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如何对 Atlas 集群中的数据执行语义搜索

在此页面上

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

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

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

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

如要完成本教程,您必须具备以下条件:

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

  • 示例数据已加载到 Atlas 集群中。

    注意

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

  • 用于在Atlas 集群上运行查询的以下客户端之一:

    • mongosh

    • 一个 MongoDB Java 驱动程序

    • MongoDB Go Driver

    • MongoDB 节点驱动程序

    • pymongo

    注意

    您可以使用任何MongoDB驱动程序在 ""聚合阶段运行"" 查询。 "" 仅包含前面列表中的驱动程序的示例。

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


➤ 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。


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

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

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

  2. 如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。

  3. 如果尚未出现,请单击侧边栏中的 Clusters(集群)。

    会显示集群页面。

2

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

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

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

    将显示 Atlas Search 页面。

  1. 单击集群的对应 Browse Collections 按钮。

  2. 展开数据库并选择集合。

  3. 单击该集合的 Search Indexes 标签页。

    将显示 Atlas Search 页面。

  1. 单击集群的名称。

  2. 单击 Atlas Search 标签页。

    将显示 Atlas Search 页面。

3
  1. 单击 Create Search Index(连接)。

  2. Atlas Vector Search下,选择JSON Editor ,然后单击Next

  3. Database and Collection(数据库和集合)部分中找到 sample_mflix 数据库,然后选择 embedded_movies 集合。

  4. Index Name 字段中输入 vector_index

  5. 将默认定义替换为以下索引定义,然后单击 Next

4
  1. 将默认定义替换为以下索引定义。

    此索引定义用于指定在 vectorSearch 类型的索引中对以下字段建立索引:

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

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

    • year 字段作为筛选器类型,用于按字段中的数值对数据进行预筛选。

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

此时将显示一个模态窗口,让您知道索引正在构建中。

6

构建索引大约需要一分钟时间。在构建时,Status 列显示 Initial Sync。构建完成后,Status 列显示 Active

1

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

2

例子

use sample_mflix
switched to db sample_mflix
3

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

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

将 Go 驱动程序作为依赖项添加到项目中:

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

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

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

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

3
go run vector-index.go
2024/10/17 09:38:21 Creating the index.
2024/10/17 09:38:22 Polling to confirm successful index creation.
2024/10/17 09:38:22 NOTE: This may take up to a minute.
2024/10/17 09:38:48 Name of Index Created: vector_index
1
  1. 将Java驾驶员版本 5.2 或更高版本添加为项目,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。

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

2

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

VectorIndex.java
1import com.mongodb.client.ListSearchIndexesIterable;
2import com.mongodb.client.MongoClient;
3import com.mongodb.client.MongoClients;
4import com.mongodb.client.MongoCollection;
5import com.mongodb.client.MongoCursor;
6import com.mongodb.client.MongoDatabase;
7import com.mongodb.client.model.SearchIndexModel;
8import com.mongodb.client.model.SearchIndexType;
9import org.bson.Document;
10import org.bson.conversions.Bson;
11
12import java.util.Arrays;
13import java.util.Collections;
14import java.util.List;
15
16public class VectorIndex {
17
18 public static void main(String[] args) {
19
20 // Replace the placeholder with your Atlas connection string
21 String uri = "<connectionString>";
22
23 // Connect to your Atlas cluster
24 try (MongoClient mongoClient = MongoClients.create(uri)) {
25
26 // Set the namespace
27 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
28 MongoCollection<Document> collection = database.getCollection("embedded_movies");
29
30 // Define the index details with the filter fields
31 String indexName = "vector_index";
32 Bson definition = new Document(
33 "fields",
34 Arrays.asList(
35 new Document("type", "vector")
36 .append("path", "plot_embedding")
37 .append("numDimensions", 1536)
38 .append("similarity", "dotProduct"),
39 new Document("type", "filter")
40 .append("path", "genres"),
41 new Document("type", "filter")
42 .append("path", "year")));
43
44 // Define the index model
45 SearchIndexModel indexModel = new SearchIndexModel(
46 indexName,
47 definition,
48 SearchIndexType.vectorSearch());
49
50 // Create the filtered index
51 try {
52 List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel));
53 System.out.println("New search index named " + result.get(0) + " is building.");
54 } catch (Exception e) {
55 throw new RuntimeException("Error creating index: " + e);
56 }
57
58 // Wait for Atlas to build the index
59 System.out.println("Polling to check if the index is ready. This may take up to a minute.");
60
61 ListSearchIndexesIterable<Document> searchIndexes = collection.listSearchIndexes();
62 Document doc = null;
63 while (doc == null) {
64 try (MongoCursor<Document> cursor = searchIndexes.iterator()) {
65 if (!cursor.hasNext()) {
66 break;
67 }
68 Document current = cursor.next();
69 String name = current.getString("name");
70 // When the index completes building, it becomes `queryable`
71 boolean queryable = current.getBoolean("queryable");
72 if (name.equals(indexName) && queryable) {
73 doc = current;
74 } else {
75 Thread.sleep(500);
76 }
77 } catch (Exception e) {
78 throw new RuntimeException("Failed to list search indexes: " + e);
79 mongoClient.close();
80 }
81 }
82 System.out.println(indexName + " is ready for querying.");
83
84 } catch (Exception e) {
85 throw new RuntimeException("Error connecting to MongoDB: " + e);
86 }
87 }
88}

