Version: User Guides (BYOC)

Use Array Fields

This guide explains how to use the array fields, such as inserting array values as well as searching and querying in array fields with basic and advanced operators.

Overview

Zilliz Cloud provides support for arrays as one of the field data types. If you want to add an array field to the schema, you have to set the data type for its element and the maximum number of elements that this field can contain. This indicates that an array in a Zilliz Cloud collection should always have elements of the same data type.

For example, the following code snippet generates a random dataset containing an array field.

Python
Java
NodeJS

colors = ["green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey"]
data = []

for i in range(1000):
    current_color = random.choice(colors)
    current_tag = random.randint(1000, 9999)
    current_coord = [ random.randint(0, 40) for _ in range(random.randint(3, 5)) ]
    data.append({
        "id": i,
        "vector": [ random.uniform(-1, 1) for _ in range(5) ],
        "color": current_color,
        "color_tag": current_tag,
        "color_coord": current_coord,
    })

print(data[0])

List<String> colors = Arrays.asList("green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey");
List<JSONObject> data = new ArrayList<>();

for (int i=0; i<1000; i++) {
    Random rand = new Random();
    String current_color = colors.get(rand.nextInt(colors.size()-1));
    Long current_tag = rand.nextLong(8999L) + 1000L;

    // Generate an random-sized array
    Long capacity = rand.nextLong(5L) + 1L;
    List<Long> current_coord = new ArrayList<>();
    current_coord.add(rand.nextLong(40L) + 1L);
    current_coord.add(rand.nextLong(40L) + 1L);
    for (int j=3; j<capacity; j++) {
        current_coord.add(rand.nextLong(40L) + 1L);
    }

    JSONObject row = new JSONObject();
    row.put("id", Long.valueOf(i));
    row.put("vector", Arrays.asList(rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat()));
    row.put("color", current_color);
    row.put("color_tag", current_tag);
    row.put("color_coord", current_coord);
    data.add(row);
}

System.out.println(JSONObject.toJSON(data.get(0)));   

const colors = ["green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey"]
var data = []

for (let i = 0; i < 1000; i++) {
    const current_color = colors[Math.floor(Math.random() * colors.length)]
    const current_tag = Math.floor(Math.random() * 8999 + 1000)
    const current_coord = Array(Math.floor(Math.random() * 5 + 1)).fill(0).map(() => Math.floor(Math.random() * 40))

    data.push({
        id: i,
        vector: Array(5).fill(0).map(() => Math.random()),
        color: current_color,
        color_tag: current_tag,
        color_coord: current_coord,
    })
}

console.log(data[0])

You can view the structure of the generated data by checking its first entry.

{
    id: 0,
    vector: [
        0.0338537420906162,
        0.6844108238358322,
        0.28410588909961754,
        0.09752595400212116,
        0.22671013058761114
    ],
    color: 'orange',
    color_tag: 5677,
    color_coord: [ 3, 0, 18, 29 ]
}

📘Notes

Elements in an array field should be of the same data type.
The number of elements in an array field should be less than or equal to the specified maximum capacity of the array field.

Define array field

To define an array field, simply follow the same procedure as defining fields of other data types.

Python
Java
NodeJS

import random, time
from pymilvus import MilvusClient, DataType

CLUSTER_ENDPOINT = "YOUR_CLUSTER_ENDPOINT"
TOKEN = "YOUR_CLUSTER_TOKEN"

# 1. Set up a Milvus client
client = MilvusClient(
    uri=CLUSTER_ENDPOINT,
    token=TOKEN 
)

# 2. Create a collection
schema = MilvusClient.create_schema(
    auto_id=False,
    enable_dynamic_field=False,
)

schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
# The dim value should be greater than 1
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=5)
schema.add_field(field_name="color", datatype=DataType.VARCHAR, max_length=512)
schema.add_field(field_name="color_tag", datatype=DataType.INT64)
# Define an array field
schema.add_field(field_name="color_coord", datatype=DataType.ARRAY, element_type=DataType.INT64, max_capacity=5)

index_params = MilvusClient.prepare_index_params()

index_params.add_index(
    field_name="id",
    index_type="STL_SORT"
)

index_params.add_index(
    field_name="vector",
    index_type="AUTOINDEX",
    metric_type="L2"
)

