||
ct.err_code: 1100,
|
||
ct.err_msg: "IsNull/IsNotNull operations are not supported on array element access",
|
||
}
|
||
self.search(client, collection_name, [vectors[0]],
|
||
filter=null_expr,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
self.drop_collection(client, collection_name)
|
||
|
||
|
||
@pytest.mark.xdist_group("TestSearchInvalidShared")
|
||
@pytest.mark.tags(CaseLabel.GPU)
|
||
class TestSearchInvalidShared(TestMilvusClientV2Base):
|
||
"""Test search with invalid parameters using shared collection.
|
||
Schema: int64(PK), float, varchar(65535), json, float_vector(128), dynamic=False
|
||
Data: 3000 rows
|
||
Index: COSINE on float_vector
|
||
"""
|
||
|
||
shared_alias = "TestSearchInvalidShared"
|
||
|
||
def setup_class(self):
|
||
super().setup_class(self)
|
||
self.collection_name = "TestSearchInvalidShared" + cf.gen_unique_str("search_invalid")
|
||
|
||
@pytest.fixture(scope="class", autouse=True)
|
||
def prepare_collection(self, request):
|
||
client = self._client(alias=self.shared_alias)
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
self.create_collection(client, self.collection_name, schema=schema, force_teardown=False)
|
||
|
||
data = cf.gen_row_data_by_schema(nb=3000, schema=schema)
|
||
self.insert(client, self.collection_name, data=data)
|
||
self.flush(client, self.collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, self.collection_name, index_params=idx)
|
||
self.load_collection(client, self.collection_name)
|
||
|
||
def teardown():
|
||
self.drop_collection(self._client(alias=self.shared_alias), self.collection_name)
|
||
request.addfinalizer(teardown)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_param_missing(self):
|
||
"""
|
||
target: test search with incomplete parameters
|
||
method: search with incomplete parameters
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_param_missing: Searching collection %s "
|
||
"with missing parameters" % self.collection_name)
|
||
self.search(client, self.collection_name,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "Either ids or data must be provided"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_vectors", ct.get_invalid_vectors)
|
||
def test_search_param_invalid_vectors(self, invalid_vectors):
|
||
"""
|
||
target: test search with invalid parameter values
|
||
method: search with invalid data
|
||
expected: raise exception and report the error
|
||
"""
|
||
if invalid_vectors in [[" "], ['a']]:
|
||
pytest.skip("['a'] and [' '] is valid now")
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_param_invalid_vectors: searching with "
|
||
"invalid vectors: {}".format(invalid_vectors))
|
||
if invalid_vectors is None:
|
||
err_msg = "Either ids or data must be provided"
|
||
else:
|
||
err_msg = "`search_data` value {} is illegal".format(invalid_vectors)
|
||
self.search(client, self.collection_name,
|
||
data=invalid_vectors, anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": err_msg})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_param_invalid_dim(self):
|
||
"""
|
||
target: test search with invalid parameter values
|
||
method: search with invalid dim
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_param_invalid_dim: searching with invalid dim")
|
||
wrong_dim = 129
|
||
wrong_vectors = [[random.random() for _ in range(wrong_dim)] for _ in range(default_nq)]
|
||
self.search(client, self.collection_name,
|
||
data=wrong_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": 'vector dimension mismatch'})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_field_name", ct.invalid_resource_names)
|
||
def test_search_param_invalid_field(self, invalid_field_name):
|
||
"""
|
||
target: test search with invalid parameter type
|
||
method: search with invalid field type
|
||
expected: raise exception and report the error
|
||
"""
|
||
if invalid_field_name in [None, ""]:
|
||
pytest.skip("None is legal")
|
||
client = self._client(alias=self.shared_alias)
|
||
error = {"err_code": 999, "err_msg": f"failed to create query plan: failed to get field schema by name"}
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=invalid_field_name,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("invalid_metric", ct.get_invalid_metric_type)
|
||
def test_search_param_invalid_metric_type(self, invalid_metric):
|
||
"""
|
||
target: test search with invalid parameter values
|
||
method: search with invalid metric type
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_param_invalid_metric_type: searching with invalid metric_type")
|
||
search_params = {"metric_type": invalid_metric, "params": {"nprobe": 10}}
|
||
if isinstance(invalid_metric, dict):
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "Dict key must be str"})
|
||
else:
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "metric type not match"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_param_metric_type_not_match(self):
|
||
"""
|
||
target: test search with invalid parameter values
|
||
method: search with invalid metric type
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_param_metric_type_not_match: searching with not matched metric_type")
|
||
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "metric type not match: invalid parameter"
|
||
"[expected=COSINE][actual=L2]"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_limit", [p for p in ct.get_invalid_ints
|
||
if not (isinstance(p, int) and p >= 0)])
|
||
def test_search_param_invalid_limit_type(self, invalid_limit):
|
||
"""
|
||
target: test search with invalid limit type
|
||
method: search with invalid limit type
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_param_invalid_limit_type: searching with "
|
||
"invalid limit: %s" % invalid_limit)
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=invalid_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "`limit` value %s is illegal" % invalid_limit})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("limit", [0, 16385])
|
||
def test_search_param_invalid_limit_value(self, limit):
|
||
"""
|
||
target: test search with invalid limit value
|
||
method: search with invalid limit: 0 and maximum
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
err_msg = f"topk [{limit}] is invalid, it should be in range [1, 16384]"
|
||
if limit == 0:
|
||
err_msg = "`limit` value 0 is illegal"
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": err_msg})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_search_expr", ["'non_existing_field'==2", 1])
|
||
def test_search_param_invalid_expr_type(self, invalid_search_expr):
|
||
"""
|
||
target: test search with invalid parameter type
|
||
method: search with invalid search expressions
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
error = {"err_code": 999, "err_msg": "failed to create query plan: cannot parse expression"}
|
||
if invalid_search_expr == 1:
|
||
error = {"err_code": 1, "err_msg": "bad argument type for built-in operation"}
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=invalid_search_expr,
|
||
check_task=CheckTasks.err_res,
|
||
check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_expr_value", ["string", 1.2, None, [1, 2, 3]])
|
||
def test_search_param_invalid_expr_value(self, invalid_expr_value):
|
||
"""
|
||
target: test search with invalid parameter values
|
||
method: search with invalid search expressions
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
invalid_search_expr = f"{ct.default_int64_field_name}=={invalid_expr_value}"
|
||
log.info("test_search_param_invalid_expr_value: searching with "
|
||
"invalid expr: %s" % invalid_search_expr)
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=invalid_search_expr,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999,
|
||
"err_msg": "failed to create query plan: cannot parse expression: %s"
|
||
% invalid_search_expr})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("expression", [f"{ct.default_int64_field_name} like 33",
|
||
f"{ct.default_float_field_name} LIKE 33"])
|
||
def test_search_with_expression_invalid_like(self, expression):
|
||
"""
|
||
target: test search int64 and float with like
|
||
method: test search int64 and float with like
|
||
expected: searched failed
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_with_expression: searching with expression: %s" % expression)
|
||
search_vectors = [[random.random() for _ in range(default_dim)]
|
||
for _ in range(default_nq)]
|
||
self.search(client, self.collection_name,
|
||
data=search_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "failed to create query plan: cannot parse "
|
||
"expression: %s" % expression})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_partitions", [[None], [1, 2]])
|
||
def test_search_partitions_invalid_type(self, invalid_partitions):
|
||
"""
|
||
target: test search invalid partition
|
||
method: search with invalid partition type
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
err_msg = "`partition_name_array` value {} is illegal".format(invalid_partitions)
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
partition_names=invalid_partitions,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999,
|
||
"err_msg": err_msg})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_partitions", [["non_existing"], [ct.default_partition_name, "non_existing"]])
|
||
def test_search_partitions_non_existing(self, invalid_partitions):
|
||
"""
|
||
target: test search invalid partition
|
||
method: search with invalid partition type
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
err_msg = "partition name non_existing not found"
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
partition_names=invalid_partitions,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": err_msg})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_output_fields", [[None], [1, 2], ct.default_int64_field_name])
|
||
def test_search_with_output_fields_invalid_type(self, invalid_output_fields):
|
||
"""
|
||
target: test search with output fields
|
||
method: search with invalid output_field
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
err_msg = f"`output_fields` value {invalid_output_fields} is illegal"
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=invalid_output_fields,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={ct.err_code: 999,
|
||
ct.err_msg: err_msg})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("non_existing_output_fields",
|
||
[["non_existing"], [ct.default_int64_field_name, "non_existing"]])
|
||
def test_search_with_output_fields_non_existing(self, non_existing_output_fields):
|
||
"""
|
||
target: test search with output fields
|
||
method: search with invalid output_field
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
err_msg = f"field non_existing not exist"
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=non_existing_output_fields,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={ct.err_code: 999,
|
||
ct.err_msg: err_msg})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("output_fields", [[default_search_field], ["*"]])
|
||
def test_search_output_field_vector(self, output_fields):
|
||
"""
|
||
target: test search with vector as output field
|
||
method: search with one vector output_field or
|
||
wildcard for vector
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_output_field_vector: Searching collection %s" %
|
||
self.collection_name)
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=output_fields,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq, "limit": default_limit,
|
||
"metric": "COSINE", "enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("output_fields", [["*%"], ["**"], ["*", "@"]])
|
||
def test_search_output_field_invalid_wildcard(self, output_fields):
|
||
"""
|
||
target: test search with invalid output wildcard
|
||
method: search with invalid output_field wildcard
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_output_field_invalid_wildcard: Searching collection %s" %
|
||
self.collection_name)
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=output_fields,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": f"field {output_fields[-1]} not exist"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_guarantee_time",
|
||
[p for p in ct.get_invalid_ints
|
||
if p != 9999999999 and p is not None])
|
||
def test_search_param_invalid_guarantee_timestamp(self, invalid_guarantee_time):
|
||
"""
|
||
target: test search with invalid guarantee timestamp
|
||
method: search with invalid guarantee timestamp
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info(
|
||
"test_search_param_invalid_guarantee_timestamp: searching with invalid guarantee timestamp")
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
guarantee_timestamp=invalid_guarantee_time,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "`guarantee_timestamp` value %s is illegal"
|
||
% invalid_guarantee_time})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("round_decimal", [7, -2, 999, 1.0, None, [1], "string", {}])
|
||
def test_search_invalid_round_decimal(self, round_decimal):
|
||
"""
|
||
target: test search with invalid round decimal
|
||
method: search with invalid round decimal
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_search_invalid_round_decimal: Searching collection %s" %
|
||
self.collection_name)
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
round_decimal=round_decimal,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": f"`round_decimal` value {round_decimal} is illegal"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nq", [16385])
|
||
def test_search_with_invalid_nq(self, nq):
|
||
"""
|
||
target: test search with invalid nq
|
||
method: search with invalid nq
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
search_vectors = [[random.random() for _ in range(default_dim)]
|
||
for _ in range(nq)]
|
||
self.search(client, self.collection_name,
|
||
data=search_vectors[:nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "nq (number of search vector per search "
|
||
"request) should be in range [1, 16384]"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_radius", [[0.1], "str"])
|
||
def test_range_search_invalid_radius(self, invalid_radius):
|
||
"""
|
||
target: test range search with invalid radius
|
||
method: range search with invalid radius
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_range_search_invalid_radius: Range searching collection %s" %
|
||
self.collection_name)
|
||
range_search_params = {"metric_type": "COSINE",
|
||
"params": {"radius": invalid_radius, "range_filter": 0}}
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=range_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": "type must be number"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("expr", [
|
||
f"{ct.default_int64_field_name} / 0 > 0",
|
||
f"{ct.default_int64_field_name} / 0 == 1",
|
||
f"{ct.default_float_field_name} / 0 == 1.0",
|
||
f"{ct.default_int64_field_name} % 0 == 1",
|
||
f"{ct.default_int64_field_name} % 0 != 0",
|
||
f"{ct.default_json_field_name}['number'] / 0 > 0",
|
||
f"{ct.default_json_field_name}['number'] % 0 == 1",
|
||
])
|
||
def test_search_filter_division_by_zero(self, expr):
|
||
"""
|
||
target: test search with division/modulo by zero in filter expression (issue #47285)
|
||
method: search with filter containing division or modulo by zero on int64/float/json fields
|
||
expected: raise error with 'by zero' message instead of crashing server (SIGFPE)
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info(f"test_search_filter_division_by_zero: searching with expr: {expr}")
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expr,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": "by zero"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("expr", [
|
||
f"{ct.default_int64_field_name} / 0 > 0",
|
||
f"{ct.default_int64_field_name} % 0 == 1",
|
||
])
|
||
def test_query_filter_division_by_zero(self, expr):
|
||
"""
|
||
target: test query with division/modulo by zero in filter expression (issue #47285)
|
||
method: query with filter containing division or modulo by zero
|
||
expected: raise error with 'by zero' message instead of crashing server (SIGFPE)
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info(f"test_query_filter_division_by_zero: querying with expr: {expr}")
|
||
self.query(client, self.collection_name,
|
||
filter=expr,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": "by zero"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("expr,expr_params", [
|
||
(f"{ct.default_int64_field_name} / {{d}} > 0", {"d": 0}),
|
||
(f"{ct.default_int64_field_name} % {{d}} == 1", {"d": 0}),
|
||
(f"{ct.default_float_field_name} / {{d}} == 1.0", {"d": 0}),
|
||
])
|
||
def test_search_filter_division_by_zero_with_expr_params(self, expr, expr_params):
|
||
"""
|
||
target: test search with division/modulo by zero via expr_params (issue #47285)
|
||
method: search with parameterized filter where divisor is zero
|
||
expected: raise error with 'by zero' message instead of crashing server (SIGFPE)
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info(f"test_search_filter_division_by_zero_with_expr_params: "
|
||
f"searching with expr: {expr}, params: {expr_params}")
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expr, filter_params=expr_params,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": "by zero"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("expr", [
|
||
f"{ct.default_int64_field_name} / 2 >= 0",
|
||
f"{ct.default_int64_field_name} % 3 == 1",
|
||
f"{ct.default_float_field_name} / 2.0 < 1000",
|
||
])
|
||
def test_search_filter_division_by_nonzero(self, expr):
|
||
"""
|
||
target: test search with valid division/modulo expressions still works (issue #47285)
|
||
method: search with filter containing division or modulo by non-zero values
|
||
expected: search succeeds without error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info(f"test_search_filter_division_by_nonzero: searching with expr: {expr}")
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expr,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq, "limit": default_limit,
|
||
"metric": "COSINE", "enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_range_filter", [[0.1], "str"])
|
||
def test_range_search_invalid_range_filter(self, invalid_range_filter):
|
||
"""
|
||
target: test range search with invalid range_filter
|
||
method: range search with invalid range_filter
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client(alias=self.shared_alias)
|
||
log.info("test_range_search_invalid_range_filter: Range searching collection %s" %
|
||
self.collection_name)
|
||
range_search_params = {"metric_type": "COSINE",
|
||
"params": {"radius": 1, "range_filter": invalid_range_filter}}
|
||
self.search(client, self.collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=range_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999, "err_msg": "type must be number"})
|
||
|
||
|
||
class TestSearchInvalidIndependent(TestMilvusClientV2Base):
|
||
""" Test case of search interface """
|
||
|
||
def _create_standard_schema(self, client, dim=default_dim):
|
||
"""Create a standard schema: int64(PK), float, varchar(65535), json, float_vector(dim)."""
