text: import pytest
  import random
  import numpy as np
  import pandas as pd
  from pymilvus import DefaultConfig
  
  from base.client_base import TestcaseBase
  from common.code_mapping import ConnectionErrorMessage as cem
  from common.code_mapping import CollectionErrorMessage as clem
  from common import common_func as cf
  from common import common_type as ct
  from common.common_type import CaseLabel, CheckTasks
  from utils.util_log import test_log as log
  
  prefix = "query"
  exp_res = "exp_res"
  default_term_expr = f'{ct.default_int64_field_name} in [0, 1]'
  default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}}
  binary_index_params = {"index_type": "BIN_IVF_FLAT", "metric_type": "JACCARD", "params": {"nlist": 64}}
  
  
  class TestQueryBase(TestcaseBase):
      """
      test Query interface
      query(collection_name, expr, output_fields=None, partition_names=None, timeout=None)
      """
  
      @pytest.mark.tags(CaseLabel.L0)
      def test_query(self):
          """
          target: test query
          method: query with term expr
          expected: verify query result
          """
          # create collection, insert default_nb, load collection
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          int_values = vectors[0][ct.default_int64_field_name].values.tolist()
          pos = 5
          term_expr = f'{ct.default_int64_field_name} in {int_values[:pos]}'
          res = vectors[0].iloc[0:pos, :1].to_dict('records')
          collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_empty_collection(self):
          """
          target: test query empty collection
          method: query on a empty collection
          expected: empty result
          """
          c_name = cf.gen_unique_str(prefix)
          collection_w = self.init_collection_wrap(name=c_name)
          collection_w.load()
          res, _ = collection_w.query(default_term_expr)
          assert len(res) == 0
  
      @pytest.mark.tags(CaseLabel.L0)
      def test_query_auto_id_collection(self):
          """
          target: test query with auto_id=True collection
          method: test query with auto id
          expected: query result is correct
          """
          self._connect()
          df = cf.gen_default_dataframe_data(ct.default_nb)
          df[ct.default_int64_field_name] = None
          insert_res, _, = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                                         primary_field=ct.default_int64_field_name,
                                                                         auto_id=True)
          assert self.collection_wrap.num_entities == ct.default_nb
          ids = insert_res[1].primary_keys
          pos = 5
          res = df.iloc[:pos, :1].to_dict('records')
          self.collection_wrap.load()
  
          # query with all primary keys
          term_expr_1 = f'{ct.default_int64_field_name} in {ids[:pos]}'
          for i in range(5):
              res[i][ct.default_int64_field_name] = ids[i]
          self.collection_wrap.query(term_expr_1, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
          # query with part primary keys
          term_expr_2 = f'{ct.default_int64_field_name} in {[ids[0], 0]}'
          self.collection_wrap.query(term_expr_2, check_task=CheckTasks.check_query_results,
                                     check_items={exp_res: res[:1]})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_auto_id_not_existed_primary_values(self):
          """
          target: test query on auto_id true collection
          method: 1.create auto_id true collection 2.query with not existed primary keys
          expected: query result is empty
          """
          schema = cf.gen_default_collection_schema(auto_id=True)
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema)
          df = cf.gen_default_dataframe_data(ct.default_nb)
          df.drop(ct.default_int64_field_name, axis=1, inplace=True)
          mutation_res, _ = collection_w.insert(data=df)
          assert collection_w.num_entities == ct.default_nb
          collection_w.load()
          term_expr = f'{ct.default_int64_field_name} in [0, 1, 2]'
          res, _ = collection_w.query(term_expr)
          assert len(res) == 0
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_none(self):
          """
          target: test query with none expr
          method: query with expr None
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          error = {ct.err_code: 0, ct.err_msg: "The type of expr must be string"}
          collection_w.query(None, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_non_string_expr(self):
          """
          target: test query with non-string expr
          method: query with non-string expr, eg 1, [] ..
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          exprs = [1, 2., [], {}, ()]
          error = {ct.err_code: 0, ct.err_msg: "The type of expr must be string"}
          for expr in exprs:
              collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_invalid_string(self):
          """
          target: test query with invalid expr
          method: query with invalid string expr
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          error = {ct.err_code: 1, ct.err_msg: "Invalid expression!"}
          exprs = ["12-s", "中文", "a", " "]
          for expr in exprs:
              collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.skip(reason="repeat with test_query, waiting for other expr")
      def test_query_expr_term(self):
          """
          target: test query with TermExpr
          method: query with TermExpr
          expected: query result is correct
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          res = vectors[0].iloc[:2, :1].to_dict('records')
          collection_w.query(default_term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_not_existed_field(self):
          """
          target: test query with not existed field
          method: query by term expr with fake field
          expected: raise exception
          """
          collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
          term_expr = 'field in [1, 2]'
          error = {ct.err_code: 1, ct.err_msg: "fieldName(field) not found"}
          collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_non_primary_fields(self):
          """
          target: test query on non-primary non-vector fields
          method: query on non-primary non-vector fields
          expected: verify query result
          """
          self._connect()
          # construct dataframe and inert data
          df = pd.DataFrame({
              ct.default_int64_field_name: pd.Series(data=[i for i in range(ct.default_nb)]),
              ct.default_int32_field_name: pd.Series(data=[np.int32(i) for i in range(ct.default_nb)], dtype="int32"),
              ct.default_int16_field_name: pd.Series(data=[np.int16(i) for i in range(ct.default_nb)], dtype="int16"),
              ct.default_float_field_name: pd.Series(data=[float(i) for i in range(ct.default_nb)], dtype="float32"),
              ct.default_double_field_name: pd.Series(data=[np.double(i) for i in range(ct.default_nb)], dtype="double"),
              ct.default_float_vec_field_name: cf.gen_vectors(ct.default_nb, ct.default_dim)
          })
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == ct.default_nb
          self.collection_wrap.load()
  
