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如何对 Atlas 集群中的数据执行语义搜索

在此页面上

  • 先决条件
  • 创建 Atlas Vector Search 索引
  • 必需的访问权限
  • 步骤
  • 使用 $vectorSearch 聚合管道阶段运行查询
  • Overview
  • 步骤

本教程介绍如何对 Atlas 集群上的 collection 中的 字段中的向量执行 ANN 搜索。plot_embeddingsample_mflix.embedded_movies为了演示这一点,它将带您完成以下步骤:

  1. sample_mflix.embedded_movies 集合中对名为 plot_embedding 的数字字段创建 Atlas Vector Search 索引。

  2. 针对 sample_mflix.embedded_movies 集合中的 plot_embedding 字段运行 Atlas Vector Search 查询。


➤ 使用选择语言下拉菜单选择用于创建 Atlas Vector Search 索引和运行查询的客户端。


提示

使用本教程的可运行版本作为Python 笔记本。

如要完成本教程,您必须具备以下条件:

  • 运行 MongoDB 版本 6.0.11、v7.0.2 或更高版本(包括RC )的 Atlas 集群。

  • 示例数据已加载到 Atlas 集群中。

    注意

    您可以将样本数据中的嵌入转换为 BSON 向量,以便在 Atlas 中高效存储和摄取向量。要了解更多信息,请参阅如何将原生嵌入转换为 BSON 向量

  • 用于在Atlas 集群上运行查询的以下客户端之一:

    注意

    您可以通过任何 MongoDB 驱动程序$vectorSearch 聚合阶段运行 Atlas Vector Search 查询。本教程仅包含前述列表中驱动程序的示例。

    您还可以将 Atlas Vector Search 与通过 Atlas CLI 创建的本地 Atlas 部署结合使用。要了解详情,请参阅创建本地 Atlas 部署

本部分演示如何在sample_mflix.embedded_moviescollection中的plot_embedding字段上创建 Atlas Vector Search 索引,以便对该字段运行查询。

要创建 Atlas Vector Search 索引,您必须拥有 Project Data Access Admin 或更高的项目访问权限。

1
  1. 如果尚未显示,请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。

  2. 如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。

  3. 如果尚未出现,请单击侧边栏中的 Clusters(集群)。

    会显示集群页面。

2

您可以从侧边栏、 Data Explorer 或集群详细信息页面转到 Atlas Search 页面。

  1. 在侧边栏中,单击 Services 标题下的 Atlas Search

    注意

    如果您没有集群,请单击 Create cluster 来创建一个。如需了解详情,请参阅 创建集群。

  2. Select data source 下拉菜单中选择您的集群并单击 Go to Atlas Search

    将显示 Atlas Search 页面。

  1. 单击集群的对应 Browse Collections 按钮。

  2. 展开数据库并选择集合。

  3. 单击该集合的 Search Indexes 标签页。

    将显示 Atlas Search 页面。

  1. 单击集群的名称。

  2. 单击 Atlas Search 标签页。

    将显示 Atlas Search 页面。

3
4

在页面上进行以下选择,然后单击 Next

Search Type

选择 Vector Search 索引类型。

Index Name and Data Source

指定以下信息:

  • Index Name: vector_index

  • Database and Collection:

    • sample_mflix database

    • embedded_movies 集合

Configuration Method

For a guided experience, select Visual Editor.

To edit the raw index definition, select JSON Editor.
5

在此步骤中,您定义 vectorSearch 类型的索引以用于以下字段:

  • plot_embedding 字段作为向量类型。 plot_embedding字段包含使用 OpenAI 的text-embedding-ada-002嵌入模型创建的嵌入。 索引定义指定了1536向量维度,并使用dotProduct来衡量相似性。

  • genres 字段作为筛选器类型,用于按字段中的字符串值对数据进行预筛选。

  • year 字段作为筛选器类型,用于按字段中的数值对数据进行预筛选。

Atlas会自动检测包含向量嵌入的字段及其对应的维度。对于 sample_mflix.embedded_movies 集合,plot_embedding 字段会显示。

要配置索引,请执行以下操作:

  1. Similarity Method 下拉列表中选择 Dot Product

  2. 单击 Advanced,然后从下拉菜单中选择 Scalar 量化。

  3. Filter Field 部分,指定 genresyear 字段以过滤数据。

将默认定义替换为以下索引定义。

1{
2 "fields": [
3 {
4 "type": "vector",
5 "path": "plot_embedding",
6 "numDimensions": 1536,
7 "similarity": "dotProduct",
8 "quantization": "scalar"
9 },
10 {
11 "type": "filter",
12 "path": "genres"
13 },
14 {
15 "type": "filter",
16 "path": "year"
17 }
18 ]
19}
6
7

