create_index()
This operation creates an index for a specific collection.
Request syntax
create_index(
collection_name: str,
index_params: IndexParams,
timeout: Optional[float] = None,
**kwargs,
)
PARAMETERS:
collection_name (str) -
[REQUIRED]
The name of an existing collection.
index_params (IndexParams) -
[REQUIRED]
An IndexParams object containing a list of IndexParam objects.
timeout (float | None) -
The timeout duration for this operation. Setting this to None indicates that this operation timeouts when any response arrives or any error occurs.
kwargs -
sync (bool)
Controls how the index is built in relation to the client’s request. Valid values:
True(default): The client waits until the index is fully built before it returns. This means you will not get a response until the process is complete.False: The client returns immediately after the request is received and the index is being built in the background. To find out if index creation has been completed, use thedescribe_index()method.
RETURN TYPE:
NoneType
RETURNS:
None
EXCEPTIONS:
MilvusException
This exception will be raised when any error occurs during this operation.
Examples
from pymilvus import MilvusClient, DataType
client = MilvusClient(
uri="http://localhost:19530",
token="root:Milvus"
)
# 1. Create schema
schema = MilvusClient.create_schema(
auto_id=False,
enable_dynamic_field=False,
)
# 2. Add fields to schema
schema.add_field(field_name="my_id", datatype=DataType.INT64, is_primary=True)
# {
# 'auto_id': False,
# 'description': '',
# 'fields': [
# {
# 'name': 'my_id',
# 'description': '',
# 'type': <DataType.INT64: 5>,
# 'is_primary': True,
# 'auto_id': False
# }
# ]
# }
schema.add_field(field_name="my_vector", datatype=DataType.FLOAT_VECTOR, dim=5)
# {
# 'auto_id': False,
# 'description': '',
# 'fields': [
# {
# 'name': 'my_id',
# 'description': '',
# 'type': <DataType.INT64: 5>,
# 'is_primary': True,
# 'auto_id': False
# },
# {
# 'name': 'my_vector',
# 'description': '',
# 'type': <DataType.FLOAT_VECTOR: 101>,
# 'params': {
# 'dim': 5
# }
# }
# ]
# }
# 3. Create index parameters
index_params = client.prepare_index_params()
# 4. Add indexes
# - For a scalar field
index_params.add_index(
field_name="my_id",
index_type="STL_SORT"
)
# - For a vector field
index_params.add_index(
field_name="my_vector",
index_type="IVF_FLAT",
metric_type="L2",
params={"nlist": 1024}
)
# 5. Create a collection
client.create_collection(
collection_name="customized_setup",
schema=schema
)
# 6. Create indexes
client.create_index(
collection_name="customized_setup",
index_params=index_params,
sync=False
)
# 6. List indexes
client.list_indexes(collection_name="customized_setup")
# ['my_id', 'my_vector']