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
return schema
|
||
|
||
"""
|
||
******************************************************************
|
||
# The followings are invalid cases
|
||
******************************************************************
|
||
"""
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_no_connection(self):
|
||
"""
|
||
target: test search without connection
|
||
method: create and delete connection, then search
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. close client connection
|
||
log.info("test_search_no_connection: closing client connection")
|
||
client.close()
|
||
log.info("test_search_no_connection: closed client connection")
|
||
|
||
# 3. search without connection
|
||
log.info("test_search_no_connection: searching without connection")
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "should create connection first"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_no_collection(self):
|
||
"""
|
||
target: test the scenario which search the non-exist collection
|
||
method: 1. create collection
|
||
2. drop collection
|
||
3. search the dropped collection
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize without data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# 2. Drop collection
|
||
self.drop_collection(client, collection_name)
|
||
|
||
# 3. Search without collection
|
||
log.info("test_search_no_collection: Searching without collection ")
|
||
self.search(client, collection_name,
|
||
data=vectors, anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "collection not found"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("index", ct.all_index_types[:8])
|
||
def test_search_invalid_params_type(self, index):
|
||
"""
|
||
target: test search with invalid search params
|
||
method: test search with invalid params type
|
||
expected: raise exception and report the error
|
||
"""
|
||
if index == "FLAT":
|
||
pytest.skip("skip in FLAT index")
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
data = cf.gen_row_data_by_schema(nb=ct.default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
# 2. create index and load
|
||
params = cf.get_index_params_params(index)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type=index, metric_type="L2", params=params)
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 3. search
|
||
invalid_search_params = cf.gen_invalid_search_params_type()
|
||
for invalid_search_param in invalid_search_params:
|
||
if index == invalid_search_param["index_type"]:
|
||
search_params = {"metric_type": "L2",
|
||
"params": invalid_search_param["search_params"]}
|
||
log.info("search_params: {}".format(search_params))
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999,
|
||
"err_msg": "fail to search on QueryNode"})
|
||
|
||
@pytest.mark.skip("not support now")
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("search_k", [-10, -1, 0, 10, 125])
|
||
def test_search_param_invalid_annoy_index(self, search_k):
|
||
"""
|
||
target: test search with invalid search params matched with annoy index
|
||
method: search with invalid param search_k out of [top_k, infinity)
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
data = cf.gen_row_data_by_schema(nb=3000, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
# 2. create annoy index and load
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="ANNOY", metric_type="L2", params={"n_trees": 512})
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 3. search
|
||
annoy_search_param = {"index_type": "ANNOY",
|
||
"search_params": {"search_k": search_k}}
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=annoy_search_param, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "Search params check failed"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("expression", cf.gen_field_compare_expressions())
|
||
def test_search_with_expression_join_two_fields(self, expression):
|
||
"""
|
||
target: test search with expressions linking two fields such as 'and'
|
||
method: create a collection and search with different conjunction
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. create a collection
|
||
nb = 1
|
||
dim = 2
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field("int64_1", DataType.INT64, is_primary=True)
|
||
schema.add_field("int64_2", DataType.INT64)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# 2. insert data
|
||
data = [{"int64_1": i, "int64_2": i,
|
||
ct.default_float_vec_field_name: [random.random() for _ in range(dim)]}
|
||
for i in range(nb)]
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
# 3. create index, load, and search with expression
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE",
|
||
index_type="FLAT")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
log.info("test_search_with_expression: searching with expression: %s" % expression)
|
||
expression = expression.replace("&&", "and").replace("||", "or")
|
||
search_vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
||
self.search(client, collection_name,
|
||
data=search_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=nb,
|
||
filter=expression,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999,
|
||
"err_msg": "failed to create query plan: "
|
||
"cannot parse expression: %s" % expression})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("invalid_expr_bool_value", [1.2, 10, "string"])
|
||
def test_search_param_invalid_expr_bool(self, invalid_expr_bool_value):
|
||
"""
|
||
target: test search with invalid parameter values
|
||
method: search with invalid bool search expressions
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_int32_field_name, DataType.INT32)
|
||
schema.add_field(ct.default_int16_field_name, DataType.INT16)
|
||
schema.add_field(ct.default_int8_field_name, DataType.INT8)
|
||
schema.add_field(ct.default_bool_field_name, DataType.BOOL)
|
||
schema.add_field(ct.default_double_field_name, DataType.DOUBLE)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. search with invalid bool expr
|
||
invalid_search_expr_bool = f"{ct.default_bool_field_name} == {invalid_expr_bool_value}"
|
||
log.info("test_search_param_invalid_expr_bool: searching with "
|
||
"invalid expr: %s" % invalid_search_expr_bool)
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=invalid_search_expr_bool,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "failed to create query plan"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_with_expression_invalid_bool(self):
|
||
"""
|
||
target: test search invalid bool
|
||
method: test search invalid bool
|
||
expected: searched failed
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_int32_field_name, DataType.INT32)
|
||
schema.add_field(ct.default_int16_field_name, DataType.INT16)
|
||
schema.add_field(ct.default_int8_field_name, DataType.INT8)
|
||
schema.add_field(ct.default_bool_field_name, DataType.BOOL)
|
||
schema.add_field(ct.default_double_field_name, DataType.DOUBLE)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
expressions = [ct.default_bool_field_name, "true", "false"]
|
||
for expression in expressions:
|
||
log.debug(f"search with expression: {expression}")
|
||
if expression == "true":
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq, "limit": default_limit,
|
||
"metric": "COSINE", "enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
elif expression == "false":
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq, "limit": 0,
|
||
"metric": "COSINE", "enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
else:
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1100,
|
||
"err_msg": "failed to create query plan: predicate is not a "
|
||
"boolean expression: %s, data type: Bool" % expression})
|
||
|
||
expression = f"!{ct.default_bool_field_name}"
|
||
log.debug(f"search with expression: {expression}")
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1100,
|
||
"err_msg": "not op can only be applied on boolean expression"})
|
||
expression = f"{ct.default_int64_field_name} > 0 and {ct.default_bool_field_name}"
|
||
log.debug(f"search with expression: {expression}")
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1100,
|
||
"err_msg": "'and' can only be used between boolean expressions"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_with_expression_invalid_array_one(self):
|
||
"""
|
||
target: test search with invalid array expressions
|
||
method: test search with invalid array expressions:
|
||
the order of array > the length of array
|
||
expected: searched successfully with correct limit(topK)
|
||
"""
|
||
# 1. create a collection
|
||
nb = ct.default_nb
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(ct.default_int32_array_field_name, DataType.ARRAY,
|
||
element_type=DataType.INT32, max_capacity=ct.default_max_capacity)
|
||
schema.add_field(ct.default_float_array_field_name, DataType.ARRAY,
|
||
element_type=DataType.FLOAT, max_capacity=ct.default_max_capacity)
|
||
schema.add_field(ct.default_string_array_field_name, DataType.ARRAY,
|
||
element_type=DataType.VARCHAR, max_capacity=ct.default_max_capacity,
|
||
max_length=100, nullable=True)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
data = cf.gen_row_data_by_schema(schema=schema)
|
||
data[1][ct.default_int32_array_field_name] = [1]
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. search (subscript > max_capacity)
|
||
expression = "int32_array[101] > 0"
|
||
res, _ = self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=nb,
|
||
filter=expression)
|
||
assert len(res[0]) == 0
|
||
|
||
# 3. search (max_capacity > subscript > actual length of array)
|
||
expression = "int32_array[51] > 0"
|
||
res, _ = self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expression)
|
||
assert len(res[0]) == default_limit
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_with_expression_invalid_array_two(self):
|
||
"""
|
||
target: test search with invalid array expressions
|
||
method: test search with invalid array expressions
|
||
expected: searched successfully with correct limit(topK)
|
||
"""
|
||
# 1. create a collection
|
||
nb = ct.default_nb
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(ct.default_int32_array_field_name, DataType.ARRAY,
|
||
element_type=DataType.INT32, max_capacity=ct.default_max_capacity)
|
||
schema.add_field(ct.default_float_array_field_name, DataType.ARRAY,
|
||
element_type=DataType.FLOAT, max_capacity=ct.default_max_capacity)
|
||
schema.add_field(ct.default_string_array_field_name, DataType.ARRAY,
|
||
element_type=DataType.VARCHAR, max_capacity=ct.default_max_capacity,
|
||
max_length=100, nullable=True)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
data = cf.gen_row_data_by_schema(schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. search
|
||
expression = "int32_array[0] - 1 < 1"
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=nb,
|
||
filter=expression)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_release_collection(self):
|
||
"""
|
||
target: test the scenario which search the released collection
|
||
method: 1. create collection
|
||
2. release collection
|
||
3. search the released collection
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize without data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. release collection
|
||
self.release_collection(client, collection_name)
|
||
|
||
# 3. Search the released collection
|
||
log.info("test_search_release_collection: Searching without collection ")
|
||
self.search(client, collection_name,
|
||
data=vectors, anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "collection not loaded"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_release_partition(self):
|
||
"""
|
||
target: test the scenario which search the released collection
|
||
method: 1. create collection
|
||
2. release partition
|
||
3. search the released partition
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# create partition and insert data
|
||
par_name = "search_partition_0"
|
||
self.create_partition(client, collection_name, partition_name=par_name)
|
||
data = cf.gen_row_data_by_schema(nb=10, schema=schema)
|
||
self.insert(client, collection_name, data=data, partition_name=par_name)
|
||
self.flush(client, collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE",
|
||
index_type="FLAT")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_partitions(client, collection_name, partition_names=[par_name])
|
||
|
||
# 2. release partition
|
||
self.release_partitions(client, collection_name, partition_names=[par_name])
|
||
|
||
# 3. Search the released partition
|
||
log.info("test_search_release_partition: Searching the released partition")
|
||
limit = 10
|
||
self.search(client, collection_name,
|
||
data=vectors, anns_field=default_search_field,
|
||
search_params=default_search_params, limit=limit,
|
||
filter=v2_invalid_search_exp,
|
||
partition_names=[par_name],
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "collection not loaded"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("vector_data_type", ct.all_dense_vector_types)
|
||
def test_search_with_empty_collection(self, vector_data_type):
|
||
"""
|
||
target: test search with empty connection
|
||
method: 1. search the empty collection before load
|
||
2. search the empty collection after load
|
||
3. search collection with data inserted but not load again
|
||
expected: 1. raise exception if not loaded
|
||
2. return topk=0 if loaded
|
||
3. return topk successfully
|
||
"""
|
||
# 1. initialize without data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_float_vec_field_name, vector_data_type, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# 2. search collection without data before load
|
||
log.info("test_search_with_empty_collection: Searching empty collection %s"
|
||
% collection_name)
|
||
err_msg = "collection not loaded"
|
||
search_vectors = cf.gen_vectors(default_nq, default_dim, vector_data_type)
|
||
self.search(client, collection_name,
|
||
data=search_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 101,
|
||
"err_msg": err_msg})
|
||
|
||
# 3. search collection without data after load
|
||
if vector_data_type == DataType.INT8_VECTOR:
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="HNSW", metric_type="L2")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
else:
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="FLAT", metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
self.search(client, collection_name,
|
||
data=search_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq,
|
||
"ids": [],
|
||
"limit": 0,
|
||
"enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
|
||
# 4. search with data inserted but not load again
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
insert_res, _ = self.insert(client, collection_name, data=data)
|
||
insert_ids = insert_res["ids"]
|
||
self.flush(client, collection_name)
|
||
self.search(client, collection_name,
|
||
data=search_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq,
|
||
"ids": insert_ids,
|
||
"limit": default_limit,
|
||
"enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_with_empty_collection_with_partition(self):
|
||
"""
|
||
target: test search with empty collection
|
||
method: 1. collection an empty collection with partitions
|
||
2. load
|
||
3. search
|
||
expected: return 0 result
|
||
"""
|
||
# 1. initialize without data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
par_name = "search_partition_0"
|
||
self.create_partition(client, collection_name, partition_name=par_name)
|
||
|
||
# 2. search collection without data after load
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="FLAT", metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq,
|
||
"ids": [],
|
||
"limit": 0,
|
||
"enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
|
||
# 3. search a partition without data after load
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
partition_names=[par_name],
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"nq": default_nq,
|
||
"ids": [],
|
||
"limit": 0,
|
||
"enable_milvus_client_api": True,
|
||
"pk_name": ct.default_int64_field_name})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_partition_deleted(self):
|
||
"""
|
||
target: test search deleted partition
|
||
method: 1. create a collection with partitions
|
||
2. delete a partition
|
||
3. search the deleted partition
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# create partition and insert data
|
||
deleted_par_name = "search_partition_0"
|
||
self.create_partition(client, collection_name, partition_name=deleted_par_name)
|
||
data = cf.gen_row_data_by_schema(nb=1000, schema=schema)
|
||
self.insert(client, collection_name, data=data, partition_name=deleted_par_name)
|
||
self.flush(client, collection_name)
|
||
|
||
# 2. delete partition
|
||
log.info("test_search_partition_deleted: deleting a partition")
|
||
self.drop_partition(client, collection_name, partition_name=deleted_par_name)
|
||
log.info("test_search_partition_deleted: deleted a partition")
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="FLAT", metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 3. search after delete partitions
|
||
log.info("test_search_partition_deleted: searching deleted partition")
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
partition_names=[deleted_par_name],
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "partition name search_partition_0 not found"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_index_partition_not_existed(self):
|
||
"""
|
||
target: test search not existed partition
|
||
method: search with not existed partition
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# 2. create index
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="IVF_FLAT", metric_type="L2", params={"nlist": 128})
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
|
||
# 3. search the non exist partition
|
||
partition_name = "search_non_exist"
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
partition_names=[partition_name],
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "partition name %s not found" % partition_name})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("reorder_k", [100])
|
||
def test_search_scann_with_invalid_reorder_k(self, reorder_k):
|
||
"""
|
||
target: test search with invalid nq
|
||
method: search with invalid nq
|
||
expected: raise exception and report the error
|
||
"""
|
||
# initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="SCANN", metric_type="L2", params={"nlist": 1024})
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# search
|
||
search_params = {"metric_type": "L2", "params": {"nprobe": 10, "reorder_k": reorder_k}}
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=search_params, limit=reorder_k + 1,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "reorder_k(100) should be larger than k(101)"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_param_invalid_binary(self):
|
||
"""
|
||
target: test search within binary data (invalid parameter)
|
||
method: search with wrong metric type
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with binary data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_binary_vec_field_name, DataType.BINARY_VECTOR, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
# 2. create index
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_binary_vec_field_name,
|
||
index_type="BIN_IVF_FLAT", metric_type="JACCARD", params={"nlist": 128})
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 3. search with exception
|
||
_, search_binary_vectors = cf.gen_binary_vectors(3000, default_dim)
|
||
wrong_search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
|
||
# err_code 65535: BIN_IVF_FLAT now returns metric type mismatch at search time
|
||
# (previously returned 1100 during index/collection migration)
|
||
self.search(client, collection_name,
|
||
data=search_binary_vectors[:default_nq],
|
||
anns_field=ct.default_binary_vec_field_name,
|
||
search_params=wrong_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "metric type not match"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_binary_flat_with_L2(self):
|
||
"""
|
||
target: search binary collection using FlAT with L2
|
||
method: search binary collection using FLAT with L2
|
||
expected: raise exception and report error
|
||
"""
|
||
# 1. initialize with binary data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_binary_vec_field_name, DataType.BINARY_VECTOR, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_binary_vec_field_name, metric_type="JACCARD")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. search and assert
|
||
_, search_binary_vectors = cf.gen_binary_vectors(2, default_dim)
|
||
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
|
||
self.search(client, collection_name,
|
||
data=search_binary_vectors[:default_nq],
|
||
anns_field=ct.default_binary_vec_field_name,
|
||
search_params=search_params, limit=default_limit,
|
||
filter=f"{ct.default_int64_field_name} >= 0",
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "metric type not match: invalid "
|
||
"parameter[expected=JACCARD][actual=L2]"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("output_fields", ["int63", ""])
|
||
@pytest.mark.parametrize("enable_dynamic", [True, False])
|
||
def test_search_with_output_fields_not_exist(self, output_fields, enable_dynamic):
|
||
"""
|
||
target: test search with output fields
|
||
method: search with non-exist output_field
|
||
expected: raise exception for non-dynamic or empty string fields
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic)[0]
|
||
schema.add_field(ct.default_int64_field_name, DataType.INT64, is_primary=True)
|
||
schema.add_field(ct.default_float_field_name, DataType.FLOAT)
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535)
|
||
schema.add_field(ct.default_json_field_name, DataType.JSON)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. search
|
||
log.info("test_search_with_output_fields_not_exist: Searching collection %s" %
|
||
collection_name)
|
||
if enable_dynamic and output_fields == "int63":
|
||
# dynamic field enabled: non-existent field name returns success (treated as dynamic field)
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=[output_fields])
|
||
elif output_fields == "":
|
||
# empty string output field
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=[output_fields],
|
||
check_task=CheckTasks.err_res,
|
||
check_items={ct.err_code: 1,
|
||
ct.err_msg: "is illegal"})
|
||
else:
|
||
# non-dynamic: non-existent field raises error
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
output_fields=[output_fields],
|
||
check_task=CheckTasks.err_res,
|
||
check_items={ct.err_code: 65535,
|
||
ct.err_msg: "field int63 not exist"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("index", ct.all_index_types[-2:])
|
||
def test_search_output_field_vector_after_gpu_index(self, index):
|
||
"""
|
||
target: test search with vector as output field
|
||
method: 1. create a collection and insert data
|
||
2. create an index which doesn't output vectors
|
||
3. load and search
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. create a collection and insert data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
# 2. create an index which doesn't output vectors
|
||
params = cf.get_index_params_params(index)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type=index, metric_type="L2", params=params)
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
|
||
# 3. load and search
|
||
self.load_collection(client, collection_name)
|
||
search_params = cf.get_search_params_params(index)
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params={"params": search_params}, limit=default_limit,
|
||
output_fields=[default_search_field],
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1,
|
||
"err_msg": "not supported"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("ignore_growing", [1.2, "string", [True]])
|
||
def test_search_invalid_ignore_growing_param(self, ignore_growing):
|
||
"""
|
||
target: test search ignoring growing segment
|
||
method: 1. create a collection, insert data, create index and load
|
||
2. insert data again
|
||
3. search with param ignore_growing invalid
|
||
expected: raise exception
|
||
"""
|
||
# 1. create a collection
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=10, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name, metric_type="COSINE")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 2. insert data again
|
||
data = cf.gen_row_data_by_schema(nb=default_nb, schema=schema, start=100)
|
||
self.insert(client, collection_name, data=data)
|
||
|
||
# 3. search with param ignore_growing=invalid
|
||
search_params = {"metric_type": "COSINE", "params": {"nprobe": 10}, "ignore_growing": ignore_growing}
|
||
vector = [[random.random() for _ in range(default_dim)] for _ in range(1)]
|
||
self.search(client, collection_name,
|
||
data=vector[:default_nq], anns_field=default_search_field,
|
||
search_params=search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 999,
|
||
"err_msg": "parse ignore growing field failed"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_range_search_invalid_radius_range_filter_L2(self):
|
||
"""
|
||
target: test range search with invalid radius and range_filter for L2
|
||
method: range search with radius smaller than range_filter
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=100, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
|
||
# 2. create index
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="FLAT", metric_type="L2")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
# 3. load
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 4. range search
|
||
log.info("test_range_search_invalid_radius_range_filter_L2: Range searching collection %s" %
|
||
collection_name)
|
||
range_search_params = {"metric_type": "L2", "params": {"nprobe": 10, "radius": 1, "range_filter": 10}}
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=range_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "must be less than radius"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_range_search_invalid_radius_range_filter_IP(self):
|
||
"""
|
||
target: test range search with invalid radius and range_filter for IP
|
||
method: range search with radius larger than range_filter
|
||
expected: raise exception and report the error
|
||
"""
|
||
# 1. initialize with data
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=100, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
|
||
# 2. create index
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="FLAT", metric_type="IP")
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
# 3. load
|
||
self.load_collection(client, collection_name)
|
||
|
||
# 4. range search
|
||
log.info("test_range_search_invalid_radius_range_filter_IP: Range searching collection %s" %
|
||
collection_name)
|
||
range_search_params = {"metric_type": "IP",
|
||
"params": {"nprobe": 10, "radius": 10, "range_filter": 1}}
|
||
self.search(client, collection_name,
|
||
data=vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=range_search_params, limit=default_limit,
|
||
filter=v2_invalid_search_exp,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "must be greater than radius"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_search_dynamic_compare_two_fields(self):
|
||
"""
|
||
target: test search compare with two fields for dynamic collection
|
||
method: 1.create collection , insert data, enable dynamic function
|
||
2.search with two fields comparisons
|
||
expected: Raise exception
|
||
"""
|
||
