milvus/tests/python_client/milvus_client/test_milvus_client_index.py

import pytest

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 *

prefix = "client_index"
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_multiple_vector_field_name = "vector_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


class TestMilvusClientIndexInvalid(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("name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
    def test_milvus_client_index_invalid_collection_name(self, name):
        """
        target: test index abnormal case
        method: create index on invalid collection name
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector")
        # 3. create index
        error = {ct.err_code: 100, ct.err_msg: f"collection not found[database=default][collection={name}"}
        self.create_index(client, name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("name", ["a".join("a" for i in range(256))])
    def test_milvus_client_index_collection_name_over_max_length(self, name):
        """
        target: test index abnormal case
        method: create index on collection name over max length
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector")
        # 3. create index
        error = {ct.err_code: 100, ct.err_msg: f"collection not found[database=default][collection={name}]"}
        self.create_index(client, name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_index_not_exist_collection_name(self):
        """
        target: test index abnormal case
        method: create index on not exist collection name
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        not_existed_collection_name = cf.gen_unique_str("not_existed_collection")
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector")
        # 3. create index
        error = {ct.err_code: 100,
                 ct.err_msg: f"collection not found[database=default][collection={not_existed_collection_name}]"}
        self.create_index(client, not_existed_collection_name, index_params,
                          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 1885")
    @pytest.mark.parametrize("index", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
    def test_milvus_client_index_invalid_index_type(self, index):
        """
        target: test index abnormal case
        method: create index on invalid index type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector", index_type=index)
        # 3. create index
        error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not "
                                               f"found[database=default][collection=not_existed]"}
        self.create_index(client, collection_name, index_params,
                          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 1885")
    @pytest.mark.parametrize("metric", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
    def test_milvus_client_index_invalid_metric_type(self, metric):
        """
        target: test index abnormal case
        method: create index on invalid metric type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector", metric_type=metric)
        # 3. create index
        error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not "
                                               f"found[database=default][collection=not_existed]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_index_drop_index_before_release(self):
        """
        target: test index abnormal case
        method: drop index before release
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        error = {ct.err_code: 65535, ct.err_msg: f"index cannot be dropped, collection is loaded, "
                                                 f"please release it first"}
        self.drop_index(client, collection_name, "vector",
                        check_task=CheckTasks.err_res, check_items=error)
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_create_multiple_diff_index_without_release(self):
        """
        target: test index abnormal case
        method: create different index on one field without release
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector", index_type="IVF_FLAT", metric_type="L2")
        # 3. create another index
        error = {ct.err_code: 65535, ct.err_msg: "CreateIndex failed: at most one distinct index is allowed per field"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)
        self.drop_collection(client, collection_name)


class TestMilvusClientIndexValid(TestMilvusClientV2Base):
    """ Test case of index interface """

    @pytest.fixture(scope="function", params=[False, True])
    def auto_id(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["COSINE", "L2", "IP"])
    def metric_type(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["TRIE", "STL_SORT", "INVERTED", "AUTOINDEX"])
    def scalar_index(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["TRIE", "INVERTED", "AUTOINDEX", ""])
    def varchar_index(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["STL_SORT", "INVERTED", "AUTOINDEX", ""])
    def numeric_index(self, request):
        yield request.param

