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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

本教程介绍了如何对 Atlas 集群上 sample_mflix.embedded_movies 集合中 plot_embedding 字段内的向量执行 ANN 搜索。为了说明这一点，本教程指导您完成以下步骤：

  1. 在 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。

使用本教程的可运行版本以作为 Python 笔记本。

先决条件

在开始之前，请完成常见先决条件。本教程包括以下客户端的示例：

创建 Atlas Vector Search 索引

本节说明了如何在 sample_mflix.embedded_movies 集合中的 plot_embedding 字段上创建 Atlas Vector Search 索引，以对该字段运行向量查询。

必需的访问权限

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。

步骤

1

在 Atlas 中，进入项目的 Clusters 页面。

  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

转到集群的 Atlas Search 页面。

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

    如果没有集群，则请单击 Create cluster 来创建一个。要了解更多信息，请参阅创建集群。

  2. 从 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

单击 Create Search Index（连接）。

4

开始您的索引配置。

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

定义 Atlas Vector Search 索引。

在此步骤中，您将为以下字段定义 vectorSearch 类型的索引：

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

  • genres 字段作为过滤器类型，用于按字段中的字符串值对数据进行预过滤。

  • year 字段作为过滤器类型，用于按字段中的数值对数据进行预过滤。

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。

要配置该索引，请执行以下操作：

  1. 从 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. 在 Filter Field 部分，指定 genres 与 year 字段以按它们来过滤该数据。

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

单击 Next 以查看索引。

7

单击 Create Vector Search Index（连接）。

Atlas 会显示一个模态窗口，让您知道您的索引正在构建中。

8

通过单击 Close 按钮关闭 You're All Set! 模态窗口。

9

检查状态。

新创建的索引会显示在 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

通过 mongosh 连接到您的集群。

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

切换到您要创建索引的数据库。

例子

use sample_mflix
switched to db sample_mflix
3

运行 db.collection.createSearchIndex() 方法。

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 genres 和 year 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# 驱动程序添加到 .NET 项目中。

将 MongoDB C# 驱动程序作为依赖项添加到项目中：

dotnet add package MongoDB.Driver
2

定义索引。

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 genres 和 year 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 。

更新 Program.cs 以初始化 IndexService 并调用该方法来创建索引。

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

保存并运行该项目。

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 驱动程序添加到您的项目中。

将 Go 驱动程序作为依赖项添加到项目中：

go get go.mongodb.org/mongo-driver/v2/mongo
2

定义索引。

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/v2/bson"
10	"go.mongodb.org/mongo-driver/v2/mongo"
11	"go.mongodb.org/mongo-driver/v2/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(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{{Key: "fields", Value: [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 genres 和 year 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

运行以下命令以创建索引。

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

将 MongoDB Java Sync 驱动程序添加到您的项目中。

  1. 将 Java 驱动程序 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

定义索引。

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 genres 和 year 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

运行该文件，以创建索引。

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.js 驱动程序添加到您的项目中。

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

定义索引。

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 genres 和 year 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

运行以下命令以创建索引。

node vector-index.js
vector_index
1

将 PyMongo 驱动程序添加到您的项目中。

将 PyMongo 驱动程序作为依赖项添加到项目中：

pip install pymongo
2

定义索引。

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 genres 和 year 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。此索引定义指定了 1536 个向量维度，使用 dotProduct 函数来测量相似性，并启用自动 scalar 量化以高效处理您的向量。

3

运行以下命令以创建索引。

python vector-index.py
vector_index

使用 $vectorSearch 聚合管道阶段运行查询

Overview

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和聚合管道操作符，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。

步骤

1

在 Atlas 中，进入项目的 Clusters 页面。

  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

Go到Collections页面。

单击集群的对应 Browse Collections 按钮。

显示数据浏览器。

3

转到集合的Aggregation标签页。

  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

针对索引字段运行 Atlas Vector Search 搜索查询。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 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：

  • 按genres字段过滤，查找不属于crime类型的电影。

  • 按 year 字段进行过滤可查找在 2015 年或之前上映的电影，按 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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22  {
23    "$project": {
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26      "genres": 1,
27      "plot": 1,
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29      "score": { $meta: "vectorSearchScore" }
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32]
1title: "Ong-bak 2"
2score: 0.7976737022399902
3year: 2008
4plot: "A young Thai boxer learns the skills and inner meaning of martial arts…"
5genres: Array (1)
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7score: 0.7929348349571228
8plot: "A handyman/martial arts master agrees to teach a bullied boy karate an…"
9genres: Array (3)
10title: "The Karate Kid"
11year: 1984
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13plot: "A young martial artist embarks on an adventure, encountering other mar…"
14genres: Array (3)
15title: "Circle of Iron"
16year: 1978
17score: 0.7852014899253845
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19plot: "A lionized account of the life of the martial arts superstar."
20genres: Array (3)
21title: "Dragon: The Bruce Lee Story"
22year: 1993
23score: 0.7763040661811829
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25score: 0.7731232643127441
26plot: "A martial arts instructor from the police force gets imprisoned after …"
27genres: Array (2)
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"
40year: 2015
41score: 0.768799901008606
42
43plot: "At his new high school, a rebellious teen is lured into an underground…"
44genres: Array (3)
45title: "Never Back Down"
46year: 2008
47score: 0.768179714679718
48
49score: 0.7679706811904907
50plot: "Three young martial arts masters emerge from the back streets of Hong …"
51genres: Array (2)
52title: "Dragon Tiger Gate"
53year: 2006
54
55title: "Shaolin Soccer"
56score: 0.7660527229309082
57year: 2001
58plot: "A young Shaolin follower reunites with his discouraged brothers to for…"
59genres: Array (3)

注意

Pipeline Output窗格会显示查询结果。

5

展开查询结果。

Atlas 用户界面可能无法显示其返回文档中的所有值。要查看在查询路径中搜索的字段的所有值，请展开这些文档中的相关字段。

1

通过 mongosh 连接到您的集群。

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

切换到 sample_mflix 数据库。

use sample_mflix
switched to db sample_mflix
3

针对索引字段运行语义Atlas Search查询。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 年份（含这两个年份）之间发行的电影。

1db.embedded_movies.aggregate([
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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1[
2  {
3    year: 1993,
4    plot: 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.',
5    genres: [ 'Action', 'Adventure', 'Thriller' ],
6    title: 'Cliffhanger',
7    score: 0.7694521546363831
8  },
9  {
10    plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
11    genres: [ 'Action', 'Adventure', 'Comedy' ],
12    title: "It's a Mad, Mad, Mad, Mad World",
13    year: 1963,
14    score: 0.7638954520225525
15  },
16  {
17    year: 1991,
18    plot: 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.',
19    genres: [ 'Action', 'Adventure', 'Comedy' ],
20    title: 'Hudson Hawk',
21    score: 0.7538407444953918
22  },
23  {
24    plot: 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.',
25    genres: [ 'Action', 'Sci-Fi' ],
26    title: 'Escape from New York',
27    year: 1981,
28    score: 0.7487208843231201
29  },
30  {
31    plot: 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.',
32    genres: [ 'Action', 'Thriller' ],
33    title: 'The Romanov Stones',
34    year: 1993,
35    score: 0.7467736005783081
36  },
37  {
38    plot: 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.',
39    genres: [ 'Action', 'Comedy', 'Adventure' ],
40    title: 'Crime Busters',
41    year: 1977,
42    score: 0.7437351942062378
43  },
44  {
45    plot: 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.',
46    genres: [ 'Action', 'Adventure', 'Comedy' ],
47    title: 'Three Kings',
48    year: 1999,
49    score: 0.7425670623779297
50  },
51  {
52    plot: 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...',
53    genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
54    title: 'Black Moon Rising',
55    year: 1986,
56    score: 0.7397696375846863
57  },
58  {
59    plot: 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.',
60    genres: [ 'Action', 'Adventure', 'Horror' ],
61    title: 'Deep Rising',
62    year: 1998,
63    score: 0.7392246127128601
64  },
65  {
66    plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
67    genres: [ 'Action', 'Adventure', 'Comedy' ],
68    title: 'That Man from Rio',
69    year: 1964,
70    score: 0.7357995510101318
71  }
72]

