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可归零和默认值

Milvus 允许你为标量字段（主字段除外）设置nullable 属性和默认值。对于标记为nullable=True 的字段，您可以在插入数据时跳过该字段，或直接将其设置为空值，系统会将其视为空值而不会导致错误。当字段具有默认值时，如果在插入过程中没有为该字段指定数据，系统将自动应用该值。

默认值和可归零属性允许处理带有空值的数据集并保留默认值设置，从而简化了从其他数据库系统到 Milvus 的数据迁移。在创建 Collections 时，也可以启用可归零属性或为可能存在不确定值的字段设置默认值。

限制

  • 只有标量字段（主字段除外）支持默认值和 nullable 属性。

  • JSON 和数组字段不支持默认值。

  • 默认值或 nullable 属性只能在创建 Collections 时配置，之后不能修改。

  • 启用了可归零属性的标量字段不能在分组搜索中用作group_by_field 。有关分组搜索的更多信息，请参阅分组搜索。

  • 标记为可归零的字段不能用作分区键。有关分区键的更多信息，请参阅使用分区键。

  • 在启用了可归零属性的标量字段上创建索引时，索引将排除空值。

可归零属性

通过nullable 属性，可以在 Collections 中存储空值，从而在处理未知数据时提供灵活性。

设置 nullable 属性

创建 Collections 时，使用nullable=True 定义可归零字段（默认为False ）。下面的示例创建了一个名为user_profiles_null 的 Collection，并将age 字段设置为可归零。

Python Java Node.js cURL
from pymilvus import MilvusClient, DataType

client = MilvusClient(uri='http://localhost:19530')

# Define collection schema
schema = client.create_schema(
    auto_id=False,
    enable_dynamic_schema=True,
)

schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=5)
schema.add_field(field_name="age", datatype=DataType.INT64, nullable=True) # Nullable field

# Set index params
index_params = client.prepare_index_params()
index_params.add_index(field_name="vector", index_type="IVF_FLAT", metric_type="L2", params={ "nlist": 128 })

# Create collection
client.create_collection(collection_name="user_profiles_null", schema=schema, index_params=index_params)


import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.common.DataType;
import io.milvus.v2.common.IndexParam;
import io.milvus.v2.service.collection.request.AddFieldReq;
import io.milvus.v2.service.collection.request.CreateCollectionReq;

import java.util.*;

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .build());
        
CreateCollectionReq.CollectionSchema schema = client.createSchema();
schema.setEnableDynamicField(true);

schema.addField(AddFieldReq.builder()
        .fieldName("id")
        .dataType(DataType.Int64)
        .isPrimaryKey(true)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("vector")
        .dataType(DataType.FloatVector)
        .dimension(5)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("age")
        .dataType(DataType.Int64)
        .isNullable(true)
        .build());

List<IndexParam> indexes = new ArrayList<>();
Map<String,Object> extraParams = new HashMap<>();
extraParams.put("nlist", 128);
indexes.add(IndexParam.builder()
        .fieldName("vector")
        .indexType(IndexParam.IndexType.IVF_FLAT)
        .metricType(IndexParam.MetricType.L2)
        .extraParams(extraParams)
        .build());

CreateCollectionReq requestCreate = CreateCollectionReq.builder()
        .collectionName("user_profiles_null")
        .collectionSchema(schema)
        .indexParams(indexes)
        .build();
client.createCollection(requestCreate);


import { MilvusClient, DataType } from "@zilliz/milvus2-sdk-node";

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

await client.createCollection({
  collection_name: "user_profiles_null",
  schema: [
    {
      name: "id",
      is_primary_key: true,
      data_type: DataType.int64,
    },
    { name: "vector", data_type: DataType.Int64, dim: 5 },

