如何对 Atlas 集群中的数据执行语义搜索

AtlasGoClusters在Atlas中，Go项目的 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（部署）。

   会显示集群页面。

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。

定义 Atlas Vector Search 索引。

1. 单击 Create Search Index（连接）。

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

3. 在 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. 在 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。

定义 Atlas Vector Search 索引。

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euclidean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": "euclidean"
   8
       },
   9
       {
   10
         "type": "filter",
   11
         "path": "genres"
   12
       },
   13
       {
   14
         "type": "filter",
   15
         "path": "year"
   16
       }
   17
     ]
   18
   }

检查索引定义，然后单击Create Search Index 。

此时将显示一个模态窗口，让您知道索引正在构建中。

Close单击 以关闭You're All Set! 模式窗口，并等待索引构建完成。

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。

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。

切换到您要创建索引的数据库。

use sample_mflix

switched to db sample_mflix

运行 <a class=\" \" target=\" \" href=\" \"> 方法。<a class=\" \" href=\" \" title=\" \"><svg xmlns=\" \" width=\" \" height=\" \" fill=\" \" viewbox=\" \" class=\" \" role=\"db.collection.createSearchIndex() \" aria-label=\" \"><path fill=\" \" d=\" \"> <path fill=\" \" d=\" \">

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 向量嵌入模型创建的向量嵌入。索引定义指定了 1536 个向量维度，并使用 euclidean 距离来衡量相似性。

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

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/mongo

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。

1
package main
2
3
import (
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
14
func 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
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	clientOptions := options.Client().ApplyURI(uri)
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	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
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	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:    "euclidean"}
54
	genreFilterDefinition := filterField{"filter", "genres"}
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	yearFilterDefinition := filterField{"filter", "year"}
56
57
	indexModel := mongo.SearchIndexModel{
58
		Definition: bson.D{{"fields", [3]interface{}{
59
			vectorDefinition,
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			genreFilterDefinition,
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			yearFilterDefinition}}},
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		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)
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	}
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	log.Println("New search index named " + searchIndexName + " is building.")
71
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	// Await the creation of the index.
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	log.Println("Polling to check if the index is ready. This may take up to a minute.")
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	searchIndexes := coll.SearchIndexes()
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	var doc bson.Raw
76
	for doc == nil {
77
		cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
78
		if err != nil {
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			fmt.Errorf("failed to list search indexes: %w", err)
80
		}
81
82
		if !cursor.Next(ctx) {
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			break
84
		}
85
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		name := cursor.Current.Lookup("name").StringValue()
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		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 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 向量嵌入模型创建的向量嵌入。索引定义指定了 1536 个向量维度，并使用 euclidean 距离来衡量相似性。

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

将 MongoDB Java Sync 驱动程序添加到您的项目中。

1. 将 Java 驱动程序版本 5.2 或更高版本作为依赖项添加到项目中：

   • 如果您使用 Maven，请将以下依赖项添加到 pom.xml 依赖项列表中：

     <dependency>
        <groupId>org.mongodb</groupId>
        <artifactId>mongodb-driver-sync</artifactId>
        <version>[5.2.0,)</version>
     </dependency>

   • 如果您使用 Gradle，请将以下依赖项添加到 build.gradle 依赖项列表中：

     dependencies {
        implementation 'org.mongodb:mongodb-driver-sync:[5.2.0,)'
     }

2. 将 Java 驱动程序的 JAR 文件添加到您的 CLASSPATH 中。

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。

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。

1
import com.mongodb.client.ListSearchIndexesIterable;
2
import com.mongodb.client.MongoClient;
3
import com.mongodb.client.MongoClients;
4
import com.mongodb.client.MongoCollection;
5
import com.mongodb.client.MongoCursor;
6
import com.mongodb.client.MongoDatabase;
7
import com.mongodb.client.model.SearchIndexModel;
8
import com.mongodb.client.model.SearchIndexType;
9
import org.bson.Document;
10
import org.bson.conversions.Bson;
11
12
import java.util.Arrays;
13
import java.util.Collections;
14
import java.util.List;
15
16
public class VectorIndex {
17
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    public static void main(String[] args) {
19
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        // 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", "euclidean"),
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 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 向量嵌入模型创建的向量嵌入。索引定义指定了 1536 个向量维度，并使用 euclidean 距离来衡量相似性。

运行该文件，以创建索引。

在 IDE 中运行文件，或命令行以运行代码。

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

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

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。

1
const { MongoClient } = require("mongodb");
2
3
// connect to your Atlas deployment
4
const uri =  "<connectionString>";
5
6
const client = new MongoClient(uri);
7
8
async 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": "euclidean"
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
}
61
run().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 向量嵌入模型创建的向量嵌入。索引定义指定了 1536 个向量维度，并使用 euclidean 距离来衡量相似性。