    """
    ******************************************************************
    #  The following are valid base cases
    ******************************************************************
    """
    @pytest.mark.tags(CaseLabel.L0)
    @pytest.mark.parametrize("index", ct.all_index_types[:7])
    def test_milvus_client_index_with_params(self, index, metric_type):
        """
        target: test index with user defined params
        method: create connection, collection, index, insert and search
        expected: index/search/query successfully
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        res = self.list_indexes(client, collection_name)[0]
        assert res == []
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        params = cf.get_index_params_params(index_type=index)
        index_params.add_index(field_name="vector", index_type=index, params=params, metric_type=metric_type)
        # 3. create index
        self.create_index(client, collection_name, index_params)
        # 4. 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)
        # 5. load collection
        self.load_collection(client, collection_name)
        # 6. 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,
                                 "limit": default_limit})
        # 7. query
        self.query(client, collection_name, filter=default_search_exp,
                   check_task=CheckTasks.check_query_results,
                   check_items={exp_res: rows,
                                "with_vec": True,
                                "primary_field": default_primary_key_field_name})
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L2)
    @pytest.mark.parametrize("index", ct.all_index_types[:7])
    def test_milvus_client_index_after_insert(self, index, metric_type):
        """
        target: test index after insert
        method: create connection, collection, insert, index and search
        expected: index/search/query successfully
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 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)
        # 3. prepare index params
        index_params = self.prepare_index_params(client)[0]
        params = cf.get_index_params_params(index)
        index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type, params=params)
        # 4. create index
        self.create_index(client, collection_name, index_params)
        # 5. load collection
        self.load_collection(client, collection_name)
        # 5. 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,
                                 "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,
                                "primary_field": default_primary_key_field_name})
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L2)
    def test_milvus_client_index_auto_index(self, numeric_index, varchar_index, metric_type):
        """
        target: test index with autoindex on both scalar and vector field
        method: create connection, collection, insert and search
        expected: index/search/query successfully
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        schema = self.create_schema(client)[0]
        schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True)
        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(default_string_field_name, DataType.VARCHAR, max_length=64)
        schema.add_field(default_float_field_name, DataType.FLOAT)
        schema.add_field(ct.default_double_field_name, DataType.DOUBLE)
        schema.add_field(ct.default_bool_field_name, DataType.BOOL)
        schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
        self.create_collection(client, collection_name, schema=schema, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        res = self.list_indexes(client, collection_name)[0]
        assert res == []
        # 2. prepare index params
        index = "AUTOINDEX"
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name=default_vector_field_name, index_type=index, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_int32_field_name, index_type=numeric_index, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_int16_field_name, index_type=numeric_index, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_int8_field_name, index_type=numeric_index, metric_type=metric_type)
        index_params.add_index(field_name=default_float_field_name, index_type=numeric_index, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_double_field_name, index_type=numeric_index, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_bool_field_name, index_type="", metric_type=metric_type)
        index_params.add_index(field_name=default_string_field_name, index_type=varchar_index, metric_type=metric_type)
        index_params.add_index(field_name=default_primary_key_field_name, index_type=numeric_index, metric_type=metric_type)
        # 3. create index
        self.create_index(client, collection_name, index_params)
        # 4. drop index
        self.drop_index(client, collection_name, default_vector_field_name)
        self.drop_index(client, collection_name, ct.default_int32_field_name)
        self.drop_index(client, collection_name, ct.default_int16_field_name)
        self.drop_index(client, collection_name, ct.default_int8_field_name)