Atlas 会显示一个模态窗口,让您知道您的索引正在构建中。

8
9

新创建的索引显示在 Atlas Search 标签页上。在构建索引期间,Status 字段显示为 Build in Progress。索引构建完成后,Status 字段将显示为Active

注意

较大的集合需要较长的索引时间。索引构建完成后,您将收到电子邮件通知。

1

在终端窗口中打开 mongosh 并连接到您的集群。有关连接的详细说明,请参阅通过 mongosh 连接

2

例子

use sample_mflix
switched to db sample_mflix
3

以下索引定义在Atlas Vector Search索引中将 plot_embedding字段索引为 vector 类型,并将 genresyear 字段索引为 filter 类型。 plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。 索引定义指定了 1536 个向量维度,并使用 dotProduct 函数测量相似性。

1db.embedded_movies.createSearchIndex(
2 "vector_index",
3 "vectorSearch",
4 {
5 "fields": [
6 {
7 "type": "vector",
8 "path": "plot_embedding",
9 "numDimensions": 1536,
10 "similarity": "dotProduct"
11 },
12 {
13 "type": "filter",
14 "path": "genres"
15 },
16 {
17 "type": "filter",
18 "path": "year"
19 }
20 ]
21 }
22);
1

将 MongoDB C# 驱动程序作为依赖项添加到项目中:

dotnet add package MongoDB.Driver
2

创建一个名为 IndexService.cs 的文件。将以下代码复制并粘贴到文件中,并替换 <connection-string> 占位符值。

IndexService.cs
1namespace query_quick_start;
2
3using MongoDB.Bson;
4using MongoDB.Driver;
5using System;
6using System.Threading;
7
8public class IndexService
9{
10 // Replace the placeholder with your Atlas connection string
11 private const string MongoConnectionString = "<connection-string>";
12 public void CreateVectorIndex()
13 {
14 try
15 {
16 // Connect to your Atlas cluster
17 var client = new MongoClient(MongoConnectionString);
18 var database = client.GetDatabase("sample_mflix");
19 var collection = database.GetCollection<BsonDocument>("embedded_movies");
20
21 var searchIndexView = collection.SearchIndexes;
22 var name = "vector_index";
23 var type = SearchIndexType.VectorSearch;
24
25 var definition = new BsonDocument
26 {
27 { "fields", new BsonArray
28 {
29 new BsonDocument
30 {
31 { "type", "vector" },
32 { "path", "plot_embedding" },
33 { "numDimensions", 1536 },
34 { "similarity", "dotProduct" },
35 { "quantization", "scalar"}
36 },
37 new BsonDocument
38 {
39 {"type", "filter"},
40 {"path", "genres"}
41 },
42 new BsonDocument
43 {
44 {"type", "filter"},
45 {"path", "year"}
46 }
47 }
48 }
49 };
50
51 var model = new CreateSearchIndexModel(name, type, definition);
52
53 searchIndexView.CreateOne(model);
54 Console.WriteLine($"New search index named {name} is building.");
55
56 // Polling for index status
57 Console.WriteLine("Polling to check if the index is ready. This may take up to a minute.");
58 bool queryable = false;
59 while (!queryable)
60 {
61 var indexes = searchIndexView.List();
62 foreach (var index in indexes.ToEnumerable())
63 {
64 if (index["name"] == name)
65 {
66 queryable = index["queryable"].AsBoolean;
67 }
68 }
69 if (!queryable)
70 {
71 Thread.Sleep(5000);
72 }
73 }
74 Console.WriteLine($"{name} is ready for querying.");
75 }
76 catch (Exception e)
77 {
78 Console.WriteLine($"Exception: {e.Message}");
79 }
80 }
81}

此索引定义在Atlas Vector Search索引中将 plot_embedding字段索引为 vector 类型,并将 genresyear 字段索引为 filter 类型。 plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。 索引定义指定了 1536 个向量维度,并使用 dotProduct 函数测量相似性。

3

更新 Program.cs 以初始化 IndexService 并调用方法创建索引。

Program.cs
using query_quick_start;
var indexService = new IndexService();
indexService.CreateVectorIndex();
4

