import os import random import math import string import json import time import uuid from functools import singledispatch import numpy as np import pandas as pd import jax.numpy as jnp from sklearn import preprocessing from npy_append_array import NpyAppendArray from faker import Faker from pathlib import Path from minio import Minio from pymilvus import DataType from base.schema_wrapper import ApiCollectionSchemaWrapper, ApiFieldSchemaWrapper from common import common_type as ct from utils.util_log import test_log as log from customize.milvus_operator import MilvusOperator import pickle import tensorflow as tf fake = Faker() """" Methods of processing data """ @singledispatch def to_serializable(val): """Used by default.""" return str(val) @to_serializable.register(np.float32) def ts_float32(val): """Used if *val* is an instance of numpy.float32.""" return np.float64(val) class ParamInfo: def __init__(self): self.param_host = "" self.param_port = "" self.param_handler = "" self.param_user = "" self.param_password = "" self.param_secure = False self.param_replica_num = ct.default_replica_num self.param_uri = "" self.param_token = "" def prepare_param_info(self, host, port, handler, replica_num, user, password, secure, uri, token): self.param_host = host self.param_port = port self.param_handler = handler self.param_user = user self.param_password = password self.param_secure = secure self.param_replica_num = replica_num self.param_uri = uri self.param_token = token param_info = ParamInfo() def gen_unique_str(str_value=None): prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8)) return "test_" + prefix if str_value is None else str_value + "_" + prefix def gen_str_by_length(length=8, letters_only=False): if letters_only: return "".join(random.choice(string.ascii_letters) for _ in range(length)) return "".join(random.choice(string.ascii_letters + string.digits) for _ in range(length)) def gen_digits_by_length(length=8): return "".join(random.choice(string.digits) for _ in range(length)) def gen_bool_field(name=ct.default_bool_field_name, description=ct.default_desc, is_primary=False, **kwargs): bool_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.BOOL, description=description, is_primary=is_primary, **kwargs) return bool_field def gen_string_field(name=ct.default_string_field_name, description=ct.default_desc, is_primary=False, max_length=ct.default_length, **kwargs): string_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.VARCHAR, description=description, max_length=max_length, is_primary=is_primary, **kwargs) return string_field def gen_json_field(name=ct.default_json_field_name, description=ct.default_desc, is_primary=False, **kwargs): json_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.JSON, description=description, is_primary=is_primary, **kwargs) return json_field def gen_array_field(name=ct.default_array_field_name, element_type=DataType.INT64, max_capacity=ct.default_max_capacity, description=ct.default_desc, is_primary=False, **kwargs): array_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.ARRAY, element_type=element_type, max_capacity=max_capacity, description=description, is_primary=is_primary, **kwargs) return array_field def gen_int8_field(name=ct.default_int8_field_name, description=ct.default_desc, is_primary=False, **kwargs): int8_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.INT8, description=description, is_primary=is_primary, **kwargs) return int8_field def gen_int16_field(name=ct.default_int16_field_name, description=ct.default_desc, is_primary=False, **kwargs): int16_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.INT16, description=description, is_primary=is_primary, **kwargs) return int16_field def gen_int32_field(name=ct.default_int32_field_name, description=ct.default_desc, is_primary=False, **kwargs): int32_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.INT32, description=description, is_primary=is_primary, **kwargs) return int32_field def gen_int64_field(name=ct.default_int64_field_name, description=ct.default_desc, is_primary=False, **kwargs): int64_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.INT64, description=description, is_primary=is_primary, **kwargs) return int64_field def gen_float_field(name=ct.default_float_field_name, is_primary=False, description=ct.default_desc, **kwargs): float_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.FLOAT, description=description, is_primary=is_primary, **kwargs) return float_field def gen_double_field(name=ct.default_double_field_name, is_primary=False, description=ct.default_desc, **kwargs): double_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.DOUBLE, description=description, is_primary=is_primary, **kwargs) return double_field def gen_float_vec_field(name=ct.default_float_vec_field_name, is_primary=False, dim=ct.default_dim, description=ct.default_desc, vector_data_type="FLOAT_VECTOR", **kwargs): if vector_data_type == "FLOAT_VECTOR": dtype = DataType.FLOAT_VECTOR elif vector_data_type == "FLOAT16_VECTOR": dtype = DataType.FLOAT16_VECTOR elif vector_data_type == "BFLOAT16_VECTOR": dtype = DataType.BFLOAT16_VECTOR float_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=dtype, description=description, dim=dim, is_primary=is_primary, **kwargs) return float_vec_field def gen_binary_vec_field(name=ct.default_binary_vec_field_name, is_primary=False, dim=ct.default_dim, description=ct.default_desc, **kwargs): binary_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.BINARY_VECTOR, description=description, dim=dim, is_primary=is_primary, **kwargs) return binary_vec_field def gen_float16_vec_field(name=ct.default_float_vec_field_name, is_primary=False, dim=ct.default_dim, description=ct.default_desc, **kwargs): float_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.FLOAT16_VECTOR, description=description, dim=dim, is_primary=is_primary, **kwargs) return float_vec_field def gen_bfloat16_vec_field(name=ct.default_float_vec_field_name, is_primary=False, dim=ct.default_dim, description=ct.default_desc, **kwargs): float_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.BFLOAT16_VECTOR, description=description, dim=dim, is_primary=is_primary, **kwargs) return float_vec_field def gen_sparse_vec_field(name=ct.default_sparse_vec_field_name, is_primary=False, description=ct.default_desc, **kwargs): sparse_vec_field, _ = ApiFieldSchemaWrapper().init_field_schema(name=name, dtype=DataType.SPARSE_FLOAT_VECTOR, description=description, is_primary=is_primary, **kwargs) return sparse_vec_field def gen_default_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, enable_dynamic_field=False, with_json=True, multiple_dim_array=[], is_partition_key=None, vector_data_type="FLOAT_VECTOR", **kwargs): if enable_dynamic_field: if primary_field is ct.default_int64_field_name: if is_partition_key is None: fields = [gen_int64_field(), gen_float_vec_field(dim=dim, vector_data_type=vector_data_type)] else: fields = [gen_int64_field(is_partition_key=(is_partition_key == ct.default_int64_field_name)), gen_float_vec_field(dim=dim, vector_data_type=vector_data_type)] elif primary_field is ct.default_string_field_name: if is_partition_key is None: fields = [gen_string_field(), gen_float_vec_field(dim=dim, vector_data_type=vector_data_type)] else: fields = [gen_string_field(is_partition_key=(is_partition_key == ct.default_string_field_name)), gen_float_vec_field(dim=dim, vector_data_type=vector_data_type)] else: log.error("Primary key only support int or varchar") assert False else: if is_partition_key is None: int64_field = gen_int64_field() vchar_field = gen_string_field() else: int64_field = gen_int64_field(is_partition_key=(is_partition_key == ct.default_int64_field_name)) vchar_field = gen_string_field(is_partition_key=(is_partition_key == ct.default_string_field_name)) fields = [int64_field, gen_float_field(), vchar_field, gen_json_field(), gen_float_vec_field(dim=dim, vector_data_type=vector_data_type)] if with_json is False: fields.remove(gen_json_field()) if len(multiple_dim_array) != 0: for other_dim in multiple_dim_array: fields.append(gen_float_vec_field(gen_unique_str("multiple_vector"), dim=other_dim, vector_data_type=vector_data_type)) schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, enable_dynamic_field=enable_dynamic_field, **kwargs) return schema def gen_all_datatype_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, enable_dynamic_field=True, **kwargs): fields = [ gen_int64_field(), gen_float_field(), gen_string_field(), gen_json_field(), gen_array_field(name="array_int", element_type=DataType.INT64), gen_array_field(name="array_float", element_type=DataType.FLOAT), gen_array_field(name="array_varchar", element_type=DataType.VARCHAR, max_length=200), gen_array_field(name="array_bool", element_type=DataType.BOOL), gen_float_vec_field(dim=dim), gen_float_vec_field(name="image_emb", dim=dim), gen_float_vec_field(name="text_emb", dim=dim), gen_float_vec_field(name="voice_emb", dim=dim), ] schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, enable_dynamic_field=enable_dynamic_field, **kwargs) return schema def gen_array_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, enable_dynamic_field=False, max_capacity=ct.default_max_capacity, max_length=100, with_json=False, **kwargs): if enable_dynamic_field: if primary_field