啟用動態欄位

本頁說明如何在集合中使用動態欄位，以彈性插入和擷取資料。

概述

Milvus 允許您通過設置每個特定欄位的名稱和數據類型來定義一個集合的模式，這樣您就可以在這些欄位中創建索引以提高搜索性能。

一旦定義了欄位，您需要在插入資料時包含這個欄位。如果某些欄位並不總是出現在您所有的資料項目中，該怎麼辦？這就是動態欄位的用武之地。

集合中的動態欄位是一個保留的 JSON 欄位，名為 $meta。它可以將非結構描述定義的欄位及其值保存為鍵值對。使用動態欄位，您可以搜尋和查詢模式定義的欄位及其可能擁有的任何非模式定義的欄位。

啟用動態欄位

為集合定義模式時，您可以將enable_dynamic_field 設為True ，以啟用保留動態欄位，表示稍後插入的任何非模式定義的欄位及其值，都會以鍵值對的形式保存在保留動態欄位中。

下面的片段用兩個模式定義的欄位（即 id 和 vector）創建了一個集合，並啟用了動態欄位。

import random, time
from pymilvus import connections, MilvusClient, DataType

SERVER_ADDR = "http://localhost:19530"

# 1. Set up a Milvus client
client = MilvusClient(
    uri=SERVER_ADDR
)

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

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="IVF_FLAT",
    metric_type="L2",
    params={"nlist": 1024}
)

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 com.google.gson.Gson;
import com.google.gson.JsonObject;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.common.DataType;
import io.milvus.v2.common.IndexParam;
import io.milvus.v2.service.collection.request.*;
import io.milvus.v2.service.vector.request.*;
import io.milvus.v2.service.vector.request.data.*;
import io.milvus.v2.service.vector.response.*;

import java.util.*;

String CLUSTER_ENDPOINT = "http://localhost:19530";

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

MilvusClientV2 client = new MilvusClientV2(connectConfig);

// 2. Create a collection in customized setup mode

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

// 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).build());

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

Map<String, Object> params = new HashMap<>();
params.put("nlist", 1024);
IndexParam indexParamForVectorField = IndexParam.builder()
        .fieldName("vector")
        .indexType(IndexParam.IndexType.IVF_FLAT)
        .metricType(IndexParam.MetricType.IP)
        .extraParams(params)
        .build();

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

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

client.createCollection(customizedSetupReq);

Thread.sleep(5000);

// 2.5 Get load state of the collection
GetLoadStateReq customSetupLoadStateReq1 = GetLoadStateReq.builder()
        .collectionName("customized_setup")
        .build();

boolean res = client.getLoadState(customSetupLoadStateReq1);

System.out.println(res);

// Output:
// true

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

const address = "http://localhost:19530"

async function main() {
// 1. Set up a Milvus Client
client = new MilvusClient({address}); 

// 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
    },
]

// 2.2 Prepare index parameters
const index_params = [{
    field_name: "id",
    index_type: "STL_SORT"
},{
    field_name: "vector",
    index_type: "IVF_FLAT",
    metric_type: "IP",
    params: { nlist: 1024}
}]

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

console.log(res.error_code)

// Output
// 
// Success
// 

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

console.log(res.state)

// Output
// 
// LoadStateLoaded
// 

插入動態資料

建立資料集後，您就可以開始插入資料，包括插入動態資料到資料集中。

準備資料

在本節中，您需要準備一些隨機產生的資料，以便稍後插入。

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)
    data.append({
        "id": i,
        "vector": [ random.uniform(-1, 1) for _ in range(5) ],
        "color": current_color,
        "tag": current_tag,
        "color_tag": f"{current_color}_{str(current_tag)}"
    })

print(data[0])

List<String> colors = Arrays.asList("green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey");
List<JsonObject> data = new ArrayList<>();
Gson gson = new Gson();
Random rand = new Random();
for (int i=0; i<1000; i++) {
    String current_color = colors.get(rand.nextInt(colors.size()-1));
    int current_tag = rand.nextInt(8999) + 1000;
    JsonObject row = new JsonObject();
    row.addProperty("id", (long) i);
    row.add("vector", gson.toJsonTree(Arrays.asList(rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat())));
    row.addProperty("color", current_color);
    row.addProperty("tag", current_tag);
    row.addProperty("color_tag", current_color + "_" + (rand.nextInt(8999) + 1000));
    data.add(row);
}

System.out.println(data.get(0).toString());