          # query by non_primary non_vector scalar field
          non_primary_field = [ct.default_int32_field_name, ct.default_int16_field_name,
                               ct.default_float_field_name, ct.default_double_field_name]
  
          # exp res: first two rows and all fields expect last vec field
          res = df.iloc[:2, :-1].to_dict('records')
          for field in non_primary_field:
              filter_values = df[field].tolist()[:2]
              term_expr = f'{field} in {filter_values}'
              self.collection_wrap.query(term_expr, output_fields=["*"],
                                         check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L2)
      @pytest.mark.xfail(reason="issue #7521 #7522")
      def test_query_expr_by_bool_field(self):
          """
          target: test query by bool field and output binary field
          method: 1.create and insert with [int64, float, bool, float_vec] fields
                  2.query by bool field, and output all int64, bool fields
          expected: verify query result and output fields
          """
          self._connect()
          df = cf.gen_default_dataframe_data()
          bool_values = pd.Series(data=[True if i % 2 == 0 else False for i in range(ct.default_nb)], dtype="bool")
          df.insert(2, ct.default_bool_field_name, bool_values)
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == ct.default_nb
          self.collection_wrap.load()
          term_expr = f'{ct.default_bool_field_name} in [True]'
          res, _ = self.collection_wrap.query(term_expr, output_fields=[ct.default_bool_field_name])
          assert len(res) == ct.default_nb / 2
          assert set(res[0].keys()) == set(ct.default_int64_field_name, ct.default_bool_field_name)
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_expr_by_int8_field(self):
          """
          target: test query by int8 field
          method: 1.create and insert with [int64, float, int8, float_vec] fields
                  2.query by int8 field, and output all scalar fields
          expected: verify query result
          """
          self._connect()
          # construct collection from dataFrame according to [int64, float, int8, float_vec]
          df = cf.gen_default_dataframe_data()
          int8_values = pd.Series(data=[np.int8(i) for i in range(ct.default_nb)], dtype="int8")
          df.insert(2, ct.default_int8_field_name, int8_values)
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == ct.default_nb
          # query expression
          term_expr = f'{ct.default_int8_field_name} in {[0]}'
          # expected query result
          res = []
          # int8 range [-128, 127] so when nb=1200, there are many repeated int8 values equal to 0
          for i in range(0, ct.default_nb, 256):
              res.extend(df.iloc[i:i + 1, :-1].to_dict('records'))
          self.collection_wrap.load()
          self.collection_wrap.query(term_expr, output_fields=["*"],
                                     check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_wrong_term_keyword(self):
          """
          target: test query with wrong term expr keyword
          method: query with wrong keyword term expr
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          expr_1 = f'{ct.default_int64_field_name} inn [1, 2]'
          error_1 = {ct.err_code: 1, ct.err_msg: f'unexpected token Identifier("inn")'}
          collection_w.query(expr_1, check_task=CheckTasks.err_res, check_items=error_1)
  
