过滤搜索

ANN 搜索查找与指定向量嵌入最相似的向量嵌入。但是，搜索结果可能并不总是正确的。您可以在搜索请求中包含过滤条件，这样 Milvus 会在进行 ANN 搜索之前先进行元数据过滤，将搜索范围从整个 collection 缩小到仅匹配指定过滤条件的 entity。

概述

在 Milvus 中，过滤搜索根据过滤应用的阶段分为两种类型——标准过滤和迭代过滤。

标准过滤

如果 collection 包含向量嵌入及其元数据，您可以在 ANN 搜索前过滤元数据以提高搜索结果的相关性。一旦 Milvus 接收到带有过滤条件的搜索请求，它就会将搜索范围限制在匹配指定过滤条件的 entity 范围内。

如上图所示，搜索请求携带 chunk like "%red%" 作为过滤条件，表示 Milvus 应该在所有 chunk field 中包含单词 red 的 entity 内进行 ANN 搜索。具体来说，Milvus 执行以下操作：

• 过滤匹配搜索请求中携带的过滤条件的 entity。

• 在过滤后的 entity 内进行 ANN 搜索。

• 返回 top-K entity。

迭代过滤

标准过滤过程有效地将搜索范围缩小到一个小范围。但是，过于复杂的过滤表达式可能导致搜索延迟非常高。在这种情况下，迭代过滤可以作为替代方案，帮助减少标量过滤的工作量。

如上图所示，使用迭代过滤的搜索以迭代方式执行向量搜索。迭代器返回的每个 entity 都经过标量过滤，此过程持续进行，直到达到指定的 topK 结果。

此方法显著减少了受标量过滤影响的 entity 数量，这对于处理高度复杂的过滤表达式特别有益。

但是，需要注意的是，迭代器一次处理一个 entity。这种顺序方法可能导致更长的处理时间或潜在的性能问题，特别是当大量 entity 受到标量过滤时。

示例

本节演示如何进行过滤搜索。本节中的代码片段假设您的 collection 中已经有以下 entity。每个 entity 有四个 field，分别是 id、vector、color 和 likes。

[
    {"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682", "likes": 165},
    {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025", "likes": 25},
    {"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781", "likes": 764},
    {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298", "likes": 234},
    {"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794", "likes": 122},
    {"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222", "likes": 12},
    {"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392", "likes": 58},
    {"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510", "likes": 775},
    {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381", "likes": 876},
    {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976", "likes": 765}
]

标准过滤搜索

以下代码片段演示了标准过滤搜索，以下代码片段中的请求携带了过滤条件和几个输出 field。

PythonJavaGoNodeJScURL

from pymilvus import MilvusClient

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

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%" and likes > 50',
    output_fields=["color", "likes"]
)

for hits in res:
    print("TopK results:")
    for hit in hits:
        print(hit)

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
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

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .token("root:Milvus")
        .build());

FloatVec queryVector = new FloatVec(new float[]{0.3580376395471989f, -0.6023495712049978f, 0.18414012509913835f, -0.26286205330961354f, 0.9029438446296592f});
SearchReq searchReq = SearchReq.builder()
        .collectionName("my_collection")
        .data(Collections.singletonList(queryVector))
        .topK(5)
        .filter("color like \"red%\" and likes > 50")
        .outputFields(Arrays.asList("color", "likes"))
        .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
// TopK results:
// SearchResp.SearchResult(entity={color=red_4794, likes=122}, score=0.5975797, id=4)
// SearchResp.SearchResult(entity={color=red_9392, likes=58}, score=-0.24996188, id=6)

import (
    "context"
    "fmt"

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

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

milvusAddr := "localhost:19530"
token := "root:Milvus"

client, err := client.New(ctx, &client.ClientConfig{
    Address: milvusAddr,
    APIKey:  token,
})
if err != nil {
    fmt.Println(err.Error())
    // handle error
}
defer client.Close(ctx)

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)},
    ).WithConsistencyLevel(entity.ClStrong).
        WithANNSField("vector").
        WithFilter("color like 'red%' and likes > 50").
        WithOutputFields("color", "likes"))
    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())
        fmt.Println("likes: ", resultSet.GetColumn("likes").FieldData().GetScalars())
    }

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

const address = "http://localhost:19530";
const token = "root:Milvus";
const client = new MilvusClient({address, token});

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

const res = await client.search({
    collection_name: "my_collection",
    data: [query_vector],
    limit: 5,
    filters: 'color like "red%" and likes > 50',
    output_fields: ["color", "likes"]
})

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%\" and likes > 50",
    "limit": 5,
    "outputFields": ["color", "likes"]
}'
# {"code":0,"cost":0,"data":[]}

搜索请求中携带的过滤条件为 color like "red%" and likes > 50。它使用 and 操作符包含两个条件：第一个条件要求 entity 在 color field 中的值以 red 开头，另一个条件要求 entity 在 likes field 中的值大于 50。只有两个 entity 满足这些要求。由于 top-K 设置为 3，Milvus 将计算这两个 entity 与查询向量的距离，并将它们作为搜索结果返回。

[
    {
        "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
        }
    },
]

有关您可以在元数据过滤中使用的操作符的更多信息，请参阅 Filtering。

迭代过滤搜索

要使用迭代过滤进行过滤搜索，您可以执行以下操作：

PythonJavaGoNodeJScURL

from pymilvus import MilvusClient

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

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%" and likes > 50',
    output_fields=["color", "likes"],
    search_params={
        "hints": "iterative_filter"
    }
)

for hits in res:
    print("TopK results:")
    for hit in hits:
        print(hit)

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
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;

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .token("root:Milvus")
        .build());

FloatVec queryVector = new FloatVec(new float[]{0.3580376395471989f, -0.6023495712049978f, 0.18414012509913835f, -0.26286205330961354f, 0.9029438446296592f});
SearchReq searchReq = SearchReq.builder()
        .collectionName("my_collection")
        .data(Collections.singletonList(queryVector))
        .topK(5)
        .filter("color like \"red%\" and likes > 50")
        .outputFields(Arrays.asList("color", "likes"))
        .searchParams(new HashMap<>("hints", "iterative_filter"))
        .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
// TopK results:
// SearchResp.SearchResult(entity={color=red_4794, likes=122}, score=0.5975797, id=4)
// SearchResp.SearchResult(entity={color=red_9392, likes=58}, score=-0.24996188, id=6)

import (
    "context"
    "fmt"

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

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

milvusAddr := "localhost:19530"
token := "root:Milvus"

client, err := client.New(ctx, &client.ClientConfig{
    Address: milvusAddr,
    APIKey:  token,
})
if err != nil {
    fmt.Println(err.Error())
    // handle error
}
defer client.Close(ctx)

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)},
).WithConsistencyLevel(entity.ClStrong).
    WithANNSField("vector").
    WithFilter("color like 'red%' and likes > 50").
    WithOutputFields("color", "likes").
    WithSearchParam("hints", "iterative_filter"))
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())
    fmt.Println("likes: ", resultSet.GetColumn("likes").FieldData().GetScalars())
}

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

const address = "http://localhost:19530";
const token = "root:Milvus";
const client = new MilvusClient({address, token});

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

const res = await client.search({
    collection_name: "filtered_search_collection",
    data: [query_vector],
    limit: 5,
    filters: 'color like "red%" and likes > 50',
    hints: "iterative_filter",
    output_fields: ["color", "likes"]
})

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%\" and likes > 50",
    "searchParams": {"hints": "iterative_filter"},
    "limit": 5,
    "outputFields": ["color", "likes"]
}'
# {"code":0,"cost":0,"data":[]}