milvus-logo
LFAI
首页
  • 用户指南
    • 插入和删除

删除实体

您可以通过筛选条件或主键删除不再需要的实体。

通过筛选条件删除实体

批量删除共享某些属性的多个实体时,可以使用过滤表达式。下面的示例代码使用in操作符批量删除了所有颜色字段设置为红色绿色的实体。你也可以使用其他操作符来构建符合你要求的过滤表达式。有关过滤表达式的更多信息,请参阅元数据过滤

from pymilvus import MilvusClient

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

res = client.delete(
    collection_name="quick_setup",
    # highlight-next-line
    filter="color in ['red_3314', 'purple_7392']"
)

print(res)

# Output
# {'delete_count': 2}

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.service.vector.request.DeleteReq;
import io.milvus.v2.service.vector.response.DeleteResp;

ilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .token("root:Milvus")
        .build());

DeleteResp deleteResp = client.delete(DeleteReq.builder()
        .collectionName("quick_setup")
        .filter("color in ['red_3314', 'purple_7392']")
        .build());


const { MilvusClient, DataType } = require("@zilliz/milvus2-sdk-node")

const address = "http://localhost:19530";
const token = "root:Milvus";
const client = new MilvusClient({address, token});

// 7. Delete entities
res = await client.delete({
    collection_name: "quick_setup",
    // highlight-next-line
    filter: "color in ['red', 'green']"
})

console.log(res.delete_cnt)

// Output
// 
// 3
// 

export CLUSTER_ENDPOINT="http://localhost:19530"
export TOKEN="root:Milvus"

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/delete" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "quick_setup",
    "filter": "color in [\"red_3314\", \"purple_7392\"]"
}'

通过主键删除实体

在大多数情况下,一个主键可以唯一标识一个实体。你可以通过在删除请求中设置实体的主键来删除实体。下面的示例代码演示了如何删除主键为1819 的两个实体。

res = client.delete(
    collection_name="quick_setup",
    # highlight-next-line
    ids=[18, 19]
)

print(res)

# Output
# {'delete_count': 2}

import io.milvus.v2.service.vector.request.DeleteReq;
import io.milvus.v2.service.vector.response.DeleteResp;

import java.util.Arrays;


DeleteResp deleteResp = client.delete(DeleteReq.builder()
        .collectionName("quick_setup")
        .ids(Arrays.asList(18, 19))
        .build());

const { MilvusClient, DataType } = require("@zilliz/milvus2-sdk-node")

res = await client.delete({
    collection_name: "quick_setup",
    ids: [18, 19]
})

console.log(res.delete_cnt)

// Output
// 
// 2
// 

export CLUSTER_ENDPOINT="http://localhost:19530"
export TOKEN="root:Milvus"

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/delete" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "quick_setup",
    "filter": "id in [18, 19]"
}'
## {"code":0,"cost":0,"data":{}}

从分区中删除实体

您还可以删除存储在特定分区中的实体。以下代码片段假定您的 Collection 中有一个名为PartitionA的分区。

res = client.delete(
    collection_name="quick_setup",
    ids=[18, 19],
    partition_name="partitionA"
)

print(res)

# Output
# {'delete_count': 2}

import io.milvus.v2.service.vector.request.DeleteReq;
import io.milvus.v2.service.vector.response.DeleteResp;

import java.util.Arrays;

DeleteResp deleteResp = client.delete(DeleteReq.builder()
        .collectionName("quick_setup")
        .ids(Arrays.asList(18, 19))
        .partitionName("partitionA")
        .build());

const { MilvusClient, DataType } = require("@zilliz/milvus2-sdk-node")

res = await client.delete({
    collection_name: "quick_setup",
    ids: [18, 19],
    partition_name: "partitionA"
})

console.log(res.delete_cnt)

// Output
// 
// 2
// 

export CLUSTER_ENDPOINT="http://localhost:19530"
export TOKEN="root:Milvus"

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/delete" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "quick_setup",
    "partitionName": "partitionA",
    "filter": "id in [18, 19]"
}'

# {
#     "code": 0,
#     "cost": 0,
#     "data": {}
# }

翻译自DeepLogo

想要更快、更简单、更好用的 Milvus SaaS服务 ?

Zilliz Cloud是基于Milvus的全托管向量数据库,拥有更高性能,更易扩展,以及卓越性价比

免费试用 Zilliz Cloud
反馈

此页对您是否有帮助?