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milvus-io
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Description

Python SDK for Milvus.

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Milvus Python SDK

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Python SDK for Milvus. To contribute code to this project, please read our contribution guidelines first.

For detailed SDK documentation, refer to API Documentation.

Get started

Prerequisites

pymilvus only supports Python 3.6 or higher.

Install pymilvus

You can install pymilvus via

pip
or
pip3
for Python3:
$ pip3 install pymilvus

The following collection shows Milvus versions and recommended pymilvus versions:

|Milvus version| Recommended pymilvus version | |:-----:|:-----:| | 0.10.5| 0.2.15, 0.4.0 | | 0.10.1 - 0.10.4| 0.2.14 | | 0.10.0 | 0.2.13 | | 0.9.1 | 0.2.12 | | 0.9.0 | 0.2.11 | | 0.8.0 | 0.2.10 | | 0.7.1 | 0.2.9 | | 0.7.0 | 0.2.8 | | 0.6.0 | 0.2.6, 0.2.7 | | 0.5.3 | 0.2.5 | | 0.5.2 | 0.2.3 | | 0.5.1 | 0.2.3 | | 0.5.0 | 0.2.3 | | 0.4.0 | 0.2.2 | | 0.3.1 | 0.1.25| | 0.3.0 | 0.1.13|

You can install a specific version of pymilvus by:

$ pip install pymilvus==0.4.0

You can upgrade

pymilvus
to the latest version by:
$ pip install --upgrade pymilvus

Examples

Refer to examples for more example programs.

Basic operations

Connect to the Milvus server

  1. Import pymilvus.
   # Import pymilvus
   >>> from milvus import Milvus, IndexType, MetricType, Status
  1. Create a client to Milvus server by using one of the following methods:
   # Connect to Milvus server
   >>> client = Milvus(host='localhost', port='19530')

Note: In the above code, default values are used for

host
and
port
parameters. Feel free to change them to the IP address and port you set for Milvus server.
   >>> client = Milvus(uri='tcp://localhost:19530')

Create/Drop collections

Create a collection

  1. Prepare collection parameters.
   # Prepare collection parameters
   >>> param = {'collection_name':'test01', 'dimension':128, 'index_file_size':1024, 'metric_type':MetricType.L2}
  1. Create collection
    test01
    with dimension size as 128, size of the data file for Milvus to automatically create indexes as 1024, and metric type as Euclidean distance (L2).
   # Create a collection
   >>> status = client.create_collection(param)
   >>> status
   Status(code=0, message='Create collection successfully!')

Drop a collection

# Drop collection
>>> status = client.drop_collection(collection_name='test01')
>>> status
Status(code=0, message='Delete collection successfully!')

Create/Drop partitions in a collection

Create a partition

You can split collections into partitions by partition tags for improved search performance. Each partition is also a collection.

# Create partition
>>> status = client.create_partition(collection_name='test01', partition_tag='tag01')
>>> status
Status(code=0, message='OK')

Use

list_partitions()
to verify whether the partition is created.
# Show partitions
>>> status, partitions = client.list_partitions(collection_name='test01')
>>> partitions
[(collection_name='test01', tag='_default'), (collection_name='test01', tag='tag01')]

Drop a partition

>>> status = client.drop_partition(collection_name='test01', partition_tag='tag01')
Status(code=0, message='OK')

Create/Drop indexes in a collection

Create an index

Note: In production, it is recommended to create indexes before inserting vectors into the collection. Index is automatically built when vectors are being imported. However, you need to create the same index again after the vector insertion process is completed because some data files may not meet the

index_file_size
and index will not be automatically built for these data files.
  1. Prepare index parameters. The following command uses
    IVF_FLAT
    index type as an example.
   # Prepare index param
   >>> ivf_param = {'nlist': 4096}
  1. Create an index for the collection.
   # Create index
   >>> status = client.create_index('test01', IndexType.IVF_FLAT, ivf_param)
   Status(code=0, message='Build index successfully!')