        self.drop_index(client, collection_name, default_float_field_name)
        self.drop_index(client, collection_name, ct.default_double_field_name)
        self.drop_index(client, collection_name, ct.default_bool_field_name)
        self.drop_index(client, collection_name, default_string_field_name)
        self.drop_index(client, collection_name, default_primary_key_field_name)
        # 5. create index
        self.create_index(client, collection_name, index_params)
        # 6. 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]),
                 ct.default_int32_field_name: np.int32(i), ct.default_int16_field_name: np.int16(i),
                 ct.default_int8_field_name: np.int8(i), default_float_field_name: i * 1.0,
                 ct.default_double_field_name: np.double(i), ct.default_bool_field_name: np.bool_(i),
                 default_string_field_name: str(i)} for i in range(default_nb)]
        self.insert(client, collection_name, rows)
        # 7. load collection
        self.load_collection(client, collection_name)
        # 8. 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,
                                 "limit": default_limit})
        # 9. query
        self.query(client, collection_name, filter=default_search_exp,
                   check_task=CheckTasks.check_query_results,
                   check_items={exp_res: rows,
                                "with_vec": True,
                                "primary_field": default_primary_key_field_name})
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_scalar_hybrid_index_small_distinct_before_insert(self, metric_type):
        """
        target: test index with autoindex on int/varchar with small distinct value (<=100)
        method: create connection, collection, insert and search
        expected: index/search/query successfully (autoindex is bitmap index indeed)
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        int64_field_name = "int"
        schema = self.create_schema(client)[0]
        schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True)
        schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
        schema.add_field(int64_field_name, DataType.INT64)
        schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
        self.create_collection(client, collection_name, schema=schema, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        res = self.list_indexes(client, collection_name)[0]
        assert res == []
        # 2. prepare index params
        index = "AUTOINDEX"
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name=default_vector_field_name, index_type=index, metric_type=metric_type)
        index_params.add_index(field_name=int64_field_name, index_type=index, metric_type=metric_type)
        index_params.add_index(field_name=default_string_field_name, index_type=index, metric_type=metric_type)
        # 3. create index
        self.create_index(client, collection_name, index_params)
        # 4. 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]),
                 int64_field_name: np.random.randint(0, 99), default_string_field_name: str(np.random.randint(0, 99))}
                for i in range(default_nb)]
        self.insert(client, collection_name, rows)
        # 5. load collection
        self.load_collection(client, collection_name)
        # 6. 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,
                                 "limit": default_limit})
        # 7. query
        self.query(client, collection_name, filter=default_search_exp,
                   check_task=CheckTasks.check_query_results,
                   check_items={exp_res: rows,
                                "with_vec": True,
                                "primary_field": default_primary_key_field_name})
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_scalar_hybrid_index_small_to_large_distinct_after_insert(self, metric_type):
        """
        target: test index with autoindex on int/varchar with small distinct value (<=100) first and
                insert to large distinct (2000+) later
        method: create connection, collection, insert and search
        expected: index/search/query successfully
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        int64_field_name = "int"
        schema = self.create_schema(client)[0]
        schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True)
        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(default_string_field_name, DataType.VARCHAR, max_length=64)
        schema.add_field(int64_field_name, DataType.INT64)
        schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
        self.create_collection(client, collection_name, schema=schema, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        res = self.list_indexes(client, collection_name)[0]
        assert res == []
        # 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]),
                 int64_field_name: np.random.randint(0, 99), ct.default_int32_field_name: np.int32(i),
                 ct.default_int16_field_name: np.int16(i), ct.default_int8_field_name: np.int8(i),
                 default_string_field_name: str(np.random.randint(0, 99))} for i in range(default_nb)]
