describe_index()
This operation describes a specific index.
Request syntax
describe_index(
collection_name: str,
index_name: str,
timeout: Optional[float] = None
) - Dict
PARAMETERS:
collection_name (str) -
[REQUIRED]
The name of an existing collection.
Setting this to a non-existing collection results in MilvusException.
index_name (str) -
[REQUIRED]
The name of the index to describe.
Setting this to a non-existing collection results in MilvusException.
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.
__RETURN TYPE: __
Dict
RETURNS:
A dictionary that contains the details of the specified index.
{
'index_type': 'IVF_FLAT',
'metric_type': 'IP',
'field_name': 'my_vector',
'index_name': 'my_vector'
}
PARAMETERS:
index_type (str) -
The algorithm that is used to build the index.
For details, refer to In-memory Index, On-disk Index and Scalar Index.
metric_type (str) -
The algorithm that is used to measure similarity between vectors. Possible values are IP, L2, and COSINE.
This is available only when the specified field is a vector field.
field_name (str) -
The name of the field on which the index has been created.
index_name (str) -
The name of the created index.
EXCEPTIONS:
MilvusException
This exception will be raised when any error occurs during this operation.
Example
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
)
# 6. List indexes
client.list_indexes(collection_name="customized_setup")
# ['my_id', 'my_vector']
# 7. Describe the indexes
client.describe_index(
collection_name="customized_setup",
index_name="my_vector"
)
# {
# 'nlist': '1024',
# 'index_type': 'IVF_FLAT',
# 'metric_type': 'L2',
# 'field_name': 'my_vector',
# 'index_name': 'my_vector'
# }
client.describe_index(
collection_name="customized_setup",
index_name="my_id"
)
# {
# 'index_type': 'STL_SORT',
# 'field_name': 'my_id',
# 'index_name': 'my_id'
# }