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Remove redundant type inference when insert data
Signed-off-by: zhenshan.cao <[email protected]>
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from pymilvus import CollectionSchema, FieldSchema, Collection, connections, DataType, Partition, utility | ||
import numpy as np | ||
import random | ||
import pandas as pd | ||
connections.connect() | ||
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dim = 128 | ||
collection_name = "test_array" | ||
arr_len = 100 | ||
nb = 10 | ||
if utility.has_collection(collection_name): | ||
utility.drop_collection(collection_name) | ||
# create collection | ||
pk_field = FieldSchema(name="int64", dtype=DataType.INT64, is_primary=True, description='pk') | ||
vector_field = FieldSchema(name="float_vector", dtype=DataType.FLOAT_VECTOR, dim=dim) | ||
int8_array = FieldSchema(name="int8_array", dtype=DataType.ARRAY, element_type=DataType.INT8, max_capacity=arr_len) | ||
int16_array = FieldSchema(name="int16_array", dtype=DataType.ARRAY, element_type=DataType.INT16, max_capacity=arr_len) | ||
int32_array = FieldSchema(name="int32_array", dtype=DataType.ARRAY, element_type=DataType.INT32, max_capacity=arr_len) | ||
int64_array = FieldSchema(name="int64_array", dtype=DataType.ARRAY, element_type=DataType.INT64, max_capacity=arr_len) | ||
bool_array = FieldSchema(name="bool_array", dtype=DataType.ARRAY, element_type=DataType.BOOL, max_capacity=arr_len) | ||
float_array = FieldSchema(name="float_array", dtype=DataType.ARRAY, element_type=DataType.FLOAT, max_capacity=arr_len) | ||
double_array = FieldSchema(name="double_array", dtype=DataType.ARRAY, element_type=DataType.DOUBLE, max_capacity=arr_len) | ||
string_array = FieldSchema(name="string_array", dtype=DataType.ARRAY, element_type=DataType.VARCHAR, max_capacity=arr_len, | ||
max_length=100) | ||
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fields = [pk_field, vector_field, int8_array, int16_array, int32_array, int64_array, | ||
bool_array, float_array, double_array, string_array] | ||
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schema = CollectionSchema(fields=fields) | ||
collection = Collection(collection_name, schema=schema) | ||
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# insert data | ||
pk_value = [i for i in range(nb)] | ||
vector_value = [[random.random() for _ in range(dim)] for i in range(nb)] | ||
int8_value = [[np.int8(j) for j in range(arr_len)] for i in range(nb)] | ||
int16_value = [[np.int16(j) for j in range(arr_len)] for i in range(nb)] | ||
int32_value = [[np.int32(j) for j in range(arr_len)] for i in range(nb)] | ||
int64_value = [[np.int64(j) for j in range(arr_len)] for i in range(nb)] | ||
bool_value = [[np.bool_(j) for j in range(arr_len)] for i in range(nb)] | ||
float_value = [[np.float32(j) for j in range(arr_len)] for i in range(nb)] | ||
double_value = [[np.double(j) for j in range(arr_len)] for i in range(nb)] | ||
string_value = [[str(j) for j in range(arr_len)] for i in range(nb)] | ||
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data = [pk_value, vector_value, | ||
int8_value,int16_value, int32_value, int64_value, | ||
bool_value, | ||
float_value, | ||
double_value, | ||
string_value | ||
] | ||
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#collection.insert(data) | ||
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data = pd.DataFrame({ | ||
'int64': pk_value, | ||
'float_vector': vector_value, | ||
"int8_array": int8_value, | ||
"int16_array": int16_value, | ||
"int32_array": int32_value, | ||
"int64_array": int64_value, | ||
"bool_array": bool_value, | ||
"float_array": float_value, | ||
"double_array": double_value, | ||
"string_array": string_value | ||
}) | ||
collection.insert(data) | ||
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index = { | ||
"index_type": "IVF_FLAT", | ||
"metric_type": "L2", | ||
"params": {"nlist": 128}, | ||
} | ||
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collection.create_index("float_vector", index) | ||
collection.load() | ||
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res = collection.query("int64 >= 0", output_fields=["int8_array"]) | ||
for hits in res: | ||
print(hits) |
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