    { name: "age", data_type: DataType.FloatVector, nullable: true },
  ],

  index_params: [
    {
      index_name: "vector_inde",
      field_name: "vector",
      metric_type: MetricType.L2,
      index_type: IndexType.AUTOINDEX,
    },
  ],
});



export pkField='{
    "fieldName": "id",
    "dataType": "Int64",
    "isPrimary": true
}'

export vectorField='{
    "fieldName": "vector",
    "dataType": "FloatVector",
    "elementTypeParams": {
        "dim": 5
    }
}'

export nullField='{
    "fieldName": "age",
    "dataType": "Int64",
    "nullable": true
}'

export schema="{
    \"autoID\": false,
    \"fields\": [
        $pkField,
        $vectorField,
        $nullField
    ]
}"

export indexParams='[
        {
            "fieldName": "vector",
            "metricType": "L2",
            "indexType": "IVF_FLAT",
            "params":{"nlist": 128}
        }
    ]'

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d "{
    \"collectionName\": \"user_profiles_null\",
    \"schema\": $schema,
    \"indexParams\": $indexParams
}"

插入实体

在可空字段中插入数据时，插入空值或直接省略该字段。

Python Java Node.js cURL
data = [
    {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4, 0.5], "age": 30},
    {"id": 2, "vector": [0.2, 0.3, 0.4, 0.5, 0.6], "age": None},
    {"id": 3, "vector": [0.3, 0.4, 0.5, 0.6, 0.7]}
]

client.insert(collection_name="user_profiles_null", data=data)


import com.google.gson.Gson;
import com.google.gson.JsonObject;

import io.milvus.v2.service.vector.request.InsertReq;
import io.milvus.v2.service.vector.response.InsertResp;

List<JsonObject> rows = new ArrayList<>();
Gson gson = new Gson();
rows.add(gson.fromJson("{\"id\": 1, \"vector\": [0.1, 0.2, 0.3, 0.4, 0.5], \"age\": 30}", JsonObject.class));
rows.add(gson.fromJson("{\"id\": 2, \"vector\": [0.2, 0.3, 0.4, 0.5, 0.6], \"age\": null}", JsonObject.class));
rows.add(gson.fromJson("{\"id\": 3, \"vector\": [0.3, 0.4, 0.5, 0.6, 0.7]}", JsonObject.class));

InsertResp insertR = client.insert(InsertReq.builder()
        .collectionName("user_profiles_null")
        .data(rows)
        .build());


const data = [
  { id: 1, vector: [0.1, 0.2, 0.3, 0.4, 0.5], age: 30 },
  { id: 2, vector: [0.2, 0.3, 0.4, 0.5, 0.6], age: null },
  { id: 3, vector: [0.3, 0.4, 0.5, 0.6, 0.7] },
];

client.insert({
  collection_name: "user_profiles_null",
  data: data,
});



curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/insert" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "data": [
        {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4, 0.5], "age": 30},
        {"id": 2, "vector": [0.2, 0.3, 0.4, 0.5, 0.6], "age": null}, 
        {"id": 3, "vector": [0.3, 0.4, 0.5, 0.6, 0.7]} 
    ],
    "collectionName": "user_profiles_null"
}'

使用空值搜索和查询

使用search 方法时，如果字段包含null 值，搜索结果将以空值返回该字段。

Python Java Node.js cURL
res = client.search(
    collection_name="user_profiles_null",
    data=[[0.1, 0.2, 0.4, 0.3, 0.128]],
    limit=2,
    search_params={"params": {"nprobe": 16}},
    output_fields=["id", "age"]
)

print(res)

# Output
# data: ["[{'id': 1, 'distance': 0.15838398039340973, 'entity': {'age': 30, 'id': 1}}, {'id': 2, 'distance': 0.28278401494026184, 'entity': {'age': None, 'id': 2}}]"] 


import io.milvus.v2.service.vector.request.SearchReq;
import io.milvus.v2.service.vector.request.data.FloatVec;
import io.milvus.v2.service.vector.response.SearchResp;

Map<String,Object> params = new HashMap<>();
params.put("nprobe", 16);
SearchResp resp = client.search(SearchReq.builder()
        .collectionName("user_profiles_null")
        .annsField("vector")
        .data(Collections.singletonList(new FloatVec(new float[]{0.1f, 0.2f, 0.3f, 0.4f, 0.5f})))
        .topK(2)
        .searchParams(params)
        .outputFields(Arrays.asList("id", "age"))
        .build());

