Milvus Python SDK

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

For detailed SDK documentation, refer to API Documentation.

New features
Get started
Basic operations
- Connect to the Milvus server
Create/Drop collections
- Create a collection
- Drop a collection
Create/Drop partitions in a collection
- Create a partition
- Drop a partition
Create/Drop indexes in a collection
- Create an index
- Drop an index
Insert/Delete vectors in collections/partitions
Flush data in one or multiple collections to disk
Compact all segments in a collection
Search entities in collections/partitions
- Search entities in a collection
- Search entities in a partition
Disconnect from the Milvus server
FAQ

New features

remove get_index_info, the index info can be obtained by get_collection_info
add get_collection_stats, more detailed collection stats.

Get started

Prerequisites

pymilvus only supports Python 3.5 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
0.3.0	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, 0.2.7
0.7.0	0.2.8
0.7.1	0.2.9
0.8.0	0.2.10
0.9.0	0.2.11
0.9.1	0.2.12
0.10.0	0.2.13
>=0.10.1, <0.11.0	0.2.14
0.11.0	0.3.0

You can install a specific version of pymilvus by:

$ pip install pymilvus==0.3.0

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

Import pymilvus.

# Import pymilvus
>>> from milvus import Milvus, DataType

Create a client to Milvus server 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

Prepare collection parameters.

# create collection name
>>> collection_name = 'test01'

# create a collection of 4 fields, fields A, B and C are int type fields
# and Vec is a float vector field.
# segment_row_limit is default as 524288 if not specified
>>> collection_param = {
...    "fields": [
...        {"name": "A", "type": DataType.INT32},
...        {"name": "B", "type": DataType.INT32},
...        {"name": "C", "type": DataType.INT64},
...        {"name": "Vec", "type": DataType.FLOAT_VECTOR, "params": {"dim": 128}}
...    ],
...    "segment_row_limit": 4096,
...    "auto_id": True
... }

Create collection test01 with dimension of 128, size of the data file for Milvus to automatically create indexes as 4096. If metric_type isn't offered, default metric type is Euclidean distance (L2). For FLOAT_VECTOR field, dim is a must.

# Create a collection
>>> client.create_collection(collection_name, collection_param)

You can check collection info by get_collection_info

>>> info = client.get_collection_info('test01')
>>> info
{'fields': [
    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
    {'name': 'Vec', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 128},
     'indexes': [{}]}
    ],
 'auto_id': True,
 'segment_row_limit': 4096
}

You can see from the info, there is an auto_id option in collection info, and its True by default. So if you have your own ids and don't want auto generated ids, you may want to set auto_id to False while creating collections.

Drop a collection

# Drop collection
>>> status = client.drop_collection('test01')
>>> status
Status(code=0, message='OK')

Create/Drop partitions in a collection

Create a partition

You can split collections into partitions by partition tags for improved search performance.

# Create partition
>>> client.create_partition(collection_name='test01', partition_tag='tag01')

Use list_partitions() to verify whether the partition is created.

# Show partitions
>>> partitions = client.list_partitions(collection_name='test01')
>>> partitions
['_default', 'tag01']

Drop a partition

# Drop partitions
>>> 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.

Create an index of IVF_FLAT with nlist = 100 for the collection.

# Create index
>>> status = client.create_index('test01', "Vec", {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}})
>>> status
Status(code=0, message='OK')

You can check index info by get_collection_info

>>> info = client.get_collection_info('test01')
>>> info
{'fields': [
    {'name': 'A', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
    {'name': 'C', 'type': <DataType.INT64: 5>, 'params': {}, 'indexes': [{}]},
    {'name': 'B', 'type': <DataType.INT32: 4>, 'params': {}, 'indexes': [{}]},
    {'name': 'Vec',
        'type': <DataType.FLOAT_VECTOR: 101>,
        'params': {'dim': 128, 'metric_type': 'L2'},
        'indexes': [{'index_type': 'IVF_FLAT', 'metric_type': 'L2', 'params': {'nlist': 100}}]}],
 'auto_id': True,
 'segment_row_limit': 4096
}

Drop an index

# Drop an index of a spacific field "Vec"
>>> status = client.drop_index('test01', "Vec")
Status(code=0, message='OK')

Insert/Delete entities in collections/partitions

Insert entities in a collection

Generate 5000 vectors of 128 dimension and an integer list.

>>> import random
>>> num = 5000

# Generate a list of integer.
>>> list_of_int = [random.randint(0, 255) for _ in range(num)]
# Generate 20 vectors of 128 dimension
>>> vectors = [[random.random() for _ in range(128)] for _ in range(num)]

Create hybrid entities

>>> hybrid_entities = [
   {"name": "A", "values": list_of_int, "type": DataType.INT32},
   {"name": "B", "values": list_of_int, "type": DataType.INT32},
   {"name": "C", "values": list_of_int, "type": DataType.INT64},
   {"name": "Vec", "values": vectors, "type":DataType.FLOAT_VECTOR}
]

Insert the hybrid entities.