运行以下命令以创建索引。

node vector-index.js

vector_index

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

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。

1
from pymongo.mongo_client import MongoClient
2
from pymongo.operations import SearchIndexModel
3
import time
4
5
# Connect to your Atlas deployment
6
uri = "<connectionString>"
7
client = MongoClient(uri)
8
9
# Access your database and collection
10
database = client["sample_mflix"]
11
collection = database["embedded_movies"]
12
13
# Create your index model, then create the search index
14
search_index_model = SearchIndexModel(
15
  definition={
16
    "fields": [
17
      {
18
        "type": "vector",
19
        "path": "plot_embedding",
20
        "numDimensions": 1536,
21
        "similarity": "euclidean"
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
37
result = collection.create_search_index(model=search_index_model)
38
print("New search index named " + result + " is building.")
39
# Wait for initial sync to complete
40
print("Polling to check if the index is ready. This may take up to a minute.")
41
predicate=None
42
if predicate is None:
43
  predicate = lambda index: index.get("queryable") is True
44
45
while True:
46
  indices = list(collection.list_search_indexes(result))
47
  if len(indices) and predicate(indices[0]):
48
    break
49
  time.sleep(5)
50
print(result + " is ready for querying.")
51
52
client.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 向量嵌入模型创建的向量嵌入。索引定义指定了 1536 个向量维度，并使用 euclidean 距离来衡量相似性。

运行以下命令以创建索引。

python vector-index.py

vector_index

AtlasGoClusters在Atlas中，Go项目的 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（部署）。

   会显示集群页面。

转到Collections 页面。

单击集群的对应 Browse Collections 按钮。

显示数据浏览器。

转到集合的 标签页。<a class=\" \" href=\" \"Aggregation title=\" \"><svg xmlns=\" \" width=\" \" height=\" \" fill=\" \" viewbox=\" \" class=\" \" role=\" \" aria-label=\" \"><path fill=\" \" d=\" \"> <path fill=\" \" d=\" \">

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。

针对索引字段运行 Atlas Vector Search 搜索查询。

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。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 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。

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。

   通过 AND 运算符进行预过滤

   使用 OR 或 AND 操作符进行预筛选

   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
   ]

   隐藏输出

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

   隐藏输出

   1
   title: "Ong-bak 2"
   2
   score: 0.7976737022399902
   3
   year: 2008
   4
   plot: "A young Thai boxer learns the skills and inner meaning of martial arts…"
   5
   genres: Array (1)
   6
   7
   score: 0.7929348349571228
   8
   plot: "A handyman/martial arts master agrees to teach a bullied boy karate an…"
   9
   genres: Array (3)
   10
   title: "The Karate Kid"
   11
   year: 1984
   12
   13
   plot: "A young martial artist embarks on an adventure, encountering other mar…"
   14
   genres: Array (3)
   15
   title: "Circle of Iron"
   16
   year: 1978
   17
   score: 0.7852014899253845
   18
   19
   plot: "A lionized account of the life of the martial arts superstar."
   20
   genres: Array (3)
   21
   title: "Dragon: The Bruce Lee Story"
   22
   year: 1993
   23
   score: 0.7763040661811829
   24
   25
   score: 0.7731232643127441
   26
   plot: "A martial arts instructor from the police force gets imprisoned after …"
   27
   genres: Array (2)
   28
   title: "Kung Fu Killer"
   29
   year: 2014
   30
   31
   plot: "A young woman is trained by a martial arts specialist to become a prof…"
   32
   genres: Array (3)
   33
   title: "Naked Killer"
   34
   year: 1992
   35
   score: 0.7693743109703064
   36
   37
   plot: "The life of the greatest karate master of a generation."
   38
   genres: Array (3)
   39
   title: "The Real Miyagi"
   40
   year: 2015
   41
   score: 0.768799901008606
   42
   43
   plot: "At his new high school, a rebellious teen is lured into an underground…"
   44
   genres: Array (3)
   45
   title: "Never Back Down"
   46
   year: 2008
   47
   score: 0.768179714679718
   48
   49
   score: 0.7679706811904907
   50
   plot: "Three young martial arts masters emerge from the back streets of Hong …"
   51
   genres: Array (2)
   52
   title: "Dragon Tiger Gate"
   53
   year: 2006
   54
   55
   title: "Shaolin Soccer"
   56
   score: 0.7660527229309082
   57
   year: 2001
   58
   plot: "A young Shaolin follower reunites with his discouraged brothers to for…"
   59
   genres: Array (3)