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