Dynamic Field

Collection schema 中定义的所有字段都必须包含在要插入的实体中。如果您希望某些字段是可选的，请考虑启用 dynamic field。本主题描述如何启用和使用 dynamic field。

概述

在 Milvus 中，您可以通过为 collection 中的每个字段设置名称和数据类型来创建 collection schema。当您向 schema 添加字段时，请确保此字段包含在您要插入的实体中。如果您希望某些字段是可选的，启用 dynamic field 是一个选择。

Dynamic field 是一个名为 $meta 的保留字段，其类型为 JavaScript Object Notation (JSON)。实体中未在 schema 中定义的任何字段都将作为键值对存储在此保留的 JSON 字段中。

对于启用了 dynamic field 的 collection，您可以使用 dynamic field 中的键进行标量过滤，就像使用在 schema 中显式定义的字段一样。

启用 dynamic field

在创建具有自定义设置的 collection 时，您可以手动启用 dynamic field。

PythonJavaNodeJSGocURL

from pymilvus import MilvusClient

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

client.create_collection(
    collection_name="my_collection",
    dimension=5,
    enable_dynamic_field=True
)

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.service.collection.request.CreateCollectionReq;

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .build());
        
CreateCollectionReq createCollectionReq = CreateCollectionReq.builder()
    .collectionName("my_collection")
    .dimension(5)
    .enableDynamicField(true)
    .build()
client.createCollection(createCollectionReq);

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

const client = new Client({
    address: 'http://localhost:19530'
});

await client.createCollection({
    collection_name: "customized_setup_2",
    schema: schema,
    enable_dynamic_field: true
});

import (
    "context"
    "fmt"

    "github.com/milvus-io/milvus/client/v2/column"
    "github.com/milvus-io/milvus/client/v2/entity"
    "github.com/milvus-io/milvus/client/v2/index"
    "github.com/milvus-io/milvus/client/v2/milvusclient"
)  

ctx, cancel := context.WithCancel(context.Background())
defer cancel()

cli, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "localhost:19530",
})
if err != nil {
    fmt.Println(err.Error())
    // handle err
}
defer client.Close(ctx)

err = client.CreateCollection(ctx, milvusclient.SimpleCreateCollectionOptions("my_collection", 5).
    WithAutoID(false).
    WithDynamicSchema(true))
if err != nil {
    fmt.Println(err.Error())
}

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "dimension": 5,
    "enableDynamicField": true
}'

使用 dynamic field

当您的 collection 中启用了 dynamic field 时，所有未在 schema 中定义的字段及其值都将作为键值对存储在 dynamic field 中。

例如，假设您的 collection schema 只定义了两个字段，名为 id 和 vector，并启用了 dynamic field。现在，将以下数据集插入到此 collection 中。

[
    {id: 0, vector: [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], color: "pink_8682"},
    {id: 1, vector: [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], color: "red_7025"},
    {id: 2, vector: [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], color: "orange_6781"},
    {id: 3, vector: [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], color: "pink_9298"},
    {id: 4, vector: [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], color: "red_4794"},
    {id: 5, vector: [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], color: "yellow_4222"},
    {id: 6, vector: [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], color: "red_9392"},
    {id: 7, vector: [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], color: "grey_8510"},
    {id: 8, vector: [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], color: "white_9381"},
    {id: 9, vector: [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], color: "purple_4976"}        
]

上述数据集包含10个实体，每个实体都包含字段 id、vector 和 color。这里，color 字段未在 schema 中定义。由于 collection 启用了 dynamic field，字段 color 将作为键值对存储在 dynamic field 中。

插入数据

以下代码演示了如何将此数据集插入到 collection 中。

PythonJavaNodeJSGocURL

data=[
    {"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682"},
    {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025"},
    {"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781"},
    {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298"},
    {"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794"},
    {"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222"},
    {"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392"},
    {"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510"},
    {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381"},
    {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976"}
]

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

print(res)

# Output
# {'insert_count': 10, 'ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}

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;
   
Gson gson = new Gson();
List<JsonObject> data = Arrays.asList(
        gson.fromJson("{\"id\": 0, \"vector\": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], \"color\": \"pink_8682\"}", JsonObject.class),
        gson.fromJson("{\"id\": 1, \"vector\": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], \"color\": \"red_7025\"}", JsonObject.class),
        gson.fromJson("{\"id\": 2, \"vector\": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], \"color\": \"orange_6781\"}", JsonObject.class),
        gson.fromJson("{\"id\": 3, \"vector\": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], \"color\": \"pink_9298\"}", JsonObject.class),
        gson.fromJson("{\"id\": 4, \"vector\": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], \"color\": \"red_4794\"}", JsonObject.class),
        gson.fromJson("{\"id\": 5, \"vector\": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], \"color\": \"yellow_4222\"}", JsonObject.class),
        gson.fromJson("{\"id\": 6, \"vector\": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], \"color\": \"red_9392\"}", JsonObject.class),
        gson.fromJson("{\"id\": 7, \"vector\": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], \"color\": \"grey_8510\"}", JsonObject.class),
        gson.fromJson("{\"id\": 8, \"vector\": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], \"color\": \"white_9381\"}", JsonObject.class),
        gson.fromJson("{\"id\": 9, \"vector\": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], \"color\": \"purple_4976\"}", JsonObject.class)
);