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：

  • 按genres字段过滤，查找不属于crime类型的电影。

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

1db.embedded_movies.aggregate([
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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1[
2    {
3      year: 2008,
4      plot: 'A young Thai boxer learns the skills and inner meaning of martial arts.',
5      genres: [ 'Action' ],
6      title: 'Ong-bak 2',
7      score: 0.7976737022399902
8    },
9    {
10      plot: 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.',
11      genres: [ 'Action', 'Drama', 'Family' ],
12      title: 'The Karate Kid',
13      year: 1984,
14      score: 0.7929348349571228
15    },
16    {
17      plot: 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.',
18      genres: [ 'Action', 'Adventure', 'Fantasy' ],
19      title: 'Circle of Iron',
20      year: 1978,
21      score: 0.7852014899253845
22    },
23    {
24      plot: 'A lionized account of the life of the martial arts superstar.',
25      genres: [ 'Action', 'Biography', 'Drama' ],
26      title: 'Dragon: The Bruce Lee Story',
27      year: 1993,
28      score: 0.7763040661811829
29    },
30    {
31      plot: 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.',
32      genres: [ 'Action', 'Thriller' ],
33      title: 'Kung Fu Killer',
34      year: 2014,
35      score: 0.7731232643127441
36    },
37    {
38      plot: 'A young woman is trained by a martial arts specialist to become a professional assassin.',
39      genres: [ 'Action', 'Crime', 'Romance' ],
40      title: 'Naked Killer',
41      year: 1992,
42      score: 0.7693743109703064
43    },
44    {
45      plot: 'The life of the greatest karate master of a generation.',
46      genres: [ 'Documentary', 'Action', 'History' ],
47      title: 'The Real Miyagi',
48      year: 2015,
49      score: 0.768799901008606
50    },
51    {
52      plot: 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.',
53      genres: [ 'Action', 'Drama', 'Sport' ],
54      title: 'Never Back Down',
55      year: 2008,
56      score: 0.768179714679718
57    },
58    {
59      plot: 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.',
60      genres: [ 'Action', 'Drama' ],
61      title: 'Dragon Tiger Gate',
62      year: 2006,
63      score: 0.7679706811904907
64    },
65    {
66      year: 2001,
67      plot: 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.',
68      genres: [ 'Action', 'Comedy', 'Sport' ],
69      title: 'Shaolin Soccer',
70      score: 0.7660527229309082
71    }
72]
73
1

创建一个名为 DataService.cs 的文件。

2

将要尝试的预过滤器操作符的代码复制并粘贴到DataService.cs文件中。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 年份（含这两个年份）之间发行的电影。

DataService.cs
1namespace query_quick_start;
2
3using MongoDB.Driver;
4using MongoDB.Bson;
5
6public class DataService
7{
8    private const string ConnectionString = "<connection-string>";
9    private static readonly MongoClient Client = new MongoClient(ConnectionString);
10    private static readonly IMongoDatabase Database = Client.GetDatabase("sample_mflix");
11    private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("embedded_movies");
12    
13    public List<BsonDocument>? PerformVectorQuery()
14    {
15        double[] queryVector = {
16            -0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 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-0.03790677, 0.0212686, 0.02982313, 0.015434778, 0.0041039507, -0.016245758, 0.012171238, -0.006415897, 0.0072464976, -0.0024362097, -0.025218857, -0.021399405, 0.036860343, 0.0056572384, 0.017004417, 0.03432276, -0.013825899, 0.028724384, 0.008528369, 0.018652538, -0.02443404, -0.025637427, 0.006497649, -0.015447859, 0.01917575, -0.016520444, -0.008678793, -0.021072397, 0.015840268, -0.006324335, 0.025925195, -0.03594472, 0.0384823, 0.01308032, 0.0054217926, 0.00448328, -0.027207067, -0.016847452, 0.0036003583, 0.01061468, -0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783,
17        };
18        var filter = Builders<BsonDocument>.Filter.And(
19            Builders<BsonDocument>.Filter.And(
20                Builders<BsonDocument>.Filter.Nin("genres", ["Drama", "Western", "Crime"]),
21                Builders<BsonDocument>.Filter.In("genres", ["Action", "Adventure", "Family"])
22            ),
23            Builders<BsonDocument>.Filter.And(
24                Builders<BsonDocument>.Filter.Gte("year", 1960),
25                Builders<BsonDocument>.Filter.Lte("year", 2000)
26            )
27        );
28  
29        var options = new VectorSearchOptions<BsonDocument>()
30        {
31            IndexName = "vector_index",
32            Filter = filter,
33            NumberOfCandidates = 200,
34        };
35        
36        var pipeline = new EmptyPipelineDefinition<BsonDocument>()
37            .VectorSearch("plot_embedding", queryVector, 10, options)
38            .Project(Builders<BsonDocument>.Projection
39                .Exclude("_id")
40                .Include("title")
41                .Include("genres")
42                .Include("plot")
43                .Include("year")
44                .Meta("score", "vectorSearchScore")
45            );
46        return Collection.Aggregate<BsonDocument>(pipeline).ToList();
47    }
48}

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：

  • 按genres字段过滤，查找不属于crime类型的电影。

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

DataService.cs
1namespace query_quick_start;
2
3using MongoDB.Driver;
4using MongoDB.Bson;
5
6public class DataService
7{
8    private const string ConnectionString = "<connection-string>";
9    private static readonly MongoClient Client = new MongoClient(ConnectionString);
10    private static readonly IMongoDatabase Database = Client.GetDatabase("sample_mflix");
11    private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("embedded_movies");
12    
13    public List<BsonDocument>? PerformVectorQuery()
14    {
15        double[] queryVector = {
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0.041802246, -0.01003436, 0.0097937435, 0.003641764, 0.022787036, -0.0017867404, -0.0026110145, 0.028145632, 0.011152902, 0.004311588, -0.021915615, -0.009182448, 0.02077106, -0.006171489, -0.0055309287, -0.019379387, 0.0022647218, -0.021213274, -0.014983257, 0.01158211, -0.0149572445, -0.021278305, -0.011725179, -0.021798559, 0.019691538, -0.04835742, 0.011536588, 0.029082086, -0.0006828307, -0.020003688, -0.0067957914, -0.16679278, 0.015646579, 0.037119977, -0.026480826, 0.0109317945, 0.0053488407, 0.013825696, -0.00035117008, -0.0013892354, -0.0014022416, 0.021915615, 0.008974346, -0.03274986, -0.0078688115, 0.023034155, 0.003518204, -0.017857648, 0.025037127, 0.0137606645, 0.02411368, 0.025765479, 0.0024338039, -0.00607069, -0.005114727, -0.009071894, -0.0094295675, 0.021096218, 0.016400944, -0.013175381, -0.0047440478, -0.015789647, -0.006548672, 0.017766604, 0.0077452515, 0.0044221417, 0.00838256, 0.0007568041, -0.015386452, -0.017649548, 0.014306929, -0.003066235, 0.02783348, -0.006984383, -0.02586953, 0.005108224, 0.02432178, 0.00885729, 0.0023297535, 0.007166471, -0.0021281557, -0.0052740546, -0.0035149525, 0.014085822, 0.00063893443, 0.031215118, -0.00425306, 0.014202879, -0.018351888, -0.0032158075, -0.0055081677, 0.003999437, -0.0017753599, 0.022748018, -0.023684472, -0.007946849, -0.019145273, -0.022487892, 0.018247837, -0.004535947, 0.008993856, -0.0043018335, 0.005498413, -0.0022647218, -0.023112195, 0.0055114194, 0.008818271, -0.00536835, 0.022604948, -0.020406883, -0.024386812, -0.014931232, 0.01761053, 0.0037588205, -0.0034986946, 0.01831287, 0.036001436, -0.000008446474, -0.024907064, -0.0036612733, -0.011725179, 0.023047162, -0.029758412, -0.014554049, -0.012934765, 0.015412465, 0.021850582, -0.011523581, -0.0022858572, -0.0040742233, -0.0056187212, 0.010307493, -0.019184291, 0.005192765, 0.008018384, 0.022722006, -0.0058138156, 0.03771827, 0.01028148, 0.029212149, -0.0054366332, 0.0014079319, -0.0009974206, 0.011627631, 0.026480826, 0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, -0.0027394518, 0.014918226, -0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914,
17        };
18        var filter = Builders<BsonDocument>.Filter.Or(
19            Builders<BsonDocument>.Filter.Ne("genres", "Crime"),
20            Builders<BsonDocument>.Filter.And(
21                Builders<BsonDocument>.Filter.Lte("year", 2015),
22                Builders<BsonDocument>.Filter.Eq("genres", "Action")
23            )
24        );
25  
26        var options = new VectorSearchOptions<BsonDocument>()
27        {
28            IndexName = "vector_index",
29            Filter = filter,
30            NumberOfCandidates = 200,
31        };
32        
33        var pipeline = new EmptyPipelineDefinition<BsonDocument>()
34            .VectorSearch("plot_embedding", queryVector, 10, options)
35            .Project(Builders<BsonDocument>.Projection
36                .Exclude("_id")
37                .Include("title")
38                .Include("genres")
39                .Include("plot")
40                .Include("year")
41                .Meta("score", "vectorSearchScore")
42            );
43        return Collection.Aggregate<BsonDocument>(pipeline).ToList();
44    }
45}
3

