创建 Scalar Index

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Zilliz Cloud 支持对标量字段（非向量字段）进行索引，从而显著加速过滤和搜索性能，尤其适用于大型数据集。

概述

对标量字段进行索引是可选操作，但如果你经常在过滤条件中访问某个特定标量字段，建议为其建立索引以加速查询性能。

Zilliz Cloud 支持对以下字段类型使用 AUTOINDEX：

字段类型	描述
VARCHAR	字符串
INT8, INT32, INT64	整数
FLOAT, DOUBLE	浮点数
BOOL	布尔值
ARRAY	标量值的同质数组

准备工作

在创建索引之前，定义一个包含向量字段和标量字段的 collection。Zilliz Cloud 要求每个 collection 都有一个向量字段。

在此示例中，我们为产品目录定义一个 schema，其中包含一个必需的向量字段（vector）和一个 DOUBLE 类型的标量字段（price）：

Python

from pymilvus import MilvusClient, DataType

client = MilvusClient(uri="YOUR_CLUSTER_ENDPOINT") # Replace with your cluster endpoint

# Define schema with dynamic field support
schema = client.create_schema(
    auto_id=False,
    # highlight-next-line
    enable_dynamic_field=True # Enable dynamic field
)

# Define fields
schema.add_field(field_name="product_id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=5) # Vector field
schema.add_field(field_name="price", datatype=DataType.DOUBLE) # Scalar field

# Create the collection
client.create_collection(
    collection_name="product_catalog",
    schema=schema
)

Java

import io.milvus.v2.client.*;
import io.milvus.v2.service.collection.request.CreateCollectionReq;
import io.milvus.v2.service.collection.request.AddFieldReq;

ConnectConfig config = ConnectConfig.builder()
        .uri("YOUR_CLUSTER_ENDPOINT")
        .build();
MilvusClientV2 client = new MilvusClientV2(config);

CreateCollectionReq.CollectionSchema schema = CreateCollectionReq.CollectionSchema.builder()
        .enableDynamicField(true)
        .build();
schema.addField(AddFieldReq.builder()
        .fieldName("product_id")
        .dataType(DataType.Int64)
        .isPrimaryKey(Boolean.TRUE)
        .build());
schema.addField(AddFieldReq.builder()
        .fieldName("vector")
        .dataType(DataType.FloatVector)
        .dimension(5)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("price")
        .dataType(DataType.Double)
        .build());

CreateCollectionReq requestCreate = CreateCollectionReq.builder()
        .collectionName("product_catalog")
        .collectionSchema(schema)
        .build();
client.createCollection(requestCreate);

NodeJS

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

// initialize client
const client = new MilvusClient({
  address: 'localhost:19530', // Replace with your cluster endpoint
});

const collectionName = 'product_catalog';

// define schema
const schema = [
  {
    name: 'product_id',
    description: 'Primary key',
    data_type: DataType.Int64,
    is_primary_key: true,
    autoID: false,
  },
  {
    name: 'vector',
    description: 'Embedding vector',
    data_type: DataType.FloatVector,
    type_params: {
      dim: '5',
    },
  },
  {
    name: 'price',
    description: 'Product price',
    data_type: DataType.Double,
  },
];

// create collection
const res = await client.createCollection({
    collection_name: collectionName,
    fields: schema,
    enable_dynamic_field: true,
});

console.log('Create collection result:', res);

Go

import (
    "context"

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

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

client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "localhost:19530",
})
if err != nil {
    return err
}

schema := entity.NewSchema().WithDynamicFieldEnabled(true)
schema.WithField(entity.NewField().
    WithName("product_id").pk
    WithDataType(entity.FieldTypeInt64).
    WithIsPrimaryKey(true),
).WithField(entity.NewField().
    WithName("vector").
    WithDataType(entity.FieldTypeFloatVector).
    WithDim(5),
).WithField(entity.NewField().
    WithName("price").
    WithDataType(entity.FieldTypeDouble),
)

err = client.CreateCollection(ctx, milvusclient.NewCreateCollectionOption("product_catalog", schema))
if err != nil {
    return err
}

cURL

export TOKEN="YOUR_CLUSTER_TOKEN"
export CLUSTER_ENDPOINT="YOUR_CLUSTER_ENDPOINT"

export productIdField='{
  "fieldName": "product_id",
  "dataType": "Int64",
  "isPrimary": true,
  "autoID": false
}'

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

export priceField='{
  "fieldName": "price",
  "dataType": "Double"
}'

export schema="{
  \"autoID\": false,
  \"enableDynamicField\": true,
  \"fields\": [
    $productIdField,
    $vectorField,
    $priceField
  ]
}"