# create collection, insert data, enable dynamic field
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self.create_schema(client, enable_dynamic_field=True)[0]
|
||
schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=65535, is_primary=True)
|
||
schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
|
||
# insert data
|
||
data = [{ct.default_string_field_name: str(i),
|
||
ct.default_float_vec_field_name: [random.random() for _ in range(default_dim)]}
|
||
for i in range(1)]
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
# create indexes
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="IVF_SQ8", metric_type="COSINE", params={"nlist": 64})
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.load_collection(client, collection_name)
|
||
|
||
# search with two fields comparison
|
||
expr = f'{ct.default_float_field_name} >= {ct.default_int64_field_name}'
|
||
search_vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
||
self.search(client, collection_name,
|
||
data=search_vectors[:default_nq], anns_field=default_search_field,
|
||
search_params=default_search_params, limit=default_limit,
|
||
filter=expr,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 1100,
|
||
"err_msg": "two column comparison with JSON type is not supported"})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_search_ef_less_than_limit(self):
|
||
"""
|
||
target: test the scenario which search with ef less than limit
|
||
method: 1. create collection
|
||
2. search with ef less than limit
|
||
expected: raise exception and report the error
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
schema = self._create_standard_schema(client)
|
||
self.create_collection(client, collection_name, schema=schema)
|
||
data = cf.gen_row_data_by_schema(nb=ct.default_nb, schema=schema)
|
||
self.insert(client, collection_name, data=data)
|
||
self.flush(client, collection_name)
|
||
|
||
idx = self.prepare_index_params(client)[0]
|
||
idx.add_index(field_name=ct.default_float_vec_field_name,
|
||
index_type="HNSW", metric_type="L2",
|
||
params={"M": 8, "efConstruction": 256})
|
||
self.create_index(client, collection_name, index_params=idx)
|
||
self.flush(client, collection_name)
|
||
self.load_collection(client, collection_name)
|
||
|
||
search_params = {"metric_type": "L2", "params": {"ef": 10}}
|
||
self.search(client, collection_name,
|
||
data=vectors, anns_field=ct.default_float_vec_field_name,
|
||
search_params=search_params, limit=100,
|
||
check_task=CheckTasks.err_res,
|
||
check_items={"err_code": 65535,
|
||
"err_msg": "query failed: N6milvus21ExecOperatorExceptionE :Operator::GetOutput failed"})
|
||
|
||
|
||
class TestMilvusClientSearchValid(TestMilvusClientV2Base):
|
||
""" Test case of search interface """
|
||
|
||
@pytest.fixture(scope="function", params=[False, True])
|
||
def auto_id(self, request):
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
||
def metric_type(self, request):
|
||
yield request.param
|
||
|
||
"""
|
||
******************************************************************
|
||
# The following are valid base cases
|
||
******************************************************************
|
||
"""
|
||
|
||
@pytest.mark.tags(CaseLabel.L0)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.INT64, DataType.INT8, DataType.INT16, DataType.INT32,
|
||
DataType.FLOAT, DataType.DOUBLE, DataType.BOOL, DataType.VARCHAR,
|
||
DataType.ARRAY, DataType.JSON])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_query_default(self, new_field_data_type, is_flush):
|
||
"""
|
||
target: test search (high level api) normal case
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 0})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
if new_field_data_type == DataType.ARRAY:
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
element_type=DataType.INT64, max_capacity=12, max_length=64, nullable=True)
|
||
else:
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, max_length=100)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = None
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is not null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is not null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.INT64, DataType.INT8, DataType.INT16, DataType.INT32])
|
||
@pytest.mark.parametrize("is_flush", [True])
|
||
@pytest.mark.skip(reason="issue #42629")
|
||
def test_milvus_client_search_query_add_new_field_with_default_value_int(self, new_field_data_type, is_flush):
|
||
"""
|
||
target: test search with add field using default value
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 2})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
if new_field_data_type == DataType.INT8:
|
||
field_type = np.int8
|
||
elif new_field_data_type == DataType.INT16:
|
||
field_type = np.int16
|
||
elif new_field_data_type == DataType.INT32:
|
||
field_type = np.int32
|
||
elif new_field_data_type == DataType.INT64:
|
||
field_type = np.int64
|
||
else:
|
||
raise Exception(f"Unsupported type {new_field_data_type}")
|
||
|
||
default_value = field_type(1)
|
||
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, default_value=default_value)
|
||
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
time.sleep(5)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = field_type(1)
|
||
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new == 1",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new == 1",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.FLOAT, DataType.DOUBLE])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
@pytest.mark.parametrize("use_numpy_float", [True, False])
|
||
def test_milvus_client_search_query_add_new_field_with_default_value_float(self, new_field_data_type, is_flush, use_numpy_float):
|
||
"""
|
||
target: test search with add field using default value
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
# Skip use_numpy_float parameter when new_field_data_type is DOUBLE
|
||
if new_field_data_type == DataType.DOUBLE and not use_numpy_float:
|
||
pytest.skip("DOUBLE type doesn't need to consider use_numpy_float parameter")
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 2})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
default_value = 1.0
|
||
if new_field_data_type == DataType.FLOAT:
|
||
if use_numpy_float:
|
||
default_value = np.float32(1.0)
|
||
elif new_field_data_type == DataType.DOUBLE:
|
||
default_value = np.float64(1.0)
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, default_value=default_value)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = default_value
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new == 1",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new == 1",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.BOOL])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_query_add_new_field_with_default_value_bool(self, new_field_data_type, is_flush):
|
||
"""
|
||
target: test search with add field using default value
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 2})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
default_value = True
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, default_value=default_value)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = default_value
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new == True",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new == True",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.VARCHAR])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_query_add_new_field_with_default_value_varchar(self, new_field_data_type, is_flush):
|
||
"""
|
||
target: test search with add field using default value
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 2})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
default_value = "1"
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, max_length=100, default_value=default_value)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = default_value
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new >='0'",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new >='0'",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.JSON])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_query_add_new_field_with_default_value_json(self, new_field_data_type, is_flush):
|
||
"""
|
||
target: test search with add field using default value
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 2})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
default_value = None
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, max_length=100, default_value=default_value)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = default_value
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is not null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is not null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("new_field_data_type", [DataType.ARRAY])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_query_add_new_field_with_default_value_array(self, new_field_data_type, is_flush):
|
||
"""
|
||
target: test search with add field using default value
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
self.using_database(client, "default")
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 2})
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 5. add field
|
||
default_value = None
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=new_field_data_type,
|
||
nullable=True, element_type=DataType.INT64, max_capacity=12, max_length=100,
|
||
default_value=default_value)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 6. check the old search is not impacted after add field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 7. check the old query is not impacted after add field
|
||
for row in rows:
|
||
row["field_new"] = default_value
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 8. search filtered with the new field
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is not null",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
# 9. query filtered with the new field
|
||
self.query(client, collection_name, filter="field_new is null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.query(client, collection_name, filter="field_new is not null",
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: [],
|
||
"pk_name": default_primary_key_field_name})
|
||
self.release_collection(client, collection_name)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
def test_milvus_client_search_query_self_creation_default(self, nullable):
|
||
"""
|
||
target: test fast create collection normal case
|
||
method: create collection
|
||
expected: create collection with default schema, index, and load successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 128
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
|
||
schema.add_field("nullable_field", DataType.INT64, nullable=True, default_value=10)
|
||
schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
||
max_length=64, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [
|
||
{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None, "array_field": None} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
if self.has_collection(client, collection_name)[0]:
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_rename_search_query_default(self):
|
||
"""
|
||
target: test search (high level api) normal case
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
old_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, old_name, default_dim, consistency_level="Strong")
|
||
collections = self.list_collections(client)[0]
|
||
assert old_name in collections
|
||
c_info = self.describe_collection(client, old_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": old_name,
|
||
"dim": default_dim,
|
||
"consistency_level": 0})[0]
|
||
|
||
rows = cf.gen_row_data_by_schema(nb=default_nb, schema=c_info)
|
||
self.insert(client, old_name, rows)
|
||
self.flush(client, old_name)
|
||
self.wait_for_index_ready(client, collection_name=old_name, index_name='vector')
|
||
|
||
vectors_to_search = cf.gen_vectors(ct.default_nq, default_dim)
|
||
insert_ids = [item.get('id') for item in rows]
|
||
old_search_res = self.search(client, old_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": ct.default_nq,
|
||
"ids": insert_ids,
|
||
"pk_name": "id",
|
||
"limit": default_limit})[0]
|
||
old_query_res = self.query(client, old_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True})[0]
|
||
|
||
new_name = old_name + "new"
|
||
self.rename_collection(client, old_name, new_name)
|
||
self.describe_collection(client, new_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": new_name,
|
||
"dim": default_dim})
|
||
|
||
# search again after rename collection
|
||
new_search_res = self.search(client, new_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": ct.default_nq,
|
||
"ids": insert_ids,
|
||
"pk_name": "id",
|
||
"limit": default_limit})[0]
|
||
new_query_res = self.query(client, new_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True})[0]
|
||
assert old_search_res[0].ids == new_search_res[0].ids
|
||
assert old_query_res == new_query_res
|
||
|
||
rows = cf.gen_row_data_by_schema(nb=200, schema=c_info, start=default_nb)
|
||
error = {ct.err_code: 0, ct.err_msg: "collection not found"}
|
||
self.insert(client, old_name, rows,
|
||
check_task=CheckTasks.err_res,
|
||
check_items=error)
|
||
self.insert(client, new_name, rows)
|
||
new_ids = [item.get('id') for item in rows]
|
||
insert_ids.extend(new_ids)
|
||
self.search(client, new_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": ct.default_nq,
|
||
"ids": insert_ids,
|
||
"pk_name": "id",
|
||
"limit": default_limit})
|
||
|
||
self.release_collection(client, new_name)
|
||
self.drop_collection(client, new_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_array_insert_search(self):
|
||
"""
|
||
target: test search (high level api) normal case
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
||
collections = self.list_collections(client)[0]
|
||
assert collection_name in collections
|
||
# 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_int32_array_field_name: [i, i + 1, i + 2],
|
||
default_string_array_field_name: [str(i), str(i + 1), str(i + 2)]
|
||
} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.skip(reason="issue 25110")
|
||
def test_milvus_client_search_query_string(self):
|
||
"""
|
||
target: test search (high level api) for string primary key
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
self.create_collection(client, collection_name, default_dim, id_type="string", max_length=ct.default_length)
|
||
self.describe_collection(client, collection_name,
|
||
check_task=CheckTasks.check_describe_collection_property,
|
||
check_items={"collection_name": collection_name,
|
||
"dim": default_dim})
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [
|
||
{default_primary_key_field_name: str(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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
assert self.num_entities(client, collection_name)[0] == default_nb
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, default_dim))
|
||
self.search(client, collection_name, vectors_to_search,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 4. query
|
||
self.query(client, collection_name, filter=default_search_exp,
|
||
check_task=CheckTasks.check_query_results,
|
||
check_items={exp_res: rows,
|
||
"with_vec": True,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_milvus_client_search_different_metric_types_not_specifying_in_search_params(self, metric_type, auto_id):
|
||
"""
|
||
target: test search (high level api) normal case
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully with limit(topK)
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 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,
|
||
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),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.skip("pymilvus issue #1866")
|
||
def test_milvus_client_search_different_metric_types_specifying_in_search_params(self, metric_type, auto_id):
|
||
"""
|
||
target: test search (high level api) normal case
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully with limit(topK)
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 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),
|
||
"pk_name": default_primary_key_field_name,
|
||
"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_collection_name_by_testcase_name()
|
||
# 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)]
|
||
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,
|
||
"pk_name": default_primary_key_field_name,
|
||
"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,
|
||
"pk_name": default_primary_key_field_name})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_delete_after_add_field(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_collection_name_by_testcase_name()
|
||
# 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)]
|
||
self.insert(client, collection_name, rows)
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=DataType.INT64,
|
||
nullable=True, max_length=100)
|
||
for row in rows:
|
||
row["field_new"] = None
|
||
# 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,
|
||
"pk_name": default_primary_key_field_name,
|
||
"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,
|
||
"pk_name": default_primary_key_field_name})
|
||
# 6. insert to the new added field
|
||
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), "field_new": i} for i in
|
||
range(delete_num)]
|
||
self.insert(client, collection_name, rows)
|
||
# 7. flush
|
||
self.flush(client, collection_name)
|
||
limit = default_nb
|
||
insert_ids = [i for i in range(default_nb)]
|
||
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,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
# 8. delete
|
||
self.delete(client, collection_name, filter=f"field_new >=0 and field_new <={delete_num}")
|
||
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,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
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_collection_name_by_testcase_name()
|
||
# 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)]
|
||
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,
|
||
"pk_name": default_primary_key_field_name,
|
||
"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,
|
||
"pk_name": 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_collection_name_by_testcase_name()
|
||
# 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)
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=DataType.VARCHAR,
|
||
nullable=True, max_length=100)
|
||
# 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"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)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_client_search_with_expr_float_vector(self):
|
||
"""
|
||
target: test search using float vector field as filter
|
||
method: create connection, collection, insert, search with float vector field as filter
|
||
expected: raise error
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
dim = 5
|
||
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])
|
||
raw_vector = [random.random() for _ in range(dim)]
|
||
vectors = np.array(raw_vector, dtype=np.float32)
|
||
error = {ct.err_code: 1100,
|
||
ct.err_msg: "failed to create query plan: cannot parse expression"}
|
||
self.search(client, collection_name, data=[search_vector], filter=f"{vector_field_name} == {raw_vector}",
|
||
search_params=default_search_params, limit=default_limit,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.search(client, collection_name, data=[search_vector], filter=f"{vector_field_name} == {vectors}",
|
||
search_params=default_search_params, limit=default_limit,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
self.drop_collection(client, collection_name)
|
||
|
||
|
||
class TestMilvusClientSearchNullExpr(TestMilvusClientV2Base):
|
||
""" Test case of search interface """
|
||
|
||
@pytest.fixture(scope="function", params=[False, True])
|
||
def auto_id(self, request):
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
||
def metric_type(self, request):
|
||
yield request.param
|
||
|
||
"""
|
||
******************************************************************
|
||
# The following are valid base cases
|
||
******************************************************************
|
||
"""
|
||
|
||
@pytest.mark.tags(CaseLabel.L0)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
||
def test_milvus_client_search_null_expr(self, nullable, null_expr_op):
|
||
"""
|
||
target: test search with null expression on int64 fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.INT64, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": i} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.add_collection_field(client, collection_name, field_name="field_new", data_type=DataType.JSON,
|
||
nullable=True, max_length=100)
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is null",
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter="field_new is not null",
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
def test_milvus_client_search_null_expr_int8(self, nullable):
|
||
"""
|
||
target: test search with null expression on int8 fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.INT8, nullable=nullable)
|
||
# schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
||
# max_length=64, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
int8_bound = np.iinfo(np.int8).max + 1
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": i % int8_bound} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
def test_milvus_client_search_null_expr_int16(self, nullable):
|
||
"""
|
||
target: test search with null expression on int16 fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.INT16, nullable=nullable)
|
||
# schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
||
# max_length=64, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": np.int16(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
############################################################
|
||
# Needs to modify to remove parameterize and use for loop ##
|
||
############################################################
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
||
def test_milvus_client_search_null_expr_int32(self, nullable, null_expr_op):
|
||
"""
|
||
target: test search with null expression on int32 fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.INT32, nullable=nullable)
|
||
# schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
||
# max_length=64, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": np.int32(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
||
def test_milvus_client_search_null_expr_float(self, nullable, null_expr_op):
|
||
"""
|
||
target: test search with null expression on float fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.FLOAT, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": i * 1.0} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
||
def test_milvus_client_search_null_expr_double(self, nullable, null_expr_op):
|
||
"""
|
||
target: test search with null expression on double fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.DOUBLE, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": np.double(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
||
def test_milvus_client_search_null_expr_bool(self, nullable, null_expr_op):
|
||
"""
|
||
target: test search with null expression on bool fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.BOOL, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": np.bool_(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
||
def test_milvus_client_search_null_expr_varchar(self, nullable, null_expr_op):
|
||
"""
|
||
target: test search with null expression on varchar fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.VARCHAR, nullable=nullable, max_length=128)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": str(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("json_flat_index", [True, False])
|
||
def test_milvus_client_search_null_expr_json(self, nullable, json_flat_index):
|
||
"""
|
||
target: test search with null expression on json fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.JSON, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
if json_flat_index:
|
||
index_params.add_index(field_name=nullable_field_name, index_name="json_index", index_type="INVERTED",
|
||
params={"json_cast_type": "json",
|
||
"json_path": f"{nullable_field_name}['a']['b']"})
|
||
index_params.add_index(field_name=nullable_field_name, index_name="json_index_1", index_type="INVERTED",
|
||
params={"json_cast_type": "json",
|
||
"json_path": f"{nullable_field_name}['a']['c']"})
|
||
else:
|
||
index_params.add_index(field_name=nullable_field_name, index_name="json_index", index_type="INVERTED",
|
||
params={"json_cast_type": "double",
|
||
"json_path": f"{nullable_field_name}['a']['b']"})
|
||
index_params.add_index(field_name=nullable_field_name, index_name="json_index_1", index_type="INVERTED",
|
||
params={"json_cast_type": "varchar",
|
||
"json_path": f"{nullable_field_name}['a']['c']"})
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), nullable_field_name: None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), nullable_field_name: {'a': {'b': i, 'c': None}}} for i in
|
||
range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
output_fields=[nullable_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
def test_milvus_client_search_null_expr_json_after_flush(self, nullable):
|
||
"""
|
||
target: test search with null expression on json fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.JSON, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
self.release_collection(client, collection_name)
|
||
self.drop_index(client, collection_name, default_vector_field_name)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), nullable_field_name: None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), nullable_field_name: {'a': {'b': i, 'c': None}}} for i in
|
||
range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. flush
|
||
self.flush(client, collection_name)
|
||
# 4. create vector and json index
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
index_params.add_index(field_name=nullable_field_name, index_name="json_index", index_type="INVERTED",
|
||
params={"json_cast_type": "DOUBLE",
|
||
"json_path": f"{nullable_field_name}['a']['b']"})
|
||
index_params.add_index(field_name=nullable_field_name, index_name="json_index_1", index_type="INVERTED",
|
||
params={"json_cast_type": "double",
|
||
"json_path": f"{nullable_field_name}['a']['c']"})
|
||
self.create_index(client, collection_name, index_params)
|
||
self.load_collection(client, collection_name)
|
||
# 5. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
output_fields=[nullable_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
|
||
self.drop_collection(client, collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("nullable", [True, False])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
@pytest.mark.parametrize("is_release", [True, False])
|
||
@pytest.mark.parametrize("is_scalar_index", [True, False])
|
||
@pytest.mark.parametrize("scalar_index_type", ["AUTOINDEX", "INVERTED", "BITMAP"])
|
||
def test_milvus_client_search_null_expr_array(self, nullable, is_flush, is_release,
|
||
is_scalar_index, scalar_index_type):
|
||
"""
|
||
target: test search with null expression on array fields
|
||
method: create connection, collection, insert and search
|
||
expected: search/query successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 128
|
||
# 1. create collection
|
||
nullable_field_name = "nullable_field"
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