        self.insert(client, collection_name, rows)
        # 3. prepare index params
        index = "AUTOINDEX"
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name=default_vector_field_name, index_type=index, metric_type=metric_type)
        index_params.add_index(field_name=int64_field_name, index_type=index, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_int32_field_name, index_type="", metric_type=metric_type)
        index_params.add_index(field_name=ct.default_int16_field_name, metric_type=metric_type)
        index_params.add_index(field_name=ct.default_int8_field_name, index_type=index, metric_type=metric_type)
        index_params.add_index(field_name=default_string_field_name, index_type=index, metric_type=metric_type)
        # 4. create index
        self.create_index(client, collection_name, index_params)
        # 5. load collection
        self.load_collection(client, collection_name)
        # 6. 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,
                                 "limit": default_limit})
        # 7. query
        self.query(client, collection_name, filter=default_search_exp,
                   check_task=CheckTasks.check_query_results,
                   check_items={exp_res: rows,
                                "with_vec": True,
                                "primary_field": default_primary_key_field_name})
        # 8. insert more distinct value to the scalar field to make the autoindex change
        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]),
                 int64_field_name: np.random.randint(0, 99), ct.default_int32_field_name: np.int32(i),
                 ct.default_int16_field_name: np.int16(i), ct.default_int8_field_name: np.int8(i),
                 default_string_field_name: str(np.random.randint(0, 99))} for i in range(default_nb, 2*default_nb)]
        self.insert(client, collection_name, rows)
        self.flush(client, collection_name)
        # 9. search
        vectors_to_search = rng.random((1, default_dim))
        insert_ids = [i for i in range(2*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,
                                 "limit": default_limit})
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L2)
    def test_milvus_client_index_multiple_vectors(self, numeric_index, metric_type):
        """
        target: test index for multiple vectors
        method: create connection, collection, index, insert and search
        expected: index/search/query successfully
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        res = self.list_indexes(client, collection_name)[0]
        assert res == []
        # 2. prepare index params
        index = "AUTOINDEX"
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type)
        index_params.add_index(field_name="id", index_type=numeric_index, metric_type=metric_type)
        # 3. create index
        self.create_index(client, collection_name, index_params)
        # 4. 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),
                 default_multiple_vector_field_name: list(rng.random((1, default_dim))[0])} for i in range(default_nb)]
        self.insert(client, collection_name, rows)
        # 5. load collection
        self.load_collection(client, collection_name)
        # 6. 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,
                                 "limit": default_limit})
        # 7. query
        self.query(client, collection_name, filter=default_search_exp,
                   check_task=CheckTasks.check_query_results,
                   check_items={exp_res: rows,
                                "with_vec": True,
                                "primary_field": default_primary_key_field_name})
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L2)
    def test_milvus_client_index_drop_create_same_index(self):
        """
        target: test index after drop and create same index twice
        method: create connection, collection, create/drop/create index, insert and search
        expected: index create/drop and search/query successfully
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        res = self.list_indexes(client, collection_name)[0]
        assert res == []
        # 2. prepare index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name="vector", index_type="HNSW", metric_type="L2")
        # 3. create index
        self.create_index(client, collection_name, index_params)
        # 4. drop index
        self.drop_index(client, collection_name, "vector")
        # 4. create same index twice
        self.create_index(client, collection_name, index_params)
        # 5. 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)
        # 6. load collection
        self.load_collection(client, collection_name)
        # 7. 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,
                                 "limit": default_limit})
        # 8. query
        self.query(client, collection_name, filter=default_search_exp,
                   check_task=CheckTasks.check_query_results,
                   check_items={exp_res: rows,
                                "with_vec": True,
                                "primary_field": default_primary_key_field_name})
        self.drop_collection(client, collection_name)