将 <connection-string> 替换为Atlas连接string ，然后保存文件。

确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

更新Program.cs 。

更新 Program.cs 以初始化 DataService 并调用该方法来执行查询。

Program.cs
1using query_quick_start;
2
3var dataService = new DataService();
4var results = dataService.PerformVectorQuery();
5if (results != null)
6{
7	foreach (var document in results)
8	{
9		Console.WriteLine(document);
10	}
11}
12else
13{
14	Console.WriteLine("The query returned no results.");
15}
5

保存并运行该项目。

保存文件，然后编译并运行您的项目以执行查询。

dotnet run query_quick_start.csproj
1{ "year" : 1993, "plot" : "A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", "genres" : ["Action", "Adventure", "Thriller"], "title" : "Cliffhanger", "score" : 0.92509341239929199 }
2{ "plot" : "The dying words of a thief spark a madcap cross-country rush to find some treasure.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "It's a Mad, Mad, Mad, Mad World", "year" : 1963, "score" : 0.92273008823394775 }
3{ "year" : 1991, "plot" : "A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "Hudson Hawk", "score" : 0.9183650016784668 }
4{ "plot" : "In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.", "genres" : ["Action", "Sci-Fi"], "title" : "Escape from New York", "year" : 1981, "score" : 0.91609716415405273 }
5{ "plot" : "Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.", "genres" : ["Action", "Thriller"], "title" : "The Romanov Stones", "year" : 1993, "score" : 0.9152265191078186 }
6{ "plot" : "An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", "genres" : ["Action", "Comedy", "Adventure"], "title" : "Crime Busters", "year" : 1977, "score" : 0.91385889053344727 }
7{ "plot" : "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "Three Kings", "year" : 1999, "score" : 0.91333001852035522 }
8{ "plot" : "A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...", "genres" : ["Action", "Sci-Fi", "Thriller"], "title" : "Black Moon Rising", "year" : 1986, "score" : 0.91205686330795288 }
9{ "plot" : "A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.", "genres" : ["Action", "Adventure", "Horror"], "title" : "Deep Rising", "year" : 1998, "score" : 0.91180777549743652 }
10{ "plot" : "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "That Man from Rio", "year" : 1964, "score" : 0.91023355722427368 }
dotnet run query_quick_start.csproj
1{ "year" : 2008, "plot" : "A young Thai boxer learns the skills and inner meaning of martial arts.", "genres" : ["Action"], "title" : "Ong-bak 2", "score" : 0.93658864498138428 }
2{ "plot" : "A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", "genres" : ["Action", "Drama", "Family"], "title" : "The Karate Kid", "year" : 1984, "score" : 0.93471556901931763 }
3{ "plot" : "A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.", "genres" : ["Action", "Adventure", "Fantasy"], "title" : "Circle of Iron", "year" : 1978, "score" : 0.93161046504974365 }
4{ "plot" : "A lionized account of the life of the martial arts superstar.", "genres" : ["Action", "Biography", "Drama"], "title" : "Dragon: The Bruce Lee Story", "year" : 1993, "score" : 0.92796128988265991 }
5{ "plot" : "A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", "genres" : ["Action", "Thriller"], "title" : "Kung Fu Killer", "year" : 2014, "score" : 0.92663633823394775 }
6{ "plot" : "A young woman is trained by a martial arts specialist to become a professional assassin.", "genres" : ["Action", "Crime", "Romance"], "title" : "Naked Killer", "year" : 1992, "score" : 0.92506062984466553 }
7{ "plot" : "The life of the greatest karate master of a generation.", "genres" : ["Documentary", "Action", "History"], "title" : "The Real Miyagi", "year" : 2015, "score" : 0.92481791973114014 }
8{ "plot" : "At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", "genres" : ["Action", "Drama", "Sport"], "title" : "Never Back Down", "year" : 2008, "score" : 0.92455542087554932 }
9{ "plot" : "Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", "genres" : ["Action", "Drama"], "title" : "Dragon Tiger Gate", "year" : 2006, "score" : 0.92446672916412354 }
10{ "year" : 2001, "plot" : "A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", "genres" : ["Action", "Comedy", "Sport"], "title" : "Shaolin Soccer", "score" : 0.92365169525146484 }
1

创建一个名为 AtlasVectorSearchTutorial.go 的文件。

2

将要尝试的预过滤器操作符的代码复制并粘贴到AtlasVectorSearchTutorial.go文件中。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 年份（含这两个年份）之间发行的电影。

