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  • Python

load_collection()

This operation loads the data of a specific collection into memory.

Request syntax

load_collection(
    collection_name: str, 
    timeout: Optional[float] = None
) -> None

PARAMETERS:

  • collection_name (str) -

    [REQUIRED]

    The name of a collection.

  • timeout (float | None) -

    The timeout duration for this operation.

    Setting this to None indicates that this operation timeouts when any response returns or error occurs.

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 a collection
client.create_collection(
    collection_name="customized_setup",
    schema=schema
)

# 4. Prepare index parameters
index_params = client.prepare_index_params()

# 5. Add indexes
index_params.add_index(
    field_name="my_id",
    index_type="STL_SORT"
)

index_params.add_index(
    field_name="my_vector", 
    index_type="IVF_FLAT",
    metric_type="L2",
    params: {nlist: 1024}
)

# 6. Create indexes
client.create_index(
    collection_name="customized_setup",
    index_params=index_params
)

# 7. Load indexes
client.load_collection(
    collection_name="customized_setup"
)