Atlas クラスターのデータに対してセマンティック検索を実行する方法

Atlas Atlasで、プロジェクトの {0 ページにGoします。GoClusters

1. まだ表示されていない場合は、希望するプロジェクトを含む組織を選択しますナビゲーション バーのOrganizationsメニュー

2. まだ表示されていない場合は、ナビゲーション バーのProjectsメニューから目的のプロジェクトを選択します。

3. まだ表示されていない場合は、サイドバーの [Clusters] をクリックします。

   [ Clusters （クラスター） ] ページが表示されます。

GoAtlas Searchクラスターの ページに します。

GoAtlas Searchページには、サイドバー、Data Explorer 、またはクラスターの詳細ページから できます。

Atlas Vector Search インデックスを定義します。

1. [Create Search Index] をクリックします。

2. [ Atlas Vector Search ] で、[ JSON Editor ] を選択し、[ Nextをクリックします。

3. Database and Collectionセクションで、 sample_mflixデータベースを検索し、 embedded_moviesコレクションを選択します。

4. Index Nameフィールドにvector_indexと入力します。

5. デフォルトの定義を次のインデックス定義に置き換え、 Nextをクリックします。

Atlas Vector Search インデックスを定義します。

1. デフォルトの定義を、以下のインデックス定義で置き換えます。

   このインデックス定義では、 vectorSearchタイプのインデックスで次のフィールドのインデックス作成を指定します。

   • plot_embedding ベクトル型としての フィールド。 plot_embeddingフィールドには、OpenAI のtext-embedding-ada-002埋め込みモデルを使用して作成された埋め込みが含まれます。 インデックス定義では、 1536ベクトル次元を指定し、 dotProductを使用して類似性を測定します。

   • genres フィールドの string 値でデータを事前にフィルタリングするフィルタータイプとして、 フィールドを使用します。

   • year フィールド には、フィールド内の数値でデータを事前にフィルタリングするためのフィルタータイプが含まれています。

   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
   }

インデックスの定義を確認し、Create Search Index をクリックします。

インデックスが作成中であることを知らせるモーダル ウィンドウが表示されます。

Close をクリックして You're All Set! モーダル ウィンドウを閉じ、インデックスの作成が完了するまで待ちます。

インデックスの構築には約 1 分かかります。 ビルド中、 Status列にはInitial Syncが読み取られます。 作成が完了すると、 Status列にはActiveが表示されます。

mongosh 内のクラスターに接続します。

ターミナル ウィンドウでmongoshを開き、クラスターに接続します。 接続の詳細な手順については、「 mongosh 経由での接続 」を参照してください。

インデックスを作成するデータベースに切り替えます。

use sample_mflix

switched to db sample_mflix

db.collection.createSearchIndex() メソッドを実行します。

次のインデックス定義は、Atlas ベクトル検索インデックスで、plot_embeddingフィールドを vector タイプとしてインデックス化し、genres フィールドと year フィールドを filter タイプとしてインデックス化します。 plot_embeddingフィールドには、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、1536ベクトル次元を指定し、dotProduct 関数を使用して類似性を測定します。

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": "dotProduct"
11
      },
12
      {
13
        "type": "filter",
14
        "path": "genres"
15
      },
16
      {
17
        "type": "filter",
18
        "path": "year"
19
      }
20
    ]
21
  }
22
);

Go ドライバーをプロジェクトに追加します。

Go ドライバーを依存関係としてプロジェクトに追加します。

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

インデックスを定義します。

vector-index.go というファイルを作成します。次のコードをコピーしてファイルに貼り付け、<connectionString> プレースホルダー値を置き換えます。

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
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
}

このインデックス定義は、Atlas ベクトル検索インデックスで、plot_embeddingフィールドを vector タイプとしてインデックス化し、genres フィールドと year フィールドを filter タイプとしてインデックス化します。 plot_embeddingフィールドには、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、1536ベクトル次元を指定し、dotProduct 関数を使用して類似性を測定します。

以下のコマンドを実行してインデックスを作成します。

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

MongoDB Java Sync Driver をプロジェクトに追加します。

1. パッケージマネージャーに応じて、 Javaドライバー バージョン 5.2 以降をプロジェクトに依存関係として追加します 。

   Maven

   Maven を使用している場合は、プロジェクトの pom.xmlファイルの dependencies 配列に次の依存関係を追加します。

   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. パッケージマネージャーを実行して、プロジェクトに依存関係をインストールします。

詳しいインストール手順やバージョンの互換性については MongoDB Java Driver のドキュメントを参照ください。

インデックスを定義します。

VectorIndex.java というファイルを作成します。次のコードをコピーしてファイルに貼り付け、<connectionString> プレースホルダー値を置き換えます。

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
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
}

このインデックス定義は、Atlas ベクトル検索インデックスで、plot_embeddingフィールドを vector タイプとしてインデックス化し、genres フィールドと year フィールドを filter タイプとしてインデックス化します。 plot_embeddingフィールドには、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、1536ベクトル次元を指定し、dotProduct 関数を使用して類似性を測定します。