1package main
2
3import (
4	"context"
5	"fmt"
6	"log"
7
8	"go.mongodb.org/mongo-driver/bson"
9	"go.mongodb.org/mongo-driver/mongo"
10	"go.mongodb.org/mongo-driver/mongo/options"
11)
12
13func main() {
14	ctx := context.Background()
15
16	// Replace the placeholder with your Atlas connection string
17	const uri = "<connection-string>"
18
19	// Connect to your Atlas cluster
20	clientOptions := options.Client().ApplyURI(uri)
21	client, err := mongo.Connect(ctx, clientOptions)
22	if err != nil {
23		log.Fatalf("failed to connect to the server: %v", err)
24	}
25	defer func() { _ = client.Disconnect(ctx) }()
26
27	// Set the namespace
28	coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30	queryVector := [1536]float64{
31		-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 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0.005209238, 0.0005939283, -0.0035807376, -0.011661106, 0.006559781, 0.0033207664, 0.0017233322, -0.00059924216, -0.000341519, -0.0140221035, 0.00084286317, -0.003306051, -0.005634348, -0.00816212, -0.009319728, -0.024447119, -0.014950806, -0.024564842, 0.0137212565, -0.010084927, 0.000044886958, -0.0033943432, 0.0025359471, 0.012478625, -0.023086766, 0.014519156, 0.020876192, -0.023282971, -0.0030804155, -0.014545316, -0.16805595, 0.01262905, 0.020719228, -0.012413224, 0.026592292, -0.0024198592, 0.041072205, 0.002658575, -0.013708176, -0.0068867886, -0.0018639456, 0.000031627806, -0.043452825, -0.028018046, -0.0105819795, 0.01266829, -0.009450532, 0.008292923, 0.0058534434, -0.006782146, 0.032229908, 0.0005955633, -0.0023103117, 0.003140912, 0.00037687673, -0.0049247406, -0.008070557, 0.017279103, -0.012759852, -0.011608784, -0.019450437, 0.016167276, 0.02248507, 0.030529467, 0.015905669, 0.0061150496, -0.016834373, 0.017344505, 0.006667693, -0.005461034, 0.0066742334, 0.01998673, 0.024591003, -0.007717389, 0.0096598165, 0.03225607, 0.018626377, -0.020248337, 0.0017740185, 0.012589809, 0.0014927916, -0.040235065, 0.01713522, 0.016206518, 0.017776156, 0.024734886, 0.0040516295, -0.009627116, 0.002001289, -0.010496957, -0.0121058365, -0.017266022, 0.008279843, -0.02122936, -0.01349889, -0.02251123, 0.004820098, -0.000071533, -0.022628954, 0.015238573, -0.01833861, -0.016572766, -0.0031523572, -0.008064018, 0.019973649, 0.0089207785, -0.03228223, 0.0040647094, -0.004784127, -0.0017920039, -0.0013775212, 0.047246117, 0.0030804155, -0.010660461, 0.02982313, 0.006088889, -0.019371955, -0.024447119, -0.011687267, -0.013708176, 0.017187541, -0.018286288, 0.019267311, 0.0011960318, 0.0046271635, 0.016886694, 0.0069129495, 0.00029062838, 0.013629694, -0.016494283, -0.017069818, 0.0058240127, 0.013943622, 0.001675916, 0.01347273, 0.023335291, 0.008129419, 0.0047187256, 0.032099105, 0.0007701039, 0.0068344674, 0.0004672127, -0.00610851, 0.026396086, -0.010738943, 0.024591003, 0.008220982, -0.019908248, 0.024682565, -0.009404751, 0.0594893, -0.009731758, -0.022628954, 0.013865139, -0.016049553, 0.0033371167, -0.107572556, -0.022341188, 0.008050937, -0.0089731, 0.004983602, 0.010771644, -0.013034539, -0.013368088, -0.0071287747, 0.0091758445, -0.017409906, -0.022118822, -0.011170594, -0.010908987, 0.050490037, 0.014584557, 0.018312449, 0.0014968792, -0.0057161, 0.024342475, -0.02699778, 0.020091372, -0.00094587065, -0.021347083, -0.003711541, 0.0016677409, -0.030738752, 0.040208906, 0.008109799, -0.017527629, -0.0009058122, 0.017776156, 0.0052779093, -0.0046206233, 0.0067952266, -0.01226934, -0.009162764, -0.01595799, 0.021582529, -0.027390191, -0.00011210243, -0.003145817, 0.01672973, -0.009999905, 0.003832534, -0.01793312, -0.0004868332, 0.027573315, 0.001756033, -0.012112376, -0.009718678, 0.0025473924, -0.027547155, -0.019084187, 0.010693162, 0.025558947, -0.02168717, -0.0068802484, -0.010869746, -0.028698223, -0.0051634563, -0.012131997, -0.014963887, 0.022210384, 0.01510777, -0.0026504, -0.013577373, 0.0058599836, 0.011281776, -0.0009393305, -0.00204053, 0.030110897, -0.029326078, 0.006491109, -0.01671665, 0.0006049648, -0.024342475, -0.008325624, 0.03722659, -0.007710849, -0.0055656764, -0.02043146, -0.015317055, -0.015212413, 0.002815539, 0.022262705, 0.00818828, 0.021778734, -0.0037409717, -0.02485261, 0.0033779927, 0.013217663, -0.0059319255, -0.018940303, 0.02409395, 0.015761785, -0.009672897, 0.011301396, -0.011582624, 0.0029725027, -0.015343216, -0.00735114, -0.075761214, 0.016821291, 0.0028040938, 0.0017233322, 0.01595799, -0.0054741143, -0.007096074, -0.011641486, -0.003554577, 0.009829861, -0.037828285, 0.024983412, 0.003793293, -0.010895907, -0.011916172, -0.017893879, 0.029640006, 0.0027452323, 0.004977062, 0.0138913, 0.0132830655, 0.010725862, 0.014205228, -0.003839074, 0.020470701, 0.0048626093, -0.010967849, 0.035343025, -0.004568302, -0.007665068, 0.0040091183, -0.02367538, -0.006821387, 0.012112376, -0.0012475356, -0.02041838, -0.030869557, -0.004865879, 0.036127847, 0.019528918, 0.00087147637, 0.0016366751, -0.006072539, -0.012380524, -0.016886694, 0.0014224849, 0.0058632535, 0.0053138803, 0.024525601, -0.008227522, 0.016167276, 0.021373244, -0.019855926, -0.011602244, -0.012223559, 0.009116983, 0.00448001, 0.0027027212, 0.0112294555, -0.025048813, 0.005958086, 0.005578757, 0.012040435, -0.019528918, -0.008096718, -0.023439934, 0.00047497914, 0.0073315194, 0.025061894, -0.016455043, 0.003992768, 0.002038895, -0.0003484679, 0.004444039, -0.014846164, 0.0018263398, 0.017305264, -0.0047154557, -0.006729825, 0.011288317, -0.009764459, -0.03220375, -0.015369376, 0.009594415, 0.031078842, 0.020967754, -0.007802411, 0.022354268, -0.010778184, 0.01833861, 0.004581382, 0.0072399573, 0.010673542, -0.012112376, -0.023073684, 0.0066448026, -0.027887244, 0.0063504954, 0.012956058, 0.032151427, -0.018103164, 0.0048855, -0.018286288, -0.036938824, -0.012354363, 0.020039052, 0.004921471, -0.03790677, 0.0212686, 0.02982313, 0.015434778, 0.0041039507, -0.016245758, 0.012171238, -0.006415897, 0.0072464976, -0.0024362097, -0.025218857, -0.021399405, 0.036860343, 0.0056572384, 0.017004417, 0.03432276, -0.013825899, 0.028724384, 0.008528369, 0.018652538, -0.02443404, -0.025637427, 0.006497649, -0.015447859, 0.01917575, -0.016520444, -0.008678793, -0.021072397, 0.015840268, -0.006324335, 0.025925195, -0.03594472, 0.0384823, 0.01308032, 0.0054217926, 0.00448328, -0.027207067, -0.016847452, 0.0036003583, 0.01061468, -0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783,
32	}
33
34	genresFilterCondition := bson.D{
35		{"$and", bson.A{
36			bson.D{{"genres", bson.D{{"$nin", bson.A{"Drama", "Western", "Crime"}}}}},
37			bson.D{{"genres", bson.D{{"$in", bson.A{"Action", "Adventure", "Family"}}}}},
38		}}}
39
40	yearFilterCondition := bson.D{
41		{"$and", bson.A{
42			bson.D{{"year", bson.D{{"$gte", 1960}}}},
43			bson.D{{"year", bson.D{{"$lte", 2000}}}},
44		}},
45	}
46
47	filter := bson.D{{"$and", bson.A{
48		genresFilterCondition, yearFilterCondition,
49	}},
50	}
51
52	vectorSearchStage := bson.D{
53		{"$vectorSearch", bson.D{
54			{"index", "vector_index"},
55			{"path", "plot_embedding"},
56			{"filter", filter},
57			{"queryVector", queryVector},
58			{"numCandidates", 200},
59			{"limit", 10},
60		}},
61	}
62
63	projectStage := bson.D{
64		{"$project", bson.D{
65			{"_id", 0},
66			{"plot", 1},
67			{"title", 1},
68			{"genres", 1},
69			{"year", 1},
70			{"score", bson.D{{"$meta", "vectorSearchScore"}}},
71		}}}
72
73	cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
74	if err != nil {
75		log.Fatalf("failed to retrieve data from the server: %v", err)
76	}
77	// display the results
78	type ProjectedMovieResult struct {
79		Title  string   `bson:"title"`
80		Plot   string   `bson:"plot"`
81		Genres []string `bson:"genres"`
82		Year   int32    `bson:"year"`
83		Score  float64  `bson:"score"`
84	}
85
86	var results []ProjectedMovieResult
87	if err = cursor.All(ctx, &results); err != nil {
88		log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err)
89	}
90	for _, result := range results {
91		fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score)
92	}
93}