is ct.default_int64_field_name: fields = [gen_int64_field(), gen_float_vec_field(dim=dim)] elif primary_field is ct.default_string_field_name: fields = [gen_string_field(), gen_float_vec_field(dim=dim)] else: log.error("Primary key only support int or varchar") assert False else: fields = [gen_int64_field(), gen_float_vec_field(dim=dim), gen_json_field(), gen_array_field(name=ct.default_int32_array_field_name, element_type=DataType.INT32, max_capacity=max_capacity), gen_array_field(name=ct.default_float_array_field_name, element_type=DataType.FLOAT, max_capacity=max_capacity), gen_array_field(name=ct.default_string_array_field_name, element_type=DataType.VARCHAR, max_capacity=max_capacity, max_length=max_length)] if with_json is False: fields.remove(gen_json_field()) schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, enable_dynamic_field=enable_dynamic_field, **kwargs) return schema def gen_bulk_insert_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, with_varchar_field=True, auto_id=False, dim=ct.default_dim, enable_dynamic_field=False, with_json=False): if enable_dynamic_field: if primary_field is ct.default_int64_field_name: fields = [gen_int64_field(), gen_float_vec_field(dim=dim)] elif primary_field is ct.default_string_field_name: fields = [gen_string_field(), gen_float_vec_field(dim=dim)] else: log.error("Primary key only support int or varchar") assert False else: fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_json_field(), gen_float_vec_field(dim=dim)] if with_json is False: fields.remove(gen_json_field()) if with_varchar_field is False: fields.remove(gen_string_field()) schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, enable_dynamic_field=enable_dynamic_field) return schema def gen_general_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, is_binary=False, dim=ct.default_dim, **kwargs): if is_binary: fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_binary_vec_field(dim=dim)] else: fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_float_vec_field(dim=dim)] schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, **kwargs) return schema def gen_string_pk_default_collection_schema(description=ct.default_desc, primary_field=ct.default_string_field_name, auto_id=False, dim=ct.default_dim, **kwargs): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_json_field(), gen_float_vec_field(dim=dim)] schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, **kwargs) return schema def gen_json_default_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, **kwargs): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_json_field(), gen_float_vec_field(dim=dim)] schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, **kwargs) return schema def gen_multiple_json_default_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, **kwargs): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_json_field(name="json1"), gen_json_field(name="json2"), gen_float_vec_field(dim=dim)] schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, **kwargs) return schema def gen_collection_schema_all_datatype(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, enable_dynamic_field=False, with_json=True, multiple_dim_array=[], **kwargs): if enable_dynamic_field: fields = [gen_int64_field()] else: fields = [gen_int64_field(), gen_int32_field(), gen_int16_field(), gen_int8_field(), gen_bool_field(), gen_float_field(), gen_double_field(), gen_string_field(), gen_json_field()] if with_json is False: fields.remove(gen_json_field()) if len(multiple_dim_array) == 0: fields.append(gen_float_vec_field(dim=dim)) else: multiple_dim_array.insert(0, dim) for i in range(len(multiple_dim_array)): fields.append(gen_float_vec_field(name=f"multiple_vector_{ct.all_float_vector_types[i%3]}", dim=multiple_dim_array[i], vector_data_type=ct.all_float_vector_types[i%3])) schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, enable_dynamic_field=enable_dynamic_field, **kwargs) return schema def gen_collection_schema(fields, primary_field=None, description=ct.default_desc, auto_id=False, **kwargs): schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, primary_field=primary_field, description=description, auto_id=auto_id, **kwargs) return schema def gen_default_binary_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, dim=ct.default_dim, **kwargs): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_binary_vec_field(dim=dim)] binary_schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, **kwargs) return binary_schema def gen_default_sparse_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name, auto_id=False, **kwargs): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_sparse_vec_field()] sparse_schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description, primary_field=primary_field, auto_id=auto_id, **kwargs) return sparse_schema def gen_schema_multi_vector_fields(vec_fields): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_float_vec_field()] fields.extend(vec_fields) primary_field = ct.default_int64_field_name schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=ct.default_desc, primary_field=primary_field, auto_id=False) return schema def gen_schema_multi_string_fields(string_fields): fields = [gen_int64_field(), gen_float_field(), gen_string_field(), gen_float_vec_field()] fields.extend(string_fields) primary_field = ct.default_int64_field_name schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=ct.default_desc, primary_field=primary_field, auto_id=False) return schema def gen_vectors(nb, dim, vector_data_type="FLOAT_VECTOR"): vectors = [] if vector_data_type == "FLOAT_VECTOR": vectors = [[random.random() for _ in range(dim)] for _ in range(nb)] elif vector_data_type == "FLOAT16_VECTOR": vectors = gen_fp16_vectors(nb, dim)[1] elif vector_data_type == "BFLOAT16_VECTOR": vectors = gen_bf16_vectors(nb, dim)[1] elif vector_data_type == "SPARSE_VECTOR": vectors = gen_sparse_vectors(nb, dim) if dim > 1: if vector_data_type == "FLOAT_VECTOR": vectors = preprocessing.normalize(vectors, axis=1, norm='l2') vectors = vectors.tolist() return vectors def gen_string(nb): string_values = [str(random.random()) for _ in range(nb)] return string_values def gen_binary_vectors(num, dim): raw_vectors = [] binary_vectors = [] for _ in range(num): raw_vector = [random.randint(0, 1) for _ in range(dim)] raw_vectors.append(raw_vector) # packs a binary-valued array into bits in a unit8 array, and bytes array_of_ints binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist())) return raw_vectors, binary_vectors def gen_default_dataframe_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, random_primary_key=False, multiple_dim_array=[], multiple_vector_field_name=[], vector_data_type="FLOAT_VECTOR", auto_id=False, primary_field = ct.default_int64_field_name): if not random_primary_key: int_values = pd.Series(data=[i for i in range(start, start + nb)]) else: int_values = pd.Series(data=random.sample(range(start, start + nb), nb)) float_values = pd.Series(data=[np.float32(i) for i in range(start, start + nb)], dtype="float32") string_values = pd.Series(data=[str(i) for i in range(start, start + nb)], dtype="string") json_values = [{"number": i, "float": i*1.0} for i in range(start, start + nb)] float_vec_values = gen_vectors(nb, dim, vector_data_type=vector_data_type) df = pd.DataFrame({ ct.default_int64_field_name: int_values, ct.default_float_field_name: float_values, ct.default_string_field_name: string_values, ct.default_json_field_name: json_values, ct.default_float_vec_field_name: float_vec_values }) if with_json is False: df.drop(ct.default_json_field_name, axis=1, inplace=True) if auto_id is True: if primary_field == ct.default_int64_field_name: df.drop(ct.default_int64_field_name, axis=1, inplace=True) elif primary_field == ct.default_string_field_name: df.drop(ct.default_string_field_name, axis=1, inplace=True) if len(multiple_dim_array) != 0: if len(multiple_vector_field_name) != len(multiple_dim_array): log.error("multiple vector feature is enabled, please input the vector field name list " "not including the default vector field") assert len(multiple_vector_field_name) == len(multiple_dim_array) for i in range(len(multiple_dim_array)): new_float_vec_values = gen_vectors(nb, multiple_dim_array[i], vector_data_type=vector_data_type) df[multiple_vector_field_name[i]] = new_float_vec_values return df def gen_general_default_list_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, random_primary_key=False, multiple_dim_array=[], multiple_vector_field_name=[], vector_data_type="FLOAT_VECTOR", auto_id=False, primary_field=ct.default_int64_field_name): insert_list = [] if not random_primary_key: int_values = pd.Series(data=[i for i in range(start, start + nb)]) else: int_values = pd.Series(data=random.sample(range(start, start + nb), nb)) float_values = pd.Series(data=[np.float32(i) for i in range(start, start + nb)], dtype="float32") string_values = pd.Series(data=[str(i) for i in range(start, start + nb)], dtype="string") json_values = [{"number": i, "float": i*1.0} for i in range(start, start + nb)] float_vec_values = gen_vectors(nb, dim, vector_data_type=vector_data_type) insert_list = [int_values, float_values, string_values] if with_json is True: insert_list.append(json_values) insert_list.append(float_vec_values) if auto_id