schema.add_field(nullable_field_name, DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
||
max_length=64, nullable=nullable)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
if is_scalar_index:
|
||
index_params.add_index(nullable_field_name, index_type=scalar_index_type)
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
if nullable:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
||
else:
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
default_string_field_name: str(i), "nullable_field": [1, 2]} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
if is_release:
|
||
self.release_collection(client, collection_name)
|
||
self.drop_index(client, collection_name, default_vector_field_name)
|
||
self.drop_index(client, collection_name, nullable_field_name)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
if is_scalar_index:
|
||
index_params.add_index(nullable_field_name, index_type=scalar_index_type)
|
||
self.create_index(client, collection_name, index_params)
|
||
self.load_collection(client, collection_name)
|
||
# 3. search
|
||
vectors_to_search = rng.random((1, dim))
|
||
null_expr_ops = ["is null", "IS NULL", "is not null", "IS NOT NULL"]
|
||
for null_expr_op in null_expr_ops:
|
||
insert_ids = [str(i) for i in range(default_nb)]
|
||
null_expr = nullable_field_name + " " + null_expr_op
|
||
if nullable:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
insert_ids = []
|
||
limit = 0
|
||
else:
|
||
limit = default_limit
|
||
else:
|
||
if "not" in null_expr or "NOT" in null_expr:
|
||
limit = default_limit
|
||
else:
|
||
insert_ids = []
|
||
limit = 0
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=null_expr,
|
||
output_fields=[nullable_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": limit})
|
||
|
||
self.drop_collection(client, collection_name)
|
||
|
||
|
||
_json_path_index_params = [
|
||
("INVERTED", "BOOL"),
|
||
("INVERTED", "DOUBLE"),
|
||
("INVERTED", "VARCHAR"),
|
||
("INVERTED", "JSON"),
|
||
("STL_SORT", "DOUBLE"),
|
||
("STL_SORT", "VARCHAR"),
|
||
("BITMAP", "BOOL"),
|
||
("BITMAP", "VARCHAR"),
|
||
]
|
||
|
||
|
||
class TestMilvusClientSearchJsonPathIndex(TestMilvusClientV2Base):
|
||
""" Test case of search interface """
|
||
|
||
@pytest.fixture(scope="function", params=_json_path_index_params, ids=[f"{t[0]}_{t[1]}" for t in _json_path_index_params])
|
||
def json_index_params(self, request):
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function")
|
||
def supported_varchar_scalar_index(self, json_index_params):
|
||
yield json_index_params[0]
|
||
|
||
@pytest.fixture(scope="function")
|
||
def supported_json_cast_type(self, json_index_params):
|
||
yield json_index_params[1]
|
||
|
||
"""
|
||
******************************************************************
|
||
# The following are valid base cases
|
||
******************************************************************
|
||
"""
|
||
|
||
@pytest.mark.tags(CaseLabel.L0)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_json_path_index_default(self, enable_dynamic_field, supported_json_cast_type,
|
||
supported_varchar_scalar_index, is_flush):
|
||
"""
|
||
target: test search after the json path index created
|
||
method: Search after creating json path index
|
||
Step: 1. create schema
|
||
2. prepare index_params with the required vector index params
|
||
3. create collection with the above schema and index params
|
||
4. insert
|
||
5. flush if specified
|
||
6. prepare json path index params
|
||
7. create json path index using the above index params created in step 6
|
||
8. create the same json path index again
|
||
9. search with expressions related with the json paths
|
||
expected: Search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
json_field_name = "my_json"
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
if not enable_dynamic_field:
|
||
schema.add_field(json_field_name, DataType.JSON)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, index_type="FLAT", metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
# 2. insert with different data distribution
|
||
vectors = cf.gen_vectors(default_nb + 60, default_dim)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': {"b": i, "c": i}}} for i in
|
||
range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: i} for i in
|
||
range(default_nb, default_nb + 10)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {}} for i in
|
||
range(default_nb + 10, default_nb + 20)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [1, 2, 3]}} for i in
|
||
range(default_nb + 20, default_nb + 30)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [{'b': 1}, 2, 3]}} for i in
|
||
range(default_nb + 30, default_nb + 40)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [{'b': None}, 2, 3]}} for i in
|
||
range(default_nb + 40, default_nb + 50)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': 1}} for i in
|
||
range(default_nb + 50, default_nb + 60)]
|
||
self.insert(client, collection_name, rows)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 2. prepare index params
|
||
index_name = "json_index"
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=default_vector_field_name, index_type="FLAT", metric_type="COSINE")
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name,
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']['b']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '1',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '2',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '3',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a'][0]['b']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '4',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a'][0]"})
|
||
# 3. create index
|
||
self.create_index(client, collection_name, index_params)
|
||
# 4. create same json index twice
|
||
self.create_index(client, collection_name, index_params)
|
||
# 5. search without filter
|
||
vectors_to_search = [vectors[0]]
|
||
insert_ids = [i for i in range(default_nb + 60)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
output_fields=[json_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
# 6. search with filter on json without output_fields
|
||
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
||
insert_ids = [default_nb / 2]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
expr = f"{json_field_name} == {default_nb + 5}"
|
||
insert_ids = [default_nb + 5]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
expr = f"{json_field_name}['a'][0] == 1"
|
||
insert_ids = [i for i in range(default_nb + 20, default_nb + 30)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
expr = f"{json_field_name}['a'][0]['b'] == 1"
|
||
insert_ids = [i for i in range(default_nb + 30, default_nb + 40)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
expr = f"{json_field_name}['a'] == 1"
|
||
insert_ids = [i for i in range(default_nb + 50, default_nb + 60)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
def test_milvus_client_search_json_path_index_default_index_name(self, enable_dynamic_field,
|
||
supported_json_cast_type,
|
||
supported_varchar_scalar_index):
|
||
"""
|
||
target: test json path index without specifying the index_name parameter
|
||
method: create json path index without specifying the index_name parameter
|
||
expected: successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
json_field_name = "my_json"
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, is_primary=True, auto_id=False,
|
||
max_length=128)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
if not enable_dynamic_field:
|
||
schema.add_field(json_field_name, DataType.JSON)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
vectors = cf.gen_vectors(default_nb, default_dim)
|
||
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. prepare index params
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
||
index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']['b']"})
|
||
# 4. create index
|
||
self.create_index(client, collection_name, index_params)
|
||
# 5. search with filter on json with output_fields
|
||
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
||
vectors_to_search = [vectors[0]]
|
||
insert_ids = [str(int(default_nb / 2))]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
output_fields=[json_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.skip(reason="issue #40636")
|
||
def test_milvus_client_search_json_path_index_on_non_json_field(self, supported_json_cast_type,
|
||
supported_varchar_scalar_index):
|
||
"""
|
||
target: test json path index on non-json field
|
||
method: create json path index on int64 field
|
||
expected: successfully with original inverted index
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
vectors = cf.gen_vectors(default_nb, default_dim)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i)} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 2. prepare index params
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
||
index_params.add_index(field_name=default_primary_key_field_name, index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{default_string_field_name}['a']['b']"})
|
||
# 3. create index
|
||
index_name = default_string_field_name
|
||
self.create_index(client, collection_name, index_params)
|
||
self.describe_index(client, collection_name, index_name,
|
||
check_task=CheckTasks.check_describe_index_property,
|
||
check_items={
|
||
# "json_cast_type": supported_json_cast_type, # issue 40426
|
||
"json_path": f"{default_string_field_name}['a']['b']",
|
||
"index_type": supported_varchar_scalar_index,
|
||
"field_name": default_string_field_name,
|
||
"index_name": index_name})
|
||
self.flush(client, collection_name)
|
||
# 5. search with filter on json with output_fields
|
||
expr = f"{default_primary_key_field_name} >= 0"
|
||
vectors_to_search = [vectors[0]]
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
output_fields=[default_string_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
def test_milvus_client_search_diff_index_same_field_diff_index_name_diff_index_params(self, enable_dynamic_field,
|
||
supported_json_cast_type,
|
||
supported_varchar_scalar_index):
|
||
"""
|
||
target: test search after different json path index with different default index name at the same time
|
||
method: Search after different json path index with different default index name at the same index_params object
|
||
expected: Search successfully
|
||
"""
|
||
if enable_dynamic_field:
|
||
pytest.skip('need to fix the field name when enabling dynamic field')
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
json_field_name = "my_json"
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
if not enable_dynamic_field:
|
||
schema.add_field(json_field_name, DataType.JSON)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
self.load_collection(client, collection_name)
|
||
# 2. insert
|
||
vectors = cf.gen_vectors(default_nb, default_dim)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. prepare index params
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']['b']"})
|
||
self.create_index(client, collection_name, index_params)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=json_field_name,
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']"})
|
||
self.create_index(client, collection_name, index_params)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=json_field_name,
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}"})
|
||
self.create_index(client, collection_name, index_params)
|
||
# 4. release and load collection to make sure new index is loaded
|
||
self.release_collection(client, collection_name)
|
||
self.load_collection(client, collection_name)
|
||
# 5. search with filter on json with output_fields
|
||
expr = f"{json_field_name}['a']['b'] >= 0"
|
||
vectors_to_search = [vectors[0]]
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
output_fields=[default_string_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
@pytest.mark.parametrize("is_release", [True, False])
|
||
def test_milvus_client_json_search_index_same_json_path_diff_field(self, enable_dynamic_field,
|
||
supported_json_cast_type,
|
||
supported_varchar_scalar_index, is_flush,
|
||
is_release):
|
||
"""
|
||
target: test search after creating same json path for different field
|
||
method: Search after creating same json path for different field
|
||
expected: Search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
json_field_name = "my_json"
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
if not enable_dynamic_field:
|
||
schema.add_field(json_field_name, DataType.JSON)
|
||
schema.add_field(json_field_name + "1", DataType.JSON)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
vectors = cf.gen_vectors(default_nb, default_dim)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': {'b': i}},
|
||
json_field_name + "1": {'a': {'b': i}}} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. flush if specified
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 3. release and drop index if specified
|
||
if is_release:
|
||
self.release_collection(client, collection_name)
|
||
self.drop_index(client, collection_name, default_vector_field_name)
|
||
# 4. prepare index params
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']['b']"})
|
||
self.create_index(client, collection_name, index_params)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=json_field_name + "1",
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}1['a']['b']"})
|
||
# 5. create index with json path index
|
||
self.create_index(client, collection_name, index_params)
|
||
if is_release:
|
||
self.load_collection(client, collection_name)
|
||
# 6. search with filter on json with output_fields on each json field
|
||
expr = f"{json_field_name}['a']['b'] >= 0"
|
||
vectors_to_search = [vectors[0]]
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
output_fields=[json_field_name],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
expr = f"{json_field_name}1['a']['b'] >= 0"
|
||
vectors_to_search = [vectors[0]]
|
||
insert_ids = [i for i in range(default_nb)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
output_fields=[json_field_name + "1"],
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_json_path_index_before_load(self, enable_dynamic_field, supported_json_cast_type,
|
||
supported_varchar_scalar_index, is_flush):
|
||
"""
|
||
target: test search after creating json path index before load
|
||
method: Search after creating json path index before load
|
||
Step: 1. create schema
|
||
2. prepare index_params with vector index params
|
||
3. create collection with the above schema and index params
|
||
4. release collection
|
||
5. insert
|
||
6. flush if specified
|
||
7. prepare json path index params
|
||
8. create index
|
||
9. load collection
|
||
10. search
|
||
expected: Search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
json_field_name = "my_json"
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
if not enable_dynamic_field:
|
||
schema.add_field(json_field_name, DataType.JSON)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
# 2. release collection
|
||
self.release_collection(client, collection_name)
|
||
# 3. insert with different data distribution
|
||
vectors = cf.gen_vectors(default_nb + 50, default_dim)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in
|
||
range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: i} for i in
|
||
range(default_nb, default_nb + 10)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {}} for i in
|
||
range(default_nb + 10, default_nb + 20)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [1, 2, 3]}} for i in
|
||
range(default_nb + 20, default_nb + 30)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [{'b': 1}, 2, 3]}} for i in
|
||
range(default_nb + 30, default_nb + 40)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [{'b': None}, 2, 3]}} for i in
|
||
range(default_nb + 40, default_nb + 50)]
|
||
self.insert(client, collection_name, rows)
|
||
# 4. flush if specified
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 5. prepare index params
|
||
index_name = "json_index"
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name,
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']['b']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '1',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '2',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '3',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a'][0]['b']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '4',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a'][0]"})
|
||
# 5. create index
|
||
self.create_index(client, collection_name, index_params)
|
||
# 6. load collection
|
||
self.load_collection(client, collection_name)
|
||
# 7. search with filter on json without output_fields
|
||
vectors_to_search = [vectors[0]]
|
||
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
||
insert_ids = [default_nb / 2]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
expr = f"{json_field_name} == {default_nb + 5}"
|
||
insert_ids = [default_nb + 5]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
expr = f"{json_field_name}['a'][0] == 1"
|
||
insert_ids = [i for i in range(default_nb + 20, default_nb + 30)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
expr = f"{json_field_name}['a'][0]['b'] == 1"
|
||
insert_ids = [i for i in range(default_nb + 30, default_nb + 40)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_json_path_index_after_release_load(self, enable_dynamic_field,
|
||
supported_json_cast_type,
|
||
supported_varchar_scalar_index, is_flush):
|
||
"""
|
||
target: test search after creating json path index after release and load
|
||
method: Search after creating json path index after release and load
|
||
Step: 1. create schema
|
||
2. prepare index_params with vector index params
|
||
3. create collection with the above schema and index params
|
||
4. insert
|
||
5. flush if specified
|
||
6. prepare json path index params
|
||
7. create index
|
||
8. release collection
|
||
9. create index again
|
||
10. load collection
|
||
11. search with expressions related with the json paths
|
||
expected: Search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create collection
|
||
json_field_name = "my_json"
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
||
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
||
if not enable_dynamic_field:
|
||
schema.add_field(json_field_name, DataType.JSON)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
||
# 2. insert with different data distribution
|
||
vectors = cf.gen_vectors(default_nb + 50, default_dim)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in
|
||
range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: i} for i in
|
||
range(default_nb, default_nb + 10)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {}} for i in
|
||
range(default_nb + 10, default_nb + 20)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [1, 2, 3]}} for i in
|
||
range(default_nb + 20, default_nb + 30)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [{'b': 1}, 2, 3]}} for i in
|
||
range(default_nb + 30, default_nb + 40)]
|
||
self.insert(client, collection_name, rows)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
||
default_string_field_name: str(i), json_field_name: {'a': [{'b': None}, 2, 3]}} for i in
|
||
range(default_nb + 40, default_nb + 50)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. flush if specified
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 4. prepare index params
|
||
index_name = "json_index"
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name,
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']['b']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '1',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '2',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '3',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a'][0]['b']"})
|
||
index_params.add_index(field_name=json_field_name, index_name=index_name + '4',
|
||
index_type=supported_varchar_scalar_index,
|
||
params={"json_cast_type": supported_json_cast_type,
|
||
"json_path": f"{json_field_name}['a'][0]"})
|
||
# 5. create json index
|
||
self.create_index(client, collection_name, index_params)
|
||
# 6. release collection
|
||
self.release_collection(client, collection_name)
|
||
# 7. create json index again
|
||
self.create_index(client, collection_name, index_params)
|
||
# 8. load collection
|
||
self.load_collection(client, collection_name)
|
||
# 9. search with filter on json without output_fields
|
||
vectors_to_search = [vectors[0]]
|
||
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
||
insert_ids = [default_nb / 2]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
expr = f"{json_field_name} == {default_nb + 5}"
|
||
insert_ids = [default_nb + 5]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 1})
|
||
expr = f"{json_field_name}['a'][0] == 1"
|
||
insert_ids = [i for i in range(default_nb + 20, default_nb + 30)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
expr = f"{json_field_name}['a'][0]['b'] == 1"
|
||
insert_ids = [i for i in range(default_nb + 30, default_nb + 40)]
|
||
self.search(client, collection_name, vectors_to_search,
|
||
filter=expr,
|
||
consistency_level="Strong",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"ids": insert_ids,
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit})
|
||
|
||
|
||
class TestMilvusClientSearchDecayRerank(TestMilvusClientV2Base):
|
||
""" Test case of search interface """
|
||
|
||
@pytest.fixture(scope="function", params=[False, True])
|
||
def auto_id(self, request):
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
||
def metric_type(self, request):
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function", params=[DataType.INT8, DataType.INT16, DataType.INT32,
|
||
DataType.FLOAT, DataType.DOUBLE])
|
||
def rerank_fields(self, request):
|
||
tags = request.config.getoption("--tags", default=['L0', 'L1', 'L2'], skip=True)
|
||
if CaseLabel.L2 not in tags:
|
||
if request.param not in [DataType.INT8, DataType.FLOAT]:
|
||
pytest.skip(f"skip rerank field type {request.param}")
|
||
yield request.param
|
||
|
||
@pytest.fixture(scope="function", params=["STL_SORT", "INVERTED", "AUTOINDEX", ""])
|
||
def scalar_index(self, request):
|
||
tags = request.config.getoption("--tags", default=['L0', 'L1', 'L2'], skip=True)
|
||
if CaseLabel.L2 not in tags:
|
||
if request.param not in ["INVERTED", ""]:
|
||
pytest.skip(f"skip scalar index type {request.param}")
|
||
yield request.param
|
||
|
||
"""
|
||
******************************************************************
|
||
# The following are valid base cases
|
||
******************************************************************
|
||
"""
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("function", ["gauss", "linear", "exp"])
|
||
@pytest.mark.parametrize("scale", [100, 10000, 100.0])
|
||
@pytest.mark.parametrize("origin", [-1, 0, 200, 2000])
|
||
@pytest.mark.parametrize("offset", [0, 10, 1.2, 2000])
|
||
@pytest.mark.parametrize("decay", [0.5])
|
||
@pytest.mark.parametrize("is_flush", [True, False])
|
||
def test_milvus_client_search_with_reranker(self, function, scale, origin, offset, decay, is_flush):
|
||
"""
|
||
target: test search with reranker
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.INT64, nullable=False)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: i} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
if is_flush:
|
||
self.flush(client, collection_name)
|
||
# 3. search
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": function,
|
||
"origin": origin,
|
||
"offset": offset,
|
||
"decay": decay,
|
||
"scale": scale
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
# search without output_fields
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
# search with output_fields
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name],
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
# range search
|
||
params = {"radius": 0, "range_filter": 1}
|
||
self.search(client, collection_name, vectors_to_search, search_params=params, ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name],
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("function", ["gauss", "linear", "exp"])
|
||
def test_milvus_client_search_with_reranker_default_offset_decay(self, function):
|
||
"""
|
||
target: test search with reranker with default offset(0) and decay(0.5) value
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.INT64, nullable=False)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: i} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": function,
|
||
"origin": 0,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=9 and {ct.default_reranker_field_name}<=4",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=0 and {ct.default_reranker_field_name}<=10",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_reranker_default_value_field(self):
|
||
"""
|
||
target: test search with reranker with default offset(0) and decay(0.5) value
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.INT64, nullable=False, default_value=0)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: i} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=9 and {ct.default_reranker_field_name}<=4",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||
@pytest.mark.parametrize("is_clustering", [True, False])
|
||
def test_milvus_client_search_with_reranker_partition_key_field(self, enable_dynamic_field, is_clustering):
|
||
"""
|
||
target: test search with reranker with partition key field
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.INT64, nullable=False, is_partition_key=True,
|
||
is_clustering_key=is_clustering)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: i} for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. compact
|
||
self.compact(client, collection_name, is_clustering=is_clustering)
|
||
# 4. search
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=9 and {ct.default_reranker_field_name}<=4",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=0 and {ct.default_reranker_field_name}<=10",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_milvus_client_search_with_reranker_all_supported_datatype_field(self, rerank_fields):
|
||
"""
|
||
target: test search with reranker with partition key field
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, rerank_fields)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
int8_bound = np.iinfo(np.int8).max + 1
|
||
rows = []
|
||
for i in range(default_nb):
|
||
if rerank_fields == DataType.INT8:
|
||
value = i % int8_bound
|
||
elif rerank_fields == DataType.INT16:
|
||
value = np.int16(i)
|
||
elif rerank_fields == DataType.INT32:
|
||
value = np.int32(i)
|
||
elif rerank_fields == DataType.FLOAT:
|
||
value = np.float32(i)
|
||
elif rerank_fields == DataType.DOUBLE:
|
||
value = np.float64(i)
|
||
single_row = {default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: value}
|
||
rows.append(single_row)
|
||
self.insert(client, collection_name, rows)
|
||
# 3. compact
|
||
self.compact(client, collection_name)
|
||
# 4. flush
|
||
self.flush(client, collection_name)
|
||
# 5. search
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=9 and {ct.default_reranker_field_name}<=4",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=0 and {ct.default_reranker_field_name}<=10",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("mmap", [True, False])
|
||
def test_milvus_client_search_with_reranker_scalar_index(self, rerank_fields, scalar_index, mmap):
|
||
"""
|
||
Test search functionality with reranker using scalar index in Milvus client.