保存文件,然后编译并运行您的项目以创建索引。

dotnet run query_quick_start.csproj
New search index named vector_index is building.
Polling to check if the index is ready. This may take up to a minute.
vector_index is ready for querying.
1

将 Go 驱动程序作为依赖项添加到项目中:

go get go.mongodb.org/mongo-driver/mongo
2

创建一个名为 vector-index.go 的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString> 占位符值。

vector-index.go
1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7 "time"
8
9 "go.mongodb.org/mongo-driver/bson"
10 "go.mongodb.org/mongo-driver/mongo"
11 "go.mongodb.org/mongo-driver/mongo/options"
12)
13
14func main() {
15 ctx := context.Background()
16
17 // Replace the placeholder with your Atlas connection string
18 const uri = "<connectionString>"
19
20 // Connect to your Atlas cluster
21 clientOptions := options.Client().ApplyURI(uri)
22 client, err := mongo.Connect(ctx, clientOptions)
23 if err != nil {
24 log.Fatalf("failed to connect to the server: %v", err)
25 }
26 defer func() { _ = client.Disconnect(ctx) }()
27
28 // Set the namespace
29 coll := client.Database("sample_mflix").Collection("embedded_movies")
30 indexName := "vector_index"
31 opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch")
32
33 type vectorDefinitionField struct {
34 Type string `bson:"type"`
35 Path string `bson:"path"`
36 NumDimensions int `bson:"numDimensions"`
37 Similarity string `bson:"similarity"`
38 Quantization string `bson:"quantization"`
39 }
40
41 type filterField struct {
42 Type string `bson:"type"`
43 Path string `bson:"path"`
44 }
45
46 type indexDefinition struct {
47 Fields []vectorDefinitionField `bson:"fields"`
48 }
49
50 vectorDefinition := vectorDefinitionField{
51 Type: "vector",
52 Path: "plot_embedding",
53 NumDimensions: 1536,
54 Similarity: "dotProduct",
55 Quantization: "scalar"}
56 genreFilterDefinition := filterField{"filter", "genres"}
57 yearFilterDefinition := filterField{"filter", "year"}
58
59 indexModel := mongo.SearchIndexModel{
60 Definition: bson.D{{"fields", [3]interface{}{
61 vectorDefinition,
62 genreFilterDefinition,
63 yearFilterDefinition}}},
64 Options: opts,
65 }
66
67 // Create the index
68 searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
69 if err != nil {
70 log.Fatalf("failed to create the search index: %v", err)
71 }
72 log.Println("New search index named " + searchIndexName + " is building.")
73
74 // Await the creation of the index.
75 log.Println("Polling to check if the index is ready. This may take up to a minute.")
76 searchIndexes := coll.SearchIndexes()
77 var doc bson.Raw
78 for doc == nil {
79 cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
80 if err != nil {
81 fmt.Errorf("failed to list search indexes: %w", err)
82 }
83
84 if !cursor.Next(ctx) {
85 break
86 }
87
88 name := cursor.Current.Lookup("name").StringValue()
89 queryable := cursor.Current.Lookup("queryable").Boolean()
90 if name == searchIndexName && queryable {
91 doc = cursor.Current
92 } else {
93 time.Sleep(5 * time.Second)
94 }
95 }
96
97 log.Println(searchIndexName + " is ready for querying.")
98}

此索引定义在Atlas Vector Search索引中将 plot_embedding字段索引为 vector 类型,并将 genresyear 字段索引为 filter 类型。 plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。 索引定义指定了 1536 个向量维度,并使用 dotProduct 函数测量相似性。

3
go run vector-index.go
2024/10/17 09:38:21 Creating the index.
2024/10/17 09:38:22 Polling to confirm successful index creation.
2024/10/17 09:38:22 NOTE: This may take up to a minute.
2024/10/17 09:38:48 Name of Index Created: vector_index
1
  1. 将 Java 驱动程序版本 5.2 或更高版本作为依赖项添加到您的项目中,具体取决于您的包管理器:

    如果使用 Maven,请将以下依赖项添加到项目的 pom.xml文件的 dependencies大量中:

    pom.xml
    <dependencies>
    <!-- MongoDB Java Sync Driver v5.2.0 or later -->
    <dependency>
    <groupId>org.mongodb</groupId>
    <artifactId>mongodb-driver-sync</artifactId>
    <version>[5.2.0,)</version>
    </dependency>
    </dependencies>
  2. 运行包管理器以安装项目的依赖项。