is True: if primary_field == ct.default_int64_field_name: index = 0 elif primary_field == ct.default_string_field_name: index = 2 del insert_list[index] if len(multiple_dim_array) != 0: if len(multiple_vector_field_name) != len(multiple_dim_array): log.error("multiple vector feature is enabled, please input the vector field name list " "not including the default vector field") assert len(multiple_vector_field_name) == len(multiple_dim_array) for i in range(len(multiple_dim_array)): new_float_vec_values = gen_vectors(nb, multiple_dim_array[i], vector_data_type=vector_data_type) insert_list.append(new_float_vec_values) return insert_list def gen_default_rows_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, multiple_dim_array=[], multiple_vector_field_name=[], vector_data_type="FLOAT_VECTOR", auto_id=False, primary_field = ct.default_int64_field_name): array = [] for i in range(start, start + nb): dict = {ct.default_int64_field_name: i, ct.default_float_field_name: i*1.0, ct.default_string_field_name: str(i), ct.default_json_field_name: {"number": i, "float": i*1.0}, ct.default_float_vec_field_name: gen_vectors(1, dim, vector_data_type=vector_data_type)[0] } if with_json is False: dict.pop(ct.default_json_field_name, None) if auto_id is True: if primary_field == ct.default_int64_field_name: dict.pop(ct.default_int64_field_name) elif primary_field == ct.default_string_field_name: dict.pop(ct.default_string_field_name) array.append(dict) if len(multiple_dim_array) != 0: for i in range(len(multiple_dim_array)): dict[multiple_vector_field_name[i]] = gen_vectors(1, multiple_dim_array[i], vector_data_type=vector_data_type)[0] log.debug("generated default row data") return array def gen_json_data_for_diff_json_types(nb=ct.default_nb, start=0, json_type="json_embedded_object"): """ Method: gen json data for different json types. Refer to RFC7159 """ if json_type == "json_embedded_object": # a json object with an embedd json object return [{json_type: {"number": i, "level2": {"level2_number": i, "level2_float": i*1.0, "level2_str": str(i)}, "float": i*1.0}, "str": str(i)} for i in range(start, start + nb)] if json_type == "json_objects_array": # a json-objects array with 2 json objects return [[{"number": i, "level2": {"level2_number": i, "level2_float": i*1.0, "level2_str": str(i)}, "float": i*1.0, "str": str(i)}, {"number": i, "level2": {"level2_number": i, "level2_float": i*1.0, "level2_str": str(i)}, "float": i*1.0, "str": str(i)} ] for i in range(start, start + nb)] if json_type == "json_array": # single array as json value return [[i for i in range(j, j + 10)] for j in range(start, start + nb)] if json_type == "json_int": # single int as json value return [i for i in range(start, start + nb)] if json_type == "json_float": # single float as json value return [i*1.0 for i in range(start, start + nb)] if json_type == "json_string": # single string as json value return [str(i) for i in range(start, start + nb)] if json_type == "json_bool": # single bool as json value return [bool(i) for i in range(start, start + nb)] else: return [] def gen_default_data_for_upsert(nb=ct.default_nb, dim=ct.default_dim, start=0, size=10000): int_values = pd.Series(data=[i for i in range(start, start + nb)]) float_values = pd.Series(data=[np.float32(i + size) for i in range(start, start + nb)], dtype="float32") string_values = pd.Series(data=[str(i + size) for i in range(start, start + nb)], dtype="string") json_values = [{"number": i, "string": str(i)} for i in range(start, start + nb)] float_vec_values = gen_vectors(nb, dim) df = pd.DataFrame({ ct.default_int64_field_name: int_values, ct.default_float_field_name: float_values, ct.default_string_field_name: string_values, ct.default_json_field_name: json_values, ct.default_float_vec_field_name: float_vec_values }) return df, float_values def gen_array_dataframe_data(nb=ct.default_nb, dim=ct.default_dim, start=0, auto_id=False, array_length=ct.default_max_capacity, with_json=False, random_primary_key=False): if not random_primary_key: int_values = pd.Series(data=[i for i in range(start, start + nb)]) else: int_values = pd.Series(data=random.sample(range(start, start + nb), nb)) float_vec_values = gen_vectors(nb, dim) json_values = [{"number": i, "float": i * 1.0} for i in range(start, start + nb)] int32_values = pd.Series(data=[[np.int32(j) for j in range(i, i + array_length)] for i in range(start, start + nb)]) float_values = pd.Series(data=[[np.float32(j) for j in range(i, i + array_length)] for i in range(start, start + nb)]) string_values = pd.Series(data=[[str(j) for j in range(i, i + array_length)] for i in range(start, start + nb)]) df = pd.DataFrame({ ct.default_int64_field_name: int_values, ct.default_float_vec_field_name: float_vec_values, ct.default_json_field_name: json_values, ct.default_int32_array_field_name: int32_values, ct.default_float_array_field_name: float_values, ct.default_string_array_field_name: string_values, }) if with_json is False: df.drop(ct.default_json_field_name, axis=1, inplace=True) if auto_id: df.drop(ct.default_int64_field_name, axis=1, inplace=True) return df def gen_dataframe_multi_vec_fields(vec_fields, nb=ct.default_nb): """ gen dataframe data for fields: int64, float, float_vec and vec_fields :param nb: num of entities, default default_nb :param vec_fields: list of FieldSchema :return: dataframe """ int_values = pd.Series(data=[i for i in range(0, nb)]) float_values = pd.Series(data=[float(i) for i in range(nb)], dtype="float32") string_values = pd.Series(data=[str(i) for i in range(nb)], dtype="string") df = pd.DataFrame({ ct.default_int64_field_name: int_values, ct.default_float_field_name: float_values, ct.default_string_field_name: string_values, ct.default_float_vec_field_name: gen_vectors(nb, ct.default_dim) }) for field in vec_fields: dim = field.params['dim'] if field.dtype == DataType.FLOAT_VECTOR: vec_values = gen_vectors(nb, dim) elif field.dtype == DataType.BINARY_VECTOR: vec_values = gen_binary_vectors(nb, dim)[1] df[field.name] = vec_values return df def gen_dataframe_multi_string_fields(string_fields, nb=ct.default_nb): """ gen dataframe data for fields: int64, float, float_vec and vec_fields :param nb: num of entities, default default_nb :param vec_fields: list of FieldSchema :return: dataframe """ int_values = pd.Series(data=[i for i in range(0, nb)]) float_values = pd.Series(data=[float(i) for i in range(nb)], dtype="float32") string_values = pd.Series(data=[str(i) for i in range(nb)], dtype="string") df = pd.DataFrame({ ct.default_int64_field_name: int_values, ct.default_float_field_name: float_values, ct.default_string_field_name: string_values, ct.default_float_vec_field_name: gen_vectors(nb, ct.default_dim) }) for field in string_fields: if field.dtype == DataType.VARCHAR: string_values = gen_string(nb) df[field.name] = string_values return df def gen_dataframe_all_data_type(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, auto_id=False, random_primary_key=False, multiple_dim_array=[], multiple_vector_field_name=[], primary_field=ct.default_int64_field_name): if not random_primary_key: int64_values = pd.Series(data=[i for i in range(start, start + nb)]) else: int64_values = pd.Series(data=random.sample(range(start, start + nb), nb)) int32_values = pd.Series(data=[np.int32(i) for i in range(start, start + nb)], dtype="int32") int16_values = pd.Series(data=[np.int16(i) for i in range(start, start + nb)], dtype="int16") int8_values = pd.Series(data=[np.int8(i) for i in range(start, start + nb)], dtype="int8") bool_values = pd.Series(data=[np.bool_(i) for i in range(start, start + nb)], dtype="bool") float_values = pd.Series(data=[np.float32(i) for i in range(start, start + nb)], dtype="float32") double_values = pd.Series(data=[np.double(i) for i in range(start, start + nb)], dtype="double") string_values = pd.Series(data=[str(i) for i in range(start, start + nb)], dtype="string") json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(i, i + ct.default_json_list_length)]} for i in range(start, start + nb)] float_vec_values = gen_vectors(nb, dim) df = pd.DataFrame({ ct.default_int64_field_name: int64_values, ct.default_int32_field_name: int32_values, ct.default_int16_field_name: int16_values, ct.default_int8_field_name: int8_values, ct.default_bool_field_name: bool_values, ct.default_float_field_name: float_values, ct.default_double_field_name: double_values, ct.default_string_field_name: string_values, ct.default_json_field_name: json_values }) if len(multiple_dim_array) == 0: df[ct.default_float_vec_field_name] = float_vec_values else: for i in range(len(multiple_dim_array)): df[multiple_vector_field_name[i]] = gen_vectors(nb, multiple_dim_array[i], ct.all_float_vector_types[i%3]) if with_json is False: df.drop(ct.default_json_field_name, axis=1, inplace=True) if auto_id: if primary_field == ct.default_int64_field_name: df.drop(ct.default_int64_field_name, axis=1, inplace=True) elif primary_field == ct.default_string_field_name: df.drop(ct.default_string_field_name, axis=1, inplace=True) log.debug("generated data completed") return df def gen_general_list_all_data_type(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, auto_id=False, random_primary_key=False, multiple_dim_array=[], multiple_vector_field_name=[], primary_field=ct.default_int64_field_name): if not