System.out.println(resp.getSearchResults());

// Output
//
// [[SearchResp.SearchResult(entity={id=1, age=30}, score=0.0, id=1), SearchResp.SearchResult(entity={id=2, age=null}, score=0.050000004, id=2)]]


client.search({
    collection_name: 'user_profiles_null',
    data: [0.3, -0.6, 0.1, 0.3, 0.5],
    limit: 2,
    output_fields: ['age', 'id'],
    filter: '25 <= age <= 35',
    params: {
        nprobe: 16
    }
});


curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "user_profiles_null",
    "data": [
        [0.1, -0.2, 0.3, 0.4, 0.5]
    ],
    "annsField": "vector",
    "limit": 5,
    "outputFields": ["id", "age"]
}'

#{"code":0,"cost":0,"data":[{"age":30,"distance":0.16000001,"id":1},{"age":null,"distance":0.28999996,"id":2},{"age":null,"distance":0.52000004,"id":3}]}

当您使用query 方法进行标量过滤时，空值的过滤结果都是 false，表明它们不会被选中。

Python Java Node.js cURL
# Reviewing previously inserted data:
# {"id": 1, "vector": [0.1, 0.2, ..., 0.128], "age": 30}
# {"id": 2, "vector": [0.2, 0.3, ..., 0.129], "age": None}
# {"id": 3, "vector": [0.3, 0.4, ..., 0.130], "age": None}  # Omitted age  column is treated as None

results = client.query(
    collection_name="user_profiles_null",
    filter="age >= 0",
    output_fields=["id", "age"]
)

# Example output:
# [
#     {"id": 1, "age": 30}
# ]
# Note: Entities with `age` as `null` (id 2 and 3) will not appear in the result.


import io.milvus.v2.service.vector.request.QueryReq;
import io.milvus.v2.service.vector.response.QueryResp;

QueryResp resp = client.query(QueryReq.builder()
        .collectionName("user_profiles_null")
        .filter("age >= 0")
        .outputFields(Arrays.asList("id", "age"))
        .build());

System.out.println(resp.getQueryResults());

// Output
//
// [QueryResp.QueryResult(entity={id=1, age=30})]


const results = await client.query(
    collection_name: "user_profiles_null",
    filter: "age >= 0",
    output_fields: ["id", "age"]
);


curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "user_profiles_null",
    "filter": "age >= 0",
    "outputFields": ["id", "age"]
}'

# {"code":0,"cost":0,"data":[{"age":30,"id":1}]}

要查询null 值的实体，请使用空表达式"" 。

Python Java Node.js cURL
null_results = client.query(
    collection_name="user_profiles_null",
    filter="",
    output_fields=["id", "age"]
)

# Example output:
# [{"id": 2, "age": None}, {"id": 3, "age": None}]


QueryResp resp = client.query(QueryReq.builder()
        .collectionName("user_profiles_null")
        .filter("")
        .outputFields(Arrays.asList("id", "age"))
        .limit(10)
        .build());

System.out.println(resp.getQueryResults());


const results = await client.query(
    collection_name: "user_profiles_null",
    filter: "",
    output_fields: ["id", "age"]
);


curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "user_profiles_null",
    "expr": "",
    "outputFields": ["id", "age"]
}'

# {"code":0,"cost":0,"data":[{"age":30,"id":1},{"age":null,"id":2},{"age":null,"id":3}]}

默认值

默认值是分配给标量字段的预设值。如果在插入时没有为有默认值的字段提供值，系统会自动使用默认值。

设置默认值

创建 Collections 时，使用default_value 参数定义字段的默认值。下面的示例显示了如何将age 的默认值设置为18 ，将status 的默认值设置为"active" 。