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

3

在 IDE 中运行该文件,或从命令行执行命令来运行代码。

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

将 MongoDB Node 驱动程序作为依赖项添加到项目中:

npm install mongodb

提示

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

2

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

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

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

3
node vector-index.js
vector_index
1

将 PyMongo 驱动程序作为依赖项添加到项目中:

pip install pymongo
2

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

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

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

3
python vector-index.py
vector_index

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

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

  2. 如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。

  3. 如果尚未出现,请单击侧边栏中的 Clusters(集群)。

    会显示集群页面。

2

单击集群的对应 Browse Collections 按钮。

显示数据浏览器

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

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

4

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。请注意,这些查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。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 titlegenresplotyear 字段。

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

  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。

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

    该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

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

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

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

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

    该过滤器使用 $or 聚合管道操作符来查找符合以下任一条件的电影文档:

    • genres字段过滤,查找不属于crime类型的电影。

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

    1[
    2 {
    3 "$vectorSearch": {
    4 "index": "vector_index",
    5 "path": "plot_embedding",
    6 "filter": {
    7 "$or": [{
    8 "genres": { "$ne": "Crime" }
    9 }, {
    10 "$and": [{
    11 "year": { "$lte": 2015 }
    12 }, {
    13 "genres": { "$eq": "Action" }
    14 }]
    15 }]
    16 },
    17 "queryVector": 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    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

Atlas用户界面可能不会显示其返回的文档中的所有值。要查看在查询路径中搜索的字段的所有值,请展开文档中的字段。

1

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

2
use sample_mflix
switched to db sample_mflix
3

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。请注意,这些查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。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 titlegenresplotyear 字段。

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

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

该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

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

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

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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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
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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
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16 {
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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
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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:

  • genres字段过滤,查找不属于crime类型的电影。

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

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
2

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。请注意,这些查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。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 titlegenresplotyear 字段。

