milvus/tests/python_client/chaos/scripts/hello_milvus.py

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# Copyright (C) 2019-2020 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License
# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
# or implied. See the License for the specific language governing permissions and limitations under the License.
import random
from pymilvus import (
connections, list_collections,
FieldSchema, CollectionSchema, DataType,
Collection
)
def hello_milvus(host="127.0.0.1"):
import time
# create connection
connections.connect(host=host, port="19530")
print(f"\nList collections...")
print(list_collections())
# create collection
dim = 128
default_fields = [
FieldSchema(name="count", dtype=DataType.INT64, is_primary=True),
FieldSchema(name="random_value", dtype=DataType.DOUBLE),
FieldSchema(name="float_vector", dtype=DataType.FLOAT_VECTOR, dim=dim)
]
default_schema = CollectionSchema(fields=default_fields, description="test collection")
print(f"\nCreate collection...")
collection = Collection(name="hello_milvus", schema=default_schema)
print(f"\nList collections...")
print(list_collections())
# insert data
nb = 3000
vectors = [[random.random() for _ in range(dim)] for _ in range(nb)]
t0 = time.time()
collection.insert(
[
[i for i in range(nb)],
[float(random.randrange(-20, -10)) for _ in range(nb)],
vectors
]
)
t1 = time.time()
print(f"\nInsert {nb} vectors cost {t1 - t0} seconds")
t0 = time.time()
print(f"\nGet collection entities...")
print(collection.num_entities)
t1 = time.time()
print(f"\nGet collection entities cost {t1 - t0} seconds")
# create index and load table
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
print(f"\nCreate index...")
collection.create_index(field_name="float_vector", index_params=default_index)
print(f"\nload collection...")
t0 = time.time()
collection.load()
t1 = time.time()
print(f"\nload collection cost {t1 - t0} seconds")
# load and search
topK = 5
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
start_time = time.time()
print(f"\nSearch...")
# define output_fields of search result
res = collection.search(
vectors[-2:], "float_vector", search_params, topK,
"count > 100", output_fields=["count", "random_value"]
)
end_time = time.time()
# show result
for hits in res:
for hit in hits:
# Get value of the random value field for search result
print(hit, hit.entity.get("random_value"))
print("search latency = %.4fs" % (end_time - start_time))
#query
expr = "count in [2,4,6,8]"
output_fields = ["count", "random_value"]
res = collection.query(expr, output_fields)
sorted_res = sorted(res, key=lambda k: k['count'])
for r in sorted_res:
print(r)
collection.release()
import argparse
parser = argparse.ArgumentParser(description='host ip')
parser.add_argument('--host', type=str, default='127.0.0.1', help='host ip')
args = parser.parse_args()
# add time stamp
import time
print(f"\nStart time: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))}")
hello_milvus(args.host)