def validate_insert(_table_name):
milvus = Milvus()
milvus.connect(**server_config)
status, count = milvus.count_table(_table_name)
assert count == 10 * 10000, "Insert validate fail. Vectors num is not matched."
milvus.disconnect()
def milvus_test(usr_features, IS_INFER, mov_features=None, ids=None):
_HOST = '127.0.0.1'
_PORT = '19530' # default value
table_name = 'recommender_demo'
milvus = Milvus()
param = {'host': _HOST, 'port': _PORT}
status = milvus.connect(**param)
if status.OK():
print("Server connected.")
else:
print("Server connect fail.")
sys.exit(1)
if IS_INFER:
status = milvus.drop_table(table_name)
time.sleep(3)
status, ok = milvus.has_table(table_name)
if not ok:
if mov_features is None:
print("Insert vectors is none!")
sys.exit(1)
param = {
'table_name': table_name,
'dimension': 200,
'index_file_size': 1024, # optional
'metric_type': MetricType.IP # optional
}
print(milvus.create_table(param))
insert_vectors = normaliz_data(mov_features)
status, ids = milvus.insert(table_name=table_name,
records=insert_vectors,
ids=ids)
time.sleep(1)
status, result = milvus.count_table(table_name)
print("rows in table recommender_demo:", result)
search_vectors = normaliz_data(usr_features)
param = {
'table_name': table_name,
'query_records': search_vectors,
'top_k': 5,
'nprobe': 16
}
time1 = time.time()
status, results = milvus.search_vectors(**param)
time2 = time.time()
print("Top\t", "Ids\t", "Title\t", "Score")
for i, re in enumerate(results[0]):
title = paddle.dataset.movielens.movie_info()[int(re.id)].title
print(i, "\t", re.id, "\t", title, "\t", float(re.distance) * 5)
def test_not_connect(self):
client = Milvus()
with pytest.raises(NotConnectError):
client.create_table({})
with pytest.raises(NotConnectError):
client.has_table("a")
with pytest.raises(NotConnectError):
client.describe_table("a")
with pytest.raises(NotConnectError):
client.drop_table("a")
with pytest.raises(NotConnectError):
client.create_index("a")
with pytest.raises(NotConnectError):
client.insert("a", [], None)
with pytest.raises(NotConnectError):
client.count_table("a")
with pytest.raises(NotConnectError):
client.show_tables()
with pytest.raises(NotConnectError):
client.search("a", 1, 2, [])
with pytest.raises(NotConnectError):
client.search_in_files("a", [], [], 2, 1)
with pytest.raises(NotConnectError):
client._cmd("")
with pytest.raises(NotConnectError):
client.preload_table("a")
with pytest.raises(NotConnectError):
client.describe_index("a")
with pytest.raises(NotConnectError):
client.drop_index("")
def milvus_test(usr_features, mov_features, ids):
_HOST = '127.0.0.1'
_PORT = '19530' # default value
milvus = Milvus()
param = {'host': _HOST, 'port': _PORT}
status = milvus.connect(**param)
if status.OK():
print("\nServer connected.")
else:
print("\nServer connect fail.")
sys.exit(1)
table_name = 'paddle_demo1'
status, ok = milvus.has_table(table_name)
if not ok:
param = {
'table_name': table_name,
'dimension': 200,
'index_file_size': 1024, # optional
'metric_type': MetricType.IP # optional
}
milvus.create_table(param)
insert_vectors = normaliz_data([usr_features.tolist()])
status, ids = milvus.insert(table_name=table_name,
records=insert_vectors,
ids=ids)
time.sleep(1)
status, result = milvus.count_table(table_name)
print("rows in table paddle_demo1:", result)
status, table = milvus.describe_table(table_name)
search_vectors = normaliz_data([mov_features.tolist()])
param = {
'table_name': table_name,
'query_records': search_vectors,
'top_k': 1,
'nprobe': 16
}
status, results = milvus.search_vectors(**param)
print("Searched ids:", results[0][0].id)
print("Score:", float(results[0][0].distance) * 5)
status = milvus.drop_table(table_name)
def main():
milvus = Milvus(handler="HTTP")
# Connect to Milvus server
# You may need to change _HOST and _PORT accordingly
param = {'host': _HOST, 'port': _PORT}
status = milvus.connect(**param)
if status.OK():
print("Server connected.")
else:
print("Server connect fail.")
sys.exit(1)
# Create table demo_table if it dosen't exist.
table_name = 'demo_tables'
status, ok = milvus.has_table(table_name)
if not ok:
param = {
'table_name': table_name,
'dimension': _DIM,
'index_file_size': _INDEX_FILE_SIZE, # optional
'metric_type': MetricType.L2 # optional
}
milvus.create_table(param)
# Show tables in Milvus server
_, tables = milvus.show_tables()
# Describe demo_table
_, table = milvus.describe_table(table_name)
print(table)
# 10000 vectors with 16 dimension
# element per dimension is float32 type
# vectors should be a 2-D array
vectors = [[random.random() for _ in range(_DIM)] for _ in range(100000)]
# You can also use numpy to generate random vectors:
# `vectors = np.random.rand(10000, 16).astype(np.float32).tolist()`
# Insert vectors into demo_table, return status and vectors id list
status, ids = milvus.insert(table_name=table_name, records=vectors)
# Wait for 6 seconds, until Milvus server persist vector data.
time.sleep(6)
# Get demo_table row count
status, result = milvus.count_table(table_name)
# create index of vectors, search more rapidly
index_param = {
'index_type': IndexType.IVFLAT, # choice ivflat index
'nlist': 2048
}
# Create ivflat index in demo_table
# You can search vectors without creating index. however, Creating index help to
# search faster
status = milvus.create_index(table_name, index_param)
# describe index, get information of index
status, index = milvus.describe_index(table_name)
print(index)
# Use the top 10 vectors for similarity search
query_vectors = vectors[0:10]
# execute vector similarity search
param = {
'table_name': table_name,
'query_records': query_vectors,
'top_k': 1,
'nprobe': 16
}
status, results = milvus.search(**param)
if status.OK():
# indicate search result
# also use by:
# `results.distance_array[0][0] == 0.0 or results.id_array[0][0] == ids[0]`
if results[0][0].distance == 0.0 or results[0][0].id == ids[0]:
print('Query result is correct')
else:
print('Query result isn\'t correct')
# print results
print(results)
# Delete demo_table
status = milvus.drop_table(table_name)
# Disconnect from Milvus
status = milvus.disconnect()