ファイルを実行してインデックスを作成します。

IDE でファイルを実行するか、コマンドラインからコマンドを実行してコードを実行します。

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

MongoDB Node.js ドライバーをプロジェクトに追加します。

MongoDB Node ドライバーを依存関係としてプロジェクトに追加します。

npm install mongodb

インデックスを定義します。

vector-index.js というファイルを作成します。次のコードをコピーしてファイルに貼り付け、<connectionString> プレースホルダー値を置き換えます。

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": "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
}
61
run().catch(console.dir);

このインデックス定義は、Atlas ベクトル検索インデックスで、plot_embeddingフィールドを vector タイプとしてインデックス化し、genres フィールドと year フィールドを filter タイプとしてインデックス化します。 plot_embeddingフィールドには、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、1536ベクトル次元を指定し、dotProduct 関数を使用して類似性を測定します。

以下のコマンドを実行してインデックスを作成します。

node vector-index.js

vector_index

PyMongo ドライバーをプロジェクトに追加します。

PyMongoドライバーを依存関係としてプロジェクトに追加します。

pip install pymongo

インデックスを定義します。

vector-index.py というファイルを作成します。次のコードをコピーしてファイルに貼り付け、<connectionString> プレースホルダー値を置き換えます。

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": "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
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()

このインデックス定義は、Atlas ベクトル検索インデックスで、plot_embeddingフィールドを vector タイプとしてインデックス化し、genres フィールドと year フィールドを filter タイプとしてインデックス化します。 plot_embeddingフィールドには、OpenAI の text-embedding-ada-002 埋め込みモデルを使用して作成された埋め込みが含まれます。インデックス定義では、1536ベクトル次元を指定し、dotProduct 関数を使用して類似性を測定します。

以下のコマンドを実行してインデックスを作成します。

python vector-index.py

vector_index

AtlasGoClustersAtlas で、プロジェクトの ページにGoします。

1. まだ表示されていない場合は、希望するプロジェクトを含む組織を選択しますナビゲーション バーのOrganizationsメニュー

2. まだ表示されていない場合は、ナビゲーション バーのProjectsメニューから目的のプロジェクトを選択します。

3. まだ表示されていない場合は、サイドバーの [Clusters] をクリックします。

   [ Clusters （クラスター） ] ページが表示されます。

Collections ページに移動します。

クラスターの [Browse Collections] ボタンをクリックします。

Data Explorerが表示されます。

[Aggregation] タブに移動してコレクションを表示します。

1. データベースのリストの下にあるsample_mflixを展開し、そのデータベース内のコレクションのリストからembedded_moviesを選択します。

2. sample_mflix.embedded_moviesコレクションのAggregationタブをクリックします。

インデックス付きフィールドに対して Atlas Vector Search クエリを実行します。

次のクエリは、 $vectorSearchパイプライン ステージを使用して、指定されたベクトル埋め込みに一致する映画を検索します。 クエリではplot_embeddingフィールドのベクトル埋め込みと同じada-002-text埋め込みが使用されていることに注意してください。 クエリでは最大100の最近傍の検索を指定し、結果を10ドキュメントのみに制限します。 クエリでは、次のアクションを実行するための$projectステージも指定します。

• _idフィールドを除外し、結果にはtitle 、 genres 、 plot 、およびyearフィールドのみが含まれます。

• 結果内の各ドキュメントのベクトル検索スコアを表示するscoreという名前のフィールドを追加します。

1. [</> Text] をクリックします。

2. 左のペインの[ ]を、試行する前フィルタ演算子のクエリに置き換えます。

   AND 演算子による事前フィルタリング

   OR または AND 演算子による事前フィルタリング

   次のクエリは、ベクトル埋め込みを使用して string履歴ハイストを検索しplot_embedding 。 クエリはada-002-text埋め込みを使用します。これはplot_embeddingフィールドのベクトル埋め込みと同じです。 セマンティック検索を実行するドキュメントを事前にフィルタリングするために、フィールドごとに複数の比較クエリ演算子を指定します。

   フィルターでは、 $and集計パイプライン演算子を使用して、次の両方の条件に一致するドキュメントを検索します。

   • genresフィールドでフィルタリングして、 drama 、 western 、またはcrimeジャンルではなく、 action 、 adventure 、またはfamilyジャンルに含まれる映画を検索します。

   • yearフィールドでフィルタリングして、 1960から2000年（両端を含む）の間にリリースされた映画を検索します。

   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
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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
   ]

   出力を非表示

   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"

   次のクエリは、ベクトル埋め込みを使用してplot_embeddingフィールドで string mongosを検索します。 クエリはada-002-text埋め込みを使用します。これはplot_embeddingフィールドのベクトル埋め込みと同じです。 集計パイプライン 演算子と比較クエリ演算子を指定し、演算子を組み合わせて使用し、データをフィルタリングする方法を示します。

   フィルターでは、 $or集計パイプライン演算子を使用して、次のいずれかの条件に一致する映画のドキュメントを検索します。

   • genresフィールドでフィルタリングして、 crimeジャンルに該当しない映画を検索します。

   • yearフィールドでフィルタリングして、 2015年以前にリリースされた映画を検索し、 genresフィールドでフィルタリングしてactionジャンルの映画を検索します。

   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
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   20
       }
   21
     },
   22
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   23
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   24
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   26
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   27
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   29
         "score": { $meta: "vectorSearchScore" }
   30
       }
   31
     }
   32
   ]

   出力を非表示

   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)