          expr_3 = f'{ct.default_int64_field_name} in not [1, 2]'
          error_3 = {ct.err_code: 1, ct.err_msg: 'right operand of the InExpr must be array'}
          collection_w.query(expr_3, check_task=CheckTasks.err_res, check_items=error_3)
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.parametrize("field", [ct.default_int64_field_name, ct.default_float_field_name])
      def test_query_expr_not_in_term(self, field):
          """
          target: test query with `not in` expr
          method: query with not in expr
          expected: verify query result
          """
          self._connect()
          df = cf.gen_default_dataframe_data()
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == ct.default_nb
          self.collection_wrap.load()
          values = df[field].tolist()
          pos = 100
          term_expr = f'{field} not in {values[pos:]}'
          res = df.iloc[:pos, :2].to_dict('records')
          self.collection_wrap.query(term_expr, output_fields=["*"],
                                     check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L2)
      @pytest.mark.parametrize("pos", [0, ct.default_nb])
      def test_query_expr_not_in_empty_and_all(self, pos):
          """
          target: test query with `not in` expr
          method: query with `not in` expr for (non)empty collection
          expected: verify query result
          """
          self._connect()
          df = cf.gen_default_dataframe_data()
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == ct.default_nb
          self.collection_wrap.load()
          int64_values = df[ct.default_int64_field_name].tolist()
          term_expr = f'{ct.default_int64_field_name} not in {int64_values[pos:]}'
          res = df.iloc[:pos, :1].to_dict('records')
          self.collection_wrap.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tag(CaseLabel.L1)
      def test_query_expr_random_values(self):
          """
          target: test query with random filter values
          method: query with random filter values, like [0, 2, 4, 3]
          expected: correct query result
          """
          self._connect()
          df = cf.gen_default_dataframe_data(nb=100)
          log.debug(df.head(5))
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == 100
          self.collection_wrap.load()
  
          # random_values = [random.randint(0, ct.default_nb) for _ in range(4)]
          random_values = [0, 2, 4, 3]
          term_expr = f'{ct.default_int64_field_name} in {random_values}'
          res = df.iloc[random_values, :1].to_dict('records')
          self.collection_wrap.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tag(CaseLabel.L1)
      def test_query_expr_not_in_random(self):
          """
          target: test query with fixed filter values
          method: query with fixed filter values
          expected: correct query result
          """
          self._connect()
          df = cf.gen_default_dataframe_data(nb=50)
          log.debug(df.head(5))
          self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                        primary_field=ct.default_int64_field_name)
          assert self.collection_wrap.num_entities == 50
          self.collection_wrap.load()
  
          random_values = [i for i in range(10, 50)]
          log.debug(f'random values: {random_values}')
          random.shuffle(random_values)
          term_expr = f'{ct.default_int64_field_name} not in {random_values}'
          res = df.iloc[:10, :1].to_dict('records')
          self.collection_wrap.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_non_array_term(self):
          """
          target: test query with non-array term expr
          method: query with non-array term expr
          expected: raise exception
          """
          exprs = [f'{ct.default_int64_field_name} in 1',
                   f'{ct.default_int64_field_name} in "in"',
                   f'{ct.default_int64_field_name} in (mn)']
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          error = {ct.err_code: 1, ct.err_msg: "right operand of the InExpr must be array"}
          for expr in exprs:
              collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_empty_term_array(self):
          """
          target: test query with empty array term expr
          method: query with empty term expr
          expected: empty result
          """
          term_expr = f'{ct.default_int64_field_name} in []'
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          res, _ = collection_w.query(term_expr)
          assert len(res) == 0
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_inconsistent_mix_term_array(self):
          """
          target: test query with term expr that field and array are inconsistent or mix type
          method: 1.query with int field and float values
                  2.query with term expr that has int and float type value
          expected: raise exception
          """
          collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
          int_values = [[1., 2.], [1, 2.]]
          error = {ct.err_code: 1, ct.err_msg: "type mismatch"}
          for values in int_values:
              term_expr = f'{ct.default_int64_field_name} in {values}'
              collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_non_constant_array_term(self):
          """
          target: test query with non-constant array term expr
          method: query with non-constant array expr
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          constants = [[1], (), {}]
          error = {ct.err_code: 1, ct.err_msg: "unsupported leaf node"}
          for constant in constants:
              term_expr = f'{ct.default_int64_field_name} in [{constant}]'
              collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_field_none_or_empty(self):
          """
          target: test query with none and empty output field
          method: query with output field=None, field=[]
          expected: return primary field
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          for fields in [None, []]:
              res, _ = collection_w.query(default_term_expr, output_fields=fields)
              assert list(res[0].keys()) == [ct.default_int64_field_name]
  
