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  • Python

add_index()

This operation adds index parameters for a specific field in a collection.

Request syntax

IndexParams.add_index(
    field_name: str,
    index_type: str,
    index_name: str,
    metric_type: str,
    params: dict
) -> None

PARAMETERS:

  • field_name (str) -

    The name of the target file to apply this object applies.

  • index_name (str) -

    The name of the index file generated after this object has been applied.

  • index_type (str) -

    The name of the algorithm used to arrange data in the specific field. For applicable algorithms, refer to In-memory Index and On-disk Index.On Zilliz Cloud, the index type is always AUTOINDEX. For details, refer to AUTOINDEX Explained.

  • 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.

  • params (dict) -

    The fine-tuning parameters for the specified index type. For details on possible keys and value ranges, refer to In-memory Index.

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

# 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.create_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}
)

Related methods

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