|
||
|
||
This test verifies the search operation works correctly when using a reranker with different scalar index types.
|
||
It covers various scenarios including:
|
||
- Different data types for rerank fields (INT8, INT16, INT32, FLOAT, DOUBLE)
|
||
- Different index types (STL_SORT, INVERTED, AUTOINDEX, "")
|
||
- Memory-mapped and non-memory-mapped configurations
|
||
|
||
The test performs the following steps:
|
||
1. Creates a collection with specified schema and index parameters
|
||
2. Inserts test data with appropriate data types
|
||
3. Builds indexes on both vector and scalar fields
|
||
4. Executes search operations with reranking function
|
||
5. Validates search results with different filter conditions
|
||
6. Cleans up by releasing collection and dropping indexes
|
||
|
||
Note: This is an L1 (basic functionality) test case.
|
||
target: test search with reranker with scalar index
|
||
method: create connection, collection, insert and search
|
||
expected: search successfully
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True,
|
||
auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, rerank_fields, mmap_enabled=mmap)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, index_type='HNSW', metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert
|
||
rng = np.random.default_rng(seed=19530)
|
||
int8_bound = np.iinfo(np.int8).max + 1
|
||
rows = []
|
||
for i in range(default_nb):
|
||
if rerank_fields == DataType.INT8:
|
||
value = i % int8_bound
|
||
elif rerank_fields == DataType.INT16:
|
||
value = np.int16(i)
|
||
elif rerank_fields == DataType.INT32:
|
||
value = np.int32(i)
|
||
elif rerank_fields == DataType.INT64:
|
||
value = i
|
||
elif rerank_fields == DataType.FLOAT:
|
||
value = np.float32(i)
|
||
elif rerank_fields == DataType.DOUBLE:
|
||
value = np.float64(i)
|
||
single_row = {default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: value}
|
||
rows.append(single_row)
|
||
self.insert(client, collection_name, rows)
|
||
# flush
|
||
self.flush(client, collection_name)
|
||
# 2. prepare index params
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=ct.default_reranker_field_name, index_type=scalar_index, params={})
|
||
# 3. create index
|
||
self.create_index(client, collection_name, index_params)
|
||
# 4. compact
|
||
self.compact(client, collection_name)
|
||
self.wait_for_index_ready(client, collection_name, index_name=ct.default_reranker_field_name)
|
||
self.wait_for_index_ready(client, collection_name, index_name=default_vector_field_name)
|
||
|
||
# 5. search
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=9 and {ct.default_reranker_field_name}<=4",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=0 and {ct.default_reranker_field_name}<=10",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
# 5. release collection
|
||
self.release_collection(client, collection_name)
|
||
self.drop_index(client, collection_name, ct.default_reranker_field_name)
|
||
self.drop_index(client, collection_name, default_vector_field_name)
|
||
# 6. create index
|
||
params = {"metric_type": "COSINE"}
|
||
if scalar_index != "STL_SORT":
|
||
params['mmap.enabled'] = mmap
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(field_name=ct.default_reranker_field_name, index_type=scalar_index, params=params)
|
||
index_params.add_index(field_name=default_vector_field_name, index_type='HNSW', params=params)
|
||
self.create_index(client, collection_name, index_params)
|
||
self.wait_for_index_ready(client, collection_name, index_name=ct.default_reranker_field_name)
|
||
self.wait_for_index_ready(client, collection_name, index_name=default_vector_field_name)
|
||
self.load_collection(client, collection_name)
|
||
# vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=9 and {ct.default_reranker_field_name}<=4",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": 0}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
filter=f"{ct.default_reranker_field_name}>=0 and {ct.default_reranker_field_name}<=10",
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@staticmethod
|
||
def _gauss_decay(origin, scale, decay, offset, distance):
|
||
adj = max(0, abs(distance - origin) - offset)
|
||
sigma_sq = scale ** 2 / math.log(decay)
|
||
return math.exp(adj ** 2 / sigma_sq)
|
||
|
||
@staticmethod
|
||
def _exp_decay(origin, scale, decay, offset, distance):
|
||
adj = max(0, abs(distance - origin) - offset)
|
||
lam = math.log(decay) / scale
|
||
return math.exp(lam * adj)
|
||
|
||
@staticmethod
|
||
def _linear_decay(origin, scale, decay, offset, distance):
|
||
adj = max(0, abs(distance - origin) - offset)
|
||
slope = (1 - decay) / scale
|
||
return max(decay, 1 - slope * adj)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("function", ["gauss", "linear", "exp"])
|
||
@pytest.mark.parametrize("decay", [0.1, 0.5, 0.9])
|
||
def test_milvus_client_search_reranker_decay_score_ordering(self, function, decay):
|
||
"""
|
||
target: verify decay reranker produces scores ordered by distance from origin
|
||
method: insert rows with identical vectors and varying reranker_field values,
|
||
search with decay reranker, check score ordering matches distance ordering
|
||
expected: results ordered by distance from origin (closer = higher score), all scores > 0
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=False)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows with identical vectors but different reranker_field values
|
||
fixed_vector = [0.5] * dim
|
||
field_values = [0, 10, 50, 100, 200, 500]
|
||
rows = [{default_primary_key_field_name: i,
|
||
default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(field_values[i])}
|
||
for i in range(len(field_values))]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": function,
|
||
"origin": 0,
|
||
"offset": 0,
|
||
"decay": decay,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = [fixed_vector]
|
||
res = self.search(client, collection_name, vectors_to_search, limit=len(field_values),
|
||
ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name])[0]
|
||
# 4. verify score ordering: closer to origin should have higher score
|
||
results = res[0]
|
||
assert len(results) == len(field_values), \
|
||
f"Expected {len(field_values)} results, got {len(results)}"
|
||
scores = [r["distance"] for r in results]
|
||
reranker_values = [r[ct.default_reranker_field_name] for r in results]
|
||
log.info(f"function={function}, decay={decay}, scores={scores}, reranker_values={reranker_values}")
|
||
# All scores must be positive
|
||
for i, score in enumerate(scores):
|
||
assert score > 0, f"Score at position {i} should be > 0, got {score}"
|
||
# Scores must be in descending order (higher score first)
|
||
for i in range(len(scores) - 1):
|
||
assert scores[i] >= scores[i + 1], \
|
||
f"Scores not in descending order: scores[{i}]={scores[i]} < scores[{i + 1}]={scores[i + 1]}"
|
||
# Distance from origin must be in ascending order (closer first)
|
||
distances = [abs(v) for v in reranker_values]
|
||
for i in range(len(distances) - 1):
|
||
assert distances[i] <= distances[i + 1], \
|
||
f"Distances not in ascending order: dist[{i}]={distances[i]} > dist[{i + 1}]={distances[i + 1]}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("function", ["gauss", "linear", "exp"])
|
||
def test_milvus_client_search_reranker_decay_score_ratio(self, function):
|
||
"""
|
||
target: verify decay reranker produces mathematically correct score ratios
|
||
method: insert rows with identical vectors at known distances, search with decay reranker,
|
||
compare actual score ratios against Python-computed expected ratios
|
||
expected: score ratios match expected decay function ratios within tolerance
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=False)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows with identical vectors at specific distances
|
||
fixed_vector = [0.5] * dim
|
||
origin = 0
|
||
scale = 100
|
||
decay_param = 0.5
|
||
offset = 0
|
||
field_values = [0, 25, 50, 75, 100]
|
||
rows = [{default_primary_key_field_name: i,
|
||
default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(field_values[i])}
|
||
for i in range(len(field_values))]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": function,
|
||
"origin": origin,
|
||
"offset": offset,
|
||
"decay": decay_param,
|
||
"scale": scale
|
||
}
|
||
)
|
||
vectors_to_search = [fixed_vector]
|
||
res = self.search(client, collection_name, vectors_to_search, limit=len(field_values),
|
||
ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name])[0]
|
||
# 4. build mapping from reranker_field value to actual score
|
||
results = res[0]
|
||
assert len(results) == len(field_values), \
|
||
f"Expected {len(field_values)} results, got {len(results)}"
|
||
actual_scores = {}
|
||
for r in results:
|
||
actual_scores[r[ct.default_reranker_field_name]] = r["distance"]
|
||
# 5. compute expected decay scores using Python formulas
|
||
decay_funcs = {"gauss": self._gauss_decay, "linear": self._linear_decay, "exp": self._exp_decay}
|
||
decay_fn = decay_funcs[function]
|
||
expected_scores = {}
|
||
for v in field_values:
|
||
expected_scores[v] = decay_fn(origin, scale, decay_param, offset, v)
|
||
log.info(f"function={function}, actual_scores={actual_scores}, expected_scores={expected_scores}")
|
||
# 6. verify score ratios match expected ratios
|
||
# Use distance=0 as reference point (decay score = 1.0, so actual score = base_score)
|
||
ref_value = 0
|
||
ref_actual = actual_scores[ref_value]
|
||
ref_expected = expected_scores[ref_value]
|
||
epsilon = 0.01
|
||
for v in field_values:
|
||
if v == ref_value:
|
||
continue
|
||
actual_ratio = actual_scores[v] / ref_actual
|
||
expected_ratio = expected_scores[v] / ref_expected
|
||
log.info(f" distance={v}: actual_ratio={actual_ratio:.6f}, expected_ratio={expected_ratio:.6f}")
|
||
assert abs(actual_ratio - expected_ratio) < epsilon, \
|
||
f"Score ratio mismatch for distance={v}: actual_ratio={actual_ratio:.6f}, " \
|
||
f"expected_ratio={expected_ratio:.6f}, diff={abs(actual_ratio - expected_ratio):.6f}"
|
||
# 7. additionally verify that score at distance=scale equals decay * score at origin
|
||
if scale in actual_scores:
|
||
actual_decay_at_scale = actual_scores[scale] / actual_scores[ref_value]
|
||
assert abs(actual_decay_at_scale - decay_param) < epsilon, \
|
||
f"At distance=scale, expected decay≈{decay_param}, got {actual_decay_at_scale:.6f}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_reranker_decay_offset_effect(self):
|
||
"""
|
||
target: verify decay reranker offset parameter works correctly
|
||
method: insert rows with identical vectors at various distances, search with decay reranker
|
||
using offset=10, verify items within offset zone have equal scores and items beyond
|
||
have decreasing scores
|
||
expected: items at distance <= offset have same score (decay=1.0), items beyond offset have
|
||
strictly decreasing scores
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=False)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows with identical vectors
|
||
fixed_vector = [0.5] * dim
|
||
field_values = [0, 5, 10, 15, 50, 100]
|
||
rows = [{default_primary_key_field_name: i,
|
||
default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(field_values[i])}
|
||
for i in range(len(field_values))]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker using offset=10
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"offset": 10,
|
||
"decay": 0.5,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = [fixed_vector]
|
||
res = self.search(client, collection_name, vectors_to_search, limit=len(field_values),
|
||
ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name])[0]
|
||
# 4. build mapping from reranker_field value to actual score
|
||
results = res[0]
|
||
assert len(results) == len(field_values), \
|
||
f"Expected {len(field_values)} results, got {len(results)}"
|
||
score_map = {}
|
||
for r in results:
|
||
score_map[r[ct.default_reranker_field_name]] = r["distance"]
|
||
log.info(f"offset_test score_map={score_map}")
|
||
# 5. verify items within offset zone (distance <= 10) have the same score
|
||
within_offset = [0, 5, 10]
|
||
epsilon = 1e-4
|
||
ref_score = score_map[within_offset[0]]
|
||
for v in within_offset:
|
||
assert abs(score_map[v] - ref_score) < epsilon, \
|
||
f"Items within offset should have equal scores: score({v})={score_map[v]}, " \
|
||
f"score({within_offset[0]})={ref_score}"
|
||
# 6. verify items beyond offset have strictly decreasing scores
|
||
beyond_offset = [15, 50, 100]
|
||
# Items within offset should have higher score than items beyond offset
|
||
for v in beyond_offset:
|
||
assert score_map[v] < ref_score, \
|
||
f"Score beyond offset should be < offset zone score: score({v})={score_map[v]}, ref={ref_score}"
|
||
# Items beyond offset should be in strictly decreasing order by distance
|
||
for i in range(len(beyond_offset) - 1):
|
||
assert score_map[beyond_offset[i]] > score_map[beyond_offset[i + 1]], \
|
||
f"Scores beyond offset not decreasing: score({beyond_offset[i]})={score_map[beyond_offset[i]]} " \
|
||
f"<= score({beyond_offset[i + 1]})={score_map[beyond_offset[i + 1]]}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("function", ["gauss", "linear", "exp"])
|
||
def test_milvus_client_search_reranker_decay_nullable_field(self, function):
|
||
"""
|
||
target: verify decay reranker works with nullable input field
|
||
method: create collection with nullable reranker field, insert rows with some null values,
|
||
search with decay reranker
|
||
expected: search successfully, results include both null and non-null rows
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection with nullable reranker field
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows: some with values, some with None
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = []
|
||
for i in range(default_nb):
|
||
row = {default_primary_key_field_name: i,
|
||
default_vector_field_name: list(rng.random((1, dim))[0])}
|
||
if i % 5 == 0:
|
||
row[ct.default_reranker_field_name] = None
|
||
else:
|
||
row[ct.default_reranker_field_name] = np.float32(i)
|
||
rows.append(row)
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": function,
|
||
"origin": 0,
|
||
"offset": 0,
|
||
"decay": 0.5,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
# search with output_fields
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name],
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_reranker_decay_nullable_field_score_ordering(self):
|
||
"""
|
||
target: verify decay reranker produces correct score ordering with nullable field,
|
||
null values should be ranked last
|
||
method: insert rows with identical vectors, some with null reranker_field values,
|
||
search with decay reranker, verify non-null rows are ranked before null rows
|
||
expected: non-null rows ranked by distance from origin, null rows ranked last
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection with nullable reranker field
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows with identical vectors: mix of non-null and null values
|
||
fixed_vector = [0.5] * dim
|
||
# ids 0-3: non-null values at known distances from origin
|
||
# ids 4-5: null values
|
||
rows = [
|
||
{default_primary_key_field_name: 0, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(0)},
|
||
{default_primary_key_field_name: 1, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(50)},
|
||
{default_primary_key_field_name: 2, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(100)},
|
||
{default_primary_key_field_name: 3, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(200)},
|
||
{default_primary_key_field_name: 4, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: None},
|
||
{default_primary_key_field_name: 5, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: None},
|
||
]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"offset": 0,
|
||
"decay": 0.5,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = [fixed_vector]
|
||
res = self.search(client, collection_name, vectors_to_search, limit=len(rows),
|
||
ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name])[0]
|
||
results = res[0]
|
||
log.info(f"nullable decay results: {results}")
|
||
# 4. verify: non-null rows should have positive scores and be ordered by distance
|
||
non_null_results = [r for r in results if r.get(ct.default_reranker_field_name) is not None]
|
||
null_results = [r for r in results if r.get(ct.default_reranker_field_name) is None]
|
||
# non-null scores should be positive and in descending order
|
||
non_null_scores = [r["distance"] for r in non_null_results]
|
||
for i, score in enumerate(non_null_scores):
|
||
assert score > 0, f"Non-null score at position {i} should be > 0, got {score}"
|
||
for i in range(len(non_null_scores) - 1):
|
||
assert non_null_scores[i] >= non_null_scores[i + 1], \
|
||
f"Non-null scores not in descending order: {non_null_scores[i]} < {non_null_scores[i + 1]}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_reranker_decay_nullable_field_null_score_last(self):
|
||
"""
|
||
target: verify decay reranker with nullable input field produces null scores
|
||
for null-field rows and always ranks them last
|
||
method: insert rows with identical vectors, some with null reranker_field values,
|
||
search with decay reranker, verify null-score rows appear at the end
|
||
expected: non-null rows ranked first with positive descending scores,
|
||
null rows ranked last with null distance
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection with nullable reranker field
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows: ids 0-3 non-null, ids 4-6 null
|
||
fixed_vector = [0.5] * dim
|
||
rows = [
|
||
{default_primary_key_field_name: 0, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(0)},
|
||
{default_primary_key_field_name: 1, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(50)},
|
||
{default_primary_key_field_name: 2, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(100)},
|
||
{default_primary_key_field_name: 3, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: np.float32(200)},
|
||
{default_primary_key_field_name: 4, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: None},
|
||
{default_primary_key_field_name: 5, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: None},
|
||
{default_primary_key_field_name: 6, default_vector_field_name: fixed_vector,
|
||
ct.default_reranker_field_name: None},
|
||
]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"offset": 0,
|
||
"decay": 0.5,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = [fixed_vector]
|
||
res = self.search(client, collection_name, vectors_to_search, limit=len(rows),
|
||
ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name])[0]
|
||
results = res[0]
|
||
log.info(f"nullable decay null-score-last results: {results}")
|
||
# 4. verify: null-field rows should be ranked last with null distance
|
||
n_total = len(results)
|
||
n_null = 3 # ids 4, 5, 6
|
||
n_non_null = n_total - n_null
|
||
# first n_non_null results should have non-null scores, positive and descending
|
||
for i in range(n_non_null):
|
||
score = results[i]["distance"]
|
||
assert score is not None, f"Expected non-null score at position {i}, got None"
|
||
assert score > 0, f"Non-null score at position {i} should be > 0, got {score}"
|
||
field_val = results[i].get(ct.default_reranker_field_name)
|
||
assert field_val is not None, f"Expected non-null field at position {i}"
|
||
non_null_scores = [results[i]["distance"] for i in range(n_non_null)]
|
||
for i in range(len(non_null_scores) - 1):
|
||
assert non_null_scores[i] >= non_null_scores[i + 1], \
|
||
f"Non-null scores not in descending order at {i}: {non_null_scores[i]} < {non_null_scores[i + 1]}"
|
||
# last n_null results should have null field values and null distance
|
||
for i in range(n_non_null, n_total):
|
||
field_val = results[i].get(ct.default_reranker_field_name)
|
||
assert field_val is None, \
|
||
f"Expected null field at position {i}, got {field_val}"
|
||
score = results[i]["distance"]
|
||
assert score is None or score == 0, \
|
||
f"Expected null/zero score for null-field row at position {i}, got {score}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L2)
|
||
def test_milvus_client_search_reranker_decay_nullable_all_types(self, rerank_fields):
|
||
"""
|
||
target: verify decay reranker works with nullable fields of all supported numeric types
|
||
method: create collection with nullable reranker field of various types,
|
||
insert rows with some null values, search with decay reranker
|
||
expected: search successfully with all supported numeric types
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection with nullable reranker field
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, rerank_fields, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows with some null values
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = []
|
||
for i in range(default_nb):
|
||
row = {default_primary_key_field_name: i,
|
||
default_vector_field_name: list(rng.random((1, dim))[0])}
|
||
if i % 5 == 0:
|
||
row[ct.default_reranker_field_name] = None
|
||
else:
|
||
if rerank_fields == DataType.INT8:
|
||
row[ct.default_reranker_field_name] = np.int8(i % 127)
|
||
elif rerank_fields == DataType.INT16:
|
||
row[ct.default_reranker_field_name] = np.int16(i)
|
||
elif rerank_fields == DataType.INT32:
|
||
row[ct.default_reranker_field_name] = np.int32(i)
|
||
elif rerank_fields == DataType.FLOAT:
|
||
row[ct.default_reranker_field_name] = np.float32(i)
|
||
elif rerank_fields == DataType.DOUBLE:
|
||
row[ct.default_reranker_field_name] = np.float64(i)
|
||
rows.append(row)
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"offset": 0,
|
||
"decay": 0.5,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name],
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_reranker_decay_nullable_all_null(self):
|
||
"""
|
||
target: verify decay reranker handles the case where all reranker field values are null
|
||
method: create collection with nullable reranker field, insert rows with all null values,
|
||
search with decay reranker
|
||
expected: search successfully, all results have null reranker field
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection with nullable reranker field
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field(ct.default_reranker_field_name, DataType.FLOAT, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
||
# 2. insert rows with all null reranker field values
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: i,
|
||
default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
ct.default_reranker_field_name: None}
|
||
for i in range(default_nb)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with decay reranker
|
||
my_rerank_fn = Function(
|
||
name="my_reranker",
|
||
input_field_names=[ct.default_reranker_field_name],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"offset": 0,
|
||
"decay": 0.5,
|
||
"scale": 100
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
res = self.search(client, collection_name, vectors_to_search, ranker=my_rerank_fn,
|
||
output_fields=[ct.default_reranker_field_name],
|
||
check_task=CheckTasks.check_search_results,
|
||
check_items={"enable_milvus_client_api": True,
|
||
"nq": len(vectors_to_search),
|
||
"pk_name": default_primary_key_field_name,
|
||
"limit": default_limit}
|
||
)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_decay_rerank_l2_metric_no_norm_score(self):
|
||
"""
|
||
target: verify decay reranker with L2 metric and norm_score=false ranks
|
||
results by "smaller distance = better match" semantics. The
|
||
decay factor is then multiplied as a [0, 1] weight.