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

res = client.get_load_state(
    collection_name="test_collection"
)

print(res)

# Output
#
# {
#     "state": "<LoadState: Loaded>"
# }

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 io.milvus.v2.service.collection.request.GetLoadStateReq;

String CLUSTER_ENDPOINT = "YOUR_CLUSTER_ENDPOINT";
String TOKEN = "YOUR_CLUSTER_TOKEN";

// 1. Connect to Milvus server
ConnectConfig connectConfig = ConnectConfig.builder()
    .uri(CLUSTER_ENDPOINT)
    .token(TOKEN)
    .build();

MilvusClientV2 client = new MilvusClientV2(connectConfig);

// 2. Create a collection in customized setup mode

// 2.1 Create schema
CreateCollectionReq.CollectionSchema schema = client.createSchema();

// 2.2 Add fields to schema
schema.addField(AddFieldReq.builder()
    .fieldName("id")
    .dataType(DataType.Int64)
    .isPrimaryKey(true)
    .autoID(false)
    .build());

schema.addField(AddFieldReq.builder()
    .fieldName("vector")
    .dataType(DataType.FloatVector)
    .dimension(5) // The dimension value should be greater than 1
    .build());
    
schema.addField(AddFieldReq.builder()
    .fieldName("color")
    .dataType(DataType.VarChar)
    .maxLength(512)
    .build());

schema.addField(AddFieldReq.builder()
    .fieldName("color_tag")
    .dataType(DataType.Int64)
    .build());

// Define an array field
schema.addField(AddFieldReq.builder()
    .fieldName("color_coord")
    .dataType(DataType.Array)
    .elementType(DataType.Int64)
    .maxCapacity(5)
    .build());

// 2.3 Prepare index parameters
IndexParam indexParamForIdField = IndexParam.builder()
    .fieldName("id")
    .indexType(IndexParam.IndexType.STL_SORT)
    .build();

IndexParam indexParamForVectorField = IndexParam.builder()
    .fieldName("vector")
    .indexType(IndexParam.IndexType.AUTOINDEX)
    .metricType(IndexParam.MetricType.IP)
    .build();

IndexParam indexParamForColorField = IndexParam.builder()
    .fieldName("color")
    .indexType(IndexParam.IndexType.TRIE)
    .build();

List<IndexParam> indexParams = new ArrayList<>();
indexParams.add(indexParamForIdField);
indexParams.add(indexParamForVectorField);
indexParams.add(indexParamForColorField);

// 2.4 Create a collection with schema and index parameters
CreateCollectionReq customizedSetupReq = CreateCollectionReq.builder()
    .collectionName("test_collection")
    .collectionSchema(schema)
    .indexParams(indexParams)         
    .build();

client.createCollection(customizedSetupReq);

// 2.5 Check if the collection is loaded
GetLoadStateReq getLoadStateReq = GetLoadStateReq.builder()
    .collectionName("test_collection")
    .build();

Boolean isLoaded = client.getLoadState(getLoadStateReq);

System.out.println(isLoaded);

// Output:
// true

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

const address = "YOUR_CLUSTER_ENDPOINT"
const token = "YOUR_CLUSTER_TOKEN"

// 1. Set up a Milvus Client
client = new MilvusClient({address, token}); 

// 2. Create a collection
// 2.1 Define fields
const fields = [
    {
        name: "id",
        data_type: DataType.Int64,
        is_primary_key: true,
        auto_id: false
    },
    {
        name: "vector",
        data_type: DataType.FloatVector,
        dim: 5 // The dim value should be greater than 1
    },
    {
        name: "color",
        data_type: DataType.VarChar,
        max_length: 512
    },
    {
        name: "color_tag",
        data_type: DataType.Int64,
    },
    {
        name: "color_coord",
        data_type: DataType.Array, // Define an array field
        element_type: DataType.Int64,
        max_capacity: 5
    }
]

// 2.2 Prepare index parameters
const index_params = [{
    field_name: "vector",
    index_type: "AUTOINDEX",
    metric_type: "IP"
}]

// 2.3 Create a collection with fields and index parameters
res = await client.createCollection({
    collection_name: "test_collection",
    fields: fields, 
    index_params: index_params
})

console.log(res.error_code)

// Output
// 
// Success
// 

res = await client.getLoadState({
    collection_name: "test_collection",
})  

console.log(res.state)

// Output
// 
// LoadStateLoaded
// 

Insert field values

After creating a collection, you can insert arrays such as the one demonstrated in Overview.

