# from pymilvus import Collection, utility, connections, db
# from pymilvus import CollectionSchema, FieldSchema, DataType
# from pymilvus import Collection, db, connections

# conn = connections.connect(host="172.18.127.124", port=19530)
# # database = db.create_database("sample_db")
# print(db.list_database())

# db.using_database("sample_db")
 
# # m_id = FieldSchema(name="m_id", dtype=DataType.INT64, is_primary=True,)
# # embeding = FieldSchema(name="embeding", dtype=DataType.FLOAT_VECTOR, dim=768,)
# # count = FieldSchema(name="count", dtype=DataType.INT64,)
# # desc = FieldSchema(name="desc", dtype=DataType.VARCHAR, max_length=256,)
# # schema = CollectionSchema(
# #   fields=[m_id, embeding, desc, count],
# #   description="Test embeding search",
# #   enable_dynamic_field=True
# # )
 
# # collection_name = "word_vector"
# # collection = Collection(name=collection_name, schema=schema, using='default', shards_num=2)

# index_params = {
#   "metric_type": "IP",
#   "index_type": "IVF_FLAT",
#   "params": {"nlist": 1024}
# }
 
# collection = Collection("word_vector")
# collection.create_index(
#   field_name="embeding",
#   index_params=index_params
# )
 
# utility.index_building_progress("word_vector")


from pymilvus import Collection, db, connections
import numpy as np
 
conn = connections.connect(host="172.18.107.78", port=19530)
database = db.create_database("XIAN")
print(db.list_database())
# db.using_database("sample_db")
# coll_name = 'word_vector'
 
# mids, embedings, counts, descs = [], [], [], []
# data_num = 100
# for idx in range(0, data_num):
#     mids.append(idx)
#     embedings.append(np.random.normal(0, 0.1, 768).tolist())
#     descs.append(f'random num {idx}')
#     counts.append(idx)
 
# collection = Collection(coll_name)
# mr = collection.insert([mids, embedings, descs, counts])
# print(mr)