InsertReq insertReq = InsertReq.builder()
        .collectionName("my_collection")
        .data(data)
        .build();

InsertResp insertResp = client.insert(insertReq);
System.out.println(insertResp);

// Output:
//
// InsertResp(InsertCnt=10, primaryKeys=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

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

// 3. Insert some data

const data = [
    {id: 0, vector: [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], color: "pink_8682"},
    {id: 1, vector: [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], color: "red_7025"},
    {id: 2, vector: [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], color: "orange_6781"},
    {id: 3, vector: [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], color: "pink_9298"},
    {id: 4, vector: [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], color: "red_4794"},
    {id: 5, vector: [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], color: "yellow_4222"},
    {id: 6, vector: [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], color: "red_9392"},
    {id: 7, vector: [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], color: "grey_8510"},
    {id: 8, vector: [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], color: "white_9381"},
    {id: 9, vector: [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], color: "purple_4976"}        
]

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

console.log(res.insert_cnt)

// Output
// 
// 10
// 

dynamicColumn := column.NewColumnString("color", []string{
    "pink_8682", "red_7025", "orange_6781", "pink_9298", "red_4794", "yellow_4222", "red_9392", "grey_8510", "white_9381", "purple_4976",
})

_, err = client.Insert(ctx, milvusclient.NewColumnBasedInsertOption("my_collection").
    WithInt64Column("id", []int64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}).
    WithFloatVectorColumn("vector", 5, [][]float32{
        {0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592},
        {0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104},
        {0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592},
        {0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345},
        {0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106},
        {0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955},
        {0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987},
        {-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052},
        {0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336},
        {0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608},
    }).
    WithColumns(dynamicColumn),
)
if err != nil {
    fmt.Println(err.Error())
    // handle err
}

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

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/insert" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "data": [
        {"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682"},
        {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025"},
        {"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781"},
        {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298"},
        {"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794"},
        {"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222"},
        {"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392"},
        {"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510"},
        {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381"},
        {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976"}        
    ],
    "collectionName": "my_collection"
}'

# {
#     "code": 0,
#     "data": {
#         "insertCount": 10,
#         "insertIds": [
#             0,
#             1,
#             2,
#             3,
#             4,
#             5,
#             6,
#             7,
#             8,
#             9
#         ]
#     }
# }

为 dynamic field 中的标量字段建立索引

当您启用 dynamic field 时，任何未定义的标量字段都将以 JSON 格式作为键值对存储。Milvus 支持为这样的未定义标量字段创建索引，实际上是通过构建 JSON 路径索引来实现的。以下是它的工作原理：

1. 选择要索引的 dynamic field 键。例如，上面示例中的 "color"。

2. 为该键处的值决定转换类型。Milvus 将解析 dynamic field，提取指定键下的值，并将它们转换为您配置的类型。

   • 支持的 json_cast_type 值为 bool（或 BOOL）、double（或 DOUBLE）和 varchar（或 VARCHAR）。

   • 如果解析或转换失败（例如，尝试将字符串解析为 double），这些行将在索引中被跳过。

3. 指定该键的 JSON 路径为 json_path。由于 dynamic field 存储为 JSON，您可以指定类似 "color" 的内容，或者如果您有嵌套结构，您可以指定更深的路径（例如 my_json["field"]["subfield"]）。

4. 创建 INVERTED 索引。目前，JSON 路径索引仅支持 INVERTED 类型。

有关参数和注意事项的详细信息，请参阅 为 JSON 字段建立索引。

以下是如何为 "color" 字段创建索引的示例：

PythonJavaGoNodeJScURL

# Prepare index parameters
index_params = client.prepare_index_params()

index_params.add_index(
    field_name="color",               # Name of the "column" you see in queries (the dynamic key).
    index_type="INVERTED",            # Currently only "INVERTED" is supported for indexing JSON fields.
    index_name="color_index",         # Assign a name to this index.
    params={
        "json_path": "color",         # JSON path to the key you want to index.
        "json_cast_type": "varchar"   # Type to which Milvus will cast the extracted values.
    }
)

# Create the index
client.create_index(
    collection_name="my_collection",
    index_params=index_params
)

import io.milvus.v2.common.IndexParam;

List<IndexParam> indexes = new ArrayList<>();