Python Java Node.js cURL
schema = client.create_schema(
    auto_id=False,
    enable_dynamic_schema=True,
)

schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=5)
schema.add_field(field_name="age", datatype=DataType.INT64, default_value=18)
schema.add_field(field_name="status", datatype=DataType.VARCHAR, default_value="active", max_length=10)

index_params = client.prepare_index_params()
index_params.add_index(field_name="vector", index_type="IVF_FLAT", metric_type="L2", params={ "nlist": 128 })

client.create_collection(collection_name="user_profiles_default", schema=schema, index_params=index_params)


import io.milvus.v2.common.DataType;
import io.milvus.v2.common.IndexParam;
import io.milvus.v2.service.collection.request.AddFieldReq;
import io.milvus.v2.service.collection.request.CreateCollectionReq;

import java.util.*;

CreateCollectionReq.CollectionSchema schema = client.createSchema();
schema.setEnableDynamicField(true);

schema.addField(AddFieldReq.builder()
        .fieldName("id")
        .dataType(DataType.Int64)
        .isPrimaryKey(true)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("vector")
        .dataType(DataType.FloatVector)
        .dimension(5)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("age")
        .dataType(DataType.Int64)
        .defaultValue(18L)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("status")
        .dataType(DataType.VarChar)
        .maxLength(10)
        .defaultValue("active")
        .build());

List<IndexParam> indexes = new ArrayList<>();
Map<String,Object> extraParams = new HashMap<>();
extraParams.put("nlist", 128);
indexes.add(IndexParam.builder()
        .fieldName("vector")
        .indexType(IndexParam.IndexType.IVF_FLAT)
        .metricType(IndexParam.MetricType.L2)
        .extraParams(extraParams)
        .build());

CreateCollectionReq requestCreate = CreateCollectionReq.builder()
        .collectionName("user_profiles_default")
        .collectionSchema(schema)
        .indexParams(indexes)
        .build();
client.createCollection(requestCreate);


import { MilvusClient, DataType } from "@zilliz/milvus2-sdk-node";

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

await client.createCollection({
  collection_name: "user_profiles_default",
  schema: [
    {
      name: "id",
      is_primary_key: true,
      data_type: DataType.int64,
    },
    { name: "vector", data_type: DataType.FloatVector, dim: 5 },
    { name: "age", data_type: DataType.Int64, default_value: 18 },
    { name: 'status', data_type: DataType.VarChar, max_length: 30, default_value: 'active'},
  ],

  index_params: [
    {
      index_name: "vector_inde",
      field_name: "vector",
      metric_type: MetricType.L2,
      index_type: IndexType.IVF_FLAT,
    },
  ],
});



export pkField='{
    "fieldName": "id",
    "dataType": "Int64",
    "isPrimary": true
}'

export vectorField='{
    "fieldName": "vector",
    "dataType": "FloatVector",
    "elementTypeParams": {
        "dim": 5
    }
}'

export defaultValueField1='{
    "fieldName": "age",
    "dataType": "Int64",
    "defaultValue": 18
}'

export defaultValueField2='{
    "fieldName": "status",
    "dataType": "VarChar",
    "defaultValue": "active",
    "elementTypeParams": {
        "max_length": 10
    }
}'

export schema="{
    \"autoID\": false,
    \"fields\": [
        $pkField,
        $vectorField,
        $defaultValueField1,
        $defaultValueField2
    ]
}"

export indexParams='[
        {
            "fieldName": "vector",
            "metricType": "L2",
            "indexType": "IVF_FLAT",
            "params":{"nlist": 128}
        }
    ]'

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d "{
    \"collectionName\": \"user_profiles_default\",
    \"schema\": $schema,
    \"indexParams\": $indexParams
}"

插入实体

插入数据时，如果省略有默认值的字段或将其值设为空，系统将使用默认值。

Python Java Node.js cURL
data = [
    {"id": 1, "vector": [0.1, 0.2, ..., 0.128], "age": 30, "status": "premium"},
    {"id": 2, "vector": [0.2, 0.3, ..., 0.129]},
    {"id": 3, "vector": [0.3, 0.4, ..., 0.130], "age": 25, "status": None}, 
    {"id": 4, "vector": [0.4, 0.5, ..., 0.131], "age": None, "status": "inactive"} 
]

client.insert(collection_name="user_profiles_default", data=data)


import com.google.gson.Gson;
import com.google.gson.JsonObject;

import io.milvus.v2.service.vector.request.InsertReq;
import io.milvus.v2.service.vector.response.InsertResp;

