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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 the describe_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="https://inxx-xxxxxxxxxxxx.api.gcp-us-west1.zillizcloud.com:19530",
    token="user:password"
)

# 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)
schema.add_field(field_name="my_vector", datatype=DataType.FLOAT_VECTOR, 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="AUTOINDEX",
    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']