|
||
method: 1. create collection with FLAT index + L2 metric + INT64 ts field
|
||
2. insert rows with vectors at progressively larger L2 distances
|
||
from a fixed query, all with ts=origin so decay factor=1.0
|
||
3. search with decay reranker, no norm_score
|
||
expected: row with smallest L2 distance ranks first.
|
||
Without the fix, raw L2 × decay is sorted DESC, putting the WORST L2
|
||
match first (i.e., the entire ordering is reversed).
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 8
|
||
# 1. create collection with L2 metric + FLAT index for exact distances
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64,
|
||
is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field("ts", DataType.INT64, nullable=False)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, index_type="FLAT",
|
||
metric_type="L2")
|
||
self.create_collection(client, collection_name, dimension=dim,
|
||
schema=schema, index_params=index_params)
|
||
# 2. insert deterministic data:
|
||
# row i has vector [0.1*i] * dim
|
||
# all rows have ts = 1000 (decay origin) → decay factor = 1.0
|
||
nrows = 5
|
||
rows = [{default_primary_key_field_name: i,
|
||
default_vector_field_name: [0.1 * i] * dim,
|
||
"ts": 1000}
|
||
for i in range(nrows)]
|
||
self.insert(client, collection_name, rows)
|
||
self.flush(client, collection_name)
|
||
# 3. search with query vector matching row 0 exactly → row 0 has L2=0
|
||
query_vector = [[0.0] * dim]
|
||
decay_fn = Function(
|
||
name="decay_l2",
|
||
input_field_names=["ts"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 1000,
|
||
"scale": 100,
|
||
"decay": 0.5,
|
||
# norm_score not set → defaults to false
|
||
}
|
||
)
|
||
res = self.search(client, collection_name, query_vector,
|
||
limit=nrows, ranker=decay_fn,
|
||
output_fields=[default_primary_key_field_name])[0]
|
||
results = res[0]
|
||
ids = [r[default_primary_key_field_name] for r in results]
|
||
scores = [r["distance"] for r in results]
|
||
log.info(f"decay+L2 no_norm result ids={ids} scores={scores}")
|
||
assert len(results) == nrows, \
|
||
f"expected {nrows} results, got {len(results)}: ids={ids}"
|
||
# Row 0 has L2=0 (perfect match) and decay=1.0 → must rank first.
|
||
# Without the fix the ordering is exactly reversed (row 4 first).
|
||
assert ids[0] == 0, \
|
||
f"row 0 (perfect L2 match) must rank first; got order {ids} with scores {scores}"
|
||
# Scores must be non-increasing — decay rerank always produces
|
||
# "larger = better" output regardless of metric direction.
|
||
for i in range(len(scores) - 1):
|
||
assert scores[i] >= scores[i + 1], \
|
||
f"decay scores must be DESC; got scores[{i}]={scores[i]} < scores[{i + 1}]={scores[i + 1]}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_decay_rerank_timestamptz_field_rejected(self):
|
||
"""
|
||
target: verify decay reranker rejects TIMESTAMPTZ input fields with a
|
||
clear runtime error. Legacy decay code listed Timestamptz in
|
||
its type-dispatch switch but the converter and GetNumericValue
|
||
paths never supported it end-to-end, so this PR preserves
|
||
legacy actual behavior — Timestamptz remains unsupported.
|
||
method: create collection with a TIMESTAMPTZ field, attempt search
|
||
with decay reranker using that field as input
|
||
expected: error reporting Timestamptz is not a numeric decay input.
|
||
Note: chain.FromSearchResultData supports Timestamptz conversion, so
|
||
the user-visible error now comes from decay reranker input validation.
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dim = 5
|
||
# 1. create collection with a TIMESTAMPTZ field
|
||
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
||
schema.add_field(default_primary_key_field_name, DataType.INT64,
|
||
is_primary=True, auto_id=False)
|
||
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
||
schema.add_field("event_time", DataType.TIMESTAMPTZ, nullable=True)
|
||
index_params = self.prepare_index_params(client)[0]
|
||
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
||
self.create_collection(client, collection_name, dimension=dim,
|
||
schema=schema, index_params=index_params)
|
||
# 2. insert data; reranker validation rejects event_time during search.
|
||
rng = np.random.default_rng(seed=19530)
|
||
rows = [{default_primary_key_field_name: i,
|
||
default_vector_field_name: list(rng.random((1, dim))[0]),
|
||
"event_time": "2025-01-01T00:00:00"}
|
||
for i in range(10)]
|
||
self.insert(client, collection_name, rows)
|
||
# 3. search with decay reranker using the TIMESTAMPTZ field as input
|
||
decay_fn = Function(
|
||
name="decay_tstz",
|
||
input_field_names=["event_time"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "decay",
|
||
"function": "gauss",
|
||
"origin": 0,
|
||
"scale": 100,
|
||
"decay": 0.5,
|
||
}
|
||
)
|
||
vectors_to_search = rng.random((1, dim))
|
||
error = {ct.err_code: 1100,
|
||
ct.err_msg: "decay input field event_time must be numeric, got Timestamptz"}
|
||
self.search(client, collection_name, vectors_to_search, ranker=decay_fn,
|
||
check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
|
||
class TestMilvusClientSearchModelRerank(TestMilvusClientV2Base):
|
||
|
||
@pytest.fixture(scope="function")
|
||
def setup_collection(self):
|
||
"""Setup collection for model rerank testing"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dense_metric_type = "COSINE"
|
||
|
||
# 1. create schema with embedding and bm25 functions
|
||
schema = client.create_schema(enable_dynamic_field=False, auto_id=True)
|
||
schema.add_field("id", DataType.INT64, is_primary=True)
|
||
schema.add_field("doc_id", DataType.VARCHAR, max_length=100)
|
||
schema.add_field("document", DataType.VARCHAR, max_length=10000, enable_analyzer=True)
|
||
schema.add_field("sparse", DataType.SPARSE_FLOAT_VECTOR)
|
||
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=768)
|
||
schema.add_field("bm25", DataType.SPARSE_FLOAT_VECTOR)
|
||
|
||
# add bm25 function
|
||
bm25_function = Function(
|
||
name="bm25",
|
||
input_field_names=["document"],
|
||
output_field_names="bm25",
|
||
function_type=FunctionType.BM25,
|
||
)
|
||
schema.add_function(bm25_function)
|
||
|
||
# 2. prepare index params
|
||
index_params = client.prepare_index_params()
|
||
index_params.add_index(field_name="dense", index_type="FLAT", metric_type=dense_metric_type)
|
||
index_params.add_index(
|
||
field_name="sparse",
|
||
index_type="SPARSE_INVERTED_INDEX",
|
||
metric_type="IP",
|
||
)
|
||
index_params.add_index(
|
||
field_name="bm25",
|
||
index_type="SPARSE_INVERTED_INDEX",
|
||
metric_type="BM25",
|
||
params={"bm25_k1": 1.2, "bm25_b": 0.75},
|
||
)
|
||
|
||
# 3. create collection
|
||
client.create_collection(
|
||
collection_name,
|
||
schema=schema,
|
||
index_params=index_params,
|
||
consistency_level="Strong",
|
||
)
|
||
|
||
# 4. insert data
|
||
rows = []
|
||
data_size = 3000
|
||
for i in range(data_size):
|
||
rows.append({
|
||
"doc_id": str(i),
|
||
"document": fake.text(),
|
||
"sparse": {random.randint(1, 10000): random.random() for _ in range(100)},
|
||
"dense": [random.random() for _ in range(768)]
|
||
})
|
||
client.insert(collection_name, rows)
|
||
|
||
return collection_name
|
||
|
||
def merge_and_dedup_hybrid_searchresults(self, result_a, result_b):
|
||
final_result = []
|
||
for i in range(len(result_a)):
|
||
tmp_result = []
|
||
tmp_ids = []
|
||
for j in range(len(result_a[i])):
|
||
tmp_result.append(result_a[i][j])
|
||
tmp_ids.append(result_a[i][j]["id"])
|
||
for j in range(len(result_b[i])):
|
||
if result_b[i][j]["id"] not in tmp_ids:
|
||
tmp_result.append(result_b[i][j])
|
||
final_result.append(tmp_result)
|
||
return final_result
|
||
|
||
def get_tei_rerank_results(self, query_texts, document_texts, tei_reranker_endpoint, enable_truncate=False):
|
||
url = f"{tei_reranker_endpoint}/rerank"
|
||
|
||
payload = json.dumps({
|
||
"query": query_texts,
|
||
"texts": document_texts
|
||
})
|
||
if enable_truncate:
|
||
payload = json.dumps({
|
||
"query": query_texts,
|
||
"texts": document_texts,
|
||
"truncate": True,
|
||
"truncation_direction": "Right"
|
||
})
|
||
headers = {
|
||
'Content-Type': 'application/json'
|
||
}
|
||
|
||
response = requests.request("POST", url, headers=headers, data=payload)
|
||
|
||
res = response.json()
|
||
reranked_results = []
|
||
for r in res:
|
||
tmp = {
|
||
"text": document_texts[r["index"]],
|
||
"score": r["score"]
|
||
}
|
||
reranked_results.append(tmp)
|
||
|
||
return reranked_results
|
||
|
||
def get_vllm_rerank_results(self, query_texts, document_texts, vllm_reranker_endpoint, enable_truncate=False):
|
||
url = f"{vllm_reranker_endpoint}/v2/rerank"
|
||
|
||
payload = json.dumps({
|
||
"query": query_texts,
|
||
"documents": document_texts
|
||
})
|
||
if enable_truncate:
|
||
payload = json.dumps({
|
||
"query": query_texts,
|
||
"documents": document_texts,
|
||
"truncate_prompt_tokens": 512
|
||
})
|
||
headers = {
|
||
'Content-Type': 'application/json'
|
||
}
|
||
|
||
response = requests.request("POST", url, headers=headers, data=payload)
|
||
|
||
res = response.json()["results"]
|
||
|
||
log.debug("vllm rerank results:\n")
|
||
for r in res:
|
||
log.debug(f"r: {r}")
|
||
reranked_results = []
|
||
for r in res:
|
||
tmp = {
|
||
"text": r["document"]["text"],
|
||
"score": r["relevance_score"]
|
||
}
|
||
reranked_results.append(tmp)
|
||
|
||
return reranked_results
|
||
|
||
def get_cohere_rerank_results(self, query_texts, document_texts,
|
||
model_name="rerank-english-v3.0", max_tokens_per_doc=4096, **kwargs):
|
||
COHERE_RERANKER_ENDPOINT = "https://api.cohere.ai"
|
||
COHERE_API_KEY = os.getenv("COHERE_API_KEY")
|
||
|
||
url = f"{COHERE_RERANKER_ENDPOINT}/v2/rerank"
|
||
|
||
payload = {
|
||
"model": model_name,
|
||
"query": query_texts,
|
||
"documents": document_texts,
|
||
"top_n": len(document_texts) # Cohere v2 uses "top_n" not "top_k"
|
||
}
|
||
|
||
if max_tokens_per_doc != 4096:
|
||
payload["max_tokens_per_doc"] = max_tokens_per_doc
|
||
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Authorization': f'Bearer {COHERE_API_KEY}'
|
||
}
|
||
|
||
response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
|
||
|
||
res = response.json()["results"]
|
||
|
||
log.debug("cohere rerank results:\n")
|
||
for r in res:
|
||
log.debug(f"r: {r}")
|
||
reranked_results = []
|
||
for r in res:
|
||
tmp = {
|
||
"text": document_texts[r["index"]], # Cohere returns index, not document text
|
||
"score": r["relevance_score"]
|
||
}
|
||
reranked_results.append(tmp)
|
||
|
||
return reranked_results
|
||
|
||
def get_voyageai_rerank_results(self, query_texts, document_texts,
|
||
model_name="rerank-2", truncation=True, **kwargs):
|
||
VOYAGEAI_RERANKER_ENDPOINT = "https://api.voyageai.com"
|
||
VOYAGEAI_API_KEY = os.getenv("VOYAGEAI_API_KEY")
|
||
|
||
url = f"{VOYAGEAI_RERANKER_ENDPOINT}/v1/rerank"
|
||
|
||
payload = {
|
||
"model": model_name,
|
||
"query": query_texts,
|
||
"documents": document_texts,
|
||
"top_k": len(document_texts),
|
||
"truncation": truncation
|
||
}
|
||
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Authorization': f'Bearer {VOYAGEAI_API_KEY}'
|
||
}
|
||
|
||
response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
|
||
|
||
res = response.json()["data"] # VoyageAI uses "data" field
|
||
|
||
log.debug("voyageai rerank results:\n")
|
||
for r in res:
|
||
log.debug(f"r: {r}")
|
||
reranked_results = []
|
||
for r in res:
|
||
tmp = {
|
||
"text": document_texts[r["index"]], # VoyageAI also returns index, not document text
|
||
"score": r["relevance_score"]
|
||
}
|
||
reranked_results.append(tmp)
|
||
|
||
return reranked_results
|
||
|
||
def get_siliconflow_rerank_results(self, query_texts, document_texts,
|
||
model_name="BAAI/bge-reranker-v2-m3", max_chunks_per_doc=None, overlap_tokens=None):
|
||
SILICONFLOW_RERANKER_ENDPOINT = "https://api.siliconflow.cn"
|
||
SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY")
|
||
|
||
url = f"{SILICONFLOW_RERANKER_ENDPOINT}/v1/rerank"
|
||
|
||
payload = {
|
||
"model": model_name,
|
||
"query": query_texts,
|
||
"documents": document_texts
|
||
}
|
||
|
||
if max_chunks_per_doc is not None:
|
||
payload["max_chunks_per_doc"] = max_chunks_per_doc
|
||
if overlap_tokens is not None:
|
||
payload["overlap_tokens"] = overlap_tokens
|
||
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Authorization': f'Bearer {SILICONFLOW_API_KEY}'
|
||
}
|
||
|
||
response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
|
||
|
||
res = response.json()["results"]
|
||
|
||
log.debug("siliconflow rerank results:\n")
|
||
for r in res:
|
||
log.debug(f"r: {r}")
|
||
reranked_results = []
|
||
for r in res:
|
||
tmp = {
|
||
"text": document_texts[r["index"]],
|
||
"score": r["relevance_score"]
|
||
}
|
||
reranked_results.append(tmp)
|
||
|
||
return reranked_results
|
||
|
||
def display_side_by_side_comparison(self, query_text, milvus_results, gt_results, doc_to_original_mapping=None, milvus_scores=None, gt_scores=None):
|
||
"""
|
||
Display side by side comparison of Milvus rerank results and ground truth results with PK values and scores
|
||
"""
|
||
log.info(f"\n{'=' * 120}")
|
||
log.info(f"Query: {query_text}")
|
||
log.info(f"{'=' * 120}")
|
||
|
||
# Display side by side comparison
|
||
log.info(f"\n{'Milvus Rerank Results':<58} | {'Ground Truth Results':<58}")
|
||
log.info(f"{'-' * 58} | {'-' * 58}")
|
||
|
||
max_len = max(len(milvus_results), len(gt_results))
|
||
|
||
for i in range(max_len):
|
||
log.info(f"\nRank {i + 1}:")
|
||
|
||
# Milvus result
|
||
if i < len(milvus_results):
|
||
milvus_doc = milvus_results[i].replace('\n', ' ')[:35] + "..." if len(milvus_results[i]) > 35 else \
|
||
milvus_results[i].replace('\n', ' ')
|
||
# Get PK if available
|
||
milvus_pk = ""
|
||
if doc_to_original_mapping and milvus_results[i] in doc_to_original_mapping:
|
||
milvus_pk = f" [PK: {doc_to_original_mapping[milvus_results[i]]['id']}]"
|
||
# Get score if available
|
||
milvus_score = ""
|
||
if milvus_scores and i < len(milvus_scores):
|
||
milvus_score = f" [Score: {milvus_scores[i]:.8f}]"
|
||
milvus_display = f"{milvus_doc}{milvus_pk}{milvus_score}"
|
||
log.info(f"{milvus_display:<58}".ljust(58) + " | " + " " * 58)
|
||
else:
|
||
log.info(f"{'(no more results)':<58}".ljust(58) + " | " + " " * 58)
|
||
|
||
# Ground truth result
|
||
if i < len(gt_results):
|
||
gt_doc = gt_results[i].replace('\n', ' ')[:35] + "..." if len(gt_results[i]) > 35 else gt_results[
|
||
i].replace('\n', ' ')
|
||
# Get PK if available
|
||
gt_pk = ""
|
||
if doc_to_original_mapping and gt_results[i] in doc_to_original_mapping:
|
||
gt_pk = f" [PK: {doc_to_original_mapping[gt_results[i]]['id']}]"
|
||
# Get score if available
|
||
gt_score = ""
|
||
if gt_scores and i < len(gt_scores):
|
||
gt_score = f" [Score: {gt_scores[i]:.8f}]"
|
||
gt_display = f"{gt_doc}{gt_pk}{gt_score}"
|
||
log.info(f"{' ' * 58} | {gt_display:<58}")
|
||
else:
|
||
log.info(f"{' ' * 58} | {'(no more results)':<58}")
|
||
|
||
# Check if documents are the same
|
||
if (i < len(milvus_results) and i < len(gt_results) and
|
||
milvus_results[i] == gt_results[i]):
|
||
log.info(f"{'✓ Same document':<58} | {'✓ Same document':<58}")
|
||
|
||
log.info(f"{'-' * 58} | {'-' * 58}")
|
||
|
||
def compare_milvus_rerank_with_origin_rerank(self, query_texts, rerank_results, results_without_rerank,
|
||
enable_truncate=False,
|
||
provider_type=None,
|
||
**kwargs):
|
||
# result length should be the same as nq
|
||
if provider_type is None:
|
||
raise Exception("provider_type parameter is required")
|
||
|
||
assert len(results_without_rerank) == len(rerank_results)
|
||
log.debug("results_without_rerank")
|
||
for r in results_without_rerank:
|
||
log.debug(r)
|
||
log.debug("rerank_results")
|
||
for r in rerank_results:
|
||
log.debug(r)
|
||
for i in range(len(results_without_rerank)):
|
||
query_text = query_texts[i]
|
||
document_texts = [x["document"] for x in results_without_rerank[i]]
|
||
distances_without_rerank = [x["distance"] for x in results_without_rerank[i]]
|
||
|
||
# Create mapping from document to original data (including pk)
|
||
doc_to_original = {}
|
||
for original_item in results_without_rerank[i]:
|
||
doc_to_original[original_item["document"]] = original_item
|
||
|
||
actual_rerank_results = [x["document"] for x in rerank_results[i]]
|
||
distances = [x["distance"] for x in rerank_results[i]]
|
||
log.debug(f"distances: {distances}")
|
||
log.debug(f"distances_without_rerank: {distances_without_rerank}")
|
||
limit = len(actual_rerank_results)
|
||
|
||
# Call the appropriate rerank method based on provider type
|
||
if provider_type == "tei":
|
||
endpoint = kwargs.get("endpoint")
|
||
if endpoint is None:
|
||
raise Exception("endpoint parameter is required for tei provider")
|
||
raw_gt = self.get_tei_rerank_results(query_text, document_texts, endpoint,
|
||
enable_truncate=enable_truncate)[:limit]
|
||
elif provider_type == "vllm":
|
||
endpoint = kwargs.get("endpoint")
|
||
if endpoint is None:
|
||
raise Exception("endpoint parameter is required for vllm provider")
|
||
raw_gt = self.get_vllm_rerank_results(query_text, document_texts, endpoint,
|
||
enable_truncate=enable_truncate)[:limit]
|
||
elif provider_type == "cohere":
|
||
raw_gt = self.get_cohere_rerank_results(query_text, document_texts,
|
||
**kwargs)[:limit]
|
||
elif provider_type == "voyageai":
|
||
raw_gt = self.get_voyageai_rerank_results(query_text, document_texts,
|
||
**kwargs)[:limit]
|
||
elif provider_type == "siliconflow":
|
||
raw_gt = self.get_siliconflow_rerank_results(query_text, document_texts,
|
||
**kwargs)[:limit]
|
||
else:
|
||
raise Exception(f"Unsupported provider_type: {provider_type}")
|
||
|
||
# Create list of (distance, pk, document) tuples for sorting
|
||
gt_with_info = []
|
||
for doc in raw_gt:
|
||
original_item = doc_to_original.get(doc["text"])
|
||
if original_item:
|
||
# Convert score to f32 precision for consistent sorting
|
||
f32_score = float(np.float32(doc["score"]))
|
||
gt_with_info.append((f32_score, original_item["id"], doc["text"]))
|
||
|
||
# Sort by score descending first, then by pk (id) ascending when scores are equal
|
||
gt_with_info.sort(key=lambda x: (-x[0], x[1]))
|
||
|
||
# Extract the sorted documents and scores
|
||
gt = [item[2] for item in gt_with_info]
|
||
gt_scores = [item[0] for item in gt_with_info]
|
||
|
||
# Side by side comparison of documents with scores
|
||
self.display_side_by_side_comparison(query_text, actual_rerank_results, gt, doc_to_original,
|
||
milvus_scores=distances, gt_scores=gt_scores)
|
