Skip to main content

Python Sdk for Milvus

Project description

Milvus Python SDK

Using Milvus python sdk for Milvus

Download

$ pip install pymilvus

Import

from milvus import Milvus, Prepare, IndexType

Getting started

Initial a Milvus instance and connect to the sever

>>> milvus = Milvus()

>>> milvus.connect(host='SERVER-HOST', port='SERVER-PORT')
Status(code=0, message="Success")

Once successfully connected, you can get the version of server

>>> milvus.server_version()
0.0.0  # this is example version, the real version may vary

Add a new table

First using Prepare to create param

>>> param = Prepare.table_schema(table_name='test01', dimension=256, index_type=IndexType.IDMAP,
                                    store_raw_vector=False)

Then create table

>>> milvus.create_table(param)
Status(message='Table test01 created!', code=0)

Describe the table we just created

>>> milvus.describe_table('test01')
(Status(code=0, message='Success!'), TableSchema(table_name='test01',dimension=256, index_type=1, store_raw_vector=False))

Add vectors into table test01

First Prepare binary vectors of 256-dimension.

  • Note that random, struct and pprint we used here is for creating fake vectors data and pretty print, you may not need them in your project
>>> import random
>>> import struct
>>> from pprint import pprint

>>> dim = 256  # Dimension of the vector

# Initialize 20 vectors of 256-dimension
>>> fake_vectors = [[random.random() for _ in range(dim)] for _ in range(20)]
>>> vectors = Prepare.records(fake_vectors)  # This will transfer fake_vector to binary data

Then add vectors into table test01

>>> status, ids = milvus.add_vectors(table_name='test01', records=vectors)
>>> print(status)
Status(code=0, message='Success')
>>> pprint(ids) # List of ids returned
23455321135511233
12245748929023489
...

Search vectors

# prepare 5 vectors of 256-dimension
>>> q_records = Prepare.records([random.random() for _ in range(dim)] for _ in range(5)]

Then get results

>>> status, results = milvus.search_vectors(table_name='test01', query_records=q_records, top_k=10)
>>> print(status)
Status(code=0, message='Success')
>>> pprint(results) # Searched top_k vectors

Disconnect with the server

>>> milvus.disconnect()
Status(code=0, message='Success')

Example python

There is a small example in examples/example.py, you can find more guide there.

Build docs

$ sphinx-build -b html doc/en/ doc/en/build

If you encounter any problems or bugs, please add new issues

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

pymilvus-0.1.1-py3-none-any.whl (21.3 kB view details)

Uploaded Python 3

File details

Details for the file pymilvus-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: pymilvus-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 21.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.8

File hashes

Hashes for pymilvus-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 563c3bbbad85d463808a0db506485438537a3551ebd45fd671ddfcbe4b15bbcc
MD5 143509ecc6f6230b1e324b43aede7b4b
BLAKE2b-256 94659053396333822db98c609fbfe7bb6c93f984870b35b856fa1ddf70ac4ddf

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page