Collection()
This is the constructor method to create a collection with the specified schema or to get an existing collection with the name.
Invocation
Collection(name, schema=None, using='default', shards_num=2, **kwargs)
Parameters
Parameter | Description | Type | Required |
---|---|---|---|
name | Name of the collection. | String | True |
schema | Schema of the collection to create. A schema specifies the properties of a collection and the fields within. See Schema for more information. | class schema.CollectionSchema | False |
using | Milvus connection used to create the collection. | String | False |
shards_num | Shard number of the collection to create. It corresponds to the number of data nodes used to insert data. | INT32 | False |
kwargs : consistency_level | Consistency level used to create the collection. | String/Integer | False |
Return
A new collection object created with the specified schema or an existing collection object by name.
Properties
Property | Description | Type |
---|---|---|
name | Name of the collection. | String |
schema | Schema of the collection. | class schema.CollectionSchema |
description | Description of the collection. | String |
is_empty | Boolean value to indicate if the collection is empty. | Bool |
num_entities | Number of entities in the collection. | Integer |
primary_field | Schema of the primary field in the collection. | class schema.FieldSchema |
partitions | List of all partitions in the collection. | list[String] |
indexes | List of all indexes in the collection. | list[String] |
Raises
CollectionNotExistException
: error if the collection does not exist.
Example
from pymilvus import CollectionSchema, FieldSchema, DataType, Collection
book_id = FieldSchema(
name="book_id",
dtype=DataType.INT64,
is_primary=True,
)
word_count = FieldSchema(
name="word_count",
dtype=DataType.INT64,
)
book_intro = FieldSchema(
name="book_intro",
dtype=DataType.FLOAT_VECTOR,
dim=2
)
schema = CollectionSchema(
fields=[book_id, word_count, book_intro],
description="Test book search"
)
collection_name = "book"
collection = Collection(
name=collection_name,
schema=schema,
using='default',
shards_num=2,
consistency_level="Strong"
)
collection.schema
{
auto_id: False
description: Test book search
fields: [{
name: book_id
description:
type: 5
is_primary: True
auto_id: False
}, {
name: word_count
description:
type: 5
}, {
name: book_intro
description:
type: 101
params: {'dim': 2}
}]
}
collection.description
'Test book search'
collection.name
'book'
collection.is_empty
True
collection.primary_field
{
name: book_id
description:
type: 5
is_primary: True
auto_id: False
}