List<JsonObject> rows = new ArrayList<>();
Gson gson = new Gson();
rows.add(gson.fromJson("{\"id\": 1, \"vector\": [0.1, 0.2, 0.3, 0.4, 0.5], \"age\": 30, \"status\": \"premium\"}", JsonObject.class));
rows.add(gson.fromJson("{\"id\": 2, \"vector\": [0.2, 0.3, 0.4, 0.5, 0.6]}", JsonObject.class));
rows.add(gson.fromJson("{\"id\": 3, \"vector\": [0.3, 0.4, 0.5, 0.6, 0.7], \"age\": 25, \"status\": null}", JsonObject.class));
rows.add(gson.fromJson("{\"id\": 4, \"vector\": [0.4, 0.5, 0.6, 0.7, 0.8], \"age\": null, \"status\": \"inactive\"}", JsonObject.class));

InsertResp insertR = client.insert(InsertReq.builder()
        .collectionName("user_profiles_default")
        .data(rows)
        .build());


const data = [
    {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4, 0.5], "age": 30, "status": "premium"},
    {"id": 2, "vector": [0.2, 0.3, 0.4, 0.5, 0.6]}, 
    {"id": 3, "vector": [0.3, 0.4, 0.5, 0.6, 0.7], "age": 25, "status": null}, 
    {"id": 4, "vector": [0.4, 0.5, 0.6, 0.7, 0.8], "age": null, "status": "inactive"}  
];

client.insert({
  collection_name: "user_profiles_default",
  data: data,
});


curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/insert" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "data": [
        {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4, 0.5], "age": 30, "status": "premium"},
        {"id": 2, "vector": [0.2, 0.3, 0.4, 0.5, 0.6]},
        {"id": 3, "vector": [0.3, 0.4, 0.5, 0.6, 0.7], "age": 25, "status": null}, 
        {"id": 4, "vector": [0.4, 0.5, 0.6, 0.7, 0.8], "age": null, "status": "inactive"}      
    ],
    "collectionName": "user_profiles_default"
}'

有关空值和默认值设置如何生效的更多信息，请参阅适用规则。

使用默认值进行搜索和查询

在向量搜索和标量过滤过程中，包含默认值的实体与其他实体的处理方式相同。您可以将默认值作为search 和query 操作符的一部分。

例如，在search 操作符中，将age 设置为默认值18 的实体将包含在结果中。

Python Java Node.js cURL
res = client.search(
    collection_name="user_profiles_default",
    data=[[0.1, 0.2, 0.4, 0.3, 0.128]],
    search_params={"params": {"nprobe": 16}},
    filter="age == 18",  # 18 is the default value of the `age` field
    limit=10,
    output_fields=["id", "age", "status"]
)

print(res)

# Output
# data: ["[{'id': 2, 'distance': 0.28278401494026184, 'entity': {'id': 2, 'age': 18, 'status': 'active'}}, {'id': 4, 'distance': 0.8315839767456055, 'entity': {'id': 4, 'age': 18, 'status': 'inactive'}}]"] 



import io.milvus.v2.service.vector.request.SearchReq;
import io.milvus.v2.service.vector.request.data.FloatVec;
import io.milvus.v2.service.vector.response.SearchResp;

Map<String,Object> params = new HashMap<>();
params.put("nprobe", 16);
SearchResp resp = client.search(SearchReq.builder()
        .collectionName("user_profiles_default")
        .annsField("vector")
        .data(Collections.singletonList(new FloatVec(new float[]{0.1f, 0.2f, 0.3f, 0.4f, 0.5f})))
        .searchParams(params)
        .filter("age == 18")
        .topK(10)
        .outputFields(Arrays.asList("id", "age", "status"))
        .build());

System.out.println(resp.getSearchResults());