Python
Java
NodeJS

res = client.insert(
    collection_name="test_collection",
    data=data
)

print(res)

# Output
#
# {
#     "insert_count": 1000,
#     "ids": [
#         0,
#         1,
#         2,
#         3,
#         4,
#         5,
#         6,
#         7,
#         8,
#         9,
#         "(990 more items hidden)"
#     ]
# }

time.sleep(5)

import com.alibaba.fastjson.JSONObject;

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

// 3.1 Insert data into the collection
InsertReq insertReq = InsertReq.builder()
    .collectionName("test_collection")
    .data(data)
    .build();

InsertResp insertResp = client.insert(insertReq);

System.out.println(JSONObject.toJSON(insertResp));

// Output:
// {"insertCnt": 1000}

Thread.sleep(5000);   // wait for a while to ensure the data is indexed

res = await client.insert({
    collection_name: "test_collection",
    data: data,
})

console.log(res.insert_cnt)

// Output
// 
// 1000
// 

await sleep(5000)

Basic scalar filtering

Once all of your data has been added, you can conduct searches and queries using the elements in the array field in the same manner as you would with a standard scalar field.

Python
Java
NodeJS

# 4. Basic search with the array field
query_vectors = [ [ random.uniform(-1, 1) for _ in range(5) ]]

res = client.search(
    collection_name="test_collection",
    data=query_vectors,
    filter="color_coord[0] < 10",
    search_params={
        "metric_type": "L2",
        "params": {"nprobe": 16}
    },
    output_fields=["id", "color", "color_tag", "color_coord"],
    limit=3
)

print(res)

# Output
#
# [
#     [
#         {
#             "id": 993,
#             "distance": 0.1538649946451187,
#             "entity": {
#                 "color_coord": [
#                     5,
#                     37,
#                     39,
#                     18
#                 ],
#                 "id": 993,
#                 "color": "black",
#                 "color_tag": 6785
#             }
#         },
#         {
#             "id": 452,
#             "distance": 0.2353954315185547,
#             "entity": {
#                 "color_coord": [
#                     2,
#                     27,
#                     34,
#                     32,
#                     30
#                 ],
#                 "id": 452,
#                 "color": "brown",
#                 "color_tag": 2075
#             }
#         },
#         {
#             "id": 862,
#             "distance": 0.27913951873779297,
#             "entity": {
#                 "color_coord": [
#                     0,
#                     19,
#                     0,
#                     26
#                 ],
#                 "id": 862,
#                 "color": "brown",
#                 "color_tag": 1787
#             }
#         }
#     ]
# ]

// 4. Basic search with an Array field

QueryReq queryReq = QueryReq.builder()
    .collectionName("test_collection")
    .filter("color_coord[0] in [7, 8, 9]")
    .outputFields(Arrays.asList("id", "color", "color_tag", "color_coord"))
    .limit(3L)
    .build();

QueryResp queryResp = client.query(queryReq);

System.out.println(JSONObject.toJSON(queryResp));

// Output:
// {"queryResults": [
//     {"entity": {
//         "color": "orange",
//         "color_tag": 2464,
//         "id": 18,
//         "color_coord": [
//             9,
//             30
//         ]
//     }},
//     {"entity": {
//         "color": "pink",
//         "color_tag": 2602,
//         "id": 22,
//         "color_coord": [
//             8,
//             34,
//             16
//         ]
//     }},
//     {"entity": {
//         "color": "pink",
//         "color_tag": 1243,
//         "id": 42,
//         "color_coord": [
//             9,
//             20
//         ]
//     }}
// ]}

// 4. Basic search with the array field
const query_vectors = [Array(5).fill(0).map(() => Math.random())]

res = await client.search({
    collection_name: "test_collection",
    data: query_vectors,
    filter: "color_coord[0] < 10",
    output_fields: ["id", "color", "color_tag", "color_coord"],
    limit: 3
})

console.log(JSON.stringify(res.results, null, 4))