random_primary_key: int64_values = pd.Series(data=[i for i in range(start, start + nb)]) else: int64_values = pd.Series(data=random.sample(range(start, start + nb), nb)) int32_values = pd.Series(data=[np.int32(i) for i in range(start, start + nb)], dtype="int32") int16_values = pd.Series(data=[np.int16(i) for i in range(start, start + nb)], dtype="int16") int8_values = pd.Series(data=[np.int8(i) for i in range(start, start + nb)], dtype="int8") bool_values = pd.Series(data=[np.bool_(i) for i in range(start, start + nb)], dtype="bool") float_values = pd.Series(data=[np.float32(i) for i in range(start, start + nb)], dtype="float32") double_values = pd.Series(data=[np.double(i) for i in range(start, start + nb)], dtype="double") string_values = pd.Series(data=[str(i) for i in range(start, start + nb)], dtype="string") json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(i, i + ct.default_json_list_length)]} for i in range(start, start + nb)] float_vec_values = gen_vectors(nb, dim) insert_list = [int64_values, int32_values, int16_values, int8_values, bool_values, float_values, double_values, string_values, json_values] if len(multiple_dim_array) == 0: insert_list.append(float_vec_values) else: for i in range(len(multiple_dim_array)): insert_list.append(gen_vectors(nb, multiple_dim_array[i], ct.all_float_vector_types[i%3])) if with_json is False: # index = insert_list.index(json_values) del insert_list[8] if auto_id: if primary_field == ct.default_int64_field_name: index = insert_list.index(int64_values) elif primary_field == ct.default_string_field_name: index = insert_list.index(string_values) del insert_list[index] log.debug("generated data completed") return insert_list def gen_default_rows_data_all_data_type(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, multiple_dim_array=[], multiple_vector_field_name=[], partition_id=0, auto_id=False, primary_field=ct.default_int64_field_name): array = [] for i in range(start, start + nb): dict = {ct.default_int64_field_name: i, ct.default_int32_field_name: i, ct.default_int16_field_name: i, ct.default_int8_field_name: i, ct.default_bool_field_name: bool(i), ct.default_float_field_name: i*1.0, ct.default_double_field_name: i * 1.0, ct.default_string_field_name: str(i), ct.default_json_field_name: {"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(i, i + ct.default_json_list_length)]} } if with_json is False: dict.pop(ct.default_json_field_name, None) if auto_id is True: if primary_field == ct.default_int64_field_name: dict.pop(ct.default_int64_field_name, None) elif primary_field == ct.default_string_field_name: dict.pop(ct.default_string_field_name, None) array.append(dict) if len(multiple_dim_array) == 0: dict[ct.default_float_vec_field_name] = gen_vectors(1, dim)[0] else: for i in range(len(multiple_dim_array)): dict[multiple_vector_field_name[i]] = gen_vectors(nb, multiple_dim_array[i], ct.all_float_vector_types[i])[0] if len(multiple_dim_array) != 0: with open(ct.rows_all_data_type_file_path + f'_{partition_id}' + f'_dim{dim}.txt', 'wb') as json_file: pickle.dump(array, json_file) log.info("generated rows data") return array def gen_default_binary_dataframe_data(nb=ct.default_nb, dim=ct.default_dim, start=0, auto_id=False, primary_field=ct.default_int64_field_name): int_values = pd.Series(data=[i for i in range(start, start + nb)]) float_values = pd.Series(data=[np.float32(i) for i in range(start, start + nb)], dtype="float32") string_values = pd.Series(data=[str(i) for i in range(start, start + nb)], dtype="string") binary_raw_values, binary_vec_values = gen_binary_vectors(nb, dim) df = pd.DataFrame({ ct.default_int64_field_name: int_values, ct.default_float_field_name: float_values, ct.default_string_field_name: string_values, ct.default_binary_vec_field_name: binary_vec_values }) if auto_id is True: if primary_field == ct.default_int64_field_name: df.drop(ct.default_int64_field_name, axis=1, inplace=True) elif primary_field == ct.default_string_field_name: df.drop(ct.default_string_field_name, axis=1, inplace=True) return df, binary_raw_values def gen_default_list_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True): int_values = [i for i in range(start, start + nb)] float_values = [np.float32(i) for i in range(start, start + nb)] string_values = [str(i) for i in range(start, start + nb)] json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(0, i)]} for i in range(start, start + nb)] float_vec_values = gen_vectors(nb, dim) if with_json is False: data = [int_values, float_values, string_values, float_vec_values] else: data = [int_values, float_values, string_values, json_values, float_vec_values] return data def gen_default_list_sparse_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=False): int_values = [i for i in range(start, start + nb)] float_values = [np.float32(i) for i in range(start, start + nb)] string_values = [str(i) for i in range(start, start + nb)] json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(0, i)]} for i in range(start, start + nb)] sparse_vec_values = gen_vectors(nb, dim, vector_data_type="SPARSE_VECTOR") if with_json: data = [int_values, float_values, string_values, json_values, sparse_vec_values] else: data = [int_values, float_values, string_values, sparse_vec_values] return data def gen_default_list_data_for_bulk_insert(nb=ct.default_nb, varchar_len=2000, with_varchar_field=True): str_value = gen_str_by_length(length=varchar_len) int_values = [i for i in range(nb)] float_values = [np.float32(i) for i in range(nb)] string_values = [f"{str(i)}_{str_value}" for i in range(nb)] # in case of large nb, float_vec_values will be too large in memory # then generate float_vec_values in each loop instead of generating all at once during generate npy or json file float_vec_values = [] # placeholder for float_vec data = [int_values, float_values, string_values, float_vec_values] if with_varchar_field is False: data = [int_values, float_values, float_vec_values] return data def prepare_bulk_insert_data(schema=None, nb=ct.default_nb, file_type="npy", minio_endpoint="127.0.0.1:9000", bucket_name="milvus-bucket"): schema = gen_default_collection_schema() if schema is None else schema dim = get_dim_by_schema(schema=schema) log.info(f"start to generate raw data for bulk insert") t0 = time.time() data = get_column_data_by_schema(schema=schema, nb=nb, skip_vectors=True) log.info(f"generate raw data for bulk insert cost {time.time() - t0} s") data_dir = "/tmp/bulk_insert_data" Path(data_dir).mkdir(parents=True, exist_ok=True) log.info(f"schema:{schema}, nb:{nb}, file_type:{file_type}, minio_endpoint:{minio_endpoint}, bucket_name:{bucket_name}") files = [] log.info(f"generate {file_type} files for bulk insert") if file_type == "json": files = gen_json_files_for_bulk_insert(data, schema, data_dir) if file_type == "npy": files = gen_npy_files_for_bulk_insert(data, schema, data_dir) log.info(f"generated {len(files)} {file_type} files for bulk insert, cost {time.time() - t0} s") log.info("upload file to minio") client = Minio(minio_endpoint, access_key="minioadmin", secret_key="minioadmin", secure=False) for file_name in files: file_size = os.path.getsize(os.path.join(data_dir, file_name)) / 1024 / 1024 t0 = time.time() client.fput_object(bucket_name, file_name, os.path.join(data_dir, file_name)) log.info(f"upload file {file_name} to minio, size: {file_size:.2f} MB, cost {time.time() - t0:.2f} s") return files def get_column_data_by_schema(nb=ct.default_nb, schema=None, skip_vectors=False, start=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields fields_not_auto_id = [] for field in fields: if not field.auto_id: fields_not_auto_id.append(field) data = [] for field in fields_not_auto_id: if field.dtype == DataType.FLOAT_VECTOR and skip_vectors is True: tmp = [] else: tmp = gen_data_by_collection_field(field, nb=nb, start=start) data.append(tmp) return data def gen_row_data_by_schema(nb=ct.default_nb, schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields fields_not_auto_id = [] for field in fields: if not field.auto_id: fields_not_auto_id.append(field) data = [] for i in range(nb): tmp = {} for field in fields_not_auto_id: tmp[field.name] = gen_data_by_collection_field(field) data.append(tmp) return data def get_fields_map(schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields fields_map = {} for field in fields: fields_map[field.name] = field.dtype return fields_map def get_int64_field_name(schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype == DataType.INT64: return field.name return None def get_float_field_name(schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype == DataType.FLOAT or field.dtype == DataType.DOUBLE: return field.name return None def get_float_vec_field_name(schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype == DataType.FLOAT_VECTOR: return field.name return None def get_float_vec_field_name_list(schema=None): vec_fields = [] if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype in [DataType.FLOAT_VECTOR, DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR]: vec_fields.append(field.name) return vec_fields def get_scalar_field_name_list(schema=None): vec_fields = [] if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype in [DataType.BOOL, DataType.INT8, DataType.INT16, DataType.INT32, DataType.INT64, DataType.FLOAT, DataType.DOUBLE, DataType.VARCHAR]: vec_fields.append(field.name) return vec_fields def get_binary_vec_field_name(schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype == DataType.BINARY_VECTOR: return field.name return None def get_binary_vec_field_name_list(schema=None): vec_fields = [] if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype in [DataType.BINARY_VECTOR]: vec_fields.append(field.name) return vec_fields def get_dim_by_schema(schema=None): if schema is None: schema = gen_default_collection_schema() fields = schema.fields for field in fields: if field.dtype == DataType.FLOAT_VECTOR or field.dtype == DataType.BINARY_VECTOR: dim = field.params['dim'] return dim return None def gen_data_by_collection_field(field, nb=None, start=None): # if nb is None, return one data, else return a list of data data_type = field.dtype if data_type == DataType.BOOL: if nb is None: return random.choice([True, False]) return [random.choice([True, False]) for _ in range(nb)] if data_type == DataType.INT8: if nb is None: return random.randint(-128, 127) return [random.randint(-128, 127) for _ in range(nb)] if data_type == DataType.INT16: if nb is None: return random.randint(-32768, 32767) return [random.randint(-32768, 32767) for _ in range(nb)] if data_type == DataType.INT32: if nb is None: return random.randint(-2147483648, 2147483647) return [random.randint(-2147483648, 2147483647) for _ in range(nb)] if data_type == DataType.INT64: if nb is None: return random.randint(-9223372036854775808, 9223372036854775807) if start is not None: return [i for i in range(start, start+nb)] return [random.randint(-9223372036854775808, 9223372036854775807) for _ in range(nb)] if data_type == DataType.FLOAT: if nb is None: return np.float32(random.random()) return [np.float32(random.random()) for _ in range(nb)] if data_type == DataType.DOUBLE: if nb is None: return np.float64(random.random()) return [np.float64(random.random()) for _ in range(nb)] if data_type == DataType.VARCHAR: max_length = field.params['max_length'] max_length = min(20, max_length-1) length = random.randint(0, max_length) if nb is None: return "".join([chr(random.randint(97, 122)) for _ in range(length)]) return ["".join([chr(random.randint(97, 122)) for _ in range(length)]) for _ in range(nb)] if data_type == DataType.JSON: if nb is None: return {"name": fake.name(), "address": fake.address()} data = [{"name": str(i), "address": i} for i in range(nb)] return data if data_type == DataType.FLOAT_VECTOR: dim = field.params['dim'] if nb is None: return [random.random() for i in range(dim)] return [[random.random() for i in range(dim)] for _ in range(nb)] if data_type == DataType.BFLOAT16_VECTOR: dim = field.params['dim'] if nb is None: raw_vector = [random.random() for _ in range(dim)] bf16_vector = jnp.array(raw_vector, dtype=jnp.bfloat16) bf16_vector = np.array(bf16_vector).view(np.uint8).tolist() return bytes(bf16_vector) bf16_vectors = [] for i in range(nb): raw_vector = [random.random() for _ in range(dim)] bf16_vector = jnp.array(raw_vector, dtype=jnp.bfloat16) bf16_vector = np.array(bf16_vector).view(np.uint8).tolist() bf16_vectors.append(bytes(bf16_vector)) return bf16_vectors if data_type == DataType.FLOAT16_VECTOR: dim = field.params['dim'] if nb is None: return [random.random() for i in range(dim)] return [[random.random() for i in range(dim)] for _ in range(nb)] if data_type == DataType.BINARY_VECTOR: dim = field.params['dim'] if nb is None: raw_vector = [random.randint(0, 1) for _ in range(dim)] binary_byte = bytes(np.packbits(raw_vector, axis=-1).tolist()) return binary_byte return [bytes(np.packbits([random.randint(0, 1) for _ in range(dim)], axis=-1).tolist()) for _ in range(nb)] if data_type == DataType.ARRAY: max_capacity = field.params['max_capacity'] element_type = field.element_type if element_type == DataType.INT32: if nb is None: return [random.randint(-2147483648, 2147483647) for _ in range(max_capacity)] return [[random.randint(-2147483648, 2147483647) for _ in range(max_capacity)] for _ in range(nb)] if element_type == DataType.INT64: if nb is None: return [random.randint(-9223372036854775808, 9223372036854775807) for _ in range(max_capacity)] return [[random.randint(-9223372036854775808, 9223372036854775807) for _ in range(max_capacity)] for _ in range(nb)] if element_type == DataType.BOOL: if nb is None: return [random.choice([True, False]) for _ in range(max_capacity)] return [[random.choice([True, False]) for _ in range(max_capacity)] for _ in range(nb)] if element_type == DataType.FLOAT: if nb is None: return [np.float32(random.random()) for _ in range(max_capacity)] return [[np.float32(random.random()) for _ in range(max_capacity)] for _ in range(nb)] if element_type == DataType.VARCHAR: max_length = field.params['max_length'] max_length = min(20, max_length - 1) length = random.randint(0, max_length) if nb is None: return ["".join([chr(random.randint(97, 122)) for _ in range(length)]) for _ in range(max_capacity)] return [["".join([chr(random.randint(97, 122)) for _ in range(length)]) for _ in range(max_capacity)] for _ in range(nb)] return None def gen_data_by_collection_schema(schema, nb, r=0): """ gen random data by collection schema, regardless of primary key or auto_id vector type only support for DataType.FLOAT_VECTOR """ data = [] start_uid = r * nb fields = schema.fields for field in fields: data.append(gen_data_by_collection_field(field, nb, start_uid)) return data def gen_json_files_for_bulk_insert(data, schema, data_dir): for d in data: if len(d) > 0: nb = len(d) dim = get_dim_by_schema(schema) vec_field_name = get_float_vec_field_name(schema) fields_name = [field.name for field in schema.fields] # get vec field index vec_field_index = fields_name.index(vec_field_name) uuid_str = str(uuid.uuid4()) log.info(f"file dir name: {uuid_str}") file_name = f"{uuid_str}/bulk_insert_data_source_dim_{dim}_nb_{nb}.json" files = [file_name] data_source = os.path.join(data_dir, file_name) Path(data_source).parent.mkdir(parents=True, exist_ok=True) log.info(f"file name: {data_source}") with open(data_source, "w") as f: f.write("{") f.write("\n") f.write('"rows":[') f.write("\n") for i in range(nb): entity_value = [None for _ in range(len(fields_name))] for j in range(len(data)): if j == vec_field_index: entity_value[j] = [random.random() for _ in range(dim)] else: entity_value[j] = data[j][i] entity = dict(zip(fields_name, entity_value)) f.write(json.dumps(entity, indent=4, default=to_serializable)) if i != nb - 1: f.write(",") f.write("\n") f.write("]") f.write("\n") f.write("}") return files def gen_npy_files_for_bulk_insert(data, schema, data_dir): for d in data: if len(d) > 0: nb = len(d) dim = get_dim_by_schema(schema) vec_field_name = get_float_vec_field_name(schema) fields_name = [field.name for field in schema.fields] files = [] uuid_str = uuid.uuid4() for field in fields_name: files.append(f"{uuid_str}/{field}.npy") for i, file in enumerate(files): data_source = os.path.join(data_dir, file) # mkdir for npy file Path(data_source).parent.mkdir(parents=True, exist_ok=True) log.info(f"save file {data_source}") if vec_field_name in file: log.info(f"generate {nb} vectors with dim {dim} for {data_source}") with NpyAppendArray(data_source, "wb") as npaa: for j in range(nb): vector = np.array([[random.random() for _ in range(dim)]]) npaa.append(vector) elif isinstance(data[i][0], dict): tmp = [] for d in data[i]: tmp.append(json.dumps(d)) data[i] = tmp np.save(data_source, np.array(data[i])) else: np.save(data_source, np.array(data[i])) return files def gen_default_tuple_data(nb=ct.default_nb, dim=ct.default_dim): int_values = [i for i in range(nb)] float_values = [np.float32(i) for i in range(nb)] string_values = [str(i) for i in range(nb)] float_vec_values = gen_vectors(nb, dim) data = (int_values, float_values, string_values, float_vec_values) return data def gen_numpy_data(nb=ct.default_nb, dim=ct.default_dim): int_values = np.arange(nb, dtype='int64') float_values = np.arange(nb, dtype='float32') string_values = [np.str_(i) for i in range(nb)] json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(i, i + ct.default_json_list_length)]} for i in range(nb)] float_vec_values = gen_vectors(nb, dim) data = [int_values, float_values, string_values, json_values, float_vec_values] return data def gen_default_binary_list_data(nb=ct.default_nb, dim=ct.default_dim): int_values = [i for i in range(nb)] float_values = [np.float32(i) for i in range(nb)] string_values = [str(i) for i in range(nb)] binary_raw_values, binary_vec_values = gen_binary_vectors(nb, dim) data = [int_values, float_values, string_values, binary_vec_values] return data, binary_raw_values def gen_simple_index(): index_params = [] for i in range(len(ct.all_index_types)): if ct.all_index_types[i] in ct.binary_support: continue elif ct.all_index_types[i] in ct.sparse_support: continue dic = {"index_type": ct.all_index_types[i], "metric_type": "L2"} dic.update({"params": ct.default_all_indexes_params[i]}) index_params.append(dic) return index_params def gen_autoindex_params(): index_params = [ {}, {"metric_type": "IP"}, {"metric_type": "L2"}, {"metric_type": "COSINE"}, {"index_type": "AUTOINDEX"}, {"index_type": "AUTOINDEX", "metric_type": "L2"}, {"index_type": "AUTOINDEX", "metric_type": "COSINE"}, {"index_type": "IVF_FLAT", "metric_type": "L2", "nlist": "1024", "m": "100"}, {"index_type": "DISKANN", "metric_type": "L2"}, {"index_type": "IVF_PQ", "nlist": "128", "m": "16", "nbits": "8", "metric_type": "IP"}, {"index_type": "IVF_SQ8", "nlist": "128", "metric_type": "COSINE"} ] return index_params def gen_invalid_field_types(): field_types = [ 6, 1.0, [[]], {}, (), "", "a" ] return field_types def gen_invalid_search_params_type(): invalid_search_key = 100 search_params = [] for index_type in ct.all_index_types: if index_type == "FLAT": continue # search_params.append({"index_type": index_type, "search_params": {"invalid_key": invalid_search_key}}) if index_type in ["IVF_FLAT", "IVF_SQ8", "IVF_PQ"]: for nprobe in ct.get_invalid_ints: ivf_search_params = {"index_type": index_type, "search_params": {"nprobe": nprobe}} search_params.append(ivf_search_params) elif index_type in ["HNSW"]: for ef in ct.get_invalid_ints: hnsw_search_param = {"index_type": index_type, "search_params": {"ef": ef}} search_params.append(hnsw_search_param) elif index_type == "ANNOY": for search_k in ct.get_invalid_ints: if isinstance(search_k, int): continue annoy_search_param = {"index_type": index_type, "search_params": {"search_k": search_k}} search_params.append(annoy_search_param) elif index_type == "SCANN": for reorder_k in ct.get_invalid_ints: if isinstance(reorder_k, int): continue scann_search_param = {"index_type": index_type, "search_params": {"nprobe": 8, "reorder_k": reorder_k}} search_params.append(scann_search_param) elif index_type == "DISKANN": for search_list in ct.get_invalid_ints[1:]: diskann_search_param = {"index_type": index_type, "search_params": {"search_list": search_list}} search_params.append(diskann_search_param) return search_params def gen_search_param(index_type, metric_type="L2"): search_params = [] if index_type in ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_PQ", "GPU_IVF_FLAT", "GPU_IVF_PQ"]: if index_type in ["GPU_FLAT"]: ivf_search_params = {"metric_type": metric_type, "params": {}} search_params.append(ivf_search_params) else: for nprobe in [64]: ivf_search_params = {"metric_type": metric_type, "params": {"nprobe": nprobe}} search_params.append(ivf_search_params) elif index_type in ["BIN_FLAT", "BIN_IVF_FLAT"]: if metric_type not in ct.binary_metrics: log.error("Metric type error: binary index only supports distance type in (%s)" % ct.binary_metrics) # default metric type for binary index metric_type = "JACCARD" for nprobe in [64, 128]: binary_search_params = {"metric_type": metric_type, "params": {"nprobe": nprobe}} search_params.append(binary_search_params) elif index_type in ["HNSW"]: for ef in [64, 1500, 32768]: hnsw_search_param = {"metric_type": metric_type, "params": {"ef": ef}} search_params.append(hnsw_search_param) elif index_type == "ANNOY": for search_k in [1000, 5000]: annoy_search_param = {"metric_type": metric_type, "params": {"search_k": search_k}} search_params.append(annoy_search_param) elif index_type == "SCANN": for reorder_k in [1200, 3000]: scann_search_param = {"metric_type": metric_type, "params": {"nprobe": 64, "reorder_k": reorder_k}} search_params.append(scann_search_param) elif index_type == "DISKANN": for search_list in [20, 300, 1500]: diskann_search_param = {"metric_type": metric_type, "params": {"search_list": search_list}} search_params.append(diskann_search_param) else: log.error("Invalid index_type.") raise Exception("Invalid index_type.") log.debug(search_params) return search_params def gen_autoindex_search_params(): search_params = [ {}, {"metric_type": "IP"}, {"nlist": "1024"}, {"efSearch": "100"}, {"search_k": "1000"} ] return search_params def gen_invalid_search_param(index_type, metric_type="L2"): search_params = [] if index_type in ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_PQ"] \ or index_type in ["BIN_FLAT", "BIN_IVF_FLAT"]: for nprobe in [-1]: ivf_search_params = {"metric_type": metric_type, "params": {"nprobe": nprobe}} search_params.append(ivf_search_params) elif index_type in ["HNSW"]: for ef in [-1]: hnsw_search_param = {"metric_type": metric_type, "params": {"ef": ef}} search_params.append(hnsw_search_param) elif index_type == "ANNOY": for search_k in ["-2"]: annoy_search_param = {"metric_type": metric_type, "params": {"search_k": search_k}} search_params.append(annoy_search_param) elif index_type == "DISKANN": for search_list in ["-1"]: diskann_search_param = {"metric_type": metric_type, "params": {"search_list": search_list}} search_params.append(diskann_search_param) elif index_type == "SCANN": for reorder_k in [-1]: scann_search_param = {"metric_type": metric_type, "params": {"reorder_k": reorder_k, "nprobe": 10}} search_params.append(scann_search_param) else: log.error("Invalid index_type.") raise Exception("Invalid index_type.") return search_params def gen_all_type_fields(): fields = [] for k, v in DataType.__members__.items(): if v != DataType.UNKNOWN: field, _ = ApiFieldSchemaWrapper().init_field_schema(name=k.lower(), dtype=v) fields.append(field) return fields def gen_normal_expressions(): expressions = [ "", "int64 > 0", "(int64 > 0 && int64 < 400) or (int64 > 500 && int64 < 1000)", "int64 not in [1, 2, 3]", "int64 in [1, 2, 3] and float != 2", "int64 == 0 || float == 10**2 || (int64 + 1) == 3", "0 <= int64 < 400 and int64 % 100 == 0", "200+300 < int64 <= 500+500", "int64 > 400 && int64 < 200", "int64 in [300/2, 900%40, -10*30+800, (100+200)*2] or float in [+3**6, 2**10/2]", "float <= -4**5/2 && float > 500-1 && float != 500/2+260" ] return expressions def gen_json_field_expressions(): expressions = [ "json_field['number'] > 0", "0 <= json_field['number'] < 400 or 1000 > json_field['number'] >= 500", "json_field['number'] not in [1, 2, 3]", "json_field['number'] in [1, 2, 3] and json_field['float'] != 2", "json_field['number'] == 0 || json_field['float'] == 10**2 || json_field['number'] + 1 == 3", "json_field['number'] < 400 and json_field['number'] >= 100 and json_field['number'] % 100 == 0", "json_field['float'] > 400 && json_field['float'] < 200", "json_field['number'] in [300/2, -10*30+800, (100+200)*2] or json_field['float'] in [+3**6, 2**10/2]", "json_field['float'] <= -4**5/2 && json_field['float'] > 500-1 && json_field['float'] != 500/2+260" ] return expressions def gen_array_field_expressions(): expressions = [ "int32_array[0] > 0", "0 <= int32_array[0] < 400 or 1000 > float_array[1] >= 500", "int32_array[1] not in [1, 2, 3]", "int32_array[1] in [1, 2, 3] and string_array[1] != '2'", "int32_array == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]", "int32_array[1] + 1 == 3 && int32_array[0] - 1 != 1", "int32_array[1] % 100 == 0 && string_array[1] in ['1', '2']", "int32_array[1] in [300/2, -10*30+800, (200-100)*2] " "or (float_array[1] <= -4**5/2 || 100 <= int32_array[1] < 200)" ] return expressions def gen_field_compare_expressions(fields1=None, fields2=None): if fields1 is None: fields1 = ["int64_1"] fields2 = ["int64_2"] expressions = [] for field1, field2 in zip(fields1, fields2): expression = [ f"{field1} | {field2} == 1", f"{field1} + {field2} <= 10 || {field1} - {field2} == 2", f"{field1} * {field2} >= 8 && {field1} / {field2} < 2", f"{field1} ** {field2} != 4 and {field1} + {field2} > 5", f"{field1} not in {field2}", f"{field1} in {field2}", ] expressions.extend(expression) return expressions def gen_normal_string_expressions(fields=None): if fields is None: fields = [ct.default_string_field_name] expressions = [] for field in fields: expression = [ f"\"0\"< {field} < \"3\"", f"{field} >= \"0\"", f"({field} > \"0\" && {field} < \"100\") or ({field} > \"200\" && {field} < \"300\")", f"\"0\" <= {field} <= \"100\"", f"{field} == \"0\"|| {field} == \"1\"|| {field} ==\"2\"", f"{field} != \"0\"", f"{field} not in [\"0\", \"1\", \"2\"]", f"{field} in [\"0\", \"1\", \"2\"]" ] expressions.extend(expression) return expressions def gen_invalid_string_expressions(): expressions = [ "varchar in [0, \"1\"]", "varchar not in [\"0\", 1, 2]" ] return expressions def gen_invalid_bool_expressions(): expressions = [ "bool", "!bool", "true", "false", "int64 > 0 and bool", "int64 > 0 or false" ] return expressions def gen_normal_expressions_field(field): expressions = [ "", f"{field} > 0", f"({field} > 0 && {field} < 400) or ({field} > 500 && {field} < 1000)", f"{field} not in [1, 2, 3]", f"{field} in [1, 2, 3] and {field} != 2", f"{field} == 0 || {field} == 1 || {field} == 2", f"0 < {field} < 400", f"500 <= {field} <= 1000", f"200+300 <= {field} <= 500+500", f"{field} in [300/2, 900%40, -10*30+800, 2048/2%200, (100+200)*2]", f"{field} in [+3**6, 2**10/2]", f"{field} <= 4**5/2 && {field} > 500-1 && {field} != 500/2+260", f"{field} > 400 && {field} < 200", f"{field} < -2**8", f"({field} + 1) == 3 || {field} * 2 == 64 || {field} == 10**2" ] return expressions def gen_integer_overflow_expressions(): expressions = [ "int8 < - 128", "int8 > 127", "int8 > -129 && int8 < 128", "int16 < -32768", "int16 >= 32768", "int16 > -32769 && int16 <32768", "int32 < -2147483648", "int32 == 2147483648", "int32 < 2147483648 || int32 == -2147483648", "int8 in [-129, 1] || int16 in [32769] || int32 in [2147483650, 0]" ] return expressions def l2(x, y): return np.linalg.norm(np.array(x) - np.array(y)) def ip(x, y): return np.inner(np.array(x), np.array(y)) def cosine(x, y): return np.dot(x, y)/(np.linalg.norm(x)*np.linalg.norm(y)) def