      @pytest.mark.tags(CaseLabel.L0)
      def test_query_output_one_field(self):
          """
          target: test query with output one field
          method: query with output one field
          expected: return one field
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_float_field_name])
          assert set(res[0].keys()) == set([ct.default_int64_field_name, ct.default_float_field_name])
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_all_fields(self):
          """
          target: test query with none output field
          method: query with output field=None
          expected: return all fields
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          df = cf.gen_default_dataframe_data()
          collection_w.insert(df)
          assert collection_w.num_entities == ct.default_nb
          all_fields = [ct.default_int64_field_name, ct.default_float_field_name, ct.default_float_vec_field_name]
          res = df.iloc[:2].to_dict('records')
          collection_w.load()
          actual_res, _ = collection_w.query(default_term_expr, output_fields=all_fields,
                                             check_task=CheckTasks.check_query_results,
                                             check_items={exp_res: res, "with_vec": True})
          assert set(actual_res[0].keys()) == set(all_fields)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_float_vec_field(self):
          """
          target: test query with vec output field
          method: specify vec field as output field
          expected: return primary field and vec field
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          df = cf.gen_default_dataframe_data()
          collection_w.insert(df)
          assert collection_w.num_entities == ct.default_nb
          fields = [[ct.default_float_vec_field_name], [ct.default_int64_field_name, ct.default_float_vec_field_name]]
          res = df.loc[:1, [ct.default_int64_field_name, ct.default_float_vec_field_name]].to_dict('records')
          collection_w.load()
          for output_fields in fields:
              collection_w.query(default_term_expr, output_fields=output_fields,
                                 check_task=CheckTasks.check_query_results,
                                 check_items={exp_res: res, "with_vec": True})
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.parametrize("vec_fields", [[cf.gen_float_vec_field(name="float_vector1")]])
      def test_query_output_multi_float_vec_field(self, vec_fields):
          """
          target: test query and output multi float vec fields
          method: a.specify multi vec field as output
                  b.specify output_fields with wildcard %
          expected: verify query result
          """
          # init collection with two float vector fields
          schema = cf.gen_schema_multi_vector_fields(vec_fields)
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema)
          df = cf.gen_dataframe_multi_vec_fields(vec_fields=vec_fields)
          collection_w.insert(df)
          assert collection_w.num_entities == ct.default_nb
  
          # query with two vec output_fields
          output_fields = [ct.default_int64_field_name, ct.default_float_vec_field_name]
          for vec_field in vec_fields:
              output_fields.append(vec_field.name)
          res = df.loc[:1, output_fields].to_dict('records')
          collection_w.load()
          collection_w.query(default_term_expr, output_fields=output_fields,
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res, "with_vec": True})
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.parametrize("vec_fields", [[cf.gen_binary_vec_field()],
                                              [cf.gen_binary_vec_field(), cf.gen_binary_vec_field("binary_vec1")]])
      def test_query_output_mix_float_binary_field(self, vec_fields):
          """
          target:  test query and output mix float and binary vec fields
          method: a.specify mix vec field as output
                  b.specify output_fields with wildcard %
          expected: output binary vector and float vec
          """
          # init collection with two float vector fields
          schema = cf.gen_schema_multi_vector_fields(vec_fields)
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema)
          df = cf.gen_dataframe_multi_vec_fields(vec_fields=vec_fields)
          collection_w.insert(df)
          assert collection_w.num_entities == ct.default_nb
  
          # query with two vec output_fields
          output_fields = [ct.default_int64_field_name, ct.default_float_vec_field_name]
          for vec_field in vec_fields:
              output_fields.append(vec_field.name)
          res = df.loc[:1, output_fields].to_dict('records')
          collection_w.load()
          collection_w.query(default_term_expr, output_fields=output_fields,
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res, "with_vec": True})
  