class TestMilvusClientJsonPathIndexInvalid(TestMilvusClientV2Base):
    """ Test case of search interface """

    @pytest.fixture(scope="function", params=["TRIE", "STL_SORT", "BITMAP"])
    def not_supported_varchar_scalar_index(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["INVERTED"])
    def supported_varchar_scalar_index(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=[DataType.JSON, DataType.ARRAY, DataType.FLOAT_VECTOR,
                                              DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR,
                                              DataType.SPARSE_FLOAT_VECTOR, DataType.INT8_VECTOR])
    def not_supported_json_cast_type(self, request):
        yield request.param

    """
    ******************************************************************
    #  The following are invalid base cases
    ******************************************************************
    """

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_json_path_index_no_index_params(self):
        """
        target: test json path index with:
                1. no all index params
                2. no json_cast_type
                3. no json_path
        method: create index on invalid collection name
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params with no 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="my_json", index_type="INVERTED")
        # 3. create index
        error = {ct.err_code: 1100, ct.err_msg: "json index must specify cast type: missing parameter"
                                                "[missing_param=json_cast_type]: invalid parameter"
                                                "[expected=valid index params][actual=invalid index params]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)
        # 4. prepare index params with no json_cast_type
        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="my_json", index_type="INVERTED", params={"json_path": "my_json['a']['b']"})
        # 5. create index
        error = {ct.err_code: 1100, ct.err_msg: "json index must specify cast type: missing parameter"
                                                "[missing_param=json_cast_type]: invalid parameter"
                                                "[expected=valid index params][actual=invalid index params]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)
        # 6. prepare index params with no json_path
        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="my_json", index_type="INVERTED", params={"json_cast_type": "varchar"})
        # 7. create index
        self.create_index(client, collection_name, index_params)
        self.drop_collection(client, collection_name)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("invalid_index_type", ["12-s", "12 s", "(mn)", "中文", "%$#"])
    def test_milvus_client_json_path_index_invalid_index_type(self, invalid_index_type):
        """
        target: test json path index with invalid index type
        method: create json path index with invalid index type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
        self.release_collection(client, collection_name)
        self.drop_index(client, collection_name, "vector")
        # 2. prepare index params with invalid index type
        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="my_json", index_type=invalid_index_type, params={"json_cast_type": "double",
                                                                                    "json_path": "my_json['a']['b']"})
        # 3. create index
        error = {ct.err_code: 1100, ct.err_msg: f"invalid parameter[expected=valid index]"
                                                f"[actual=invalid index type: {invalid_index_type}]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_json_path_index_not_support_index_type(self, enable_dynamic_field, not_supported_varchar_scalar_index):
        """
        target: test json path index with not supported index type
        method: create json path index with not supported index type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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, default_dim)
        # 2. prepare index params with invalid json index type
        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=not_supported_varchar_scalar_index,
                               params={"json_cast_type": "double", "json_path": "my_json['a']['b']"})
        # 3. create index
        if not_supported_varchar_scalar_index == "TRIE":
            supported_field_type = "varchar"
        if not_supported_varchar_scalar_index == "STL_SORT":
            supported_field_type = "numeric"
        if not_supported_varchar_scalar_index == "BITMAP":
            supported_field_type = "bool, int, string and array"
            not_supported_varchar_scalar_index = "bitmap index"
        error = {ct.err_code: 1100, ct.err_msg: f"{not_supported_varchar_scalar_index} are only supported on "
                                                f"{supported_field_type} field: invalid parameter[expected=valid "
                                                f"index params][actual=invalid index params]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.skip(reason="issue 40420")
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    @pytest.mark.parametrize("invalid_json_cast_type", ["12-s", "12 s", "(mn)", "中文", "%$#"])
    def test_milvus_client_json_path_index_invalid_json_cast_type(self, enable_dynamic_field, invalid_json_cast_type,
                                                                  supported_varchar_scalar_index):
        """
        target: test json path index with invalid json_cast_type
        method: create json path index with invalid json_cast_type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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, default_dim)
        # 2. prepare index params with invalid json index type
        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="json_index", index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": invalid_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
        # 3. create index
        error = {ct.err_code: 1100, ct.err_msg: f"index params][actual=invalid index params]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.skip(reason="issue 40420")
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_json_path_index_not_supported_json_cast_type(self, enable_dynamic_field, not_supported_json_cast_type,
                                                                        supported_varchar_scalar_index):
        """
        target: test json path index with not supported json_cast_type
        method: create json path index with not supported json_cast_type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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, default_dim)
        # 2. prepare index params with invalid json index type
        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="json_index", index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": not_supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
        # 3. create index
        error = {ct.err_code: 1100, ct.err_msg: f"index params][actual=invalid index params]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.skip(reason="issue 40423")
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    @pytest.mark.parametrize("invalid_json_path", ["12-s", "12 s", "(mn)", "中文", "%$#"])
    def test_milvus_client_json_path_index_invalid_json_path(self, enable_dynamic_field, invalid_json_path,
                                                             supported_varchar_scalar_index):
        """
        target: test json path index with invalid json_cast_type
        method: create json path index with invalid json_cast_type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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, default_dim)
        # 2. prepare index params with invalid json index type
        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="json_index",
                               index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": "double", "json_path": invalid_json_path})
        # 3. create index
        error = {ct.err_code: 1100, ct.err_msg: f"index params][actual=invalid index params]"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_milvus_client_json_path_index_not_exist_field_non_dynamic(self, supported_varchar_scalar_index):
        """
        target: test json path index with not exist field in non dynamic field scenario
        method: create json path index with not exist field with enable_dynamic_field disabled
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 1. create collection
        json_field_name = "my_json"
        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(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": "double", "json_path": f"{json_field_name}['a']"})
        error = {ct.err_code: 65535, ct.err_msg: f"cannot create index on non-exist field: {json_field_name}"}
        self.create_collection(client, collection_name, schema=schema, index_params=index_params,
                               check_task = CheckTasks.err_res, check_items = error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_different_index_same_json_path(self, enable_dynamic_field, supported_varchar_scalar_index):
        """
        target: test json path index with invalid json_cast_type
        method: create json path index with invalid json_cast_type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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, default_dim)
        # 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=json_field_name, index_name="json_index",
                               index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": "double", "json_path": f"{json_field_name}['a']"})
        self.create_index(client, collection_name, index_params)
        # 4. prepare another index params
        index_params = self.prepare_index_params(client)[0]
        index_params.add_index(field_name=json_field_name, index_name="json_index",
                               index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": "varchar", "json_path": f"{json_field_name}['a']"})
        # 5. create index
        error = {ct.err_code: 65535, ct.err_msg: "CreateIndex failed: at most one distinct index is allowed per field"}
        self.create_index(client, collection_name, index_params,
                          check_task=CheckTasks.err_res, check_items=error)