有关更详细的安装说明和版本兼容性信息,请参阅 MongoDB Java 驱动程序文档。

2

创建一个名为 VectorIndex.java 的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString> 占位符值。

VectorIndex.java
1import com.mongodb.client.MongoClient;
2import com.mongodb.client.MongoClients;
3import com.mongodb.client.MongoCollection;
4import com.mongodb.client.MongoDatabase;
5import com.mongodb.client.model.SearchIndexModel;
6import com.mongodb.client.model.SearchIndexType;
7import org.bson.Document;
8import org.bson.conversions.Bson;
9
10import java.util.Arrays;
11import java.util.Collections;
12import java.util.List;
13
14public class VectorIndex {
15
16 public static void main(String[] args) {
17
18 // Replace the placeholder with your Atlas connection string
19 String uri = "<connectionString>";
20
21 // Connect to your Atlas cluster
22 try (MongoClient mongoClient = MongoClients.create(uri)) {
23
24 // Set the namespace
25 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26 MongoCollection<Document> collection = database.getCollection("embedded_movies");
27
28 // Define the index details with the filter fields
29 String indexName = "vector_index";
30 Bson definition = new Document(
31 "fields",
32 Arrays.asList(
33 new Document("type", "vector")
34 .append("path", "plot_embedding")
35 .append("numDimensions", 1536)
36 .append("similarity", "dotProduct")
37 .append("quantization", "scalar"),
38 new Document("type", "filter")
39 .append("path", "genres"),
40 new Document("type", "filter")
41 .append("path", "year")));
42
43 // Define the index model
44 SearchIndexModel indexModel = new SearchIndexModel(
45 indexName,
46 definition,
47 SearchIndexType.vectorSearch());
48
49 // Create the index using the defined model
50 List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel));
51 System.out.println("Successfully created vector index named: " + result.get(0));
52 System.out.println("It may take up to a minute for the index to leave the BUILDING status and become queryable.");
53
54 // Wait for Atlas to build the index
55 System.out.println("Polling to confirm the index has left the BUILDING status.");
56 // No special handling in case of a timeout. Custom handling can be implemented.
57 waitForIndex(collection, indexName);
58 }
59 }
60
61 /**
62 * Polls the collection to check whether the specified index is ready to query.
63 */
64 public static <T> boolean waitForIndex(final MongoCollection<T> collection, final String indexName) {
65 long startTime = System.nanoTime();
66 long timeoutNanos = TimeUnit.SECONDS.toNanos(60);
67 while (System.nanoTime() - startTime < timeoutNanos) {
68 Document indexRecord = StreamSupport.stream(collection.listSearchIndexes().spliterator(), false)
69 .filter(index -> indexName.equals(index.getString("name")))
70 .findAny().orElse(null);
71 if (indexRecord != null) {
72 if ("FAILED".equals(indexRecord.getString("status"))) {
73 throw new RuntimeException("Search index has FAILED status.");
74 }
75 if (indexRecord.getBoolean("queryable")) {
76 System.out.println(indexName + " index is ready to query");
77 return true;
78 }
79 }
80 try {
81 Thread.sleep(100); // busy-wait, avoid in production
82 } catch (InterruptedException e) {
83 Thread.currentThread().interrupt();
84 throw new RuntimeException(e);
85 }
86 }
87 return false;
88 }
89}

此索引定义在Atlas Vector Search索引中将 plot_embedding字段索引为 vector 类型,并将 genresyear 字段索引为 filter 类型。 plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。 索引定义指定了 1536 个向量维度,并使用 dotProduct 函数测量相似性。

3

在 IDE 中运行该文件,或从命令行执行命令来运行代码。

javac VectorIndex.java
java VectorIndex
Successfully created a vector index named: [vector_index]
It may take up to a minute for the index to build before you can query using it.
Polling to confirm the index has completed building.
vector_index index is ready to query
1