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

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

该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

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

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

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, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, -0.014623798, 0.00067322777, -0.00083427917, -0.028070368, 0.03714811, -0.004529061, 0.0054087127, 0.0028727653, 0.008384486, 0.010026066, -0.006190262, -0.0002493436, 0.0029953935, -0.026226042, -0.018417092, 0.009941043, 0.0036494094, -0.00982332, 0.013551212, 0.02574207, -0.0022645304, -0.0006004685, 0.012805633, -0.024303235, 0.008194821, -0.014179068, -0.02977081, 0.003095131, -0.0015941641, 0.029953934, 0.0052680993, 0.025388902, -0.031392768, -0.021386323, 0.014898485, 0.022419669, 0.00897964, 0.013243824, 0.006854088, 0.0066415328, -0.003839074, -0.01877026, 0.021216279, -0.015055449, -0.0015508354, 0.013211124, -0.008783435, 0.0052157775, -0.68938524, -0.01221702, -0.04125533, -0.016232677, 0.020039052, -0.0026422248, -0.0037050007, 0.0064682183, -0.0047579664, 0.0032749851, -0.0035382267, 0.031942144, -0.00035643874, -0.011628405, -0.043086577, -0.0196074, -0.0066088317, -0.014872325, 0.028331975, 0.010294212, -0.013930541, 0.031994462, -0.018626377, 0.017462227, 0.026343765, -0.010274592, 0.0046827546, -0.029430721, -0.011746128, 0.0024362097, 0.0023054064, 0.0027730279, -0.002406779, 0.003917556, 0.059436977, 0.008665713, -0.0018901062, 0.06037876, 0.017880797, 0.05185039, 0.0067102043, -0.020300657, 0.005604917, 0.018704858, 0.012073136, 0.0144145135, 0.012413224, -0.0074819434, 0.015801027, -0.0061412104, 0.008613391, -0.0039077457, -0.0036232488, 0.008469507, 0.014087505, 0.0124066835, 0.019267311, -0.002573553, 0.005055544, -0.009417831, -0.009103903, 0.011150973, -0.012046975, 0.0058567133, -0.0053727417, 0.018260127, -0.005588567, 0.015591742, 0.007495024, -0.02567667, 0.024211673, 0.021386323, -0.012890656, -0.016114954, 0.009515933, 0.009679437, 0.025532786, -0.0076454473, -0.02575515, 0.008319084, -0.0068410076, -0.017082898, -0.026173722, -0.0049901423, 0.01918883, -0.008646091, -0.031759016, 0.014820003, 0.011850771, 0.01836477, 0.012700991, -0.0011437106, 0.005058814, 0.0151993325, -0.0060692686, 0.027416352, 0.0037344315, 0.0013546307, 0.018325528, -0.03152357, -0.008809595, 0.014649959, -0.008345244, 0.0066415328, -0.005523165, 0.0043492066, -0.0015892589, 0.0048855, 0.034453563, -0.03837766, 0.0068410076, -0.0042151334, -0.0067429054, 0.0055689462, -0.011733048, -0.0212032, 0.016847452, -0.0022220195, 0.0059351954, -0.00449963, 0.02251123, -0.01020265, 0.023361452, -0.0032455544, 0.016180357, 0.0049443613, -0.0064747585, -0.03259616, 0.012321662, 0.020104453, 0.009954124, -0.019411195, 0.0048102876, -0.000392614, 0.012184318, 0.0044276887, 0.005634348, -0.020562263, 0.015722545, -0.005179807, -0.0067952266, 0.0027861083, 0.0024198592, -0.0020585153, 0.0018525004, -0.045100946, -0.010176489, -0.012956058, 0.0013497255, 0.0105361985, 0.003796563, -0.0106016, -0.013126101, 0.0050359233, 0.015003128, -0.0075800456, -0.015722545, -0.01755379, -0.00978408, -0.02940456, 0.017606111, 0.016612006, -0.016912855, 0.025441224, 0.0054741143, 0.00448001, 0.009470152, 0.015382457, -0.008332164, -0.019123428, 0.024564842, 0.016860534, 0.008286383, -0.007141855, 0.006559781, 0.016625088, -0.01840401, -0.011602244, -0.00489858, -0.0073184394, -0.008809595, -0.0018459603, -0.01629808, -0.005542786, 0.0064257076, 0.010379234, 0.014663039, 0.034872133, -0.013355007, 0.027285548, 0.011654565, -0.004032009, 0.02323065, -0.02653997, -0.0009941043, 0.002946342, 0.010667001, 0.008345244, 0.018626377, 0.04821406, 0.031392768, 0.010281132, 0.026069079, 0.002735422, 0.01182461, -0.01593183, 0.006585941, -0.010071847, 0.024564842, -0.0025261368, 0.004293615, -0.0068606283, -0.0066448026, -0.0074100015, -0.0014347476, 0.021530207, -0.010418476, 0.018495573, -0.0034924455, -0.014165987, -0.004784127, -0.012472086, 0.004417878, -0.0030313642, -0.010084927, -0.010954768, 0.01508161, 0.0010047321, 0.0042347535, -0.03345946, -0.00027346043, 0.014793842, -0.019882087, 0.012772933, 0.021490967, 0.0031932332, 0.0093589695, 0.00090172456, 0.0048102876, 0.0070045115, -0.0045584915, 0.015840268, 0.024342475, -0.0091300635, 0.0039796876, 0.003796563, 0.025022654, -0.008103259, -0.025022654, 0.03021554, -0.008201361, -0.0070502926, 0.0011821339, 0.021072397, 0.004849529, -0.02495725, 0.012184318, 0.0019228071, -0.007226877, 0.020562263, 0.018861823, -0.0017593032, 0.01345965, 0.0022727058, 0.003023189, -0.026971621, -0.0030558899, 0.017723834, -0.01998673, -0.010608139, 0.011491061, -0.025179617, 0.0069652707, 0.003924096, 0.021177039, 0.0045650317, -0.0009973744, 0.007586586, -0.004032009, -0.008129419, -0.010091467, -0.04279881, 0.019790525, 0.01595799, 