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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.
pymilvus only supports Python 3.5 or higher.
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 |
You can install a specific version of pymilvus by:
$ pip install pymilvus==0.2.9
You can upgrade pymilvus to the latest version by:
$ pip install --upgrade pymilvus
Refer to examples for more example programs.
Import pymilvus.
# Import pymilvus
>>> from milvus import Milvus, IndexType, MetricType, Status
Connect to Milvus server by using one of the following methods:
# Connect to Milvus server
>>> milvus = Milvus()
>>> milvus.connect(host='localhost', port='19530')
Note: In the above code, default values are used for
hostandportparameters. Feel free to change them to the IP address and port you set for Milvus server.
>>> milvus.connect(uri='tcp://localhost:19530')
Prepare collection parameters.
# Prepare collection parameters
>>> param = {'collection_name':'test01', 'dimension':256, 'index_file_size':1024, 'metric_type':MetricType.L2}
Create collection test01 with dimension size as 256, size of the data file for Milvus to automatically create indexes as 1024, and metric type as Euclidean distance (L2).
# Create a collection
>>> milvus.create_collection(param)
# Drop collection
>>> milvus.drop_collection(collection_name='test01')
You can split collections into partitions by partition tags for improved search performance. Each partition is also a collection.
# Create partition
>>> milvus.create_partition(collection_name='test01', partition_tag='tag01')
Use show_partitions() to verify whether the partition is created.
# Show partitions
>>> milvus.show_partitions(collection_name='test01')
>>> milvus.drop_partition(collection_name='test01', partition_tag='tag01')
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_sizeand index will not be automatically built for these data files.
Prepare index parameters. The following command uses IVF_FLAT index type as an example.
# Prepare index param
>>> ivf_param = {'nlist': 16384}
Create an index for the collection.
# Create index
>>> milvus.create_index('test01', IndexType.IVF_FLAT, ivf_param)
>>> milvus.drop_index('test01')
Generate 20 vectors of 256 dimension.
>>> import random
# Generate 20 vectors of 256 dimension
>>> vectors = [[random.random() for _ in range(dim)] for _ in range(20)]
Insert the list of vectors. If you do not specify vector ids, Milvus automatically generates IDs for the vectors.
# Insert vectors
>>> milvus.insert(collection_name='test01', records=vectors)
Alternatively, you can also provide user-defined vector ids:
>>> vector_ids = [id for id in range(20)]
>>> milvus.insert(collection_name='test01', records=vectors, ids=vector_ids)
>>> milvus.insert('test01', vectors, partition_tag="tag01")
To verify the vectors you have inserted, use get_vector_by_id(). Assume you have a vector with the following ID.
>>> status, vector = milvus.get_vector_by_id(collection_name='test01', vector_id=0)
Assume you have some vectors with the following IDs:
>>> ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
You can delete these vectors by:
>>> milvus.delete_by_id('test01', ids)
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.
>>> milvus.flush(['test01'])
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.
>>> milvus.compact(collection_name='test01')
>>> search_param = {'nprobe': 16}
# create 5 vectors of 32-dimension
>>> q_records = [[random.random() for _ in range(dim)] for _ in range(5)]
# search vectors
>>> milvus.search(collection_name='test01', query_records=q_records, top_k=2, params=search_param)
# create 5 vectors of 32-dimension
>>> q_records = [[random.random() for _ in range(dim)] for _ in range(5)]
>>> milvus.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.
>>> milvus.disconnect()
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