mirror of https://github.com/milvus-io/milvus.git
145 lines
4.8 KiB
Python
145 lines
4.8 KiB
Python
import pdb
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import random
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import logging
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import json
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import time, datetime
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from multiprocessing import Process
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import numpy
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import sklearn.preprocessing
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from milvus import Milvus, IndexType, MetricType
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logger = logging.getLogger("milvus_acc.client")
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SERVER_HOST_DEFAULT = "127.0.0.1"
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SERVER_PORT_DEFAULT = 19530
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def time_wrapper(func):
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"""
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This decorator prints the execution time for the decorated function.
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"""
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def wrapper(*args, **kwargs):
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start = time.time()
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result = func(*args, **kwargs)
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end = time.time()
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logger.info("Milvus {} run in {}s".format(func.__name__, round(end - start, 2)))
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return result
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return wrapper
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class MilvusClient(object):
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def __init__(self, table_name=None, host=None, port=None):
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self._milvus = Milvus()
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self._table_name = table_name
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try:
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if not host:
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self._milvus.connect(
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host = SERVER_HOST_DEFAULT,
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port = SERVER_PORT_DEFAULT)
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else:
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self._milvus.connect(
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host = host,
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port = port)
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except Exception as e:
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raise e
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def __str__(self):
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return 'Milvus table %s' % self._table_name
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def check_status(self, status):
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if not status.OK():
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logger.error(status.message)
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raise Exception("Status not ok")
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def create_table(self, table_name, dimension, index_file_size, metric_type):
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if not self._table_name:
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self._table_name = table_name
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if metric_type == "l2":
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metric_type = MetricType.L2
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elif metric_type == "ip":
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metric_type = MetricType.IP
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else:
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logger.error("Not supported metric_type: %s" % metric_type)
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self._metric_type = metric_type
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create_param = {'table_name': table_name,
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'dimension': dimension,
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'index_file_size': index_file_size,
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"metric_type": metric_type}
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status = self._milvus.create_table(create_param)
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self.check_status(status)
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@time_wrapper
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def insert(self, X, ids):
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if self._metric_type == MetricType.IP:
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logger.info("Set normalize for metric_type: Inner Product")
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X = sklearn.preprocessing.normalize(X, axis=1, norm='l2')
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X = X.astype(numpy.float32)
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status, result = self._milvus.add_vectors(self._table_name, X.tolist(), ids=ids)
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self.check_status(status)
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return status, result
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@time_wrapper
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def create_index(self, index_type, nlist):
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if index_type == "flat":
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index_type = IndexType.FLAT
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elif index_type == "ivf_flat":
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index_type = IndexType.IVFLAT
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elif index_type == "ivf_sq8":
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index_type = IndexType.IVF_SQ8
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elif index_type == "ivf_sq8h":
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index_type = IndexType.IVF_SQ8H
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elif index_type == "mix_nsg":
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index_type = IndexType.MIX_NSG
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index_params = {
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"index_type": index_type,
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"nlist": nlist,
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}
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logger.info("Building index start, table_name: %s, index_params: %s" % (self._table_name, json.dumps(index_params)))
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status = self._milvus.create_index(self._table_name, index=index_params, timeout=6*3600)
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self.check_status(status)
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def describe_index(self):
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return self._milvus.describe_index(self._table_name)
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def drop_index(self):
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logger.info("Drop index: %s" % self._table_name)
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return self._milvus.drop_index(self._table_name)
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@time_wrapper
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def query(self, X, top_k, nprobe):
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if self._metric_type == MetricType.IP:
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logger.info("Set normalize for metric_type: Inner Product")
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X = sklearn.preprocessing.normalize(X, axis=1, norm='l2')
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X = X.astype(numpy.float32)
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status, results = self._milvus.search_vectors(self._table_name, top_k, nprobe, X.tolist())
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self.check_status(status)
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ids = []
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for result in results:
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tmp_ids = []
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for item in result:
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tmp_ids.append(item.id)
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ids.append(tmp_ids)
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return ids
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def count(self):
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return self._milvus.get_table_row_count(self._table_name)[1]
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def delete(self, table_name):
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logger.info("Start delete table: %s" % table_name)
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return self._milvus.delete_table(table_name)
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def describe(self):
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return self._milvus.describe_table(self._table_name)
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def exists_table(self, table_name):
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return self._milvus.has_table(table_name)
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def get_server_version(self):
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status, res = self._milvus.server_version()
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self.check_status(status)
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return res
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@time_wrapper
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def preload_table(self):
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return self._milvus.preload_table(self._table_name)
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