动态字段
Collections 的 Schema 中定义的所有字段都必须包含在要插入的实体中。如果希望某些字段是可选的,可以考虑启用动态字段。本主题将介绍如何启用和使用动态字段。
概述
在 Milvus 中,您可以通过设置 Collections 中每个字段的名称和数据类型来创建 Collections Schema。向 Schema 中添加字段时,请确保该字段包含在要插入的实体中。如果希望某些字段是可选的,启用动态字段是一种选择。
动态字段是一个保留字段,名为$meta
,属于 JavaScript Object Notation(JSON)类型。实体中任何未在 Schema 中定义的字段都将以键值对的形式存储在这个保留的 JSON 字段中。
对于启用了动态字段的 Collections,可以使用动态字段中的键进行标量过滤,就像使用模式中明确定义的字段一样。
启用动态字段
使用 "立即创建集合"中描述的方法创建的集合默认已启用动态字段。也可以在创建具有自定义设置的 Collections 时手动启用动态字段。
from pymilvus import MilvusClient
client= MilvusClient(uri="http://localhost:19530")
client.create_collection(
collection_name="my_dynamic_collection",
dimension=5,
# highlight-next-line
enable_dynamic_field=True
)
import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.service.collection.request.CreateCollectionReq;
MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
.uri("http://localhost:19530")
.build());
CreateCollectionReq createCollectionReq = CreateCollectionReq.builder()
.collectionName("my_dynamic_collection")
.dimension(5)
// highlight-next-line
.enableDynamicField(true)
.build()
client.createCollection(createCollectionReq);
import { MilvusClient, DataType } from "@zilliz/milvus2-sdk-node";
const client = new Client({
address: 'http://localhost:19530'
});
await client.createCollection({
collection_name: "customized_setup_2",
schema: schema,
// highlight-next-line
enable_dynamic_field: true
});
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
"collectionName": "my_dynamic_collection",
"dimension": 5,
"enableDynamicField": true
}'
使用动态字段
在集合中启用动态字段后,所有未在 Schema 中定义的字段及其值都将作为键值对存储在动态字段中。
例如,假设您的 Collections Schema 只定义了两个字段,名为id
和vector
,并启用了动态字段。现在,在此 Collections 中插入以下数据集。
[
{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"}
]
上面的数据集包含 10 个实体,每个实体都包括字段id
,vector
, 和color
。这里,Schema 中没有定义color
字段。由于 Collections 启用了动态字段,因此字段color
将作为键值对存储在动态字段中。
插入数据
以下代码演示了如何将此数据集插入 Collections。
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_dynamic_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_dynamic_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
var 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"}
]
var res = await client.insert({
collection_name: "quick_setup",
data: data,
})
console.log(res.insert_cnt)
// Output
//
// 10
//
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_dynamic_collection"
}'
# {
# "code": 0,
# "data": {
# "insertCount": 10,
# "insertIds": [
# 0,
# 1,
# 2,
# 3,
# 4,
# 5,
# 6,
# 7,
# 8,
# 9
# ]
# }
# }
使用动态字段进行查询和搜索
Milvus 支持在查询和搜索过程中使用过滤表达式,允许您指定在结果中包含哪些字段。下面的示例演示了如何通过动态字段使用color
字段执行查询和搜索,该字段在 Schema 中没有定义。
query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
res = client.search(
collection_name="my_dynamic_collection",
data=[query_vector],
limit=5,
# highlight-start
filter='color like "red%"',
output_fields=["color"]
# highlight-end
)
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_dynamic_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]
res = await client.search({
collection_name: "quick_setup",
data: [query_vector],
limit: 5,
// highlight-start
filters: "color like \"red%\"",
output_fields: ["color"]
// highlight-end
})
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_dynamic_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"红色 "开头。在示例数据中,只有两个实体符合这一条件。因此,当limit
(topK) 设置为3
或更少时,将返回这两个实体。
[
{
"id": 4,
"distance": 0.3345786594834839,
"entity": {
"vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106],
"color": "red_4794",
"likes": 122
}
},
{
"id": 6,
"distance": 0.6638239834383389,
"entity": {
"vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987],
"color": "red_9392",
"likes": 58
}
},
]