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：

  • 按genres字段过滤，查找不属于crime类型的电影。

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

1package main
2
3import (
4	"context"
5	"fmt"
6	"log"
7
8	"go.mongodb.org/mongo-driver/bson"
9	"go.mongodb.org/mongo-driver/mongo"
10	"go.mongodb.org/mongo-driver/mongo/options"
11)
12
13func main() {
14	ctx := context.Background()
15
16	// Replace the placeholder with your Atlas connection string
17	const uri = "<connection-string>"
18
19	// Connect to your Atlas cluster
20	clientOptions := options.Client().ApplyURI(uri)
21	client, err := mongo.Connect(ctx, clientOptions)
22	if err != nil {
23		log.Fatalf("failed to connect to the server: %v", err)
24	}
25	defer func() { _ = client.Disconnect(ctx) }()
26
27	// Set the namespace
28	coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30	queryVector := [1536]float64{
31		-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 0.008558145, 0.013786677, 0.027235191, -0.0075046346, -0.015308415, -0.0031442728, 0.009136925, 0.010548109, 0.009455579, -0.0070624207, -0.001147806, 0.008928824, 0.011042348, -0.019314354, -0.0035702293, 0.019691538, 0.013929747, -0.00071615935, -0.0016477356, -0.0029556816, 0.009592146, 0.011699166, -0.045183886, 0.018533977, -0.025063138, 0.002194813, -0.013968766, 0.0037848332, -0.014775156, 0.0138777215, -0.023112195, -0.019288342, -0.02252691, -0.009813253, -0.0030792414, 0.00088036386, 0.0048285886, -0.019691538, -0.021863589, 0.0068413136, 0.01641395, -0.04494977, -0.0027475806, -0.014710125, 0.0077192388, -0.0030954992, 0.005417124, -0.008219982, 0.014046803, 0.01662205, 0.027235191, 0.0015266144, 0.036261562, 0.02006872, -0.032489736, -0.0019379386, 0.004106739, -0.006808798, 0.048825648, 0.0096766865, 0.0132794315, 0.0066722315, -0.026116649, 0.019912645, -0.017441448, -0.0014128092, -0.041880284, -0.018599007, 0.018729072, 0.00619425, -0.01947043, -0.009748221, -0.012121871, 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0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, -0.0027394518, 0.014918226, -0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914,
32	}
33
34	genresFilterCondition := bson.D{{"genres", bson.D{{"$ne", "Crime"}}}}
35
36	yearAndGenresFilterCondition := bson.D{
37		{"$and", bson.A{
38			bson.D{{"year", bson.D{{"$lte", 2015}}}},
39			bson.D{{"genres", bson.D{{"$eq", "Action"}}}},
40		}},
41	}
42
43	filter := bson.D{{"$or", bson.A{
44		genresFilterCondition, yearAndGenresFilterCondition,
45	}},
46	}
47
48	vectorSearchStage := bson.D{
49		{"$vectorSearch", bson.D{
50			{"index", "vector_index"},
51			{"path", "plot_embedding"},
52			{"filter", filter},
53			{"queryVector", queryVector},
54			{"numCandidates", 200},
55			{"limit", 10},
56		}},
57	}
58
59	projectStage := bson.D{
60		{"$project", bson.D{
61			{"_id", 0},
62			{"plot", 1},
63			{"title", 1},
64			{"genres", 1},
65			{"year", 1},
66			{"score", bson.D{{"$meta", "vectorSearchScore"}}},
67		}}}
68
69	cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
70	if err != nil {
71		log.Fatalf("failed to retrieve data from the server: %v", err)
72	}
73	// display the results
74	type ProjectedMovieResult struct {
75		Title  string   `bson:"title"`
76		Plot   string   `bson:"plot"`
77		Genres []string `bson:"genres"`
78		Year   int32    `bson:"year"`
79		Score  float64  `bson:"score"`
80	}
81
82	var results []ProjectedMovieResult
83	if err = cursor.All(ctx, &results); err != nil {
84		log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err)
85	}
86	for _, result := range results {
87		fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score)
88	}
89}
3

将 <connection-string> 替换为Atlas连接string ，然后保存文件。

确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

编译并运行 AtlasVectorSearchTutorial.go 文件。

go run AtlasVectorSearchTutorial.go
1Title: Cliffhanger
2Plot: A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.
3Genres: [Action Adventure Thriller]
4Year: 1993
5Score: 0.7694521546363831
6
7Title: It's a Mad, Mad, Mad, Mad World
8Plot: The dying words of a thief spark a madcap cross-country rush to find some treasure.
9Genres: [Action Adventure Comedy]
10Year: 1963
11Score: 0.7638954520225525
12
13Title: Hudson Hawk
14Plot: A cat burglar is forced to steal Da Vinci works of art for a world domination plot.
15Genres: [Action Adventure Comedy]
16Year: 1991
17Score: 0.7538407444953918
18
19Title: Escape from New York
20Plot: In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.
21Genres: [Action Sci-Fi]
22Year: 1981
23Score: 0.7487208843231201
24
25Title: The Romanov Stones
26Plot: Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.
27Genres: [Action Thriller]
28Year: 1993
29Score: 0.7467736005783081
30
31Title: Crime Busters
32Plot: An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.
33Genres: [Action Comedy Adventure]
34Year: 1977
35Score: 0.7437351942062378
36
37Title: Three Kings
38Plot: In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.
39Genres: [Action Adventure Comedy]
40Year: 1999
41Score: 0.7425670623779297
42
43Title: Black Moon Rising
44Plot: A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...
45Genres: [Action Sci-Fi Thriller]
46Year: 1986
47Score: 0.7397696375846863
48
49Title: Deep Rising
50Plot: A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.
51Genres: [Action Adventure Horror]
52Year: 1998
53Score: 0.7392246127128601
54
55Title: That Man from Rio
56Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.
57Genres: [Action Adventure Comedy]
58Year: 1964
59Score: 0.7357995510101318
go run AtlasVectorSearchTutorial.go
1Title: Ong-bak 2
2Plot: A young Thai boxer learns the skills and inner meaning of martial arts.
3Genres: [Action]
4Year: 2008
5Score: 0.7976737022399902
6
7Title: The Karate Kid
8Plot: A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.
9Genres: [Action Drama Family]
10Year: 1984
11Score: 0.7929348349571228
12
13Title: Circle of Iron
14Plot: A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.
15Genres: [Action Adventure Fantasy]
16Year: 1978
17Score: 0.7852014899253845
18
19Title: Dragon: The Bruce Lee Story
20Plot: A lionized account of the life of the martial arts superstar.
21Genres: [Action Biography Drama]
22Year: 1993
23Score: 0.7763040661811829
24
25Title: Kung Fu Killer
26Plot: A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.
27Genres: [Action Thriller]
28Year: 2014
29Score: 0.7731232643127441
30
31Title: Naked Killer
32Plot: A young woman is trained by a martial arts specialist to become a professional assassin.
33Genres: [Action Crime Romance]
34Year: 1992
35Score: 0.7693743109703064
36
37Title: The Real Miyagi
38Plot: The life of the greatest karate master of a generation.
39Genres: [Documentary Action History]
40Year: 2015
41Score: 0.768799901008606
42
43Title: Never Back Down
44Plot: At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.
45Genres: [Action Drama Sport]
46Year: 2008
47Score: 0.768179714679718
48
49Title: Dragon Tiger Gate
50Plot: Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.
51Genres: [Action Drama]
52Year: 2006
53Score: 0.7679706811904907
54
55Title: Shaolin Soccer
56Plot: A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.
57Genres: [Action Comedy Sport]
58Year: 2001
59Score: 0.7660527229309082
1