jaccard(x, y): x = np.asarray(x, np.bool_) y = np.asarray(y, np.bool_) return 1 - np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum()) def hamming(x, y): x = np.asarray(x, np.bool_) y = np.asarray(y, np.bool_) return np.bitwise_xor(x, y).sum() def tanimoto(x, y): x = np.asarray(x, np.bool_) y = np.asarray(y, np.bool_) res = np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum()) if res == 0: value = float("inf") else: value = -np.log2(res) return value def tanimoto_calc(x, y): x = np.asarray(x, np.bool_) y = np.asarray(y, np.bool_) return np.double((len(x) - np.bitwise_xor(x, y).sum())) / (len(y) + np.bitwise_xor(x, y).sum()) def substructure(x, y): x = np.asarray(x, np.bool_) y = np.asarray(y, np.bool_) return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(y) def superstructure(x, y): x = np.asarray(x, np.bool_) y = np.asarray(y, np.bool_) return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(x) def compare_distance_2d_vector(x, y, distance, metric, sqrt): for i in range(len(x)): for j in range(len(y)): if metric == "L2": distance_i = l2(x[i], y[j]) if not sqrt: distance_i = math.pow(distance_i, 2) elif metric == "IP": distance_i = ip(x[i], y[j]) elif metric == "HAMMING": distance_i = hamming(x[i], y[j]) elif metric == "TANIMOTO": distance_i = tanimoto_calc(x[i], y[j]) elif metric == "JACCARD": distance_i = jaccard(x[i], y[j]) else: raise Exception("metric type is invalid") assert abs(distance_i - distance[i][j]) < ct.epsilon return True def compare_distance_vector_and_vector_list(x, y, metric, distance): """ target: compare the distance between x and y[i] with the expected distance array method: compare the distance between x and y[i] with the expected distance array expected: return true if all distances are matched """ if not isinstance(y, list): log.error("%s is not a list." % str(y)) assert False for i in range(len(y)): if metric == "L2": distance_i = l2(x, y[i]) elif metric == "IP": distance_i = ip(x, y[i]) elif metric == "COSINE": distance_i = cosine(x, y[i]) else: raise Exception("metric type is invalid") if abs(distance_i - distance[i]) > ct.epsilon: log.error("The distance between %f and %f is not equal with %f" % (x, y[i], distance[i])) assert abs(distance_i - distance[i]) < ct.epsilon return True def modify_file(file_path_list, is_modify=False, input_content=""): """ file_path_list : file list -> list[] is_modify : does the file need to be reset input_content ：the content that need to insert to the file """ if not isinstance(file_path_list, list): log.error("[modify_file] file is not a list.") for file_path in file_path_list: folder_path, file_name = os.path.split(file_path) if not os.path.isdir(folder_path): log.debug("[modify_file] folder(%s) is not exist." % folder_path) os.makedirs(folder_path) if not os.path.isfile(file_path): log.error("[modify_file] file(%s) is not exist." % file_path) else: if is_modify is True: log.debug("[modify_file] start modifying file(%s)..." % file_path) with open(file_path, "r+") as f: f.seek(0) f.truncate() f.write(input_content) f.close() log.info("[modify_file] file(%s) modification is complete." % file_path_list) def index_to_dict(index): return { "collection_name": index.collection_name, "field_name": index.field_name, # "name": index.name, "params": index.params } def get_index_params_params(index_type): """get default params of index params by index type""" return ct.default_all_indexes_params[ct.all_index_types.index(index_type)].copy() def get_search_params_params(index_type): """get default params of search params by index type""" return ct.default_all_search_params_params[ct.all_index_types.index(index_type)].copy() def assert_json_contains(expr, list_data): opposite = False if expr.startswith("not"): opposite = True expr = expr.split("not ", 1)[1] result_ids = [] expr_prefix = expr.split('(', 1)[0] exp_ids = eval(expr.split(', ', 1)[1].split(')', 1)[0]) if expr_prefix in ["json_contains", "JSON_CONTAINS", "array_contains", "ARRAY_CONTAINS"]: for i in range(len(list_data)): if exp_ids in list_data[i]: result_ids.append(i) elif expr_prefix in ["json_contains_all", "JSON_CONTAINS_ALL", "array_contains_all", "ARRAY_CONTAINS_ALL"]: for i in range(len(list_data)): set_list_data = set(tuple(element) if isinstance(element, list) else element for element in list_data[i]) if set(exp_ids).issubset(set_list_data): result_ids.append(i) elif expr_prefix in ["json_contains_any", "JSON_CONTAINS_ANY", "array_contains_any", "ARRAY_CONTAINS_ANY"]: for i in range(len(list_data)): set_list_data = set(tuple(element) if isinstance(element, list) else element for element in list_data[i]) if set(exp_ids) & set_list_data: result_ids.append(i) else: log.warning("unknown expr: %s" % expr) if opposite: result_ids = [i for i in range(len(list_data)) if i not in result_ids] return result_ids def assert_equal_index(index_1, index_2): return index_to_dict(index_1) == index_to_dict(index_2) def gen_partitions(collection_w, partition_num=1): """ target: create extra partitions except for _default method: create more than one partitions expected: return collection and raw data """ log.info("gen_partitions: creating partitions") for i in range(partition_num): partition_name = "search_partition_" + str(i) collection_w.create_partition(partition_name=partition_name, description="search partition") par = collection_w.partitions assert len(par) == (partition_num + 1) log.info("gen_partitions: created partitions %s" % par) def insert_data(collection_w, nb=ct.default_nb, is_binary=False, is_all_data_type=False, auto_id=False, dim=ct.default_dim, insert_offset=0, enable_dynamic_field=False, with_json=True, random_primary_key=False, multiple_dim_array=[], primary_field=ct.default_int64_field_name, vector_data_type="FLOAT_VECTOR"): """ target: insert non-binary/binary data method: insert non-binary/binary data into partitions if any expected: return collection and raw data """ par = collection_w.partitions num = len(par) vectors = [] binary_raw_vectors = [] insert_ids = [] start = insert_offset log.info(f"inserting {nb} data into collection {collection_w.name}") # extract the vector field name list vector_name_list = extract_vector_field_name_list(collection_w) # prepare data for i in range(num): log.debug("Dynamic field is enabled: %s" % enable_dynamic_field) if not is_binary: if not is_all_data_type: if not enable_dynamic_field: if vector_data_type == "FLOAT_VECTOR": default_data = gen_default_dataframe_data(nb // num, dim=dim, start=start, with_json=with_json, random_primary_key=random_primary_key, multiple_dim_array=multiple_dim_array, multiple_vector_field_name=vector_name_list, vector_data_type=vector_data_type, auto_id=auto_id, primary_field=primary_field) elif vector_data_type == "FLOAT16_VECTOR" or "BFLOAT16_VECTOR": default_data = gen_general_default_list_data(nb // num, dim=dim, start=start, with_json=with_json, random_primary_key=random_primary_key, multiple_dim_array=multiple_dim_array, multiple_vector_field_name=vector_name_list, vector_data_type=vector_data_type, auto_id=auto_id, primary_field=primary_field) else: default_data = gen_default_rows_data(nb // num, dim=dim, start=start, with_json=with_json, multiple_dim_array=multiple_dim_array, multiple_vector_field_name=vector_name_list, vector_data_type=vector_data_type, auto_id=auto_id, primary_field=primary_field) else: if not enable_dynamic_field: if vector_data_type == "FLOAT_VECTOR": default_data = gen_general_list_all_data_type(nb // num, dim=dim, start=start, with_json=with_json, random_primary_key=random_primary_key, multiple_dim_array=multiple_dim_array, multiple_vector_field_name=vector_name_list, auto_id=auto_id, primary_field=primary_field) elif vector_data_type == "FLOAT16_VECTOR" or "BFLOAT16_VECTOR": default_data = gen_general_list_all_data_type(nb // num, dim=dim, start=start, with_json=with_json, random_primary_key=random_primary_key, multiple_dim_array=multiple_dim_array, multiple_vector_field_name=vector_name_list, auto_id=auto_id, primary_field=primary_field) else: if os.path.exists(ct.rows_all_data_type_file_path + f'_{i}' + f'_dim{dim}.txt'): with open(ct.rows_all_data_type_file_path + f'_{i}' + f'_dim{dim}.txt', 'rb') as f: default_data = pickle.load(f) else: default_data = gen_default_rows_data_all_data_type(nb // num, dim=dim, start=start, with_json=with_json, multiple_dim_array=multiple_dim_array, multiple_vector_field_name=vector_name_list, partition_id=i, auto_id=auto_id, primary_field=primary_field) else: default_data, binary_raw_data = gen_default_binary_dataframe_data(nb // num, dim=dim, start=start, auto_id=auto_id, primary_field=primary_field) binary_raw_vectors.extend(binary_raw_data) insert_res = collection_w.insert(default_data, par[i].name)[0] log.info(f"inserted {nb // num} data into collection {collection_w.name}") time_stamp = insert_res.timestamp insert_ids.extend(insert_res.primary_keys) vectors.append(default_data) start += nb // num return collection_w, vectors, binary_raw_vectors, insert_ids, time_stamp def _check_primary_keys(primary_keys, nb): if primary_keys is None: raise Exception("The primary_keys is None") assert len(primary_keys) == nb for i in range(nb - 1): if primary_keys[i] >= primary_keys[i + 1]: return False return True def get_segment_distribution(res): """ Get segment distribution """ from collections import defaultdict segment_distribution = defaultdict(lambda: {"sealed": []}) for r in res: for node_id in r.nodeIds: if r.state == 3: segment_distribution[node_id]["sealed"].append(r.segmentID) return segment_distribution def percent_to_int(string): """ transform percent(0%--100%) to int """ new_int = -1 if not isinstance(string, str): log.error("%s is not a string" % string) return new_int if "%" not in string: log.error("%s is not a percent" % string) else: new_int = int(string.strip("%")) return new_int def gen_grant_list(collection_name): grant_list = [{"object": "Collection", "object_name": collection_name, "privilege": "Load"}, {"object": "Collection", "object_name": collection_name, "privilege": "Release"}, {"object": "Collection", "object_name": collection_name, "privilege": "Compaction"}, {"object": "Collection", "object_name": collection_name, "privilege": "Delete"}, {"object": "Collection", "object_name": collection_name, "privilege": "GetStatistics"}, {"object": "Collection", "object_name": collection_name, "privilege": "CreateIndex"}, {"object": "Collection", "object_name": collection_name, "privilege": "IndexDetail"}, {"object": "Collection", "object_name": collection_name, "privilege": "DropIndex"}, {"object": "Collection", "object_name": collection_name, "privilege": "Search"}, {"object": "Collection", "object_name": collection_name, "privilege": "Flush"}, {"object": "Collection", "object_name": collection_name, "privilege": "Query"}, {"object": "Collection", "object_name": collection_name, "privilege": "LoadBalance"}, {"object": "Collection", "object_name": collection_name, "privilege": "Import"}, {"object": "Global", "object_name": "*", "privilege": "All"}, {"object": "Global", "object_name": "*", "privilege": "CreateCollection"}, {"object": "Global", "object_name": "*", "privilege": "DropCollection"}, {"object": "Global", "object_name": "*", "privilege": "DescribeCollection"}, {"object": "Global", "object_name": "*", "privilege": "ShowCollections"}, {"object": "Global", "object_name": "*", "privilege": "CreateOwnership"}, {"object": "Global", "object_name": "*", "privilege": "DropOwnership"}, {"object": "Global", "object_name": "*", "privilege": "SelectOwnership"}, {"object": "Global", "object_name": "*", "privilege": "ManageOwnership"}, {"object": "User", "object_name": "*", "privilege": "UpdateUser"}, {"object": "User", "object_name": "*", "privilege": "SelectUser"}] return grant_list def install_milvus_operator_specific_config(namespace, milvus_mode, release_name, image, rate_limit_enable, collection_rate_limit): """ namespace : str milvus_mode : str -> standalone or cluster release_name : str image: str -> image tag including repository rate_limit_enable: str -> true or false, switch for rate limit collection_rate_limit: int -> collection rate limit numbers input_content ：the content that need to insert to the file return: milvus host name """ if not isinstance(namespace, str): log.error("[namespace] is not a string.") if not isinstance(milvus_mode, str): log.error("[milvus_mode] is not a string.") if not isinstance(release_name, str): log.error("[release_name] is not a string.") if not isinstance(image, str): log.error("[image] is not a string.") if not isinstance(rate_limit_enable, str): log.error("[rate_limit_enable] is not a string.") if not isinstance(collection_rate_limit, int): log.error("[collection_rate_limit] is not an integer.") if milvus_mode not in ["standalone", "cluster"]: log.error("[milvus_mode] is not 'standalone' or 'cluster'") if rate_limit_enable not in ["true", "false"]: log.error("[rate_limit_enable] is not 'true' or 'false'") data_config = { 'metadata.namespace': namespace, 'spec.mode': milvus_mode, 'metadata.name': release_name, 'spec.components.image': image, 'spec.components.proxy.serviceType': 'LoadBalancer', 'spec.components.dataNode.replicas': 2, 'spec.config.common.retentionDuration': 60, 'spec.config.quotaAndLimits.enable': rate_limit_enable, 'spec.config.quotaAndLimits.ddl.collectionRate': collection_rate_limit, } mil = MilvusOperator() mil.install(data_config) if mil.wait_for_healthy(release_name, NAMESPACE, timeout=TIMEOUT): host = mic.endpoint(release_name, NAMESPACE).split(':')[0] else: raise MilvusException(message=f'Milvus healthy timeout 1800s') return host def get_wildcard_output_field_names(collection_w, output_fields): all_fields = [field.name for field in collection_w.schema.fields] output_fields = output_fields.copy() if "*" in output_fields: output_fields.remove("*") output_fields.extend(all_fields) return output_fields def extract_vector_field_name_list(collection_w): """ extract the vector field name list collection_w : the collection object to be extracted thea name of all the vector fields return: the vector field name list without the default float vector field name """ schema_dict = collection_w.schema.to_dict() fields = schema_dict.get('fields') vector_name_list = [] for field in fields: if str(field['type']) in ["101", "102", "103"]: if field['name'] != ct.default_float_vec_field_name: vector_name_list.append(field['name']) for field in fields: if str(field['type']) == 'DataType.FLOAT_VECTOR' \ or str(field['type']) == 'DataType.FLOAT16_VECTOR' \ or str(field['type']) == 'DataType.BFLOAT16_VECTOR': if field['name'] != ct.default_float_vec_field_name: vector_name_list.append(field['name']) return vector_name_list def get_activate_func_from_metric_type(metric_type): activate_function = lambda x: x if metric_type == "COSINE": activate_function = lambda x: (1 + x) * 0.5 elif metric_type == "IP": activate_function = lambda x: 0.5 + math.atan(x)/ math.pi else: activate_function = lambda x: 1.0 - 2*math.atan(x) / math.pi return activate_function def get_hybrid_search_base_results_rrf(search_res_dict_array, round_decimal=-1): """ merge the element in the dicts array search_res_dict_array : the dict array in which the elements to be merged return: the sorted id and score answer """ # calculate hybrid search base line search_res_dict_merge = {} ids_answer = [] score_answer = [] for i, result in enumerate(search_res_dict_array, 0): for key, distance in result.items(): search_res_dict_merge[key] = search_res_dict_merge.get(key, 0) + distance if round_decimal != -1 : for k, v in search_res_dict_merge.items(): multiplier = math.pow(10.0, round_decimal) v = math.floor(v*multiplier+0.5) / multiplier search_res_dict_merge[k] = v sorted_list = sorted(search_res_dict_merge.items(), key=lambda x: x[1], reverse=True) for sort in sorted_list: ids_answer.append(int(sort[0])) score_answer.append(float(sort[1])) return ids_answer, score_answer def get_hybrid_search_base_results(search_res_dict_array, weights, metric_types, round_decimal=-1): """ merge the element in the dicts array search_res_dict_array : the dict array in which the elements to be merged return: the sorted id and score answer """ # calculate hybrid search base line search_res_dict_merge = {} ids_answer = [] score_answer = [] for i, result in enumerate(search_res_dict_array, 0): activate_function = get_activate_func_from_metric_type(metric_types[i]) for key, distance in result.items(): activate_distance = activate_function(distance) weight = weights[i] search_res_dict_merge[key] = search_res_dict_merge.get(key, 0) + activate_function(distance) * weights[i] if round_decimal != -1 : for k, v in search_res_dict_merge.items(): multiplier = math.pow(10.0, round_decimal) v = math.floor(v*multiplier+0.5) / multiplier search_res_dict_merge[k] = v sorted_list = sorted(search_res_dict_merge.items(), key=lambda x: x[1], reverse=True) for sort in sorted_list: ids_answer.append(int(sort[0])) score_answer.append(float(sort[1])) return ids_answer, score_answer def gen_bf16_vectors(num, dim): """ generate brain float16 vector data raw_vectors : the vectors bf16_vectors: the bytes used for insert return: raw_vectors and bf16_vectors """ raw_vectors = [] bf16_vectors = [] for _ in range(num): raw_vector = [random.random() for _ in range(dim)] raw_vectors.append(raw_vector) bf16_vector = tf.cast(raw_vector, dtype=tf.bfloat16).numpy() bf16_vectors.append(bf16_vector) return raw_vectors, bf16_vectors def gen_fp16_vectors(num, dim): """ generate float16 vector data raw_vectors : the vectors fp16_vectors: the bytes used for insert return: raw_vectors and fp16_vectors """ raw_vectors = [] fp16_vectors = [] for _ in range(num): raw_vector = [random.random() for _ in range(dim)] raw_vectors.append(raw_vector) fp16_vector = np.array(raw_vector, dtype=np.float16) fp16_vectors.append(fp16_vector) return raw_vectors, fp16_vectors def gen_sparse_vectors(nb, dim): """ generate sparse vector data return sparse_vectors """ rng = np.random.default_rng() entities = [ { d: rng.random() for d in random.sample(range(dim), random.randint(1, 1)) } for _ in range(nb) ] return entities def gen_vectors_based_on_vector_type(num, dim, vector_data_type): """ generate float16 vector data raw_vectors : the vectors fp16_vectors: the bytes used for insert return: raw_vectors and fp16_vectors """ if vector_data_type == "FLOAT_VECTOR": vectors = [[random.random() for _ in range(dim)] for _ in range(num)] elif vector_data_type == "FLOAT16_VECTOR": vectors = gen_fp16_vectors(num, dim)[1] elif vector_data_type == "BFLOAT16_VECTOR": vectors = gen_bf16_vectors(num, dim)[1] return vectors