Map<String,Object> extraParams = new HashMap<>();
extraParams.put("json_path", "color");
extraParams.put("json_cast_type", "varchar");
indexes.add(IndexParam.builder()
        .fieldName("color")
        .indexName("color_index")
        .indexType(IndexParam.IndexType.INVERTED)
        .extraParams(extraParams)
        .build());

client.createIndex(CreateIndexReq.builder()
        .collectionName("my_collection")
        .indexParams(indexes)
        .build());

indexTask, err := client.CreateIndex(ctx, milvusclient.NewCreateIndexOption("my_collection", "color",
    index.NewJSONPathIndex(index.Inverted, "varchar", "color")))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

err = indexTask.Await(ctx)
if err != nil {
    fmt.Println(err.Error())
    // handler err
}

const index_params = {
    field_name: "color",               // Name of the "column" you see in queries (the dynamic key).
    index_type: "INVERTED",            // Currently only "INVERTED" is supported for indexing JSON fields.
    index_name: "color_index",         // Assign a name to this index.
    params:{
        "json_path": "color",          // JSON path to the key you want to index.
        "json_cast_type": "varchar"   // Type to which Milvus will cast the extracted values.
    }
}

// Create the index
await client.create_index({
    collection_name: "my_collection",
    index_params: index_params
});

# restful
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/indexes/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "indexParams": [
        {
            "fieldName": "color",
            "indexName": "color_index",
            "indexType": "INVERTED",
            "params": {
                "json_path": "color",
                "json_cast_type": "varchar"
            }
        }
    ]
}'

使用 dynamic field 查询和搜索

Milvus 支持在查询和搜索过程中使用过滤表达式，允许您指定要在结果中包含的字段。以下示例演示了如何通过使用 dynamic field 来使用未在 schema 中定义的 color 字段执行查询和搜索。

PythonJavaNodeJSGocURL

query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]

res = client.search(
    collection_name="my_collection",
    data=[query_vector],
    limit=5,
    filter='color like "red%"',
    output_fields=["color"]
)

print(res)

# Output
# data: ["[{'id': 1, 'distance': 0.6290165185928345, 'entity': {'color': 'red_7025'}}, {'id': 4, 'distance': 0.5975797176361084, 'entity': {'color': 'red_4794'}}, {'id': 6, 'distance': -0.24996188282966614, 'entity': {'color': 'red_9392'}}]"] 

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

FloatVec queryVector = new FloatVec(new float[]{0.3580376395471989f, -0.6023495712049978f, 0.18414012509913835f, -0.26286205330961354f, 0.9029438446296592f});
SearchResp resp = client.search(SearchReq.builder()
        .collectionName("my_collection")
        .annsField("vector")
        .data(Collections.singletonList(queryVector))
        .outputFields(Collections.singletonList("color"))
        .filter("color like \"red%\"")
        .topK(5)
        .consistencyLevel(ConsistencyLevel.STRONG)
        .build());

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

// Output
//
// [[
//    SearchResp.SearchResult(entity={color=red_7025}, score=0.6290165, id=1),
//    SearchResp.SearchResult(entity={color=red_4794}, score=0.5975797, id=4), 
//    SearchResp.SearchResult(entity={color=red_9392}, score=-0.24996188, id=6)
//]]

const query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]

const res = await client.search({
    collection_name: "quick_setup",
    data: [query_vector],
    limit: 5,
    # highlight-start
    filters: "color like \"red%\"",
    output_fields: ["color"]
    # highlight-end
});

queryVector := []float32{0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592}

resultSets, err := client.Search(ctx, milvusclient.NewSearchOption(
    "my_collection", // collectionName
    5,                       // limit
    []entity.Vector{entity.FloatVector(queryVector)},
).WithFilter("color like \"red%\"").
    WithANNSField("vector").
    WithOutputFields("color"))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

for _, resultSet := range resultSets {
    fmt.Println("IDs: ", resultSet.IDs.FieldData().GetScalars())
    fmt.Println("Scores: ", resultSet.Scores)
    fmt.Println("color: ", resultSet.GetColumn("color").FieldData().GetScalars())
}

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

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "data": [
        [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
    ],
    "annsField": "vector",
    "filter": "color like \"red%\"",
    "limit": 3,
    "outputFields": ["color"]
}'
# {"code":0,"cost":0,"data":[{"color":"red_7025","distance":0.6290165,"id":1},{"color":"red_4794","distance":0.5975797,"id":4},{"color":"red_9392","distance":-0.24996185,"id":6}]}

在上面代码示例中使用的过滤表达式 color like "red%" and likes > 50 中，条件指定 color 字段的值必须以 "red" 开头。在示例数据中，只有两个实体满足此条件。因此，当 limit（topK）设置为 3 或更少时，这两个实体都将被返回。

[
    {
        "id": 1, 
        "distance": 0.6290165,
        "entity": {
            "color": "red_7025"
        }
    },
    {
        "id": 4, 
        "distance": 0.5975797,
        "entity": {
            "color": "red_4794"
        }
    },
    {
        "id": 6, 
        "distance": -0.24996188，
        "entity": {
            "color": "red_9392"
        }
    },
]