创建一个名为 AtlasVectorSearchTutorial.java 的文件。

2

将要尝试的预过滤器操作符的代码复制并粘贴到AtlasVectorSearchTutorial.java文件中。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 年份（含这两个年份）之间发行的电影。

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static java.util.Arrays.asList;
5
6import com.mongodb.client.MongoClient;
7import com.mongodb.client.MongoClients;
8import com.mongodb.client.MongoCollection;
9import com.mongodb.client.MongoDatabase;
10import com.mongodb.client.model.*;
11import com.mongodb.client.model.search.VectorSearchOptions;
12import java.util.Arrays;
13import java.util.List;
14import com.mongodb.client.model.search.FieldSearchPath;
15import org.bson.Document;
16import org.bson.conversions.Bson;
17
18public class AtlasVectorSearchTutorial {
19  public static void main( String[] args ) {
20    // specify connection
21    String uri = "<connection-string>";
22
23    // establish connection and set namespace
24    try (MongoClient mongoClient = MongoClients.create(uri)) {
25      MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26      MongoCollection<Document> collection = database.getCollection("embedded_movies");
27      
28  	  // define $vectorSearch query options 
29	    List<Double> queryVector = (asList(-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 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-0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783));
30	    String indexName = "vector_index";
31	    FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
32	    int numCandidates = 200;
33      int limit = 10;
34      List<String> genresNotInCriteria = Arrays.asList("Drama", "Western", "Crime");
35      List<String> genresInCriteria = Arrays.asList("Action", "Adventure", "Family");
36      Bson criteria = Filters.and(
37                    Filters.nin("genres", genresNotInCriteria), 
38                    Filters.in("genres", genresInCriteria), 
39                    Filters.gte("year", 1960), 
40                    Filters.lte("year", 2000));
41      VectorSearchOptions options = VectorSearchOptions
42                    .approximateVectorSearchOptions(numCandidates)
43                    .filter(criteria);
44      
45      // define pipeline 
46      List<Bson> pipeline = asList(
47          vectorSearch(
48           fieldSearchPath,
49           queryVector,
50           indexName,
51           limit,
52           options),
53          project(
54           fields(exclude("_id"), include("title"), include("plot"), include("year"),
55        		   include("genres"), metaVectorSearchScore("score"))));
56      
57	  // run query and print results
58      collection.aggregate(pipeline)
59        .forEach(doc -> System.out.println(doc.toJson()));	
60    }
61  }
62}

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：

  • 按genres字段过滤，查找不属于crime类型的电影。

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

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static java.util.Arrays.asList;
5
6import com.mongodb.client.MongoClient;
7import com.mongodb.client.MongoClients;
8import com.mongodb.client.MongoCollection;
9import com.mongodb.client.MongoDatabase;
10import com.mongodb.client.model.*;
11import com.mongodb.client.model.search.VectorSearchOptions;
12import java.util.List;
13import com.mongodb.client.model.search.FieldSearchPath;
14import org.bson.Document;
15import org.bson.conversions.Bson;
16
17public class AtlasVectorSearchTutorial {
18  public static void main( String[] args ) {
19    // specify connection
20    String uri = "<connection-string>";
21
22    // establish connection and set namespace
23    try (MongoClient mongoClient = MongoClients.create(uri)) {
24      MongoDatabase database = mongoClient.getDatabase("sample_mflix");
25      MongoCollection<Document> collection = database.getCollection("embedded_movies");
26      
27  	  // define $vectorSearch query options 
28	    List<Double> queryVector = 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29	    String indexName = "vector_index";
30	    FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
31	    int numCandidates = 200;
32      int limit = 10;
33      Bson criteria = Filters.or(
34                    Filters.lte("year", 2015),
35                    Filters.and(
36                            Filters.lte("year", 2015),
37                            Filters.eq("genres", "Action")));
38      VectorSearchOptions options = VectorSearchOptions
39                    .approximateVectorSearchOptions(numCandidates)
40                    .filter(criteria);
41      
42      // define pipeline 
43      List<Bson> pipeline = asList(
44          vectorSearch(
45           fieldSearchPath,
46           queryVector,
47           indexName,
48           limit,
49           options),
50          project(
51           fields(exclude("_id"), include("title"), include("plot"), include("year"),
52        		   include("genres"), metaVectorSearchScore("score"))));
53      
54	    // run query and print results
55      collection.aggregate(pipeline)
56        .forEach(doc -> System.out.println(doc.toJson()));	
57    }
58  }
59}
3

将 <connection-string> 替换为Atlas连接string ，然后保存文件。

确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

编译并运行 AtlasVectorSearchTutorial.java 文件。

javac AtlasVectorSearchTutorial.java
java AtlasVectorSearchTutorial
1{
2    "year": 1993,
3    "plot": "A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", 
4    "genres": ["Action", "Adventure", "Thriller"], 
5    "title": "Cliffhanger", 
6    "score": 0.7694521546363831
7}
8{
9    "plot": "The dying words of a thief spark a madcap cross-country rush to find some treasure.", 
10    "genres": ["Action", "Adventure", "Comedy"], 
11    "title": "It's a Mad, Mad, Mad, Mad World", 
12    "year": 1963, 
13    "score": 0.7638954520225525
14}
15{
16    "year": 1991, 
17    "plot": "A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", 
18    "genres": ["Action", "Adventure", "Comedy"], 
19    "title": "Hudson Hawk", 
20    "score": 0.7538407444953918
21}
22{
23    "plot": "In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.", 
24    "genres": ["Action", "Sci-Fi"], 
25    "title": "Escape from New York", 
26    "year": 1981, 
27    "score": 0.7487208843231201
28}
29{
30    "plot": "Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.", 
31    "genres": ["Action", "Thriller"], 
32    "title": "The Romanov Stones", 
33    "year": 1993, 
34    "score": 0.7467736005783081
35}
36{
37    "plot": "An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", 
38    "genres": ["Action", "Comedy", "Adventure"], 
39    "title": "Crime Busters", 
40    "year": 1977, 
41    "score": 0.7437351942062378
42}
43{
44    "plot": "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.", 
45    "genres": ["Action", "Adventure", "Comedy"], 
46    "title": "Three Kings", 
47    "year": 1999, 
48    "score": 0.7425670623779297
49}
50{
51    "plot": "A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...", 
52    "genres": ["Action", "Sci-Fi", "Thriller"], 
53    "title": "Black Moon Rising", 
54    "year": 1986, 
55    "score": 0.7397696375846863
56}
57{
58    "plot": "A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.", 
59    "genres": ["Action", "Adventure", "Horror"], 
60    "title": "Deep Rising", 
61    "year": 1998, 
62    "score": 0.7392246127128601
63}
64{
65    "plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", 
66    "genres": ["Action", "Adventure", "Comedy"], 
67    "title": "That Man from Rio", 
68    "year": 1964, 
69    "score": 0.7357995510101318
70}
javac AtlasVectorSearchTutorial.java
java AtlasVectorSearchTutorial
1{
2    "year": 2008, 
3    "plot": "A young Thai boxer learns the skills and inner meaning of martial arts.", 
4    "genres": ["Action"], 
5    "title": "Ong-bak 2", 
6    "score": 0.7976737022399902
7}
8{
9    "plot": "A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", 
10    "genres": ["Action", "Drama", "Family"], 
11    "title": "The Karate Kid", 
12    "year": 1984, 
13    "score": 0.7929348349571228
14}
15{
16    "plot": "A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.", 
17    "genres": ["Action", "Adventure", "Fantasy"], 
18    "title": "Circle of Iron", 
19    "year": 1978, 
20    "score": 0.7852014899253845
21}
22{
23    "plot": "A lionized account of the life of the martial arts superstar.", 
24    "genres": ["Action", "Biography", "Drama"], 
25    "title": "Dragon: The Bruce Lee Story", 
26    "year": 1993, 
27    "score": 0.7763040661811829
28}
29{
30    "plot": "A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", 
31    "genres": ["Action", "Thriller"], 
32    "title": "Kung Fu Killer", 
33    "year": 2014, 
34    "score": 0.7731232643127441
35}
36{
37    "plot": "A young woman is trained by a martial arts specialist to become a professional assassin.", 
38    "genres": ["Action", "Crime", "Romance"], 
39    "title": "Naked Killer", 
40    "year": 1992, 
41    "score": 0.7693743109703064
42}
43{
44    "plot": "The life of the greatest karate master of a generation.", 
45    "genres": ["Documentary", "Action", "History"], 
46    "title": "The Real Miyagi", 
47    "year": 2015, 
48    "score": 0.768799901008606
49}
50{
51    "plot": "At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", 
52    "genres": ["Action", "Drama", "Sport"], 
53    "title": "Never Back Down", 
54    "year": 2008, 
55    "score": 0.768179714679718
56}
57{
58    "plot": "Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", 
59    "genres": ["Action", "Drama"], 
60    "title": "Dragon Tiger Gate", 
61    "year": 2006, 
62    "score": 0.7679706811904907
63}
64{
65    "year": 2001, 
66    "plot": "A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", 
67    "genres": ["Action", "Comedy", "Sport"], 
68    "title": "Shaolin Soccer", 
69    "score": 0.7660527229309082
70}
1