class TestMilvusClientJsonPathIndexValid(TestMilvusClientV2Base):
    """ Test case of search interface """

    @pytest.fixture(scope="function", params=["TRIE", "STL_SORT", "BITMAP"])
    def not_supported_varchar_scalar_index(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["INVERTED"])
    def supported_varchar_scalar_index(self, request):
        yield request.param

    @pytest.fixture(scope="function", params=["bool", "double", "varchar"])
    def supported_json_cast_type(self, request):
        yield request.param

    """
    ******************************************************************
    #  The following are valid base cases
    ******************************************************************
    """

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_json_path_index_default(self, enable_dynamic_field, supported_json_cast_type,
                                                   supported_varchar_scalar_index):
        """
        target: test json path index with not supported json_cast_type
        method: create json path index with not supported json_cast_type
        expected: raise exception
        """
        if enable_dynamic_field:
            pytest.skip('need to fix the field name when enabling dynamic field')
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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 + 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': None}, 2, 3]}} for i in
                range(default_nb + 30, default_nb + 40)]
        self.insert(client, collection_name, rows)
        # 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="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]"})
        # 3. create index
        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"{json_field_name}['a']['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})
        # 4. create json index on different json path
        self.create_index(client, collection_name, index_params)
        # 5. create same json index twice
        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"{json_field_name}['a']['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})
        self.describe_index(client, collection_name, index_name + '1',
                            check_task=CheckTasks.check_describe_index_property,
                            check_items={
                                # "json_cast_type": supported_json_cast_type, # issue 40426
                                "json_path": f"{json_field_name}['a']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name + '1'})
        self.describe_index(client, collection_name, index_name +'2',
                            check_task=CheckTasks.check_describe_index_property,
                            check_items={
                                # "json_cast_type": supported_json_cast_type, # issue 40426
                                "json_path": f"{json_field_name}",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name + '2'})
        self.describe_index(client, collection_name, index_name + '3',
                            check_task=CheckTasks.check_describe_index_property,
                            check_items={
                                # "json_cast_type": supported_json_cast_type, # issue 40426
                                "json_path": f"{json_field_name}['a'][0]['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name + '3'})
        self.describe_index(client, collection_name, index_name + '4',
                            check_task=CheckTasks.check_describe_index_property,
                            check_items={
                                # "json_cast_type": supported_json_cast_type, # issue 40426
                                "json_path": f"{json_field_name}['a'][0]",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name + '4'})

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_json_path_index_default_index_name(self, enable_dynamic_field, supported_json_cast_type,
                                                              supported_varchar_scalar_index):
        """
        target: test json path index with not supported json_cast_type
        method: create json path index with not supported json_cast_type
        expected: raise exception
        """
        if enable_dynamic_field:
            pytest.skip('issue 40374')
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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
        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)
        # 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=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']"})
        # 3. create index
        index_name = json_field_name + '/a/b'
        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"{json_field_name}['a']['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})

