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  • Python
    • MilvusClient

delete()

This operation deletes entities by their IDs or with a boolean expression.

Request syntax

delete(
    collection_name: str,
    ids: Optional[Union[list, str, int]] = None,
    timeout: Optional[float] = None,
    filter: Optional[str] = "",
    partition_name: Optional[str] = "",
    **kwargs,
) -> dict

PARAMETERS:

  • collection_name (str) -

    [REQUIRED]

    The name of an existing collection.

  • ids (list | str | int) -

    [REQUIRED]

    A specific entity ID or a list of entity IDs.

    The value defaults to None, indicating that a scalar filtering condition applies. Setting both ids and filter results in a ParamError exception.

  • 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.

  • filter (str) -

    A scalar filtering condition to filter matching entities.

    The value defaults to an empty string, indicating that no condition applies. Setting both ids and filter results in a ParamError exception.

    You can set this parameter to an empty string to skip scalar filtering. To build a scalar filtering condition, refer to Boolean Expression Rules.

  • partition_name (str | "") -

    The name of the partition to delete entities from.

    The value defaults to an empty string. If specified, entities will be deleted from the specified partition.

    This parameter is not applicable to Milvus Lite. For more information on Milvus Lite limits, refer to Run Milvus Lite.

RETURN TYPE:

dict

RETURNS:

A dictionary contains the number of deleted entities.

{
    "delete_cnt": int
}

EXCEPTIONS:

  • MilvusException

    This exception will be raised when any error occurs during this operation.

  • ParamError

    This exception will be raised when both ids and filter are specified.

  • DataTypeNotMatchException

    This exception will be raised when a parameter value doesn't match the required data type.

Examples

from pymilvus import MilvusClient

# 1. Set up a milvus client
client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

# 2. Create a collection
client.create_collection(
    collection_name="test_collection",
    dimension=5
)

# 3. Insert data
client.insert(
    collection_name="test_collection",
    data=[
        {"id": 0, "vector": [0.5, 0.09, 0.2, 0.15, 0.05], "color": "green"},
        {"id": 1, "vector": [0.04, 0.09, 0.33, 0.03, 0.35], "color": "blue"},
        {"id": 2, "vector": [0.1, 0.21, 0.41, 0.36, 0.9], "color": "orange"},
        {"id": 3, "vector": [0.75, 0.24, 0.09, 0.81, 0.41], "color": "red"},
        {"id": 4, "vector": [0.13, 0.27, 0.3, 0.23, 0.17], "color": "yellow"},
        {"id": 5, "vector": [0.17, 0.3, 0.13, 0.9, 0.29], "color": "white"},
        {"id": 6, "vector": [0.33, 0.22, 0.39, 0.17, 0.18], "color": "black"},
        {"id": 7, "vector": [0.16, 0.13, 0.03, 0.13, 0.12], "color": "purple"},
        {"id": 8, "vector": [0.12, 0.16, 0.25, 0.2, 0.16], "color": "pink"},
        {"id": 9, "vector": [0.07, 0.38, 0.36, 0.03, 0.47], "color": "brown"}
    ]
)

# {'insert_count': 10}

# 4. Delete entities
client.delete(
    collection_name="test_collection",
    ids=[3, 6, 7]
)

# {'delete_count': 3}

client.delete(
    collection_name="test_collection",
    filter="id in [1, 8, 9] and color like 'b%'"
)

# {'delete_count': 2}

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