创建一个名为 atlas-vector-search-tutorial.js 的文件。

2

复制您要尝试的预过滤操作符的示例查询，并将其粘贴到 atlas-vector-search-tutorial.js 文件中。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 年份（含这两个年份）之间发行的电影。

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9  try {
10    await client.connect();
11
12    // set namespace
13    const database = client.db("sample_mflix");
14    const coll = database.collection("embedded_movies");
15
16    // define pipeline
17    const agg = [
18      {
19        '$vectorSearch': {
20          'index': 'vector_index', 
21          'path': 'plot_embedding', 
22          'filter': {
23            '$and': [
24              {
25                'genres': {
26                  '$nin': [
27                    'Drama', 'Western', 'Crime'
28                  ], 
29                  '$in': [
30                    'Action', 'Adventure', 'Family'
31                  ]
32                }
33              }, {
34                'year': {
35                  '$gte': 1960, 
36                  '$lte': 2000
37                }
38              }
39            ]
40          }, 
41          'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 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0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783], 
42          'numCandidates': 200, 
43          'limit': 10
44        }
45      }, {
46        '$project': {
47          '_id': 0, 
48          'title': 1, 
49          'genres': 1, 
50          'plot': 1, 
51          'year': 1, 
52          'score': {
53            '$meta': 'vectorSearchScore'
54          }
55        }
56      }
57    ];
58
59    // run pipeline
60    const result = coll.aggregate(agg);
61
62    // print results
63    await result.forEach((doc) => console.dir(JSON.stringify(doc)));
64  } finally {
65    await client.close();
66  }
67}
68run().catch(console.dir);

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：

  • 按genres字段过滤，查找不属于crime类型的电影。

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

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9  try {
10    await client.connect();
11
12    // set namespace
13    const database = client.db("sample_mflix");
14    const coll = database.collection("embedded_movies");
15
16    // define pipeline
17    const agg = [
18      {
19        '$vectorSearch': {
20          'index': 'vector_index', 
21          'path': 'plot_embedding', 
22          'filter': {
23            '$or': [
24              {
25                'genres': {
26                  '$ne': 'Crime'
27                }
28              }, {
29                '$and': [
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31                    'year': {
32                      '$lte': 2015
33                    }
34                  }, {
35                    'genres': {
36                      '$eq': 'Action'
37                    }
38                  }
39                ]
40              }
41            ]
42          }, 
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0.011627631, 0.026480826, 0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, 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0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 
44          'numCandidates': 200, 
45          'limit': 10
46        }
47      }, {
48        '$project': {
49          '_id': 0, 
50          'title': 1, 
51          'genres': 1, 
52          'plot': 1, 
53          'released': 1, 
54          'score': {
55            '$meta': 'vectorSearchScore'
56          }
57        }
58      }
59    ];
60
61    // run pipeline
62    const result = coll.aggregate(agg);
63
64    // print results
65    await result.forEach((doc) => console.dir(JSON.stringify(doc)));
66  } finally {
67    await client.close();
68  }
69}
70run().catch(console.dir);
3

替换查询中的 <connection-string>，然后保存该文件。

确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

查询您的集合。

运行以下命令来查询您的集合：

node atlas-vector-search-tutorial.js
'{
    "year":1993,
    "plot":"A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.",
    "genres":["Action","Adventure","Thriller"],
    "title":"Cliffhanger",
    "score":0.7694521546363831
}'
`{
    "plot":"The dying words of a thief spark a madcap cross-country rush to find some treasure.",
    "genres":["Action","Adventure","Comedy"],
    "title":"It's a Mad, Mad, Mad, Mad World",
    "year":1963,
    "score":0.7638954520225525
}`
'{
    "year":1991,
    "plot":"A cat burglar is forced to steal Da Vinci works of art for a world domination plot.",
    "genres":["Action","Adventure","Comedy"],
    "title":"Hudson Hawk",
    "score":0.7538407444953918
}'
'{
    "plot":"In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.",
    "genres":["Action","Sci-Fi"],
    "title":"Escape from New York",
    "year":1981,
    "score":0.7487208843231201
}'
'{
    "plot":"Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.",
    "genres":["Action","Thriller"],
    "title":"The Romanov Stones",
    "year":1993,
    "score":0.7467736005783081
}'
'{
    "plot":"An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.",
    "genres":["Action","Comedy","Adventure"],
    "title":"Crime Busters",
    "year":1977,
    "score":0.7437351942062378
}'
'{
    "plot":"In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.",
    "genres":["Action","Adventure","Comedy"],
    "title":"Three Kings",
    "year":1999,
    "score":0.7425670623779297
}'
'{
    "plot":"A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...",
    "genres":["Action","Sci-Fi","Thriller"],
    "title":"Black Moon Rising",
    "year":1986,
    "score":0.7397696375846863
}'
'{
    "plot":"A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.","genres":["Action","Adventure","Horror"],"title":"Deep Rising","year":1998,"score":0.7392246127128601
}'
'{
    "plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.",
    "genres":["Action","Adventure","Comedy"],
    "title":"That Man from Rio",
    "year":1964,
    "score":0.7357995510101318
}'
node atlas-search-tutorial.js
'{
    "year":2008,
    "plot":"A young Thai boxer learns the skills and inner meaning of martial arts.",
    "genres":["Action"],
    "title":"Ong-bak 2",
    "score":0.7976737022399902
}'
'{
    "plot":"A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.",
    "genres":["Action","Drama","Family"],
    "title":"The Karate Kid",
    "year":1984,
    "score":0.7929348349571228
}'
'{
    "plot":"A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.","
    genres":["Action","Adventure","Fantasy"],
    "title":"Circle of Iron",
    "year":1978,
    "score":0.7852014899253845
}'
'{
    "plot":"A lionized account of the life of the martial arts superstar.",
    "genres":["Action","Biography","Drama"],
    "title":"Dragon: The Bruce Lee Story",
    "year":1993,
    "score":0.7763040661811829
}'
'{
    "plot":"A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.",
    "genres":["Action","Thriller"],
    "title":"Kung Fu Killer",
    "year":2014,
    "score":0.7731232643127441
}'
'{
    "plot":"A young woman is trained by a martial arts specialist to become a professional assassin.",
    "genres":["Action","Crime","Romance"],
    "title":"Naked Killer",
    "year":1992,
    "score":0.7693743109703064
}'
'{
    "plot":"The life of the greatest karate master of a generation.",
    "genres":["Documentary","Action","History"],
    "title":"The Real Miyagi",
    "year":2015,
    "score":0.768799901008606
}'
'{
    "plot":"At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.",
    "genres":["Action","Drama","Sport"],
    "title":"Never Back Down",
    "year":2008,
    "score":0.768179714679718
}'
'{
    "plot":"Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.",
    "genres":["Action","Drama"],
    "title":"Dragon Tiger Gate",
    "year":2006,
    "score":0.7679706811904907
}'
'{
    "year":2001,
    "plot":"A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.",
    "genres":["Action","Comedy","Sport"],
    "title":"Shaolin Soccer",
    "score":0.7660527229309082
}'
1