||
|
||
# Use strict comparison since scores are now normalized to f32 precision
|
||
assert gt == actual_rerank_results, "Rerank result is different from ground truth rerank result"
|
||
|
||
@pytest.mark.parametrize("ranker_model", [
|
||
pytest.param("tei", marks=pytest.mark.tags(CaseLabel.L1)),
|
||
pytest.param("vllm", marks=pytest.mark.tags(CaseLabel.L3)),
|
||
])
|
||
@pytest.mark.parametrize("enable_truncate", [False, True])
|
||
def test_milvus_client_single_vector_search_with_model_rerank(self, setup_collection, ranker_model, enable_truncate,
|
||
tei_reranker_endpoint, vllm_reranker_endpoint):
|
||
"""
|
||
target: test single vector search with model rerank using SciFact dataset
|
||
method: test dense/sparse/bm25 search with model reranker separately and compare results with origin reranker
|
||
expected: result should be the same
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
# 5. prepare search parameters for reranker
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
if enable_truncate:
|
||
# make query texts larger
|
||
query_texts = [" ".join([fake.word() for _ in range(1024)]) for _ in range(nq)]
|
||
tei_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
"truncate": enable_truncate,
|
||
"truncation_direction": "Right"
|
||
},
|
||
)
|
||
vllm_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "vllm",
|
||
"queries": query_texts,
|
||
"endpoint": vllm_reranker_endpoint,
|
||
"truncate": enable_truncate,
|
||
"truncate_prompt_tokens": 512
|
||
},
|
||
)
|
||
|
||
# 6. execute search with reranker
|
||
if ranker_model == "tei":
|
||
ranker = tei_ranker
|
||
else:
|
||
ranker = vllm_ranker
|
||
|
||
for search_type in ["dense", "sparse", "bm25"]:
|
||
log.info(f"Executing {search_type} search with model reranker")
|
||
rerank_results = []
|
||
results_without_rerank = None
|
||
if search_type == "dense":
|
||
|
||
data = [[random.random() for _ in range(768)] for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
results_without_rerank = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
|
||
elif search_type == "sparse":
|
||
data = [{random.randint(1, 10000): random.random() for _ in range(100)} for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="sparse",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
results_without_rerank = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="sparse",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
elif search_type == "bm25":
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=query_texts,
|
||
anns_field="bm25",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = client.search(
|
||
collection_name,
|
||
data=query_texts,
|
||
anns_field="bm25",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
if ranker_model == "tei":
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
enable_truncate=enable_truncate,
|
||
provider_type="tei",
|
||
endpoint=tei_reranker_endpoint)
|
||
else:
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
enable_truncate=enable_truncate,
|
||
provider_type="vllm",
|
||
endpoint=vllm_reranker_endpoint)
|
||
|
||
@pytest.mark.parametrize("ranker_model", [
|
||
pytest.param("tei", marks=pytest.mark.tags(CaseLabel.L1)),
|
||
pytest.param("vllm", marks=pytest.mark.tags(CaseLabel.L3)),
|
||
])
|
||
def test_milvus_client_hybrid_vector_search_with_model_rerank(self, setup_collection, ranker_model,
|
||
tei_reranker_endpoint, vllm_reranker_endpoint):
|
||
"""
|
||
target: test hybrid vector search with model rerank
|
||
method: test dense+sparse/dense+bm25/sparse+bm25 search with model reranker
|
||
expected: search successfully with model reranker
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
# 5. prepare search parameters for reranker
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
tei_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
vllm_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "vllm",
|
||
"queries": query_texts,
|
||
"endpoint": vllm_reranker_endpoint,
|
||
},
|
||
)
|
||
if ranker_model == "tei":
|
||
ranker = tei_ranker
|
||
else:
|
||
ranker = vllm_ranker
|
||
# 6. execute search with reranker
|
||
for search_type in ["dense+sparse", "dense+bm25", "sparse+bm25"]:
|
||
log.info(f"Executing {search_type} search with model reranker")
|
||
rerank_results = []
|
||
dense_search_param = {
|
||
"data": [[random.random() for _ in range(768)] for _ in range(nq)],
|
||
"anns_field": "dense",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
dense = AnnSearchRequest(**dense_search_param)
|
||
|
||
sparse_search_param = {
|
||
"data": [{random.randint(1, 10000): random.random() for _ in range(100)} for _ in range(nq)],
|
||
"anns_field": "sparse",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25_search_param = {
|
||
"data": query_texts,
|
||
"anns_field": "bm25",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25 = AnnSearchRequest(**bm25_search_param)
|
||
|
||
sparse = AnnSearchRequest(**sparse_search_param)
|
||
results_without_rerank = None
|
||
if search_type == "dense+sparse":
|
||
|
||
rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[dense, sparse],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, sparse_results)
|
||
elif search_type == "dense+bm25":
|
||
rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[dense, bm25],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, bm25_results)
|
||
elif search_type == "sparse+bm25":
|
||
rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[sparse, bm25],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(sparse_results, bm25_results)
|
||
if ranker_model == "tei":
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
provider_type="tei",
|
||
endpoint=tei_reranker_endpoint)
|
||
else:
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
provider_type="vllm",
|
||
endpoint=vllm_reranker_endpoint)
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("model_name", ["rerank-english-v3.0", "rerank-multilingual-v3.0"])
|
||
@pytest.mark.parametrize("max_tokens_per_doc", [4096, 2048])
|
||
def test_milvus_client_search_with_cohere_rerank_specific_params(self, setup_collection, model_name,
|
||
max_tokens_per_doc):
|
||
"""
|
||
target: test search with Cohere rerank model using specific parameters
|
||
method: test dense search with Cohere reranker using different model_name and max_tokens_per_doc values
|
||
expected: search successfully with Cohere reranker and specific parameters
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
# prepare search parameters for reranker
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
|
||
cohere_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "cohere",
|
||
"queries": query_texts,
|
||
"model_name": model_name,
|
||
"max_tokens_per_doc": max_tokens_per_doc
|
||
},
|
||
)
|
||
|
||
# execute dense search with Cohere reranker
|
||
data = [[random.random() for _ in range(768)] for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=cohere_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
|
||
results_without_rerank = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
provider_type="cohere",
|
||
model_name=model_name,
|
||
max_tokens_per_doc=max_tokens_per_doc)
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("model_name", ["rerank-2", "rerank-2-lite"])
|
||
@pytest.mark.parametrize("truncation", [True, False])
|
||
def test_milvus_client_search_with_voyageai_rerank_specific_params(self, setup_collection, model_name,
|
||
truncation):
|
||
"""
|
||
target: test search with VoyageAI rerank model using specific parameters
|
||
method: test dense search with VoyageAI reranker using different model_name and truncation values
|
||
expected: search successfully with VoyageAI reranker and specific parameters
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
# prepare search parameters for reranker
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
|
||
voyageai_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "voyageai",
|
||
"queries": query_texts,
|
||
"model_name": model_name,
|
||
"truncation": truncation
|
||
},
|
||
)
|
||
|
||
# execute dense search with VoyageAI reranker
|
||
data = [[random.random() for _ in range(768)] for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=voyageai_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
|
||
results_without_rerank = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
provider_type="voyageai",
|
||
model_name=model_name,
|
||
truncation=truncation)
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("model_name", ["BAAI/bge-reranker-v2-m3", "netease-youdao/bce-reranker-base_v1"])
|
||
@pytest.mark.parametrize("max_chunks_per_doc,overlap_tokens", [(10, 80), (20, 120)])
|
||
def test_milvus_client_search_with_siliconflow_rerank_specific_params(self, setup_collection, model_name,
|
||
max_chunks_per_doc, overlap_tokens):
|
||
"""
|
||
target: test search with SiliconFlow rerank model using specific parameters
|
||
method: test dense search with SiliconFlow reranker using different model_name, max_chunks_per_doc and overlap_tokens values
|
||
expected: search successfully with SiliconFlow reranker and specific parameters
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
# prepare search parameters for reranker
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
|
||
siliconflow_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "siliconflow",
|
||
"queries": query_texts,
|
||
"model_name": model_name,
|
||
"max_chunks_per_doc": max_chunks_per_doc,
|
||
"overlap_tokens": overlap_tokens
|
||
},
|
||
)
|
||
|
||
# execute dense search with SiliconFlow reranker
|
||
data = [[random.random() for _ in range(768)] for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=siliconflow_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
|
||
results_without_rerank = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, rerank_results, results_without_rerank,
|
||
provider_type="siliconflow",
|
||
model_name=model_name,
|
||
max_chunks_per_doc=max_chunks_per_doc,
|
||
overlap_tokens=overlap_tokens)
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("model_name", ["rerank-english-v3.0", "rerank-multilingual-v3.0"])
|
||
@pytest.mark.parametrize("max_tokens_per_doc", [4096, 2048])
|
||
def test_milvus_client_hybrid_search_with_cohere_rerank_specific_params(self, setup_collection, model_name, max_tokens_per_doc):
|
||
"""
|
||
target: test hybrid search with cohere rerank specific parameters
|
||
method: test hybrid search with different cohere model names and max_tokens_per_doc values
|
||
expected: hybrid search successfully with cohere reranker
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "cohere",
|
||
"queries": query_texts,
|
||
"model_name": model_name,
|
||
"max_tokens_per_doc": max_tokens_per_doc
|
||
},
|
||
)
|
||
|
||
# Test different hybrid search combinations
|
||
for search_type in ["dense+sparse", "dense+bm25", "sparse+bm25"]:
|
||
log.info(f"Executing {search_type} hybrid search with cohere reranker")
|
||
|
||
dense_search_param = {
|
||
"data": [[random.random() for _ in range(768)] for _ in range(nq)],
|
||
"anns_field": "dense",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
dense = AnnSearchRequest(**dense_search_param)
|
||
|
||
sparse_search_param = {
|
||
"data": [{random.randint(1, 10000): random.random() for _ in range(100)} for _ in range(nq)],
|
||
"anns_field": "sparse",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
sparse = AnnSearchRequest(**sparse_search_param)
|
||
|
||
bm25_search_param = {
|
||
"data": query_texts,
|
||
"anns_field": "bm25",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25 = AnnSearchRequest(**bm25_search_param)
|
||
|
||
if search_type == "dense+sparse":
|
||
reqs = [dense, sparse]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, sparse_results)
|
||
elif search_type == "dense+bm25":
|
||
reqs = [dense, bm25]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, bm25_results)
|
||
else: # sparse+bm25
|
||
reqs = [sparse, bm25]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(sparse_results, bm25_results)
|
||
|
||
# Compare Milvus rerank results with origin rerank results
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, hybrid_results, results_without_rerank,
|
||
provider_type="cohere",
|
||
model_name=model_name,
|
||
max_tokens_per_doc=max_tokens_per_doc)
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("model_name", ["rerank-2", "rerank-1"])
|
||
@pytest.mark.parametrize("truncation", [True, False])
|
||
def test_milvus_client_hybrid_search_with_voyageai_rerank_specific_params(self, setup_collection, model_name, truncation):
|
||
"""
|
||
target: test hybrid search with voyageai rerank specific parameters
|
||
method: test hybrid search with different voyageai model names and truncation values
|
||
expected: hybrid search successfully with voyageai reranker
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "voyageai",
|
||
"queries": query_texts,
|
||
"model_name": model_name,
|
||
"truncation": truncation
|
||
},
|
||
)
|
||
|
||
# Test different hybrid search combinations
|
||
for search_type in ["dense+sparse", "dense+bm25", "sparse+bm25"]:
|
||
log.info(f"Executing {search_type} hybrid search with voyageai reranker")
|
||
|
||
dense_search_param = {
|
||
"data": [[random.random() for _ in range(768)] for _ in range(nq)],
|
||
"anns_field": "dense",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
dense = AnnSearchRequest(**dense_search_param)
|
||
|
||
sparse_search_param = {
|
||
"data": [{random.randint(1, 10000): random.random() for _ in range(100)} for _ in range(nq)],
|
||
"anns_field": "sparse",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
sparse = AnnSearchRequest(**sparse_search_param)
|
||
|
||
bm25_search_param = {
|
||
"data": query_texts,
|
||
"anns_field": "bm25",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25 = AnnSearchRequest(**bm25_search_param)
|
||
|
||
if search_type == "dense+sparse":
|
||
reqs = [dense, sparse]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, sparse_results)
|
||
elif search_type == "dense+bm25":
|
||
reqs = [dense, bm25]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, bm25_results)
|
||
else: # sparse+bm25
|
||
reqs = [sparse, bm25]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(sparse_results, bm25_results)
|
||
|
||
# Compare Milvus rerank results with origin rerank results
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, hybrid_results, results_without_rerank,
|
||
provider_type="voyageai",
|
||
model_name=model_name,
|
||
truncation=truncation)
|
||
|
||
|
||
@pytest.mark.tags(CaseLabel.L3)
|
||
@pytest.mark.parametrize("model_name", ["BAAI/bge-reranker-v2-m3", "netease-youdao/bce-reranker-base_v1"])
|
||
@pytest.mark.parametrize("max_chunks_per_doc", [10, 5])
|
||
@pytest.mark.parametrize("overlap_tokens", [80, 40])
|
||
def test_milvus_client_hybrid_search_with_siliconflow_rerank_specific_params(self, setup_collection, model_name, max_chunks_per_doc, overlap_tokens):
|
||
"""
|
||
target: test hybrid search with siliconflow rerank specific parameters
|
||
method: test hybrid search with different siliconflow model names, max_chunks_per_doc and overlap_tokens values
|
||
expected: hybrid search successfully with siliconflow reranker
|
||
"""
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
nq = 2
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "siliconflow",
|
||
"queries": query_texts,
|
||
"model_name": model_name,
|
||
"max_chunks_per_doc": max_chunks_per_doc,
|
||
"overlap_tokens": overlap_tokens
|
||
},
|
||
)
|
||
|
||
# Test different hybrid search combinations
|
||
for search_type in ["dense+sparse", "dense+bm25", "sparse+bm25"]:
|
||
log.info(f"Executing {search_type} hybrid search with siliconflow reranker")
|
||
|
||
dense_search_param = {
|
||
"data": [[random.random() for _ in range(768)] for _ in range(nq)],
|
||
"anns_field": "dense",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
dense = AnnSearchRequest(**dense_search_param)
|
||
|
||
sparse_search_param = {
|
||
"data": [{random.randint(1, 10000): random.random() for _ in range(100)} for _ in range(nq)],
|
||
"anns_field": "sparse",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
sparse = AnnSearchRequest(**sparse_search_param)
|
||
|
||
bm25_search_param = {
|
||
"data": query_texts,
|
||
"anns_field": "bm25",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25 = AnnSearchRequest(**bm25_search_param)
|
||
|
||
if search_type == "dense+sparse":
|
||
reqs = [dense, sparse]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, sparse_results)
|
||
elif search_type == "dense+bm25":
|
||
reqs = [dense, bm25]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
dense_results = client.search(
|
||
collection_name,
|
||
data=dense_search_param["data"],
|
||
anns_field="dense",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(dense_results, bm25_results)
|
||
else: # sparse+bm25
|
||
reqs = [sparse, bm25]
|
||
# Get hybrid search results with reranker
|
||
hybrid_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=reqs,
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
# Get results without rerank by using search separately and merging them
|
||
sparse_results = client.search(
|
||
collection_name,
|
||
data=sparse_search_param["data"],
|
||
anns_field="sparse",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
)
|
||
bm25_results = client.search(
|
||
collection_name,
|
||
data=bm25_search_param["data"],
|
||
anns_field="bm25",
|
||
limit=5,
|
||
output_fields=["doc_id", "document"],
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
results_without_rerank = self.merge_and_dedup_hybrid_searchresults(sparse_results, bm25_results)
|
||
|
||
# Compare Milvus rerank results with origin rerank results
|
||
self.compare_milvus_rerank_with_origin_rerank(query_texts, hybrid_results, results_without_rerank,
|
||
provider_type="siliconflow",
|
||
model_name=model_name,
|
||
max_chunks_per_doc=max_chunks_per_doc,
|
||
overlap_tokens=overlap_tokens)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_tei_model_rerank_nullable_field(self, tei_reranker_endpoint):
|
||
"""
|
||
target: verify model reranker (TEI) works with nullable VarChar input field
|
||
method: create collection with nullable document field, insert rows with some null values,
|
||
search with TEI model reranker
|
||
expected: search successfully, null document rows treated as empty strings for reranking
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create schema with nullable document field
|
||
schema = client.create_schema(enable_dynamic_field=False, auto_id=True)
|
||