// Output
// 
// [
//     {
//         "score": 2.015894889831543,
//         "id": "260",
//         "color": "green",
//         "color_tag": "5320",
//         "color_coord": [
//             "1",
//             "7",
//             "33",
//             "13",
//             "23"
//         ]
//     },
//     {
//         "score": 2.006500720977783,
//         "id": "720",
//         "color": "green",
//         "color_tag": "4939",
//         "color_coord": [
//             "0",
//             "19",
//             "5",
//             "30",
//             "15"
//         ]
//     },
//     {
//         "score": 1.9539016485214233,
//         "id": "243",
//         "color": "red",
//         "color_tag": "2403",
//         "color_coord": [
//             "4"
//         ]
//     }
// ]
// 

Advanced scalar filtering

As what we have in a JSON field, Zilliz Cloud also provides advanced filtering operators for arrays, namely ARRAY_CONTAINS, ARRAY_CONTAINS_ALL, ARRAY_CONTAINS_ANY, and ARRAY_LENGTH.

Filters all entities having a 10 in their color coordinators.

Python
Java
NodeJS

# 5. Advanced search within the array field

res = client.query(
    collection_name="test_collection",
    filter="ARRAY_CONTAINS(color_coord, 10)",
    output_fields=["id", "color", "color_tag", "color_coord"],
    limit=3
)

print(res)

# Output
#
# [
#     {
#         "id": 21,
#         "color": "white",
#         "color_tag": 4202,
#         "color_coord": [
#             10,
#             5,
#             5
#         ]
#     },
#     {
#         "id": 31,
#         "color": "grey",
#         "color_tag": 7386,
#         "color_coord": [
#             8,
#             10,
#             23,
#             7,
#             31
#         ]
#     },
#     {
#         "id": 45,
#         "color": "purple",
#         "color_tag": 6126,
#         "color_coord": [
#             0,
#             10,
#             24
#         ]
#     }
# ]

// 5. Advanced search within an Array field
queryReq = QueryReq.builder()
    .collectionName("test_collection")
    .filter("ARRAY_CONTAINS(color_coord, 10)")
    .outputFields(Arrays.asList("id", "color", "color_tag", "color_coord"))
    .limit(3)
    .build();

queryResp = client.query(queryReq);

System.out.println(JSONObject.toJSON(queryResp));

// Output:
// {"queryResults": [
//     {"entity": {
//         "color": "blue",
//         "color_tag": 4337,
//         "id": 17,
//         "color_coord": [
//             11,
//             33,
//             10,
//             20
//         ]
//     }},
//     {"entity": {
//         "color": "white",
//         "color_tag": 5219,
//         "id": 25,
//         "color_coord": [
//             10,
//             15
//         ]
//     }},
//     {"entity": {
//         "color": "red",
//         "color_tag": 7120,
//         "id": 35,
//         "color_coord": [
//             19,
//             10,
//             10,
//             14
//         ]
//     }}
// ]}

// 5. Advanced search within the array field
res = await client.search({
    collection_name: "test_collection",
    data: query_vectors,
    filter: "ARRAY_CONTAINS(color_coord, 10)",
    output_fields: ["id", "color", "color_tag", "color_coord"],
    limit: 3
})

console.log(JSON.stringify(res.results, null, 4))

// Output
// 
// [
//     {
//         "score": 1.7962548732757568,
//         "id": "696",
//         "color": "red",
//         "color_tag": "1798",
//         "color_coord": [
//             "33",
//             "10",
//             "37"
//         ]
//     },
//     {
//         "score": 1.7126177549362183,
//         "id": "770",
//         "color": "red",
//         "color_tag": "1962",
//         "color_coord": [
//             "21",
//             "23",
//             "10"
//         ]
//     },
//     {
//         "score": 1.6707111597061157,
//         "id": "981",
//         "color": "yellow",
//         "color_tag": "3100",
//         "color_coord": [
//             "28",
//             "39",
//             "10",
//             "6"
//         ]
//     }
// ]
// 

Filters all entities having a 7 and an 8 in their color coordinators.