    @pytest.mark.tags(CaseLabel.L2)
    def test_milvus_client_json_path_index_on_non_json_field(self, supported_json_cast_type,
                                                             supported_varchar_scalar_index):
        """
        target: test json path index with not supported json_cast_type
        method: create json path index with not supported json_cast_type
        expected: raise exception
        """
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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)
        # 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_string_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})

    @pytest.mark.tags(CaseLabel.L2)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_different_json_path_index_same_field_same_index_name(self, enable_dynamic_field, supported_json_cast_type,
                                                                                supported_varchar_scalar_index):
        """
        target: test different json path index with same index name at the same time
        method: test different json path index with same index name at the same index_params object
        expected: create index successfully using the last index params with the same index name
        """
        if enable_dynamic_field:
            pytest.skip('need to fix the field name when enabling dynamic field')
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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
        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)
        # 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="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,
                               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,
                               index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": supported_json_cast_type,
                                       "json_path": f"{json_field_name}"})
        # 3. create index
        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"{json_field_name}",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.skip(reason="issue 40442")
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_different_json_path_index_same_field_different_index_name(self, enable_dynamic_field, supported_json_cast_type,
                                                                                     supported_varchar_scalar_index):
        """
        target: test different json path index with different default index name at the same time
        method: test different json path index with different default index name at the same index_params object
        expected: create index successfully using the last index params with the same index name
        """
        if enable_dynamic_field:
            pytest.skip('need to fix the field name when enabling dynamic field')
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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
        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)
        # 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="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,
                               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,
                               index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": supported_json_cast_type,
                                       "json_path": f"{json_field_name}"})
        # 3. create index
        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"{json_field_name}",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_diff_index_same_field_diff_index_name_diff_index_params(self, enable_dynamic_field,
                                                                                   supported_json_cast_type,
                                                                                   supported_varchar_scalar_index):
        """
        target: test different json path index with different default index name at the same time
        method: test different json path index with different default index name at the same index_params object
        expected: create index successfully using the last index params with the same index name
        """
        if enable_dynamic_field:
            pytest.skip('need to fix the field name when enabling dynamic field')
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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
        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)
        # 2. 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)
        # 3. create index
        index_name = f"{json_field_name}/a/b"
        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": "my_json['a']['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})
        index_name = f"{json_field_name}/a"
        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": "my_json['a']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})
        index_name = f"{json_field_name}"
        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": "my_json",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                #"index_name": index_name # issue 40441
                            })

    @pytest.mark.tags(CaseLabel.L2)
    @pytest.mark.parametrize("enable_dynamic_field", [True, False])
    def test_milvus_client_json_index_same_json_path_diff_field(self, enable_dynamic_field, supported_json_cast_type,
                                                                supported_varchar_scalar_index):
        """
        target: test different json path index with different default index name at the same time
        method: test different json path index with different default index name at the same index_params object
        expected: create index successfully using the last index params with the same index name
        """
        if enable_dynamic_field:
            pytest.skip('need to fix the field name when enabling dynamic field')
        client = self._client()
        collection_name = cf.gen_unique_str(prefix)
        # 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)
        # 2. 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 + "1",
                               index_type=supported_varchar_scalar_index,
                               params={"json_cast_type": supported_json_cast_type,
                                       "json_path": f"{json_field_name}1['a']['b']"})
        self.create_index(client, collection_name, index_params)
        # 3. create index
        index_name = f"{json_field_name}/a/b"
        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"{json_field_name}['a']['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name,
                                "index_name": index_name})
        index_name = f"{json_field_name}1/a/b"
        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"{json_field_name}1['a']['b']",
                                "index_type": supported_varchar_scalar_index,
                                "field_name": json_field_name + "1",
                                "index_name": index_name})