Drop an index

>>> status = client.drop_index('test01')
Status(code=0, message='OK')

Insert/Delete vectors in collections/partitions

Insert vectors in a collection

  1. Generate 20 vectors of 128 dimension.
   >>> import random
   >>> dim = 128
   # Generate 20 vectors of 128 dimension
   >>> vectors = [[random.random() for _ in range(dim)] for _ in range(20)]
  1. Insert the list of vectors. If you do not specify vector ids, Milvus automatically generates IDs for the vectors.
   # Insert vectors
   >>> status, inserted_vector_ids = client.insert(collection_name='test01', records=vectors)
   >>> inserted_vector_ids 
   [1592028661511657000, 1592028661511657001, 1592028661511657002, 1592028661511657003, 1592028661511657004, 1592028661511657005, 1592028661511657006, 1592028661511657007, 1592028661511657008, 1592028661511657009, 1592028661511657010, 1592028661511657011, 1592028661511657012, 1592028661511657013, 1592028661511657014, 1592028661511657015, 1592028661511657016, 1592028661511657017, 1592028661511657018, 1592028661511657019]

Alternatively, you can also provide user-defined vector ids:

   >>> vector_ids = [id for id in range(20)]
   >>> status, inserted_vector_ids = client.insert(collection_name='test01', records=vectors, ids=vector_ids)

Insert vectors in a partition

>>> status, inserted_vector_ids = client.insert('test01', vectors, partition_tag="tag01")

To verify the vectors you have inserted, use

get_vector_by_id()
. Assume you have vector with the following ID.
>>> status, vector = client.get_entity_by_id(collection_name='test01', ids=inserted_vector_ids[:10])

Delete vectors by ID

You can delete these vectors by:

>>> status = client.delete_entity_by_id('test01', inserted_vector_ids[:10])
>>> status
Status(code=0, message='OK')

Flush data in one or multiple collections to disk

When performing operations related to data changes, you can flush the data from memory to disk to avoid possible data loss. Milvus also supports automatic flushing, which runs at a fixed interval to flush the data in all collections to disk. You can use the Milvus server configuration file to set the interval.

>>> status = client.flush(['test01'])
>>> status
Status(code=0, message='OK')

Compact all segments in a collection

A segment is a data file that Milvus automatically creates by merging inserted vector data. A collection can contain multiple segments. If some vectors are deleted from a segment, the space taken by the deleted vectors cannot be released automatically. You can compact segments in a collection to release space.

>>> status = client.compact(collection_name='test01')
>>> status
Status(code=0, message='OK')

Search vectors in collections/partitions

Search vectors in a collection

  1. Prepare search parameters.
>>> search_param = {'nprobe': 16}
  1. Search vectors.
# create 5 vectors of 32-dimension
>>> q_records = [[random.random() for _ in range(dim)] for _ in range(5)]
# search vectors
>>> status, results = client.search(collection_name='test01', query_records=q_records, top_k=2, params=search_param)
>>> results
[
[(id:1592028661511657012, distance:19.450458526611328), (id:1592028661511657017, distance:20.13418197631836)],
[(id:1592028661511657012, distance:19.12230682373047), (id:1592028661511657018, distance:20.221458435058594)],
[(id:1592028661511657014, distance:20.423980712890625), (id:1592028661511657016, distance:20.984281539916992)],
[(id:1592028661511657018, distance:18.37057876586914), (id:1592028661511657019, distance:19.366962432861328)],
[(id:1592028661511657013, distance:19.522361755371094), (id:1592028661511657010, distance:20.304216384887695)]
]

Search vectors in a partition

# create 5 vectors of 32-dimension
>>> q_records = [[random.random() for _ in range(dim)] for _ in range(5)]
>>> client.search(collection_name='test01', query_records=q_records, top_k=1, partition_tags=['tag01'], params=search_param)

Note: If you do not specify

partition_tags
, Milvus searches the whole collection.

close client

>>> client.close()

FAQ

I'm getting random "socket operation on non-socket" errors from gRPC when connecting to Milvus from an application served on Gunicorn

Make sure to set the environment variable

GRPC_ENABLE_FORK_SUPPORT=1
. For reference, see https://zhuanlan.zhihu.com/p/136619485

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