Python
Java
NodeJS

res = client.query(
    collection_name="test_collection",
    filter="ARRAY_CONTAINS_ALL(color_coord, [7, 8])",
    output_fields=["id", "color", "color_tag", "color_coord"],
    limit=3
)

print(res)

# Output
#
# [
#     {
#         "color": "grey",
#         "color_tag": 7386,
#         "color_coord": [
#             8,
#             10,
#             23,
#             7,
#             31
#         ],
#         "id": 31
#     },
#     {
#         "color": "purple",
#         "color_tag": 7823,
#         "color_coord": [
#             38,
#             8,
#             36,
#             38,
#             7
#         ],
#         "id": 258
#     },
#     {
#         "color": "purple",
#         "color_tag": 6356,
#         "color_coord": [
#             34,
#             32,
#             11,
#             8,
#             7
#         ],
#         "id": 348
#     }
# ]

queryReq = QueryReq.builder()
    .collectionName("test_collection")
    .filter("ARRAY_CONTAINS_ALL(color_coord, [7, 8, 9])")
    .outputFields(Arrays.asList("id", "color", "color_tag", "color_coord"))
    .limit(3)
    .build();

queryResp = client.query(queryReq);

System.out.println(JSONObject.toJSON(queryResp));     

// Output:
// {"queryResults": [{"entity": {
//     "color": "red",
//     "color_tag": 6986,
//     "id": 423,
//     "color_coord": [
//         26,
//         7,
//         8,
//         9
//     ]
// }}]}

res = await client.search({
    collection_name: "test_collection",
    data: query_vectors,
    filter: "ARRAY_CONTAINS_ALL(color_coord, [7, 8])",
    output_fields: ["id", "color", "color_tag", "color_coord"],
    limit: 3
})

console.log(JSON.stringify(res.results, null, 4))

// Output
// 
// [
//     {
//         "score": 0.8267516493797302,
//         "id": "913",
//         "color": "brown",
//         "color_tag": "8897",
//         "color_coord": [
//             "39",
//             "31",
//             "8",
//             "29",
//             "7"
//         ]
//     },
//     {
//         "score": 0.6889009475708008,
//         "id": "826",
//         "color": "blue",
//         "color_tag": "4903",
//         "color_coord": [
//             "7",
//             "25",
//             "5",
//             "12",
//             "8"
//         ]
//     },
//     {
//         "score": 0.5851659774780273,
//         "id": "167",
//         "color": "blue",
//         "color_tag": "1550",
//         "color_coord": [
//             "8",
//             "27",
//             "7"
//         ]
//     }
// ]
// 

Filters all entities having either 7, 8, or 9 in their color coordinators.

Python
Java
NodeJS

res = client.query(
    collection_name="test_collection",
    filter="ARRAY_CONTAINS_ANY(color_coord, [7, 8, 9])",
    output_fields=["id", "color", "color_tag", "color_coord"],
    limit=3
)

print(res)

# Output
#
# [
#     {
#         "id": 0,
#         "color": "green",
#         "color_tag": 9212,
#         "color_coord": [
#             37,
#             36,
#             36,
#             7,
#             9
#         ]
#     },
#     {
#         "id": 5,
#         "color": "blue",
#         "color_tag": 9643,
#         "color_coord": [
#             8,
#             20,
#             20,
#             11
#         ]
#     },
#     {
#         "id": 12,
#         "color": "blue",
#         "color_tag": 3075,
#         "color_coord": [
#             29,
#             7,
#             17
#         ]
#     }
# ]

queryReq = QueryReq.builder()
    .collectionName("test_collection")
    .filter("ARRAY_CONTAINS_ANY(color_coord, [7, 8, 9])")
    .outputFields(Arrays.asList("id", "color", "color_tag", "color_coord"))
    .limit(3)
    .build();

queryResp = client.query(queryReq);

System.out.println(JSONObject.toJSON(queryResp));   

// Output:
// {"queryResults": [
//     {"entity": {
//         "color": "orange",
//         "color_tag": 2464,
//         "id": 18,
//         "color_coord": [
//             9,
//             30
//         ]
//     }},
//     {"entity": {
//         "color": "pink",
//         "color_tag": 2602,
//         "id": 22,
//         "color_coord": [
//             8,
//             34,
//             16
//         ]
//     }},
//     {"entity": {
//         "color": "pink",
//         "color_tag": 1243,
//         "id": 42,
//         "color_coord": [
//             9,
//             20
//         ]
//     }}
// ]}

res = await client.search({
    collection_name: "test_collection",
    data: query_vectors,
    filter: "ARRAY_CONTAINS_ANY(color_coord, [7, 8, 9])",
    output_fields: ["id", "color", "color_tag", "color_coord"],
    limit: 3
})

console.log(JSON.stringify(res.results, null, 4))