If you create a new collection with auto_id = True, Milvus automatically generates IDs for the vectors.

# Insert vectors
>>> ids = client.insert('test01', hybrid_entities)

If you create a new collection with auto_id = False, you have to provide user-defined vector ids:

# Generate fake custom ids
>>> vector_ids = [id for id in range(num)]
# Insert to the non-auto-id collection
>>> ids = client.insert('test01', hybrid_entities, ids=vector_ids)

The examples below are based on collection with auto_id = True.

Insert entities in a partition

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

To verify the entities you have inserted, use get_entity_by_id().

>>> entities = client.get_entity_by_id(collection_name='test01', ids=inserted_vector_ids[:10])

Delete entities by ID

You can delete these entities by:

>>> status = client.delete_entity_by_id('test01', 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.

>>> client.flush(['test01'])

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

Search entities in collections/partitions

Search entities in a collection

Prepare search parameters. "term" and "range" is optional, "params" in "vector" stands for index params.

# This dsl will search topk `entities` that are
# close to vectors[:1] searched by `IVF_FLAT` index with `nprobe = 10` and `metric_type = L2`,
# AND field "A" in [1, 2, 5],
# AND field "B" greater than 1 less than 100
>>> dsl = {
...     "bool": {
...         "must":[
...             {
...                 "term": {"A": [1, 2, 5]}
...             },
...             {
...                 "range": {"B": {"GT": 1, "LT": 100}}
...             },
...             {
...                 "vector": {
...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
...                 }
...             }
...         ]
...     }
... }

A search without hybrid conditions with IVF_FLAT index would be like:

>>> dsl = {
...     "bool": {
...         "must":[
...             {
...                 "vector": {
...                    "Vec": {"topk": 10, "query": vectors[:1], "metric_type": "L2", "params": {"nprobe": 10}}
...                 }
...             }
...         ]
...     }
... }

A FLAT search doesn't need index params, so the query would be like:

>>> dsl = {
...     "bool": {
...         "must":[
...             {
...                 "vector": {
...                    "Vec": {"topk": 10, "query": vectors[0], "metric_type": "L2"}
...                 }
...             }
...         ]
...     }
... }

Search entities.

With fields=["B"], not only can you get entity ids and distances, but also values of a spacific field B.

# search entities and get entity field B back
>>> results = client.search('test01', dsl, fields=["B"])

You can obtain ids, distances and fields by entities in results.

# Results consist of number-of-query entities
>>> entities = results[0]

# Entities consists of topk entity
>>> entity = entities[0]

# You can get all ids and distances by entities
>>> all_ids = entities.ids
>>> all_distances = entities.distances

# Or you can get them one by one by entity
>>> a_id = entity.id
>>> a_distance = entity.distance
>>> a_field = entity.entity.B # getattr(entity.entity, "B")

Note: If you don't provide fields in search, you will only get ids and distances.

Search entities in a partition

# Search entities in a partition `tag01`
>>> client.search(collection_name='test01', dsl=dsl, partition_tags=['tag01'])

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

Release files for pymilvus 0.3.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pymilvus 0.3.0
File	Size	Uploaded
pymilvus-0.3.0.tar.gz	49.1 kB	Oct 16, 2020	Details

Built distribution (wheel)

Table of built distributions (wheels) for pymilvus 0.3.0
File	Interpreter	ABI	Platform	Reset
pymilvus-0.3.0-py3-none-any.whl 56.6 kB Oct 16, 2020	Python 3	none	any	Details

Total release size: 105.7 kB

Release files / pymilvus-0.3.0.tar.gz

Download URL	pymilvus-0.3.0.tar.gz
Size	49.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`68cdabe02021b87efced07aecd234acd927cb755f28817c47b28e1a1d38f54e7`
BLAKE2b-256 checksum How to use checksums	`293e7c16b638a1d47b133371046442740a3ac84ccb875ca869081fe1deeae3a9`
Upload date	Oct 16, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.9`

Release files / pymilvus-0.3.0-py3-none-any.whl

Download URL	pymilvus-0.3.0-py3-none-any.whl
Size	56.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`3a18f577bc08e59996e9a9ef58b093be11969304c791db9549faf0648310a762`
BLAKE2b-256 checksum How to use checksums	`b966f96a3bb69a482cd3f7964606df577b323deed3f1aae6509eb6149502a450`
Upload date	Oct 16, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.9`