Python Sdk for Milvus
Project description
Milvus Python SDK -- pymilvus
If you want to contribute to this repo, please read our contribution guidelines.
You can find api doc in Reference Doc
Using Milvus python sdk for Milvus Download
Pymilvus only supports python >= 3.5
, is fully tested under 3.5, 3.6, 3.7.
Pymilvus can be downloaded via pip
or pip3
for python3
$ pip install pymilvus
Different versions of Milvus and recommended pymilvus version supported accordingly
Milvus version | Recommended pymilvus version |
---|---|
0.3.0 | 0.1.13 |
0.3.1 | 0.1.25 |
0.4.0 | 0.2.2 |
0.5.0 | 0.2.3 |
0.5.1 | 0.2.3 |
0.5.2 | 0.2.3 |
0.5.3 | 0.2.5 |
0.6.0 | 0.2.6 |
You can download a specific version by:
$ pip install pymilvus==0.2.6
If you want to upgrade pymilvus
to newest version
$ pip install --upgrade pymilvus
Import
from milvus import Milvus, IndexType, MetricType, Status
Getting started
Initial a Milvus
instance and connect
to the sever
Create table
>>> milvus = Milvus()
>>> milvus.connect(host='localhost', port='19530')
Status(code=0, message='Successfully connected! localhost:19530')
Once successfully connected, you can get the version of server
>>> milvus.server_version()
(Status(code=0, message='Success'), '0.5.0') # this is example version, the real version may vary
Add a new table
First set param
>>> dim = 32 # Dimension of the vector
>>> param = {'table_name':'test01', 'dimension':dim, 'index_file_size':1024, 'metric_type':MetricType.L2}
Then create table
>>> milvus.create_table(param)
Status(code=0, message='Create table successfully!')
Describe the table we just created
>>> milvus.describe_table('test01')
(Status(code=0, message='Describe table successfully!'), TableSchema(table_name='test01', dimension=32, index_file_size=1024, metric_type=<MetricType: L2>))
Insert vectors
Add vectors into table test01
First create 20 vectors of 256-dimension.
- Note that
random
andpprint
we used here is for creating fake vectors data and pretty print, you may not need them in your project
>>> import random
>>> from pprint import pprint
# Initialize 20 vectors of 256-dimension
>>> vectors = [[random.random() for _ in range(dim)] for _ in range(20)]
Then add vectors into table test01
>>> status, ids = milvus.insert(table_name='test01', records=vectors)
>>> print(status)
Status(code=0, message='Add vectors successfully!')
>>> pprint(ids) # List of ids returned
[1571123848227800000,
1571123848227800001,
...........
1571123848227800018,
1571123848227800019]
You can also specify vectors id
>>> vector_ids = [i for i in range(20)]
>>> status, ids = milvus.insert(table_name='test01', records=vectors, ids=vector_ids)
>>> pprint(ids)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
Get vectors num
>>> milvus.count_table('test01')
(Status(code=0, message='Success!'), 20)
Load vectors into memory
>>> milvus.preload_table('test01')
Status(code=0, message='')
Create index
Create index
>>> index_param = {'index_type': IndexType.FLAT, 'nlist': 128}
>>> milvus.create_index('test01', index_param)
Status(code=0, message='Build index successfully!')
Then show index information
>>> milvus.describe_index('test01')
(Status(code=0, message='Successfully'), IndexParam(_table_name='test01', _index_type=<IndexType: FLAT>, _nlist=128))
Search vectors
# create 5 vectors of 256-dimension
>>> q_records = [[random.random() for _ in range(dim)] for _ in range(5)]
Then get results
>>> status, results = milvus.search(table_name='test01', query_records=q_records, top_k=1, nprobe=16)
>>> print(status)
Status(code=0, message='Search vectors successfully!')
>>> pprint(results) # Searched top_k vectors
[
[(id:15, distance:2.855304718017578),
(id:16, distance:3.040700674057007)],
[(id:11, distance:3.673950433731079),
(id:15, distance:4.183730602264404)],
........
[(id:6, distance:4.065953254699707),
(id:1, distance:4.149323463439941)]
]
Partition operations
Create table named demo01
>>> param = {'table_name':'demo01', 'dimension':dim, 'index_file_size':1024, 'metric_type':MetricType.L2}
>>> milvus.create_table(param)
Create a new partition named partition01
under table demo01
, and specify tag tag01
>>> milvus.create_partition('demo01', 'partition01', 'tag01')
Status(code=0, message='OK')
Specify partition vectors insert into
>>> status = milvus.insert('demo01', vectors, partition_tag="tag01")
>>> status
(Status(code=0, message='Add vectors successfully!')
Show partitions
milvus.show_partitions(table_name='demo01')
Search vectors in a designated partition
>>> milvus.search(table_name='test01', query_records=q_records, top_k=1, nprobe=16, partition_tags=['tag01'])
When you not specify partition_tags
, milvus will search in whole table.
Drop operations
Drop index
>>> milvus.drop_index('test01')
Status(code=0, message='')
Delete the table we just created
>>> milvus.drop_table(table_name='test01')
Status(code=0, message='Delete table successfully!')
Disconnect with the server
>>> milvus.disconnect()
Status(code=0, message='Disconnect successfully')
Example python
There are some small examples in examples/
, you can find more guide there.
If you encounter any problems or bugs, please open new issues
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