// Output
// 
// [
//     {
//         "score": 2.015894889831543,
//         "id": "260",
//         "color": "green",
//         "color_tag": "5320",
//         "color_coord": [
//             "1",
//             "7",
//             "33",
//             "13",
//             "23"
//         ]
//     },
//     {
//         "score": 1.783075213432312,
//         "id": "593",
//         "color": "orange",
//         "color_tag": "4079",
//         "color_coord": [
//             "8",
//             "19"
//         ]
//     },
//     {
//         "score": 1.7713876962661743,
//         "id": "874",
//         "color": "blue",
//         "color_tag": "7029",
//         "color_coord": [
//             "14",
//             "8",
//             "15"
//         ]
//     }
// ]
// 

Filters entities that have exactly four elements.

Python
Java
NodeJS

res = client.query(
    collection_name="test_collection",
    filter="ARRAY_LENGTH(color_coord) == 4",
    output_fields=["id", "color", "color_tag", "color_coord"],
    limit=3
)

print(res)

# Output
#
# [
#     {
#         "id": 1,
#         "color": "pink",
#         "color_tag": 6708,
#         "color_coord": [
#             15,
#             36,
#             38,
#             2
#         ]
#     },
#     {
#         "id": 4,
#         "color": "green",
#         "color_tag": 5386,
#         "color_coord": [
#             13,
#             32,
#             35,
#             5
#         ]
#     },
#     {
#         "id": 5,
#         "color": "blue",
#         "color_tag": 9643,
#         "color_coord": [
#             8,
#             20,
#             20,
#             11
#         ]
#     }
# ]

queryReq = QueryReq.builder()
    .collectionName("test_collection")
    .filter("ARRAY_LENGTH(color_coord) == 4")
    .outputFields(Arrays.asList("id", "color", "color_tag", "color_coord"))
    .limit(3)
    .build();

queryResp = client.query(queryReq);

System.out.println(JSONObject.toJSON(queryResp));   

// Output:
// {"queryResults": [
//     {"entity": {
//         "color": "green",
//         "color_tag": 2984,
//         "id": 2,
//         "color_coord": [
//             27,
//             31,
//             23,
//             29
//         ]
//     }},
//     {"entity": {
//         "color": "black",
//         "color_tag": 6867,
//         "id": 4,
//         "color_coord": [
//             37,
//             3,
//             30,
//             33
//         ]
//     }},
//     {"entity": {
//         "color": "brown",
//         "color_tag": 3464,
//         "id": 10,
//         "color_coord": [
//             31,
//             38,
//             21,
//             28
//         ]
//     }}
// ]}

res = await client.search({
collection_name: "test_collection",
data: query_vectors,
filter: "ARRAY_LENGTH(color_coord) == 4",
output_fields: ["id", "color", "color_tag", "color_coord"],
limit: 3
})

console.log(JSON.stringify(res.results, null, 4))

// Output
// 
// [
//     {
//         "score": 2.0404388904571533,
//         "id": "439",
//         "color": "orange",
//         "color_tag": "7096",
//         "color_coord": [
//             "27",
//             "34",
//             "26",
//             "39"
//         ]
//     },
//     {
//         "score": 1.9059759378433228,
//         "id": "918",
//         "color": "purple",
//         "color_tag": "2903",
//         "color_coord": [
//             "28",
//             "19",
//             "36",
//             "35"
//         ]
//     },
//     {
//         "score": 1.8385567665100098,
//         "id": "92",
//         "color": "yellow",
//         "color_tag": "4693",
//         "color_coord": [
//             "1",
//             "23",
//             "2",
//             "3"
//         ]
//     }
// ]
// 

Overview​

Define array field​

Insert field values​

Basic scalar filtering​

Advanced scalar filtering​

Overview

Define array field

Insert field values

Basic scalar filtering

Advanced scalar filtering