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

为标量字段建立索引

您可以使用 AUTOINDEX 为任何非 JSON 标量字段创建索引。无需额外的索引参数。以下示例展示了如何为 price 字段建立索引：

Python

index_params = client.prepare_index_params() # Prepare an empty IndexParams object, without having to specify any index parameters

index_params.add_index(
    field_name="price", # Name of the scalar field to be indexed
    # highlight-next-line
    index_type="AUTOINDEX", # Type of index to be created
    index_name="price_index" # Name of the index to be created
)

Java

import io.milvus.v2.common.IndexParam;

List<IndexParam> indexParams = new ArrayList<>();
indexParams.add(IndexParam.builder()
        .fieldName("price")
        .indexName("price_index")
        .indexType(IndexParam.IndexType.AUTOINDEX)
        .build());

NodeJS

const indexParams = [{
    collection_name: collectionName,
    field_name: 'price',
    index_type: 'AUTOINDEX',
    index_name: 'price_index'
}];

Go

import (
    "github.com/milvus-io/milvus/client/v2/index"
)

indexOpt := client.NewCreateIndexOption("product_catalog", "price",
        index.NewInvertedIndex())

cURL

export priceIndex='{
  "fieldName": "price",
  "indexName": "price_index",
  "params": {
    "index_type": "AUTOINDEX"
  }
}'

定义索引参数后，您可以使用 create_index() 将它们应用到 collection：

Python

client.create_index(
    collection_name="product_catalog",
    index_params=index_params
)

Java

import io.milvus.v2.service.index.request.CreateIndexReq;

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

NodeJS

client.createIndex(indexParams)

Go

indexTask1, err := client.CreateIndex(ctx, indexOpt1)
if err != nil {
    return err
}
indexTask2, err := client.CreateIndex(ctx, indexOpt2)
if err != nil {
    return err
}
indexTask3, err := client.CreateIndex(ctx, indexOpt3)
if err != nil {
    return err
}
indexTask4, err := client.CreateIndex(ctx, indexOpt4)
if err != nil {
    return err
}

cURL

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/indexes/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--data "{
  \"collectionName\": \"product_catalog\",
  \"indexParams\": [$priceIndex]
}"

查看索引详情

创建索引后，您可以查看其详细信息：

Python

# Describe index
res = client.list_indexes(
    collection_name="product_catalog"
)

print(res)

res = client.describe_index(
    collection_name="product_catalog",
    index_name="price_index"
)

print(res)

Java

import io.milvus.v2.service.index.request.DescribeIndexReq;
import io.milvus.v2.service.index.response.DescribeIndexResp;

// Describe index
// List the index names
ListIndexesReq listIndexesReq = ListIndexesReq.builder()
    .collectionName("product_catalog")
    .build();

List<String> indexNames = client.listIndexes(listIndexesReq);

System.out.println(indexNames);

// Describe an index
DescribeIndexReq describeIndexReq = DescribeIndexReq.builder()
    .collectionName("product_catalog")
    .indexName("price_index")
    .build();

DescribeIndexResp describeIndexResp = client.describeIndex(describeIndexReq);

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

NodeJS

// Describe the index
res = await client.describeIndex({
    collection_name: "product_catalog",
    index_name: "price_index"
})

console.log(JSON.stringify(res.index_descriptions, null, 2))

删除索引

当您不需要索引时，可以考虑将其删除。

Python

# Drop index
client.drop_index(
    collection_name="product_catalog",
    index_name="price_index"
)

Java

// Drop index

DropIndexReq dropIndexReq = DropIndexReq.builder()
    .collectionName("product_catalog")
    .indexName("price_index")
    .build();

client.dropIndex(dropIndexReq);

NodeJS

// Drop the index
res = await client.dropIndex({
    collection_name: "product_catalog",
    index_name: "price_index"
})

console.log(res.error_code)

高级功能

除了基础功能外，标量索引还提供了一些进阶特性，能够满足更复杂的数据检索需求。

NGRAM [READ MORE]

在 Zilliz Cloud 中，NGRAM 索引 用于加速对 VARCHAR 字段 或 JSON 字段中指定路径的 LIKE 查询。在建立索引之前，Zilliz Cloud 会将文本拆分为固定长度 n 的 重叠子串（n-gram）。例如，当 `n = 3` 时，单词 `"Milvus"` 会被拆分为以下 3-gram：`"Mil"`, `"ilv"`, `"lvu"`, `"vus"`。这些 n-gram 随后会存储在倒排索引中，每个 gram 都映射到包含它的文档 ID。在查询时，该索引使 Zilliz Cloud 能快速缩小候选范围，从而显著加速查询执行。