// Output
//
// [[SearchResp.SearchResult(entity={id=2, age=18, status=active}, score=0.050000004, id=2), SearchResp.SearchResult(entity={id=4, age=18, status=inactive}, score=0.45000002, id=4)]]


client.search({
    collection_name: 'user_profiles_default',
    data: [0.3, -0.6, 0.1, 0.3, 0.5],
    limit: 2,
    output_fields: ['age', 'id', 'status'],
    filter: 'age == 18',
    params: {
        nprobe: 16
    }
});


curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "user_profiles_default",
    "data": [
        [0.1, 0.2, 0.3, 0.4, 0.5]
    ],
    "annsField": "vector",
    "limit": 5,
    "filter": "age == 18",
    "outputFields": ["id", "age", "status"]
}'

# {"code":0,"cost":0,"data":[{"age":18,"distance":0.050000004,"id":2,"status":"active"},{"age":18,"distance":0.45000002,"id":4,"status":"inactive"}]}

在query 操作符中，可以直接通过默认值进行匹配或过滤。

Python Java Node.js cURL
# Query all entities where `age` equals the default value (18)
default_age_results = client.query(
    collection_name="user_profiles_default",
    filter="age == 18",
    output_fields=["id", "age", "status"]
)

# Query all entities where `status` equals the default value ("active")
default_status_results = client.query(
    collection_name="user_profiles_default",
    filter='status == "active"',
    output_fields=["id", "age", "status"]
)


import io.milvus.v2.service.vector.request.QueryReq;
import io.milvus.v2.service.vector.response.QueryResp;

QueryResp ageResp = client.query(QueryReq.builder()
        .collectionName("user_profiles_default")
        .filter("age == 18")
        .outputFields(Arrays.asList("id", "age", "status"))
        .build());

System.out.println(ageResp.getQueryResults());

// Output
//
// [QueryResp.QueryResult(entity={id=2, age=18, status=active}), QueryResp.QueryResult(entity={id=4, age=18, status=inactive})]

QueryResp statusResp = client.query(QueryReq.builder()
        .collectionName("user_profiles_default")
        .filter("status == \"active\"")
        .outputFields(Arrays.asList("id", "age", "status"))
        .build());

System.out.println(statusResp.getQueryResults());

// Output
//
// [QueryResp.QueryResult(entity={id=2, age=18, status=active}), QueryResp.QueryResult(entity={id=3, age=25, status=active})]


// Query all entities where `age` equals the default value (18)
const default_age_results = await client.query(
    collection_name: "user_profiles_default",
    filter: "age == 18",
    output_fields: ["id", "age", "status"]
);
// Query all entities where `status` equals the default value ("active")
const default_status_results = await client.query(
    collection_name: "user_profiles_default",
    filter: 'status == "active"',
    output_fields: ["id", "age", "status"]
)


curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "user_profiles_default",
    "filter": "age == 18",
    "outputFields": ["id", "age", "status"]
}'

# {"code":0,"cost":0,"data":[{"age":18,"id":2,"status":"active"},{"age":18,"id":4,"status":"inactive"}]}

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "user_profiles_default",
    "filter": "status == \"active\"",
    "outputFields": ["id", "age", "status"]
}'

# {"code":0,"cost":0,"data":[{"age":18,"id":2,"status":"active"},{"age":25,"id":3,"status":"active"}]}

适用规则

下表总结了可归零列和默认值在不同配置组合下的行为。这些规则决定了在尝试插入空值或未提供字段值时，Milvus 如何处理数据。

可归零默认值默认值类型用户输入结果示例
✅✅非空无/空使用默认值
  • 字段：age
  • 默认值：18
  • 用户输入：空
  • 结果：存储为18
✅❌-无/空存储为空
  • 字段：middle_name
  • 默认值： -用户
  • 输入：空
  • 结果：存储为空
❌✅非空无/空使用默认值
  • 字段：status
  • 默认值："active"
  • 用户输入：空
  • 结果：存储为"active"
❌❌-无/空抛出错误
  • 字段：email
  • 默认值： -用户
  • 输入：空
  • 结果：操作被拒绝，系统抛出错误
❌✅空无/空抛出错误
  • 字段：username
  • 默认值：空用户
  • 输入：空
  • 结果：操作被拒绝，系统提示错误

翻译自DeepL

目录

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