0.0044309585, -0.0033747226, -0.018665617, -0.012818714, -0.016206518, 0.014113666, -0.0020912162, 0.01427063, -0.020248337, -0.0112752365, -0.020588424, -0.011039791, 0.008744194, -0.015147011, 0.0022269245, -0.010438096, -0.0017772885, -0.028750544, -0.008861917, -0.016991336, 0.033668745, 0.034636687, 0.009888723, 0.0023953337, 0.006991431, -0.003346927, 0.003103306, -0.0044571194, 0.011249076, 0.0033779927, 0.00012446742, -0.0027027212, -0.025859794, -0.011942333, 0.02694546, 0.028227331, 0.0064289775, -0.03385187, -0.020719228, 0.00489531, 0.10663077, 0.041752383, -0.021700252, -0.008103259, 0.0049574412, -0.01675589, -0.020182934, -0.006585941, 0.007684688, -0.002859685, 0.027023941, 0.00856107, 0.0037017306, 0.016978256, 0.025885954, -0.010372694, 0.0025964435, 0.011706887, 0.021360163, -0.021674091, -0.024983412, 0.0034074234, 0.0032030435, 0.022262705, -0.01266829, -0.002249815, 0.032779284, -0.0034303141, -0.016101874, -0.005156916, -0.0212032, 0.005362931, 0.009077743, -0.013917461, -0.0017315074, 0.010980929, -0.019450437, 0.013865139, 0.028227331, -0.008757275, -0.0033649125, -0.012857955, 0.011039791, 0.009764459, 0.00029594224, -0.026317604, 0.025048813, 0.037749805, -0.025807472, -0.005425063, 0.021791814, -0.010012985, -0.00066995766, -0.016952096, 0.0031147513, -0.016598927, 0.0084368065, 0.004787397, -0.0064355177, 0.0015164997, -0.021216279, -0.023845425, 0.013969782, -0.011255615, 0.0042576445, -0.024250913, 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-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:

  • genres字段过滤,查找不属于crime类型的电影。

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

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, 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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,
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

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
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
2

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。请注意,这些查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。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 titlegenresplotyear 字段。

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

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

该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

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

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

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:

  • genres字段过滤,查找不属于crime类型的电影。

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

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

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
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
2

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。请注意,这些查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。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 titlegenresplotyear 字段。

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

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

该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

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

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

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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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:

  • genres字段过滤,查找不属于crime类型的电影。

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

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': [
30 {
31 'year': {
32 '$lte': 2015
33 }
34 }, {
35 'genres': {
36 '$eq': 'Action'
37 }
38 }
39 ]
40 }
41 ]
42 },
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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],
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

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
2

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。请注意,这些查询使用 ada-002-text 嵌入,它与 plot_embedding 字段中的向量嵌入相同。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 titlegenresplotyear 字段。

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

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

该过滤器使用 $and 聚合管道操作符查找符合以下两个条件的电影文档:

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

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

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 },
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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:

  • genres字段过滤,查找不属于crime类型的电影。

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

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, 0.024894057, 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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

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
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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Tutorials