          # query with wildcard %
          collection_w.query(default_term_expr, output_fields=["%"],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res, "with_vec": True})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_binary_vec_field(self):
          """
          target: test query with binary vec output field
          method: specify binary vec field as output field
          expected: return primary field and binary vec field
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True, is_binary=True)[0:2]
          fields = [[ct.default_binary_vec_field_name], [ct.default_int64_field_name, ct.default_binary_vec_field_name]]
          for output_fields in fields:
              res, _ = collection_w.query(default_term_expr, output_fields=output_fields)
              assert list(res[0].keys()) == fields[-1]
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_primary_field(self):
          """
          target: test query with output field only primary field
          method: specify int64 primary field as output field
          expected: return int64 field
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_int64_field_name])
          assert list(res[0].keys()) == [ct.default_int64_field_name]
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_not_existed_field(self):
          """
          target: test query output not existed field
          method: query with not existed output field
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          error = {ct.err_code: 1, ct.err_msg: 'Field int not exist'}
          output_fields = [["int"], [ct.default_int64_field_name, "int"]]
          for fields in output_fields:
              collection_w.query(default_term_expr, output_fields=fields, check_task=CheckTasks.err_res,
                                 check_items=error)
  
      @pytest.mark.tags(CaseLabel.L2)
      @pytest.mark.xfail(reason="exception not MilvusException")
      def test_query_invalid_output_fields(self):
          """
          target: test query with invalid output fields
          method: query with invalid field fields
          expected: raise exception
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          output_fields = ["12-s", 1, [1, "2", 3], (1,), {1: 1}]
          error = {ct.err_code: 0, ct.err_msg: f'Invalid query format. \'output_fields\' must be a list'}
          for fields in output_fields:
              collection_w.query(default_term_expr, output_fields=fields, check_task=CheckTasks.err_res,
                                 check_items=error)
  
      @pytest.mark.tags(CaseLabel.L0)
      def test_query_output_fields_simple_wildcard(self):
          """
          target: test query output_fields with simple wildcard (* and %)
          method: specify output_fields as "*" and "*", "%"
          expected: output all scale field; output all fields
          """
          # init collection with fields: int64, float, float_vec, float_vector1
          collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix))
          collection_w.load()
  
          # query with wildcard scale(*)
          output_fields = [ct.default_int64_field_name, ct.default_float_field_name]
          res = df.loc[:1, output_fields].to_dict('records')
          collection_w.query(default_term_expr, output_fields=["*"],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res})
  
          # query with wildcard %
          output_fields2 = [ct.default_int64_field_name, ct.default_float_vec_field_name, ct.another_float_vec_field_name]
          res2 = df.loc[:1, output_fields2].to_dict('records')
          collection_w.query(default_term_expr, output_fields=["%"],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res2, "with_vec": True})
  
          # query with wildcard all fields: vector(%) and scale(*)
          res3 = df.iloc[:2].to_dict('records')
          collection_w.query(default_term_expr, output_fields=["*", "%"],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res3, "with_vec": True})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_fields_part_scale_wildcard(self):
          """
          target: test query output_fields with part wildcard
          method: specify output_fields as wildcard and part field
          expected: verify query result
          """
          # init collection with fields: int64, float, float_vec, float_vector1
          collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix))
  
          # query with output_fields=["*", float_vector)
          res = df.iloc[:2, :3].to_dict('records')
          collection_w.load()
          collection_w.query(default_term_expr, output_fields=["*", ct.default_float_vec_field_name],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res, "with_vec": True})
  
          # query with output_fields=["*", float)
          res2 = df.iloc[:2, :2].to_dict('records')
          collection_w.load()
          collection_w.query(default_term_expr, output_fields=["*", ct.default_float_field_name],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res2})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_fields_part_vector_wildcard(self):
          """
          target: test query output_fields with part wildcard
          method: specify output_fields as wildcard and part field
          expected: verify query result
          """
          # init collection with fields: int64, float, float_vec, float_vector1
          collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix))
          collection_w.load()
  
          # query with output_fields=["%", float), expected: all fields
          res = df.iloc[:2].to_dict('records')
          collection_w.query(default_term_expr, output_fields=["%", ct.default_float_field_name],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res, "with_vec": True})
  
          # query with output_fields=["%", float_vector), expected: int64, float_vector, float_vector1
          output_fields = [ct.default_int64_field_name, ct.default_float_vec_field_name, ct.another_float_vec_field_name]
          res2 = df.loc[:1, output_fields].to_dict('records')
          collection_w.query(default_term_expr, output_fields=["%", ct.default_float_vec_field_name],
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res2, "with_vec": True})
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.parametrize("output_fields", [["*%"], ["**"], ["*", "@"]])
      def test_query_invalid_wildcard(self, output_fields):
          """
          target: test query with invalid output wildcard
          method: output_fields is invalid output wildcard
          expected: raise exception
          """
          # init collection with fields: int64, float, float_vec, float_vector1
          collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix))
          collection_w.load()
  