创建一个名为 atlas-vector-search-tutorial.py 的文件。

2

将您想要尝试的预过滤操作符的查询复制并粘贴到 atlas-vector-search-tutorial.py 文件中。

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

  • 在结果中排除 _id 字段，而仅包含 title、genres、plot 和 year 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：

  • 按 genres 字段过滤，以查找不属于 drama、western 或 crime 类型但属于 action、adventure 或 family 类型的电影。

  • 按 year 字段过滤，以查找在 1960 和 2000 年份（含这两个年份）之间发行的电影。

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8  {
9    '$vectorSearch': {
10      'index': 'vector_index', 
11        'path': 'plot_embedding', 
12        'filter': {
13          '$and': [
14            {
15              'genres': {
16                '$nin': [
17                  'Drama', 'Western', 'Crime'
18                ], 
19                '$in': [
20                  'Action', 'Adventure', 'Family'
21                ]
22              }
23            }, {
24              'year': {
25                '$gte': 1960, 
26                '$lte': 2000
27              }
28            }
29          ]
30        }, 
31        'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 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0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783], 
32       'numCandidates': 200, 
33       'limit': 10
34    }
35  }, {
36    '$project': {
37      '_id': 0, 
38      'title': 1, 
39      'genres': 1, 
40      'plot': 1, 
41      'year': 1, 
42      'score': {
43        '$meta': 'vectorSearchScore'
44      }
45    }
46  }
47]
48
49# run pipeline
50result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
51
52# print results
53for i in result:
54    print(i)

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：

  • 按genres字段过滤，查找不属于crime类型的电影。

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

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8  {
9    '$vectorSearch': {
10      'index': 'vector_index', 
11      'path': 'plot_embedding', 
12      'filter': {
13        '$or': [
14          {
15            'genres': {
16              '$ne': 'Crime'
17            }
18          }, {
19            '$and': [
20              {
21                'year': {
22                  '$lte': 2015
23                }
24              }, {
25                'genres': {
26                  '$eq': 'Action'
27                }
28              }
29            ]
30          }
31        ]
32      }, 
33      'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 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0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 
34      'numCandidates': 200, 
35      'limit': 10
36    }
37  }, {
38    '$project': {
39      '_id': 0, 
40      'title': 1, 
41      'genres': 1, 
42      'plot': 1, 
43      'year': 1, 
44      'score': {
45        '$meta': 'vectorSearchScore'
46      }
47    }
48  }
49]
50
51# run pipeline
52result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
53
54# print results
55for i in result:
56    print(i)
3

替换查询中的 <connection-string>，然后保存该文件。

确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

运行命令以查询您的集合。

python atlas-vector-search-tutorial.py
{
    'year': 1993, 
    'plot': 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.', 
    'genres': ['Action', 'Adventure', 'Thriller'], 
    'title': 'Cliffhanger', 
    'score': 0.7694521546363831
}
{
    'plot': 'The dying words of a thief spark a madcap cross-country rush to find some treasure.', 
    'genres': ['Action', 'Adventure', 'Comedy'], 
    'title': "It's a Mad, Mad, Mad, Mad World", 
    'year': 1963, 
    'score': 0.7638954520225525
}
{
    'year': 1991, 
    'plot': 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.', 
    'genres': ['Action', 'Adventure', 'Comedy'], 
    'title': 'Hudson Hawk', 
    'score': 0.7538407444953918
}
{
    'plot': 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.', 
    'genres': ['Action', 'Sci-Fi'], 
    'title': 'Escape from New York', 
    'year': 1981, 
    'score': 0.7487208843231201
}
{
    'plot': 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.', 
    'genres': ['Action', 'Thriller'], 
    'title': 'The Romanov Stones', 
    'year': 1993, 
    'score': 0.7467736005783081
}
{
    'plot': 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.', 
    'genres': ['Action', 'Comedy', 'Adventure'], 
    'title': 'Crime Busters', 
    'year': 1977, 
    'score': 0.7437351942062378
}
{
    'plot': 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.', 
    'genres': ['Action', 'Adventure', 'Comedy'], 
    'title': 'Three Kings', 
    'year': 1999, 
    'score': 0.7425670623779297
}
{
    'plot': 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...', 'genres': ['Action', 'Sci-Fi', 'Thriller'], 'title': 'Black Moon Rising', 'year': 1986, 'score': 0.7397696375846863
}
{
    'plot': 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.', 
    'genres': ['Action', 'Adventure', 'Horror'], 
    'title': 'Deep Rising', 
    'year': 1998, 
    'score': 0.7392246127128601
}
{
    'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 
    'genres': ['Action', 'Adventure', 'Comedy'], 
    'title': 'That Man from Rio', 
    'year': 1964, 
    'score': 0.7357995510101318
}
python atlas-vector-search-tutorial.py
{
    'year': 2008, 
    'plot': 'A young Thai boxer learns the skills and inner meaning of martial arts.', 
    'genres': ['Action'], 
    'title': 'Ong-bak 2', 
    'score': 0.7976737022399902
}
{
    'plot': 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.', 
    'genres': ['Action', 'Drama', 'Family'], 
    'title': 'The Karate Kid', 
    'year': 1984, 
    'score': 0.7929348349571228
}
{
    'plot': 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.', 
    'genres': ['Action', 'Adventure', 'Fantasy'], 
    'title': 'Circle of Iron', 
    'year': 1978, 
    'score': 0.7852014899253845
}
{
    'plot': 'A lionized account of the life of the martial arts superstar.', 
    'genres': ['Action', 'Biography', 'Drama'], 
    'title': 'Dragon: The Bruce Lee Story', 
    'year': 1993, 
    'score': 0.7763040661811829
}
{
    'plot': 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.', 
    'genres': ['Action', 'Thriller'], 
    'title': 'Kung Fu Killer', 
    'year': 2014, 
    'score': 0.7731232643127441
}
{
    'plot': 'A young woman is trained by a martial arts specialist to become a professional assassin.', 
    'genres': ['Action', 'Crime', 'Romance'], '
    title': 'Naked Killer', 
    'year': 1992, 
    'score': 0.7693743109703064
}
{
    'plot': 'The life of the greatest karate master of a generation.', 
    'genres': ['Documentary', 'Action', 'History'], 
    'title': 'The Real Miyagi', 
    'year': 2015, 
    'score': 0.768799901008606
}
{
    'plot': 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.', 
    'genres': ['Action', 'Drama', 'Sport'], 
    'title': 'Never Back Down', 
    'year': 2008, 
    'score': 0.768179714679718
}
{
    'plot': 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.', 
    'genres': ['Action', 'Drama'], 
    'title': 'Dragon Tiger Gate', 
    'year': 2006, 
    'score': 0.7679706811904907
}
{
    'year': 2001, 
    'plot': 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.', 
    'genres': ['Action', 'Comedy', 'Sport'], 
    'title': 'Shaolin Soccer', '
    score': 0.7660527229309082
}

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