schema.add_field("id", DataType.INT64, is_primary=True)
|
||
schema.add_field("document", DataType.VARCHAR, max_length=10000, nullable=True)
|
||
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=768)
|
||
# 2. prepare index params
|
||
index_params = client.prepare_index_params()
|
||
index_params.add_index(field_name="dense", index_type="FLAT", metric_type="COSINE")
|
||
# 3. create collection
|
||
client.create_collection(
|
||
collection_name,
|
||
schema=schema,
|
||
index_params=index_params,
|
||
consistency_level="Strong",
|
||
)
|
||
# 4. insert data: some rows with null document
|
||
rows = []
|
||
data_size = 100
|
||
for i in range(data_size):
|
||
row = {
|
||
"document": None if i % 10 == 0 else fake.text(),
|
||
"dense": [random.random() for _ in range(768)]
|
||
}
|
||
rows.append(row)
|
||
client.insert(collection_name, rows)
|
||
# 5. search with TEI model reranker
|
||
nq = 1
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
tei_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
data = [[random.random() for _ in range(768)] for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["document"],
|
||
ranker=tei_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
assert len(rerank_results) == nq
|
||
assert len(rerank_results[0]) > 0
|
||
# verify scores are in descending order
|
||
scores = [r["distance"] for r in rerank_results[0]]
|
||
for i in range(len(scores) - 1):
|
||
assert scores[i] >= scores[i + 1], \
|
||
f"Scores not in descending order: scores[{i}]={scores[i]} < scores[{i + 1}]={scores[i + 1]}"
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_tei_model_rerank_nullable_all_null(self, tei_reranker_endpoint):
|
||
"""
|
||
target: verify model reranker (TEI) handles the case where all document field values are null
|
||
method: create collection with nullable document field, insert rows with all null values,
|
||
search with TEI model reranker
|
||
expected: search successfully, all null documents treated as empty strings
|
||
"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
# 1. create schema with nullable document field
|
||
schema = client.create_schema(enable_dynamic_field=False, auto_id=True)
|
||
schema.add_field("id", DataType.INT64, is_primary=True)
|
||
schema.add_field("document", DataType.VARCHAR, max_length=10000, nullable=True)
|
||
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=768)
|
||
# 2. prepare index params
|
||
index_params = client.prepare_index_params()
|
||
index_params.add_index(field_name="dense", index_type="FLAT", metric_type="COSINE")
|
||
# 3. create collection
|
||
client.create_collection(
|
||
collection_name,
|
||
schema=schema,
|
||
index_params=index_params,
|
||
consistency_level="Strong",
|
||
)
|
||
# 4. insert data with all null documents
|
||
rows = []
|
||
data_size = 100
|
||
for i in range(data_size):
|
||
row = {
|
||
"document": None,
|
||
"dense": [random.random() for _ in range(768)]
|
||
}
|
||
rows.append(row)
|
||
client.insert(collection_name, rows)
|
||
# 5. search with TEI model reranker
|
||
nq = 1
|
||
query_texts = [fake.text() for _ in range(nq)]
|
||
tei_ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
data = [[random.random() for _ in range(768)] for _ in range(nq)]
|
||
rerank_results = client.search(
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=10,
|
||
output_fields=["document"],
|
||
ranker=tei_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
assert len(rerank_results) == nq
|
||
assert len(rerank_results[0]) > 0
|
||
# verify all returned documents are null and scores are valid
|
||
# model reranker treats null as empty string "", so scores are non-null float values
|
||
scores = [r["distance"] for r in rerank_results[0]]
|
||
for r in rerank_results[0]:
|
||
assert r.get("document") is None, \
|
||
f"Expected null document, got {r.get('document')}"
|
||
for i in range(len(scores) - 1):
|
||
assert scores[i] >= scores[i + 1], \
|
||
f"Scores not in descending order: scores[{i}]={scores[i]} < scores[{i + 1}]={scores[i + 1]}"
|
||
|
||
|
||
class TestMilvusClientSearchModelRerankNegative(TestMilvusClientV2Base):
|
||
""" Test case of model rerank negative scenarios """
|
||
|
||
@pytest.fixture(scope="function")
|
||
def setup_collection(self):
|
||
"""Setup collection for negative testing"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
|
||
# 1. create schema
|
||
schema = client.create_schema(enable_dynamic_field=False, auto_id=True)
|
||
schema.add_field("id", DataType.INT64, is_primary=True)
|
||
schema.add_field("doc_id", DataType.VARCHAR, max_length=100)
|
||
schema.add_field("document", DataType.VARCHAR, max_length=10000)
|
||
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=128)
|
||
|
||
# 2. prepare index params
|
||
index_params = client.prepare_index_params()
|
||
index_params.add_index(field_name="dense", index_type="FLAT", metric_type="L2")
|
||
|
||
# 3. create collection
|
||
client.create_collection(
|
||
collection_name,
|
||
schema=schema,
|
||
index_params=index_params,
|
||
consistency_level="Strong",
|
||
)
|
||
|
||
# 4. insert data
|
||
rows = []
|
||
for i in range(100):
|
||
rows.append({
|
||
"doc_id": str(i),
|
||
"document": fake.text()[:500],
|
||
"dense": [random.random() for _ in range(128)]
|
||
})
|
||
client.insert(collection_name, rows)
|
||
|
||
yield client, collection_name
|
||
|
||
# cleanup
|
||
client.drop_collection(collection_name)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("invalid_provider", ["invalid_provider", "openai", "", None, 123])
|
||
def test_milvus_client_search_with_model_rerank_invalid_provider(self, setup_collection, invalid_provider,
|
||
tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with invalid provider
|
||
method: use invalid provider values
|
||
expected: raise exception
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": invalid_provider,
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[random.random() for _ in range(128)]]
|
||
error = {ct.err_code: 65535, ct.err_msg: "unknown rerank model provider"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("invalid_endpoint", ["", "invalid_url", "ftp://invalid.com", "localhost", None])
|
||
def test_milvus_client_search_with_model_rerank_invalid_endpoint(self, setup_collection, invalid_endpoint):
|
||
"""
|
||
target: test model rerank with invalid endpoint
|
||
method: use invalid endpoint values
|
||
expected: raise exception
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": invalid_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "not a valid http/https link"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_unreachable_endpoint(self, setup_collection):
|
||
"""
|
||
target: test model rerank with unreachable endpoint
|
||
method: use unreachable endpoint
|
||
expected: raise connection error
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": "http://192.168.999.999:8080", # unreachable IP
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "call service failed"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("invalid_queries", [None, "", 123, {"key": "value"}])
|
||
def test_milvus_client_search_with_model_rerank_invalid_queries(self, setup_collection, invalid_queries,
|
||
tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with invalid queries parameter
|
||
method: use invalid queries values
|
||
expected: raise exception
|
||
"""
|
||
client, collection_name = setup_collection
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": invalid_queries,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "parse rerank params [queries] failed"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_missing_queries(self, setup_collection, tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank without queries parameter
|
||
method: omit queries parameter
|
||
expected: raise exception for missing required parameter
|
||
"""
|
||
client, collection_name = setup_collection
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"endpoint": tei_reranker_endpoint,
|
||
# missing "queries" parameter
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "rerank function missing required param: queries"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_missing_endpoint(self, setup_collection):
|
||
"""
|
||
target: test model rerank without endpoint parameter
|
||
method: omit endpoint parameter
|
||
expected: raise exception for missing required parameter
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
# missing "endpoint" parameter
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "is not a valid http/https link"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("invalid_reranker_type", ["invalid", None, 123])
|
||
def test_milvus_client_search_with_invalid_reranker_type(self, setup_collection, invalid_reranker_type,
|
||
tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with invalid reranker type
|
||
method: use invalid reranker type values
|
||
expected: raise exception
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": invalid_reranker_type,
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "unsupported reranker"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_empty_reranker_type(self, setup_collection, tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with empty reranker type
|
||
method: use empty string as reranker type
|
||
expected: raise exception
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "reranker name not specified"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_query_mismatch(self, setup_collection, tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with query count mismatch
|
||
method: provide multiple queries but single search data
|
||
expected: raise exception for query mismatch
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["query1", "query2", "query3"] # 3 queries
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128] # single search data
|
||
error = {ct.err_code: 65535, ct.err_msg: "queries count (3) != nq count (1)"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_non_text_field(self, setup_collection, tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with non-text input field
|
||
method: use numeric field for reranking input
|
||
expected: raise exception for unsupported field type
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["id"], # numeric field instead of text
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "model input field id must be VarChar, got Int64"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5, output_fields=["doc_id", "document"],
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_nonexistent_field(self, setup_collection, tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with non-existent input field
|
||
method: use field that doesn't exist in collection
|
||
expected: raise exception for field not found
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["nonexistent_field"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 1, ct.err_msg: "field not found"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_multiple_input_fields(self, setup_collection,
|
||
tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with multiple input fields
|
||
method: specify multiple fields for reranking input
|
||
expected: raise exception for multiple input fields not supported
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document", "doc_id"], # multiple fields
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
error = {ct.err_code: 65535, ct.err_msg: "model reranker requires exactly 1 input field, got 2"}
|
||
self.search(client, collection_name, data, anns_field="dense", limit=5,
|
||
ranker=ranker, check_task=CheckTasks.err_res, check_items=error)
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
def test_milvus_client_search_with_model_rerank_extra_params(self, setup_collection, tei_reranker_endpoint):
|
||
"""
|
||
target: test model rerank with extra unknown parameters
|
||
method: add unknown parameters to params
|
||
expected: search should work but ignore unknown parameters or raise warning
|
||
"""
|
||
client, collection_name = setup_collection
|
||
query_texts = ["test query"]
|
||
|
||
ranker = Function(
|
||
name="rerank_model",
|
||
input_field_names=["document"],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "model",
|
||
"provider": "tei",
|
||
"queries": query_texts,
|
||
"endpoint": tei_reranker_endpoint,
|
||
"unknown_param": "value", # extra parameter
|
||
"another_param": 123,
|
||
},
|
||
)
|
||
|
||
data = [[0.1] * 128]
|
||
# This might succeed with warning, or fail depending on implementation
|
||
res, result = self.search(
|
||
client,
|
||
collection_name,
|
||
data=data,
|
||
anns_field="dense",
|
||
limit=5,
|
||
ranker=ranker,
|
||
)
|
||
assert result is True
|
||
|
||
|
||
class TestMilvusClientSearchRRFWeightedRerank(TestMilvusClientV2Base):
|
||
|
||
@pytest.fixture(scope="function")
|
||
def setup_collection(self):
|
||
"""Setup collection for rrf/weighted rerank testing"""
|
||
client = self._client()
|
||
collection_name = cf.gen_collection_name_by_testcase_name()
|
||
dense_metric_type = "COSINE"
|
||
|
||
# 1. create schema with embedding and bm25 functions
|
||
schema = client.create_schema(enable_dynamic_field=False, auto_id=True)
|
||
schema.add_field("id", DataType.INT64, is_primary=True)
|
||
schema.add_field("doc_id", DataType.VARCHAR, max_length=100)
|
||
schema.add_field("document", DataType.VARCHAR, max_length=10000, enable_analyzer=True)
|
||
schema.add_field("sparse", DataType.SPARSE_FLOAT_VECTOR)
|
||
schema.add_field("dense", DataType.FLOAT_VECTOR, dim=768)
|
||
schema.add_field("bm25", DataType.SPARSE_FLOAT_VECTOR)
|
||
|
||
# add bm25 function
|
||
bm25_function = Function(
|
||
name="bm25",
|
||
input_field_names=["document"],
|
||
output_field_names="bm25",
|
||
function_type=FunctionType.BM25,
|
||
)
|
||
schema.add_function(bm25_function)
|
||
|
||
# 2. prepare index params
|
||
index_params = client.prepare_index_params()
|
||
index_params.add_index(field_name="dense", index_type="FLAT", metric_type=dense_metric_type)
|
||
index_params.add_index(
|
||
field_name="sparse",
|
||
index_type="SPARSE_INVERTED_INDEX",
|
||
metric_type="IP",
|
||
)
|
||
index_params.add_index(
|
||
field_name="bm25",
|
||
index_type="SPARSE_INVERTED_INDEX",
|
||
metric_type="BM25",
|
||
params={"bm25_k1": 1.2, "bm25_b": 0.75},
|
||
)
|
||
|
||
# 3. create collection
|
||
client.create_collection(
|
||
collection_name,
|
||
schema=schema,
|
||
index_params=index_params,
|
||
consistency_level="Strong",
|
||
)
|
||
|
||
# 4. insert data
|
||
rows = []
|
||
data_size = 3000
|
||
for i in range(data_size):
|
||
rows.append({
|
||
"doc_id": str(i),
|
||
"document": fake.text(),
|
||
"sparse": {random.randint(1, 10000): random.random() for _ in range(100)},
|
||
"dense": [random.random() for _ in range(768)]
|
||
})
|
||
client.insert(collection_name, rows)
|
||
|
||
return collection_name
|
||
|
||
@pytest.mark.tags(CaseLabel.L1)
|
||
@pytest.mark.parametrize("ranker_model", ["rrf", "weight"])
|
||
def test_milvus_client_hybrid_vector_search_with_rrf_weight_rerank(self, setup_collection, ranker_model):
|
||
"""
|
||
target: test hybrid vector search with rrf/weight rerank
|
||
method: test dense+sparse/dense+bm25/sparse+bm25 search with rrf/weight reranker
|
||
expected: search successfully with rrf/weight reranker
|
||
"""
|
||
from pymilvus import WeightedRanker, RRFRanker
|
||
client = self._client()
|
||
collection_name = setup_collection
|
||
|
||
# 5. prepare search parameters for reranker
|
||
query_texts = [fake.text() for _ in range(10)]
|
||
rrf_func_ranker = Function(
|
||
name="rrf_ranker",
|
||
input_field_names=[],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "rrf",
|
||
"k": 100
|
||
},
|
||
)
|
||
weight_func_ranker = Function(
|
||
name="weight_ranker",
|
||
input_field_names=[],
|
||
function_type=FunctionType.RERANK,
|
||
params={
|
||
"reranker": "weighted",
|
||
"weights": [0.1, 0.9],
|
||
"norm_score": True
|
||
},
|
||
)
|
||
func_ranker = None
|
||
original_ranker = None
|
||
if ranker_model == "rrf":
|
||
func_ranker = rrf_func_ranker
|
||
original_ranker = RRFRanker(k=100)
|
||
|
||
if ranker_model == "weight":
|
||
func_ranker = weight_func_ranker
|
||
original_ranker = WeightedRanker(0.1, 0.9, norm_score=True)
|
||
# 6. execute search with reranker
|
||
for search_type in ["dense+sparse", "dense+bm25", "sparse+bm25"]:
|
||
log.info(f"Executing {search_type} search with rrf/weight reranker")
|
||
dense_search_param = {
|
||
"data": [[random.random() for _ in range(768)] for _ in range(10)],
|
||
"anns_field": "dense",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
dense = AnnSearchRequest(**dense_search_param)
|
||
|
||
sparse_search_param = {
|
||
"data": [{random.randint(1, 10000): random.random() for _ in range(100)} for _ in range(10)],
|
||
"anns_field": "sparse",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25_search_param = {
|
||
"data": query_texts,
|
||
"anns_field": "bm25",
|
||
"param": {},
|
||
"limit": 5,
|
||
}
|
||
bm25 = AnnSearchRequest(**bm25_search_param)
|
||
|
||
sparse = AnnSearchRequest(**sparse_search_param)
|
||
if search_type == "dense+sparse":
|
||
|
||
function_rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[dense, sparse],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=func_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
original_rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[dense, sparse],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=original_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
elif search_type == "dense+bm25":
|
||
function_rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[dense, bm25],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=func_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
original_rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[dense, bm25],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=original_ranker,
|
||
consistency_level="Strong",
|
||
)
|
||
elif search_type == "sparse+bm25":
|
||
function_rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[sparse, bm25],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=func_ranker,
|
||
consistency_level="Strong",
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
original_rerank_results = client.hybrid_search(
|
||
collection_name,
|
||
reqs=[sparse, bm25],
|
||
limit=10,
|
||
output_fields=["doc_id", "document"],
|
||
ranker=original_ranker,
|
||
consistency_level="Strong",
|
||
search_params={"metric_type": "BM25"}
|
||
)
|
||
assert function_rerank_results == original_rerank_results
|