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modified example_tls2.py through MilvusClient #2077

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May 14, 2024
Merged

modified example_tls2.py through MilvusClient #2077

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162 changes: 61 additions & 101 deletions examples/example_tls2.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,8 @@
import random

from pymilvus import (
connections,
MilvusClient,
FieldSchema, CollectionSchema, DataType,
Collection,
utility
)

Expand All @@ -17,6 +16,7 @@

_HOST = '127.0.0.1'
_PORT = '19530'
_URI = f"https://{_HOST}:{_PORT}"

# Const names
_COLLECTION_NAME = 'demo'
Expand All @@ -35,132 +35,92 @@
_TOPK = 3


# Create a Milvus connection
def create_connection():
def main():
# create a connection
print(f"\nCreate connection...")
connections.connect(host=_HOST, port=_PORT, secure=True, client_pem_path="cert/client.pem",
client_key_path="cert/client.key",
ca_pem_path="cert/ca.pem", server_name="localhost")
print(f"\nList connections:")
print(connections.list_connections())
milvus_client = MilvusClient(uri=_URI,
secure=True,
client_pem_path="cert/client.pem",
client_key_path="cert/client.key",
ca_pem_path="cert/ca.pem",
server_name='localhost')
print(f"\nList connection:")
print(milvus_client._get_connection())

# drop collection if the collection exists
if milvus_client.has_collection(_COLLECTION_NAME):
milvus_client.drop_collection(_COLLECTION_NAME)

# Create a collection named 'demo'
def create_collection(name, id_field, vector_field):
field1 = FieldSchema(name=id_field, dtype=DataType.INT64, description="int64", is_primary=True)
field2 = FieldSchema(name=vector_field, dtype=DataType.FLOAT_VECTOR, description="float vector", dim=_DIM,
# create collection
field1 = FieldSchema(name=_ID_FIELD_NAME, dtype=DataType.INT64, description="int64", is_primary=True)
field2 = FieldSchema(name=_VECTOR_FIELD_NAME, dtype=DataType.FLOAT_VECTOR, description="float vector", dim=_DIM,
is_primary=False)
schema = CollectionSchema(fields=[field1, field2], description="collection description")
collection = Collection(name=name, data=None, schema=schema)
print("\ncollection created:", name)
return collection


def has_collection(name):
return utility.has_collection(name)
milvus_client.create_collection(collection_name=_COLLECTION_NAME,schema=schema)
milvus_client.describe_collection(collection_name=_COLLECTION_NAME)

print("\ncollection created:", _COLLECTION_NAME)

# Drop a collection in Milvus
def drop_collection(name):
collection = Collection(name)
collection.drop()
print("\nDrop collection: {}".format(name))


# List all collections in Milvus
def list_collections():
# show collections
print("\nlist collections:")
print(utility.list_collections())


def insert(collection, num, dim):
data = [
[i for i in range(num)],
[[random.random() for _ in range(dim)] for _ in range(num)],
]
collection.insert(data)
return data[1]
print(milvus_client.list_collections())

# insert 10000 vectors with 128 dimension
data_dict = []
for i in range(10000):
entity = {
"id_field": i+1, # Assuming id_field is the _COLLECTION_NAME of the field corresponding to the ID
"float_vector_field": [random.random() for _ in range(_DIM)]
}
data_dict.append(entity)
insert_result = milvus_client.insert(collection_name=_COLLECTION_NAME,data=data_dict)

def get_entity_num(collection):
print("\nThe number of entity:")
print(collection.num_entities)


def create_index(collection, filed_name):
index_param = {
"index_type": _INDEX_TYPE,
"params": {"nlist": _NLIST},
"metric_type": _METRIC_TYPE}
collection.create_index(filed_name, index_param)
print("\nCreated index:\n{}".format(collection.index().params))


def drop_index(collection):
collection.drop_index()
print("\nDrop index sucessfully")

# get the number of entities
print(f"\nThe number of entity: {insert_result['insert_count']}")

def load_collection(collection):
collection.load()
# create index
index_params = milvus_client.prepare_index_params()

index_params.add_index(
field_name=_VECTOR_FIELD_NAME,
index_type=_INDEX_TYPE,
metric_type=_METRIC_TYPE,
params={"nlist": _NLIST}
)

def release_collection(collection):
collection.release()
milvus_client.create_index(
collection_name=_COLLECTION_NAME,
index_params=index_params
)
print("\nCreated index")

# load data to memory
milvus_client.load_collection(_COLLECTION_NAME)
vector = data_dict[1]
vectors = [vector["float_vector_field"]]

def search(collection, vector_field, id_field, search_vectors):
# search
search_param = {
"data": search_vectors,
"anns_field": vector_field,
"anns_field": _VECTOR_FIELD_NAME,
"param": {"metric_type": _METRIC_TYPE, "params": {"nprobe": _NPROBE}},
"limit": _TOPK,
"expr": "id_field > 0"}
results = collection.search(**search_param)
"expr": f"{_ID_FIELD_NAME} > 0"}
results = milvus_client.search(collection_name=_COLLECTION_NAME,data=vectors,limit= _TOPK,search_params=search_param)
for i, result in enumerate(results):
print("\nSearch result for {}th vector: ".format(i))
for j, res in enumerate(result):
print("Top {}: {}".format(j, res))


def main():
# create a connection
create_connection()

# drop collection if the collection exists
if has_collection(_COLLECTION_NAME):
drop_collection(_COLLECTION_NAME)

# create collection
collection = create_collection(_COLLECTION_NAME, _ID_FIELD_NAME, _VECTOR_FIELD_NAME)

# show collections
list_collections()

# insert 10000 vectors with 128 dimension
vectors = insert(collection, 10000, _DIM)

# get the number of entities
get_entity_num(collection)

# create index
create_index(collection, _VECTOR_FIELD_NAME)

# load data to memory
load_collection(collection)

# search
search(collection, _VECTOR_FIELD_NAME, _ID_FIELD_NAME, vectors[:3])

# release memory
release_collection(collection)
milvus_client.release_collection(_COLLECTION_NAME)

# drop collection index
drop_index(collection)
milvus_client.drop_index(_COLLECTION_NAME,index_name=_VECTOR_FIELD_NAME)
print("\nDrop index sucessfully")

# drop collection
drop_collection(_COLLECTION_NAME)
milvus_client.drop_collection(_COLLECTION_NAME)
print("\nDrop collection: {}".format(_COLLECTION_NAME))


if __name__ == '__main__':
main()
main()