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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] = "",
    **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.

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}