          # query with invalid output_fields
          error = {ct.err_code: 1, ct.err_msg: f"Field {output_fields[-1]} not exist"}
          collection_w.query(default_term_expr, output_fields=output_fields,
                             check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L0)
      def test_query_partition(self):
          """
          target: test query on partition
          method: create a partition and query
          expected: verify query result
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          partition_w = self.init_partition_wrap(collection_wrap=collection_w)
          df = cf.gen_default_dataframe_data(ct.default_nb)
          partition_w.insert(df)
          assert collection_w.num_entities == ct.default_nb
          partition_w.load()
          res = df.iloc[:2, :1].to_dict('records')
          collection_w.query(default_term_expr, partition_names=[partition_w.name],
                             check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_partition_without_loading(self):
          """
          target: test query on partition without loading
          method: query on partition and no loading
          expected: raise exception
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          partition_w = self.init_partition_wrap(collection_wrap=collection_w)
          df = cf.gen_default_dataframe_data(ct.default_nb)
          partition_w.insert(df)
          assert partition_w.num_entities == ct.default_nb
          error = {ct.err_code: 1, ct.err_msg: f'collection {collection_w.name} was not loaded into memory'}
          collection_w.query(default_term_expr, partition_names=[partition_w.name],
                             check_task=CheckTasks.err_res, check_items=error)
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_default_partition(self):
          """
          target: test query on default partition
          method: query on default partition
          expected: verify query result
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
          res = vectors[0].iloc[:2, :1].to_dict('records')
          collection_w.query(default_term_expr, partition_names=[ct.default_partition_name],
                             check_task=CheckTasks.check_query_results, check_items={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_empty_partition(self):
          """
          target: test query on empty partition
          method: query on a empty collection
          expected: empty query result
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          partition_w = self.init_partition_wrap(collection_wrap=collection_w)
          assert partition_w.is_empty
          partition_w.load()
          res, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name])
          assert len(res) == 0
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_not_existed_partition(self):
          """
          target: test query on a not existed partition
          method: query on not existed partition
          expected: raise exception
          """
          collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
          collection_w.load()
          partition_names = cf.gen_unique_str()
          error = {ct.err_code: 1, ct.err_msg: f'PartitonName: {partition_names} not found'}
          collection_w.query(default_term_expr, partition_names=[partition_names],
                             check_task=CheckTasks.err_res, check_items=error)
  
  
  class TestQueryOperation(TestcaseBase):
      """
      ******************************************************************
        The following cases are used to test query interface operations
      ******************************************************************
      """
  
      @pytest.mark.tags(CaseLabel.L2)
      # @pytest.mark.parametrize("collection_name", [cf.gen_unique_str(prefix)])
      def test_query_without_connection(self):
          """
          target: test query without connection
          method: close connect and query
          expected: raise exception
          """
  
          # init a collection with default connection
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
  
          # remove default connection
          self.connection_wrap.remove_connection(alias=DefaultConfig.DEFAULT_USING)
  
          # list connection to check
          self.connection_wrap.list_connections(check_task=ct.CheckTasks.ccr, check_items={ct.list_content: []})
  
          # query after remove default connection
          collection_w.query(default_term_expr, check_task=CheckTasks.err_res,
                             check_items={ct.err_code: 0, ct.err_msg: cem.ConnectFirst})
  
      @pytest.mark.tags(CaseLabel.L1)
      # @pytest.mark.parametrize("collection_name, data",
      # [(cf.gen_unique_str(prefix), cf.gen_default_list_data(ct.default_nb))])
      def test_query_without_loading(self):
          """
          target: test query without loading
          method: no loading before query
          expected: raise exception
          """
  
          # init a collection with default connection
          collection_name = cf.gen_unique_str(prefix)
          collection_w = self.init_collection_wrap(name=collection_name)
  
          # insert data to collection
          collection_w.insert(data=cf.gen_default_list_data(ct.default_nb))
  
          # check number of entities and that method calls the flush interface
          assert collection_w.num_entities == ct.default_nb
  