将 MongoDB Node 驱动程序作为依赖项添加到项目中:

npm install mongodb

提示

本页上的示例假设您的项目将模块作为 CommonJS 模块进行管理。而如果您使用的是 ES 模块,则必须修改导入语法。

2

创建一个名为 vector-index.js 的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString> 占位符值。

vector-index.js
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas deployment
4const uri = "<connectionString>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9 try {
10 const database = client.db("sample_mflix");
11 const collection = database.collection("embedded_movies");
12
13 // define your Atlas Vector Search index
14 const index = {
15 name: "vector_index",
16 type: "vectorSearch",
17 definition: {
18 "fields": [
19 {
20 "type": "vector",
21 "numDimensions": 1536,
22 "path": "plot_embedding",
23 "similarity": "dotProduct",
24 "quantization": "scalar"
25 },
26 {
27 "type": "filter",
28 "path": "genres"
29 },
30 {
31 "type": "filter",
32 "path": "year"
33 }
34 ]
35 }
36 }
37
38 // run the helper method
39 const result = await collection.createSearchIndex(index);
40 console.log(`New search index named ${result} is building.`);
41
42 // wait for the index to be ready to query
43 console.log("Polling to check if the index is ready. This may take up to a minute.")
44 let isQueryable = false;
45 while (!isQueryable) {
46 const cursor = collection.listSearchIndexes();
47 for await (const index of cursor) {
48 if (index.name === result) {
49 if (index.queryable) {
50 console.log(`${result} is ready for querying.`);
51 isQueryable = true;
52 } else {
53 await new Promise(resolve => setTimeout(resolve, 5000));
54 }
55 }
56 }
57 }
58 } finally {
59 await client.close();
60 }
61}
62run().catch(console.dir);

此索引定义在Atlas Vector Search索引中将 plot_embedding字段索引为 vector 类型,并将 genresyear 字段索引为 filter 类型。 plot_embedding字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。 索引定义指定了 1536 个向量维度,并使用 dotProduct 函数测量相似性。

3
node vector-index.js
vector_index
1

将 PyMongo 驱动程序作为依赖项添加到项目中:

pip install pymongo
2

创建一个名为 vector-index.py 的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString> 占位符值。

vector-index.py
1from pymongo.mongo_client import MongoClient
2from pymongo.operations import SearchIndexModel
3import time
4
5# Connect to your Atlas deployment
6uri = "<connectionString>"
7client = MongoClient(uri)
8
9# Access your database and collection
10database = client["sample_mflix"]
11collection = database["embedded_movies"]
12
13# Create your index model, then create the search index
14search_index_model = SearchIndexModel(
15 definition={
16 "fields": [
17 {
18 "type": "vector",
19 "path": "plot_embedding",
20 "numDimensions": 1536,
21 "similarity": "dotProduct",
22 "quantization": "scalar"
23 },
24 {
25 "type": "filter",
26 "path": "genres"
27 },
28 {
29 "type": "filter",
30 "path": "year"
31 }
32 ]
33 },
34 name="vector_index",
35 type="vectorSearch",
36)
37
38result = collection.create_search_index(model=search_index_model)
39print("New search index named " + result + " is building.")
40
41# Wait for initial sync to complete
42print("Polling to check if the index is ready. This may take up to a minute.")
43predicate=None
44if predicate is None:
45 predicate = lambda index: index.get("queryable") is True
46
47while True:
48 indices = list(collection.list_search_indexes(result))
49 if len(indices) and predicate(indices[0]):
50 break
51 time.sleep(5)
52print(result + " is ready for querying.")
53
54client.close()

此索引定义在 Atlas Vector Search 索引中将 plot_embedding 字段索引为 vector 类型,并将 genresyear 字段索引为 filter 类型。plot_embedding 字段包含使用 OpenAI 的 text-embedding-ada-002 嵌入模型创建的嵌入。索引定义指定了 1536 个向量维度,使用 dotProduct 函数测量相似性,并启用自动 scalar 量化以高效处理您的向量。

3
python vector-index.py
vector_index

本节演示如何使用 $vectorSearch 阶段查询 sample_mflix.embedded_movies 集合中的索引向量数据。这些示例查询还演示了各种查询聚合管道操作符,我们可以在查询中使用它们来对我们要执行语义搜索的数据进行预过滤。

1
  1. 如果尚未显示,请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。

  2. 如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。

  3. 如果尚未出现,请单击侧边栏中的 Clusters(集群)。

    会显示集群页面。

2

单击集群的对应 Browse Collections 按钮。

显示数据浏览器

3
  1. 展开数据库列表下的sample_mflix ,然后从该数据库的collection列表中选择embedded_movies

  2. 单击sample_mflix.embedded_movies集合的Aggregation标签页。

4
  1. 单击 </> Text(连接)。

  2. 将左窗格中的[ ]替换为对要尝试的预过滤操作符的查询。

以下查询使用$vectorSearch 管道阶段来搜索与指定向量嵌入匹配的电影。查询指定最多搜索100 个最近邻,并将结果限制为仅10 个文档。查询还指定了一个$project 阶段来执行以下操作:

  • 在结果中排除 _id 字段,而仅包含 titlegenresplotyear 字段。

  • 添加一个名为 score 的字段,以显示结果中的每个文档的向量搜索分数。

以下查询使用字符串 historical heist 的向量嵌入搜索 plot_embedding 字段。该查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。它为每个字段指定多个比较查询操作符,以对要执行语义搜索的文档进行预过滤。

该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

  • genres 字段过滤,以查找不属于 dramawesterncrime 类型但属于 actionadventurefamily 类型的电影。

  • year 字段过滤,以查找在 19602000 年份(含这两个年份)之间发行的电影。

1[
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "filter": {
7 "$and": [{
8 "genres": {
9 "$nin": ["Drama", "Western", "Crime"] ,
10 "$in": ["Action", "Adventure", "Family"]
11 },
12 }, {
13 "year": {
14 "$gte": 1960,
15 "$lte": 2000
16 }
17 }]
18 },
19 "queryVector": 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20 "numCandidates": 200,
21 "limit": 10
22 }
23 },
24 {
25 "$project": {
26 "_id": 0,
27 "title": 1,
28 "genres": 1,
29 "plot": 1,
30 "year": 1,
31 "score": { $meta: "vectorSearchScore" }
32 }
33 }
34]
1year: 1993
2plot: "A botched mid-air heist results in suitcases full of cash being search…"
3genres: Array (3)
4title: "Cliffhanger"
5score: 0.7694521546363831
6
7genres: Array (3)
8title: "It's a Mad, Mad, Mad, Mad World"
9year: 1963
10score: 0.7638954520225525
11plot: "The dying words of a thief spark a madcap cross-country rush to find s…"
12
13score: 0.7538407444953918
14year: 1991
15plot: "A cat burglar is forced to steal Da Vinci works of art for a world dom…"
16genres: Array (3)
17title: "Hudson Hawk"
18
19plot: "In 1997, when the US President crashes into Manhattan, now a giant max…"
20genres: Array (2)
21title: "Escape from New York"
22year: 1981
23score: 0.7487208843231201
24
25title: "The Romanov Stones"
26year: 1993
27score: 0.7467736005783081
28plot: "Patrick and Tony are hired by the wealthy gambler to steal the pricele…"
29genres: Array (2)
30
31plot: "An attempted robbery turns to be an unexpected recruitment when two un…"
32genres: Array (3)
33title: "Crime Busters"
34year: 1977
35score: 0.7437351942062378
36
37plot: "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal …"
38genres: Array (3)
39title: "Three Kings"
40year: 1999
41score: 0.7425670623779297
42
43plot: "A professional thief is hired by the FBI to steal a data tape from a c…"
44genres: Array (3)
45title: "Black Moon Rising"
46year: 1986
47score: 0.7397696375846863
48
49plot: "A group of heavily armed hijackers board a luxury ocean liner in the S…"
50genres: Array (3)
51title: "Deep Rising"
52year: 1998
53score: 0.7392246127128601
54
55year: 1964
56score: 0.7357995510101318
57plot: "A young man comes to the rescue of his girlfriend abducted by thieves …"
58genres: Array (3)
59title: "That Man from Rio"

以下查询使用向量嵌入在 plot_embedding 字段中搜索字符串 martial arts。该查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。

该过滤器使用 $or 聚合管道操作符来查找符合以下任一条件的电影文档:

  • genres字段过滤,查找不属于crime类型的电影。

  • year 字段进行过滤可查找在 2015 年或之前上映的电影genres 字段进行过滤可查找属于 action 类型的电影。

1[
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "filter": {
7 "$or": [{
8 "genres": { "$ne": "Crime" }
9 }, {
10 "$and": [{
11 "year": { "$lte": 2015 }
12 }, {
13 "genres": { "$eq": "Action" }
14 }]
15 }]
16 },
17 "queryVector": 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13plot: "A young martial artist embarks on an adventure, encountering other mar…"
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28title: "Kung Fu Killer"
29year: 2014
30
31plot: "A young woman is trained by a martial arts specialist to become a prof…"
32genres: Array (3)
33title: "Naked Killer"
34year: 1992
35score: 0.7693743109703064
36
37plot: "The life of the greatest karate master of a generation."
38genres: Array (3)
39title: "The Real Miyagi"