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)
    data.push({
        id: i,
        vector: [Math.random(), Math.random(), Math.random(), Math.random(), Math.random()],
        color: current_color,
        tag: current_tag,
        color_tag: `${current_color}_${current_tag}`
    })
}

console.log(data[0])

您可以檢視所產生資料的結構，方法是檢查其第一個項目。

{
    id: 0,
    vector: [
        0.1275656405044483,
        0.47417858592773277,
        0.13858264437643286,
        0.2390904907020377,
        0.8447862593689635
    ],
    color: 'blue',
    tag: 2064,
    color_tag: 'blue_2064'
}

插入資料

然後您就可以安全地將資料插入集合中。

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)

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

InsertResp insertResp = client.insert(insertReq);

System.out.println(insertResp.getInsertCnt());

// Output:
// 1000

Thread.sleep(5000);

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

console.log(res.insert_cnt)

// Output
// 
// 1000
// 

await sleep(5000)

使用動態欄位搜尋

如果您已建立啟用動態欄位的集合，並插入非結構描述定義的欄位，您可以在搜尋或查詢的篩選表達式中使用這些欄位，如下所示。

# 4. Search with dynamic fields
query_vectors = [[0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]]

res = client.search(
    collection_name="test_collection",
    data=query_vectors,
    filter="color in [\"red\", \"green\"]",
    search_params={"metric_type": "L2", "params": {"nprobe": 10}},
    limit=3
)

print(res)

# Output
#
# [
#     [
#         {
#             "id": 863,
#             "distance": 0.188413605093956,
#             "entity": {
#                 "id": 863,
#                 "color_tag": "red_2371"
#             }
#         },
#         {
#             "id": 799,
#             "distance": 0.29188022017478943,
#             "entity": {
#                 "id": 799,
#                 "color_tag": "red_2235"
#             }
#         },
#         {
#             "id": 564,
#             "distance": 0.3492690920829773,
#             "entity": {
#                 "id": 564,
#                 "color_tag": "red_9186"
#             }
#         }
#     ]
# ]

// 4. Search with non-schema-defined fields
List<BaseVector> queryVectors = Collections.singletonList(new FloatVec(new float[]{0.3580376395471989f, -0.6023495712049978f, 0.18414012509913835f, -0.26286205330961354f, 0.9029438446296592f}));

SearchReq searchReq = SearchReq.builder()
        .collectionName("customized_setup")
        .data(queryVectors)
        .filter("$meta[\"color\"] in [\"red\", \"green\"]")
        .outputFields(Arrays.asList("id", "color_tag"))
        .topK(3)
        .build();

SearchResp searchResp = client.search(searchReq);

List<List<SearchResp.SearchResult>> searchResults = searchResp.getSearchResults();
for (List<SearchResp.SearchResult> results : searchResults) {
    System.out.println("TopK results:");
    for (SearchResp.SearchResult result : results) {
        System.out.println(result);
    }
}

// Output:
// SearchResp.SearchResult(entity={color_tag=green_2205, id=556}, score=1.134007, id=556)
// SearchResp.SearchResult(entity={color_tag=red_2786, id=310}, score=0.9072295, id=310)
// SearchResp.SearchResult(entity={color_tag=red_9493, id=215}, score=0.8819287, id=215)

// 4. Search with non-schema-defined fields
const query_vectors = [[0.1, 0.2, 0.3, 0.4, 0.5]]

res = await client.search({
    collection_name: "test_collection",
    data: query_vectors,
    filter: "color in [\"red\", \"green\"]",
    output_fields: ["color_tag"],
    limit: 3
})

console.log(res.results)

// Output
// 
// [
//   { score: 1.2284551858901978, id: '301', color_tag: 'red_1270' },
//   { score: 1.2195171117782593, id: '205', color_tag: 'red_2780' },
//   { score: 1.2055039405822754, id: '487', color_tag: 'red_6653' }
// ]
// 

重覆

值得注意的是，color、tag和color_tag在您定義集合模式時並不存在，但您可以在進行搜尋和查詢時，將它們當作模式定義的欄位來使用。

如果非模式定義欄位的名稱包含數位、字母和下劃線以外的字元，如加號 (+)、星號 (*) 或美元符號 () 等，當您在布林表達式中使用它或在輸出欄位中包含它時，您必須在 __meta[]__ 中包含該關鍵字，如以下程式碼片段所示。

... 
filter='$meta["$key"] in ["a", "b", "c"]', 
output_fields='$meta["$key"]'  
...