          # query without load
          collection_w.query(default_term_expr, check_task=CheckTasks.err_res,
                             check_items={ct.err_code: 1, ct.err_msg: clem.CollNotLoaded % collection_name})
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.parametrize("term_expr", [f'{ct.default_int64_field_name} in [0]'])
      def test_query_expr_single_term_array(self, term_expr):
          """
          target: test query with single array term expr
          method: query with single array value
          expected: query result is one entity
          """
  
          # init a collection and insert data
          collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[0:3]
  
          # query the first row of data
          check_vec = vectors[0].iloc[:, [0]][0:1].to_dict('records')
          collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
  
      @pytest.mark.tags(CaseLabel.L1)
      @pytest.mark.parametrize("term_expr", [f'{ct.default_int64_field_name} in [0]'])
      def test_query_binary_expr_single_term_array(self, term_expr, check_content):
          """
          target: test query with single array term expr
          method: query with single array value
          expected: query result is one entity
          """
  
          # init a collection and insert data
          collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True,
                                                                                   is_binary=True)[0:3]
  
          # query the first row of data
          check_vec = vectors[0].iloc[:, [0]][0:1].to_dict('records')
          collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_expr_all_term_array(self):
          """
          target: test query with all array term expr
          method: query with all array value
          expected: verify query result
          """
  
          # init a collection and insert data
          collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[0:3]
  
          # data preparation
          int_values = vectors[0][ct.default_int64_field_name].values.tolist()
          term_expr = f'{ct.default_int64_field_name} in {int_values}'
          check_vec = vectors[0].iloc[:, [0]][0:len(int_values)].to_dict('records')
  
          # query all array value
          collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_expr_half_term_array(self):
          """
          target: test query with half array term expr
          method: query with half array value
          expected: verify query result
          """
  
          half = ct.default_nb // 2
          collection_w, partition_w, df_partition, df_default = self.insert_entities_into_two_partitions_in_half(half)
  
          int_values = df_default[ct.default_int64_field_name].values.tolist()
          term_expr = f'{ct.default_int64_field_name} in {int_values}'
          res, _ = collection_w.query(term_expr)
          assert len(res) == len(int_values)
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_expr_repeated_term_array(self):
          """
          target: test query with repeated term array on primary field with unique value
          method: query with repeated array value
          expected: return hit entities, no repeated
          """
          collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[0:3]
          int_values = [0, 0, 0, 0]
          term_expr = f'{ct.default_int64_field_name} in {int_values}'
          res, _ = collection_w.query(term_expr)
          assert len(res) == 1
          assert res[0][ct.default_int64_field_name] == int_values[0]
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_dup_ids_dup_term_array(self):
          """
          target: test query on duplicate primary keys with dup term array
          method: 1.create collection and insert dup primary keys
                  2.query with dup term array
          expected: todo
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          df = cf.gen_default_dataframe_data(nb=100)
          df[ct.default_int64_field_name] = 0
          mutation_res, _ = collection_w.insert(df)
          assert mutation_res.primary_keys == df[ct.default_int64_field_name].tolist()
          collection_w.load()
          term_expr = f'{ct.default_int64_field_name} in {[0, 0, 0]}'
          res = df.iloc[:, :2].to_dict('records')
          collection_w.query(term_expr, output_fields=["*"], check_items=CheckTasks.check_query_results,
                             check_task={exp_res: res})
  
      @pytest.mark.tags(CaseLabel.L0)
      def test_query_after_index(self):
          """
          target: test query after creating index
          method: query after index
          expected: query result is correct
          """
          collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[0:3]
  
          default_field_name = ct.default_float_vec_field_name
          collection_w.create_index(default_field_name, default_index_params)
  
          collection_w.load()
  
          int_values = [0]
          term_expr = f'{ct.default_int64_field_name} in {int_values}'
          check_vec = vectors[0].iloc[:, [0]][0:len(int_values)].to_dict('records')
          collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_after_search(self):
          """
          target: test query after search
          method: query after search
          expected: query result is correct
          """
  
          limit = 1000
          nb_old = 500
          collection_w, vectors, binary_raw_vectors, insert_ids = \
              self.init_collection_general(prefix, True, nb_old)
  
          # 2. search for original data after load
          vectors_s = [[random.random() for _ in range(ct.default_dim)] for _ in range(ct.default_nq)]
          collection_w.search(vectors_s[:ct.default_nq], ct.default_float_vec_field_name,
                              ct.default_search_params, limit, "int64 >= 0",
                              check_task=CheckTasks.check_search_results,
                              check_items={"nq": ct.default_nq, "limit": nb_old, "ids": insert_ids})
  
          # check number of entities and that method calls the flush interface
          assert collection_w.num_entities == nb_old
  
          term_expr = f'{ct.default_int64_field_name} in [0, 1]'
          check_vec = vectors[0].iloc[:, [0]][0:2].to_dict('records')
          collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
  
      @pytest.mark.tags(CaseLabel.L1)
      def test_query_output_vec_field_after_index(self):
          """
          target: test query output vec field after index
          method: create index and specify vec field as output field
          expected: return primary field and vec field
          """
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
          df = cf.gen_default_dataframe_data(nb=5000)
          collection_w.insert(df)
          assert collection_w.num_entities == 5000
          fields = [ct.default_int64_field_name, ct.default_float_vec_field_name]
          collection_w.create_index(ct.default_float_vec_field_name, default_index_params)
          assert collection_w.has_index()[0]
          res = df.loc[:1, [ct.default_int64_field_name, ct.default_float_vec_field_name]].to_dict('records')
          collection_w.load()
          collection_w.query(default_term_expr, output_fields=fields,
                             check_task=CheckTasks.check_query_results,
                             check_items={exp_res: res, "with_vec": True})
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_output_binary_vec_field_after_index(self):
          """
          target: test query output vec field after index
          method: create index and specify vec field as output field
          expected: return primary field and vec field
          """
          collection_w, vectors = self.init_collection_general(prefix, insert_data=True, is_binary=True)[0:2]
          fields = [ct.default_int64_field_name, ct.default_binary_vec_field_name]
          collection_w.create_index(ct.default_binary_vec_field_name, binary_index_params)
          assert collection_w.has_index()[0]
          res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_binary_vec_field_name])
          assert list(res[0].keys()) == fields
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_partition_repeatedly(self):
          """
          target: test query repeatedly on partition
          method: query on partition twice
          expected: verify query result
          """
  
          # create connection
          self._connect()
  
          # init collection
          collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
  
          # init partition
          partition_w = self.init_partition_wrap(collection_wrap=collection_w)
  
          # insert data to partition
          df = cf.gen_default_dataframe_data(ct.default_nb)
          partition_w.insert(df)
  
          # check number of entities and that method calls the flush interface
          assert collection_w.num_entities == ct.default_nb
  
          # load partition
          partition_w.load()
  
          # query twice
          res_one, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name])
          res_two, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name])
          assert res_one == res_two
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_another_partition(self):
          """
          target: test query another partition
          method: 1. insert entities into two partitions
                  2.query on one partition and query result empty
          expected: query result is empty
          """
          half = ct.default_nb // 2
          collection_w, partition_w, _, _ = self.insert_entities_into_two_partitions_in_half(half)
  
          term_expr = f'{ct.default_int64_field_name} in [{half}]'
          # half entity in _default partition rather than partition_w
          collection_w.query(term_expr, partition_names=[partition_w.name], check_task=CheckTasks.check_query_results,
                             check_items={exp_res: []})
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_multi_partitions_multi_results(self):
          """
          target: test query on multi partitions and get multi results
          method: 1.insert entities into two partitions
                  2.query on two partitions and query multi result
          expected: query results from two partitions
          """
          half = ct.default_nb // 2
          collection_w, partition_w, _, _ = self.insert_entities_into_two_partitions_in_half(half)
  
          term_expr = f'{ct.default_int64_field_name} in [{half - 1}, {half}]'
          # half entity in _default, half-1 entity in partition_w
          res, _ = collection_w.query(term_expr, partition_names=[ct.default_partition_name, partition_w.name])
          assert len(res) == 2
  
      @pytest.mark.tags(CaseLabel.L2)
      def test_query_multi_partitions_single_result(self):
          """
          target: test query on multi partitions and get single result
          method: 1.insert into two partitions
                  2.query on two partitions and query single result
          expected: query from two partitions and get single result
          """
          half = ct.default_nb // 2
          collection_w, partition_w, df_partition, df_default = self.insert_entities_into_two_partitions_in_half(half)
  
          term_expr = f'{ct.default_int64_field_name} in [{half}]'
          # half entity in _default
          res, _ = collection_w.query(term_expr, partition_names=[ct.default_partition_name, partition_w.name])
          assert len(res) == 1
          assert res[0][ct.default_int64_field_name] == half
  